diff --git "a/\320\232\320\276\320\277\320\270\321\217 \320\261\320\273\320\276\320\272\320\275\320\276\321\202\320\260 _Untitled2.ipynb_" "b/\320\232\320\276\320\277\320\270\321\217 \320\261\320\273\320\276\320\272\320\275\320\276\321\202\320\260 _Untitled2.ipynb_"
new file mode 100644
index 0000000..945ac1b
--- /dev/null
+++ "b/\320\232\320\276\320\277\320\270\321\217 \320\261\320\273\320\276\320\272\320\275\320\276\321\202\320\260 _Untitled2.ipynb_"	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1gflZGIkz8_-SFrkGwKsRes8PHpImpLQA","timestamp":1773052233896}],"mount_file_id":"1gflZGIkz8_-SFrkGwKsRes8PHpImpLQA","authorship_tag":"ABX9TyNsyR7zLSilAOALaBnEPVoK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"26511a612fb240878e45b0be1b4f1aab":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_dc8907c29c8742be945f91c053aee93b","IPY_MODEL_267de66128304dbeaf02517d855023bc","IPY_MODEL_ded69f943c254be1a499dc4a462334f4"],"layout":"IPY_MODEL_7748bdb577c9422c9cc8d3b3b5a216c1"}},"dc8907c29c8742be945f91c053aee93b":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_b8002cdfb5584cc7b84ea812ee460974","placeholder":"​","style":"IPY_MODEL_ad009a7315cd4cb6817c33d1927b4c41","value":"lr: 100%"}},"267de66128304dbeaf02517d855023bc":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_e6ba9cef5521425fa5aa8f8ed050fbc9","max":8,"min":0,"orientation":"horizontal","style":"IPY_MODEL_bddeb3ad8290486ea3106771e4e26001","value":8}},"ded69f943c254be1a499dc4a462334f4":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_e40191a8e4984203bed0647f11ef7f4a","placeholder":"​","style":"IPY_MODEL_b5eead6a813749c9976f56b4415c0fbb","value":" 8/8 [08:44&lt;00:00, 101.31s/it]"}},"7748bdb577c9422c9cc8d3b3b5a216c1":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"b8002cdfb5584cc7b84ea812ee460974":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"ad009a7315cd4cb6817c33d1927b4c41":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"e6ba9cef5521425fa5aa8f8ed050fbc9":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"bddeb3ad8290486ea3106771e4e26001":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"e40191a8e4984203bed0647f11ef7f4a":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"b5eead6a813749c9976f56b4415c0fbb":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"e145436d63874a97bce53dc7e29b71bc":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_24474a7c66ba4f5da622dab2c4f2afba","IPY_MODEL_1064448d917a417da9517e5adca120e5","IPY_MODEL_58e69002306e4c3f87ef1e5ff3ea7d92"],"layout":"IPY_MODEL_dde89e54d5d342f6b528520204657ea4"}},"24474a7c66ba4f5da622dab2c4f2afba":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_2b78d4bda1c8452caad279dd527e093a","placeholder":"​","style":"IPY_MODEL_013a5e417c8340bfaf5e35cf71561aea","value":"batch: 100%"}},"1064448d917a417da9517e5adca120e5":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_8ecd1af312f545c9a4bc47ba7e8387ea","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_e36ec1ed388345e69a775db490afe9aa","value":10}},"58e69002306e4c3f87ef1e5ff3ea7d92":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_a705a40461a14ea2914d1c41afeb4ad8","placeholder":"​","style":"IPY_MODEL_0c90285b641c483d9401f313eda143f9","value":" 10/10 [00:01&lt;00:00,  7.00it/s]"}},"dde89e54d5d342f6b528520204657ea4":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"2b78d4bda1c8452caad279dd527e093a":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"013a5e417c8340bfaf5e35cf71561aea":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"8ecd1af312f545c9a4bc47ba7e8387ea":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"e36ec1ed388345e69a775db490afe9aa":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"a705a40461a14ea2914d1c41afeb4ad8":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"0c90285b641c483d9401f313eda143f9":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"7254ae5628694a73a104b99aa1cb8077":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_46de6bd3b22240a69ac859a5590dcc30","IPY_MODEL_b0ff7eefd5a542beb6844988e7b328da","IPY_MODEL_404057be731144fa8448384bd5bd5514"],"layout":"IPY_MODEL_a489308bb89644f0ad7b98a3c41e219f"}},"46de6bd3b22240a69ac859a5590dcc30":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_5b828dc6657e4f1c8c424e0986dcdadf","placeholder":"​","style":"IPY_MODEL_b9938872da8d4d97862462d93124367b","value":"batch:  60%"}},"b0ff7eefd5a542beb6844988e7b328da":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_62e058f786d24a04b8e47a3fc7d38e9e","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_3f5e5f24664040edad3bf1599cca8a9f","value":10}},"404057be731144fa8448384bd5bd5514":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_b08300dc0653425dbe983bdc56c634dc","placeholder":"​","style":"IPY_MODEL_1d2ef1fbf7314991a1e9818c49dc0cf8","value":" 6/10 [00:00&lt;00:00, 56.44it/s]"}},"a489308bb89644f0ad7b98a3c41e219f":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"5b828dc6657e4f1c8c424e0986dcdadf":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"b9938872da8d4d97862462d93124367b":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"62e058f786d24a04b8e47a3fc7d38e9e":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"3f5e5f24664040edad3bf1599cca8a9f":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"b08300dc0653425dbe983bdc56c634dc":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"1d2ef1fbf7314991a1e9818c49dc0cf8":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"031d3bb7b1a24a6194b5d43f53e5748f":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_63f8c04350964d2b903f28bea7b1a082","IPY_MODEL_237bb39b162c4d6ca329ee2a969617ef","IPY_MODEL_2f8a3fcd5e9d4d2ea9eb13133106a32c"],"layout":"IPY_MODEL_e8597177b14e4ced8f2f863013ca1b09"}},"63f8c04350964d2b903f28bea7b1a082":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_37d257fa0f564cc4af65e247876ec743","placeholder":"​","style":"IPY_MODEL_e83bc113542d46d3a84eef4bca859a5a","value":"batch:   0%"}},"237bb39b162c4d6ca329ee2a969617ef":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_d738a0434ce2419a8ea58c52100ee123","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_7307b7a2a26f4b18b835be10bdf6d407","value":10}},"2f8a3fcd5e9d4d2ea9eb13133106a32c":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_efd411c908d74ca1a5320f9815d3fc21","placeholder":"​","style":"IPY_MODEL_f70be4dd46f94aaf89a44d46647cb0fc","value":" 0/10 [00:00&lt;?, ?it/s]"}},"e8597177b14e4ced8f2f863013ca1b09":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"37d257fa0f564cc4af65e247876ec743":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"e83bc113542d46d3a84eef4bca859a5a":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"d738a0434ce2419a8ea58c52100ee123":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"7307b7a2a26f4b18b835be10bdf6d407":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"efd411c908d74ca1a5320f9815d3fc21":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"f70be4dd46f94aaf89a44d46647cb0fc":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"ab3edb3e720d47998ef2ae30cb286cac":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_62b5dc58d5d145a685fd665e1b8019d1","IPY_MODEL_4351daab7af443b791e2d927dae6bb73","IPY_MODEL_fd5a069b59284c2b886aacea3ce16534"],"layout":"IPY_MODEL_b04b27f4a3d94461b7f5f5fcd0f4a9d3"}},"62b5dc58d5d145a685fd665e1b8019d1":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_ad735e3b855543f5ab9ff26dfb9313a7","placeholder":"​","style":"IPY_MODEL_351e4494c83e402db049a7c4b0c81f80","value":"batch:   0%"}},"4351daab7af443b791e2d927dae6bb73":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_85c64386d03742e4a67e4a8496f86f40","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_2eaee0b62f8e4bb6868bd16ff4f2f235","value":10}},"fd5a069b59284c2b886aacea3ce16534":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_c49da9dbc29c45bd81acf7e4b2daf5e7","placeholder":"​","style":"IPY_MODEL_75585ed3495c44d7b0b85bbc2b1fe046","value":" 0/10 [00:00&lt;?, ?it/s]"}},"b04b27f4a3d94461b7f5f5fcd0f4a9d3":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"ad735e3b855543f5ab9ff26dfb9313a7":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"351e4494c83e402db049a7c4b0c81f80":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"85c64386d03742e4a67e4a8496f86f40":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"2eaee0b62f8e4bb6868bd16ff4f2f235":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"c49da9dbc29c45bd81acf7e4b2daf5e7":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"75585ed3495c44d7b0b85bbc2b1fe046":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"051cfe00efec4bf08978527130603332":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_74e4d33cedb54836a8239b9fe273fbc2","IPY_MODEL_1f125c02b0d1494e86dce61d927a5d80","IPY_MODEL_9b57cd2efc3a478fbb1c30e17492e623"],"layout":"IPY_MODEL_c29815289a744a558da61f594798f2c0"}},"74e4d33cedb54836a8239b9fe273fbc2":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_52e3ec2ebccb45e2aa560114b91f9188","placeholder":"​","style":"IPY_MODEL_4a1e6417afab4014a352dc414e309a29","value":"batch:   0%"}},"1f125c02b0d1494e86dce61d927a5d80":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_d0ad16a0be6c43ebaefe3cf711cc76d1","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_a69cdba6b2294d94aa5206f1042be7d5","value":10}},"9b57cd2efc3a478fbb1c30e17492e623":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_db0e58b34a5a4540b7dfce7727a3ca00","placeholder":"​","style":"IPY_MODEL_db84ce4ca3cd4e0f978a88dd4dcc1124","value":" 0/10 [00:00&lt;?, ?it/s]"}},"c29815289a744a558da61f594798f2c0":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"52e3ec2ebccb45e2aa560114b91f9188":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"4a1e6417afab4014a352dc414e309a29":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"d0ad16a0be6c43ebaefe3cf711cc76d1":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"a69cdba6b2294d94aa5206f1042be7d5":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"db0e58b34a5a4540b7dfce7727a3ca00":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"db84ce4ca3cd4e0f978a88dd4dcc1124":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"16d9592bb1c34bc7b5ffe9dfea26a6ea":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_827f6bf4f198406b801c1e0b419df66f","IPY_MODEL_567160aef16a4489961e9822f8b97829","IPY_MODEL_0e96c54d43104601ad9af688e72f9f5e"],"layout":"IPY_MODEL_8217b3374c4147ee8228760cf7bec747"}},"827f6bf4f198406b801c1e0b419df66f":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_1c32d8a3565e42a18d4785caf3bdf478","placeholder":"​","style":"IPY_MODEL_56c1ddd51deb4f3eb3ac8b0bf4490ec6","value":"batch: 100%"}},"567160aef16a4489961e9822f8b97829":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_99fa959ce4f14901b2af137675bbb741","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_2d62375f068a4afd805943edcf65fc2b","value":10}},"0e96c54d43104601ad9af688e72f9f5e":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_789b9ce8e2da4b5792f5c3e49ad01da1","placeholder":"​","style":"IPY_MODEL_1fbca0165b79445889d808c23215eadf","value":" 10/10 [02:53&lt;00:00, 17.27s/it]"}},"8217b3374c4147ee8228760cf7bec747":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"1c32d8a3565e42a18d4785caf3bdf478":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"56c1ddd51deb4f3eb3ac8b0bf4490ec6":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"99fa959ce4f14901b2af137675bbb741":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"2d62375f068a4afd805943edcf65fc2b":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"789b9ce8e2da4b5792f5c3e49ad01da1":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"1fbca0165b79445889d808c23215eadf":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"1149024c43c64af5bb020ca86c52877f":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_f7cf1c4374fe4c2d82c3e66bb6ae6480","IPY_MODEL_18a850bc9f9f4ea883ffc692b353fee4","IPY_MODEL_abe5f6c082394cc59e3e9dd80070b291"],"layout":"IPY_MODEL_30ac12042af54310973009ff68ccbc5a"}},"f7cf1c4374fe4c2d82c3e66bb6ae6480":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_fb65ccaff5df43e19e3c561e29b0fdae","placeholder":"​","style":"IPY_MODEL_d944ea247484426abe3f8c7fbe066eb2","value":"batch: 100%"}},"18a850bc9f9f4ea883ffc692b353fee4":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_e2910d5e333045669e00452b04729b02","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_685a7520c2a5493a8ba33e35f3e2c546","value":10}},"abe5f6c082394cc59e3e9dd80070b291":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_360c977b32b34685a35b175079b3da8b","placeholder":"​","style":"IPY_MODEL_77c1aac3802a44baa0a619b021f46aab","value":" 10/10 [02:53&lt;00:00, 17.44s/it]"}},"30ac12042af54310973009ff68ccbc5a":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"fb65ccaff5df43e19e3c561e29b0fdae":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"d944ea247484426abe3f8c7fbe066eb2":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"e2910d5e333045669e00452b04729b02":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"685a7520c2a5493a8ba33e35f3e2c546":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"360c977b32b34685a35b175079b3da8b":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"77c1aac3802a44baa0a619b021f46aab":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"35c8fcdd43ff458197b8a3bcd640cf22":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_ce92a3f149ef4e5dad97a6187607660a","IPY_MODEL_1764eafebd4d450a86c79efcb5f477fa","IPY_MODEL_c23ea4c554eb4bdfaab0221d817e030d"],"layout":"IPY_MODEL_2db47c7de2e14af989f5eee18dac447c"}},"ce92a3f149ef4e5dad97a6187607660a":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_bd2d411042d24686a0edeefd7a601429","placeholder":"​","style":"IPY_MODEL_327a8bf1af2141208f0f00c972c96779","value":"batch: 100%"}},"1764eafebd4d450a86c79efcb5f477fa":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"","description_tooltip":null,"layout":"IPY_MODEL_573979e176b64750aa51313ac67b5b05","max":10,"min":0,"orientation":"horizontal","style":"IPY_MODEL_d63b0f24a3d645a19679364a90a59107","value":10}},"c23ea4c554eb4bdfaab0221d817e030d":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_62c379887aa94436b8dd61ba1858d93d","placeholder":"​","style":"IPY_MODEL_9b65caa913554fae9550e8dc2fc744c5","value":" 10/10 [02:55&lt;00:00, 17.60s/it]"}},"2db47c7de2e14af989f5eee18dac447c":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":"hidden","width":null}},"bd2d411042d24686a0edeefd7a601429":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"327a8bf1af2141208f0f00c972c96779":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"573979e176b64750aa51313ac67b5b05":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"d63b0f24a3d645a19679364a90a59107":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"62c379887aa94436b8dd61ba1858d93d":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"9b65caa913554fae9550e8dc2fc744c5":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"FUjXwauVwuYb"},"outputs":[],"source":[]},{"cell_type":"markdown","source":["# Новый раздел"],"metadata":{"id":"Ab2caDlCwy66"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных ----------\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы для независимых методов ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция оценки ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    if method == 'ne':\n","        return mse_ne\n","    if method == 'gd':\n","        w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","        return mean_squared_error(y, Xwb @ w)\n","    if method == 'sgd':\n","        w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","        return mean_squared_error(y, Xwb @ w)\n","    if method == 'lr_sk':\n","        return mse_lr_sk\n","    if method == 'sgd_sk':\n","        sgd_sk = SGDRegressor(fit_intercept=True, max_iter=epochs, eta0=lr,\n","                              learning_rate='constant', penalty=None, tol=None,\n","                              batch_size=batch_size, random_state=42)\n","        sgd_sk.fit(X, y)\n","        return mean_squared_error(y, sgd_sk.predict(X))\n","    raise ValueError('Unknown method')\n","\n","# ---------- Диапазоны параметров ----------\n","lr_range = np.logspace(-8, 0, 9)\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- График 1: learning rate ----------\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n","    plt.semilogx(lr_range, mses, marker='o', label=label)\n","plt.xlabel('Learning rate')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от learning rate')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# ---------- График 2: эпохи ----------\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n","    plt.semilogx(epochs_range, mses, marker='o', label=label)\n","plt.xlabel('Epochs')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от количества эпох')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# ---------- График 3: размер батча ----------\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n","    plt.semilogx(batch_range, mses, marker='o', label=label)\n","plt.xlabel('Batch size')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от размера батча')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":953},"id":"oEzoSPneyUfG","executionInfo":{"status":"error","timestamp":1773046361844,"user_tz":-420,"elapsed":684,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"28e789d9-70b6-4787-c8e0-907213e76edb"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipykernel_406/3602992315.py:25: RuntimeWarning: overflow encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/tmp/ipykernel_406/3602992315.py:25: RuntimeWarning: invalid value encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n"]},{"output_type":"error","ename":"ValueError","evalue":"Input contains NaN.","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_406/3602992315.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmethods\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0mmses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcompute_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEFAULT_EPOCHS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEFAULT_BATCH\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlr_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Learning rate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_406/3602992315.py\u001b[0m in \u001b[0;36mcompute_mse\u001b[0;34m(method, lr, epochs, batch_size)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgradient_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXwb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sgd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msgd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m                     )\n\u001b[1;32m    215\u001b[0m                 ):\n\u001b[0;32m--> 216\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_namespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m     _, y_true, y_pred, sample_weight, multioutput = (\n\u001b[0;32m--> 565\u001b[0;31m         _check_reg_targets_with_floating_dtype(\n\u001b[0m\u001b[1;32m    566\u001b[0m             \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         )\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets_with_floating_dtype\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, xp)\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mdtype_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_matching_floating_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     )\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype, xp)\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1107\u001b[0;31m             _assert_all_finite(\n\u001b[0m\u001b[1;32m   1108\u001b[0m                 \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m     _assert_all_finite_element_wise(\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    167\u001b[0m                 \u001b[0;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m             )\n\u001b[0;32m--> 169\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Input contains NaN."]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAzoAAAH+CAYAAABUV3s/AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAIaxJREFUeJzt3X2QlfV58PGLXeqyqe42+ILyJhQlhhWlvhA1NdFItERJ6pgxRloRJ1aDE9tkIF0mAz6MrjsQSpuh0Zkm1qwVTDANqY2tjSUaRMwQUWmNtZhHQQoITQx7eIk07p7nD+s+oizskb33sJefz8z+sffevz2/c7GyfL3vPTugXC6XAwAAIJGaam8AAACgtwkdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASGdgtTfQE52dnbFly5Y46qijYsCAAdXeDgAAUCXlcjl27twZQ4cOjZqa7q/b9IvQ2bJlS4wYMaLa2wAAAA4TmzZtiuHDh3f78X4ROkcddVREvPFkGhoaqrwbAACgWkqlUowYMaKrEbrTL0LnzdvVGhoahA4AAHDQH2nxYgQAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQTkWh09HREXPmzInRo0dHfX19jBkzJm699dYol8sHXLd37974yle+EieeeGLU1dXFqFGj4m//9m8PaeMAAADdGVjJyfPnz48777wz2traoqmpKZ588smYPn16NDY2xs0339ztuiuvvDK2bdsWd911V5x00kmxdevW6OzsPOTNAwAA7E9FobN69er41Kc+FZdeemlERIwaNSruu+++WLNmTbdrHnroofjxj38cL774YgwePLhrHQAAQFEqunXtvPPOixUrVsT69esjImLdunWxatWqmDx5crdrHnjggTjrrLNiwYIFMWzYsBg7dmzMnDkzfv3rX3e7Zu/evVEqlfZ5AwAA6KmKrug0NzdHqVSKU045JWpra6OjoyNaWlpi6tSp3a558cUXY9WqVTFo0KBYvnx5/OIXv4gZM2bEL3/5y7j77rv3u6a1tTXmzZtX2TMBAAD4XwPKB3slgbf49re/HbNmzYqvfvWr0dTUFM8880z82Z/9WSxatCimTZu23zUXX3xxPPbYY/HKK69EY2NjRER873vfi09/+tOxe/fuqK+vf8eavXv3xt69e7veL5VKMWLEiGhvb4+GhoZKnyMAAJBEqVSKxsbGg7ZBRVd0Zs2aFc3NzXHVVVdFRMT48eNj48aN0dra2m3onHDCCTFs2LCuyImI+OAHPxjlcjn+67/+K04++eR3rKmrq4u6urpKtgYAANClop/R2bNnT9TU7Luktrb2gK+g9uEPfzi2bNkSu3bt6jq2fv36qKmpieHDh1e4XQAAgIOrKHSmTJkSLS0t8eCDD8aGDRti+fLlsWjRorj88su7zpk9e3Zcc801Xe9fffXVcfTRR8f06dPjueeei5UrV8asWbPiuuuu2+9tawAAAIeqolvXFi9eHHPmzIkZM2bE9u3bY+jQoXHDDTfE3Llzu87ZunVrvPzyy13vH3nkkfHwww/HF77whTjrrLPi6KOPjiuvvDJuu+223nsWAAAAb1HRixFUS09/4AgAAMitp21Q0a1rAAAA/YHQAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0hE6AABAOkIHAABIR+gAAADpCB0AACAdoQMAAKQjdAAAgHSEDgAAkI7QAQAA0qkodDo6OmLOnDkxevToqK+vjzFjxsStt94a5XK5R+sff/zxGDhwYEyYMOHd7BUAAKBHBlZy8vz58+POO++Mtra2aGpqiieffDKmT58ejY2NcfPNNx9w7Y4dO+Kaa66Jiy66KLZt23ZImwYAADiQikJn9erV8alPfSouvfTSiIgYNWpU3HfffbFmzZqDrr3xxhvj6quvjtra2vj+97//rjYLAADQExXdunbeeefFihUrYv369RERsW7duli1alVMnjz5gOvuvvvuePHFF+OWW27p0ePs3bs3SqXSPm8AAAA9VdEVnebm5iiVSnHKKadEbW1tdHR0REtLS0ydOrXbNS+88EI0NzfHY489FgMH9uzhWltbY968eZVsDQAAoEtFV3SWLVsWS5YsiaVLl8ZTTz0VbW1tsXDhwmhra9vv+R0dHXH11VfHvHnzYuzYsT1+nNmzZ0d7e3vX26ZNmyrZJgAA8B43oNzTl0yLiBEjRkRzc3PcdNNNXcduu+22uPfee+P5559/x/k7duyI97///VFbW9t1rLOzM8rlctTW1sYPf/jD+NjHPnbQxy2VStHY2Bjt7e3R0NDQ0+0CAADJ9LQNKrp1bc+ePVFTs+9FoNra2ujs7Nzv+Q0NDfHv//7v+xy744474kc/+lF897vfjdGjR1fy8AAAAD1SUehMmTIlWlpaYuTIkdHU1BRPP/10LFq0KK677rquc2bPnh2bN2+Oe+65J2pqauLUU0/d53Mcd9xxMWjQoHccBwAA6C0Vhc7ixYtjzpw5MWPGjNi+fXsMHTo0brjhhpg7d27XOVu3bo2XX3651zcKAADQUxX9jE61+BkdAAAgoudtUNGrrgEAAPQHQgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSGVjtDfQnHZ3lWPPSq7F952tx3FGDYuLowVFbM6Da20rDfItlvsUy32KZb7HMt1jmWzwzLlZ/na/Q6aGHnt0a8/7xudja/lrXsRMaB8UtU8bFH5x6QhV3loP5Fst8i2W+xTLfYplvscy3eGZcrP483wHlcrlc7U0cTKlUisbGxmhvb4+GhoY+f/yHnt0an7/3qXj7oN7s2Dv/6IzD/g/6cGa+xTLfYplvscy3WOZbLPMtnhkX63Cdb0/bwBWdg+joLMe8f3zuHX/AERHleOMP+v888Fx8+KRj+sUlvMNNR2c5bnngZ+ZbEPMtlvkWy3yLZb7FMt/imXGxejLfef/4XHx83PGH7Xxd0TmIJ/7vL+Oz3/hJnz4mAAD0B/ddf06cO+boPn3MnraBV107iO07Xzv4SQAA8B50OP9b2a1rB3HcUYN6dN63pp8dE0cPLng3+ax56dW49u6fHvQ88313zLdY5lss8y2W+RbLfItnxsXq6Xx7+m/lahA6BzFx9OA4oXFQvNL+2n7vURwQEcc3DorzTz72sL0/8XB2/snHmm+BzLdY5lss8y2W+RbLfItnxsXq6XwP54h069pB1NYMiFumjIuI//8KE2968/1bpozzH9C7ZL7FMt9imW+xzLdY5lss8y2eGRcrw3yFTg/8waknxJ1/dEYc37jvpbnjGwd52cJeYL7FMt9imW+xzLdY5lss8y2eGRerv8/Xq65VoL/+Vtj+wnyLZb7FMt9imW+xzLdY5ls8My7W4TbfnraB0AEAAPoNLy8NAAC8ZwkdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANKpKHQ6Ojpizpw5MXr06Kivr48xY8bErbfeGuVyuds13/ve9+LjH/94HHvssdHQ0BDnnntu/Mu//MshbxwAAKA7FYXO/Pnz484774y//uu/jv/4j/+I+fPnx4IFC2Lx4sXdrlm5cmV8/OMfj3/6p3+KtWvXxoUXXhhTpkyJp59++pA3DwAAsD8Dyge6HPM2l112WQwZMiTuuuuurmNXXHFF1NfXx7333tvjB21qaorPfOYzMXfu3B6dXyqVorGxMdrb26OhoaHHjwMAAOTS0zao6IrOeeedFytWrIj169dHRMS6deti1apVMXny5B5/js7Ozti5c2cMHjy423P27t0bpVJpnzcAAICeGljJyc3NzVEqleKUU06J2tra6OjoiJaWlpg6dWqPP8fChQtj165dceWVV3Z7Tmtra8ybN6+SrQEAAHSp6IrOsmXLYsmSJbF06dJ46qmnoq2tLRYuXBhtbW09Wr906dKYN29eLFu2LI477rhuz5s9e3a0t7d3vW3atKmSbQIAAO9xFV3RmTVrVjQ3N8dVV10VERHjx4+PjRs3Rmtra0ybNu2Aa7/97W/H5z73ubj//vtj0qRJBzy3rq4u6urqKtkaAABAl4qu6OzZsydqavZdUltbG52dnQdcd99998X06dPjvvvui0svvbTyXQIAAFSgois6U6ZMiZaWlhg5cmQ0NTXF008/HYsWLYrrrruu65zZs2fH5s2b45577omIN25XmzZtWnzta1+LD33oQ/HKK69ERER9fX00Njb24lMBAAB4Q0UvL71z586YM2dOLF++PLZv3x5Dhw6Nz372szF37tw44ogjIiLi2muvjQ0bNsSjjz4aEREXXHBB/PjHP37H55o2bVp861vf6tHjenlpAAAgoudtUFHoVIvQAQAAIgr6PToAAAD9gdABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSqSh0Ojo6Ys6cOTF69Oior6+PMWPGxK233hrlcvmA6x599NE444wzoq6uLk466aT41re+dSh7BgAAOKCBlZw8f/78uPPOO6OtrS2ampriySefjOnTp0djY2PcfPPN+13z0ksvxaWXXho33nhjLFmyJFasWBGf+9zn4oQTTohLLrmkV54EAADAWw0oH+xyzFtcdtllMWTIkLjrrru6jl1xxRVRX18f9957737X/Pmf/3k8+OCD8eyzz3Ydu+qqq2LHjh3x0EMP9ehxS6VSNDY2Rnt7ezQ0NPR0uwAAQDI9bYOKbl0777zzYsWKFbF+/fqIiFi3bl2sWrUqJk+e3O2aJ554IiZNmrTPsUsuuSSeeOKJbtfs3bs3SqXSPm8AAAA9VdGta83NzVEqleKUU06J2tra6OjoiJaWlpg6dWq3a1555ZUYMmTIPseGDBkSpVIpfv3rX0d9ff071rS2tsa8efMq2RoAAECXiq7oLFu2LJYsWRJLly6Np556Ktra2mLhwoXR1tbWq5uaPXt2tLe3d71t2rSpVz8/AACQW0VXdGbNmhXNzc1x1VVXRUTE+PHjY+PGjdHa2hrTpk3b75rjjz8+tm3bts+xbdu2RUNDw36v5kRE1NXVRV1dXSVbAwAA6FLRFZ09e/ZETc2+S2pra6Ozs7PbNeeee26sWLFin2MPP/xwnHvuuZU8NAAAQI9VFDpTpkyJlpaWePDBB2PDhg2xfPnyWLRoUVx++eVd58yePTuuueaarvdvvPHGePHFF+PLX/5yPP/883HHHXfEsmXL4otf/GLvPQsAAIC3qOjWtcWLF8ecOXNixowZsX379hg6dGjccMMNMXfu3K5ztm7dGi+//HLX+6NHj44HH3wwvvjFL8bXvva1GD58eHzzm9/0O3QAAIDCVPR7dKrF79EBAAAiCvo9OgAAAP2B0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJCO0AEAANIROgAAQDpCBwAASEfoAAAA6QgdAAAgHaEDAACkI3QAAIB0hA4AAJDOwGpvoCfK5XJERJRKpSrvBAAAqKY3m+DNRuhOvwidnTt3RkTEiBEjqrwTAADgcLBz585obGzs9uMDygdLocNAZ2dnjB07NtauXRsDBgzo9ryzzz47fvrTn/b4Y92d//bjb32/VCrFiBEjYtOmTdHQ0PBunk5FDvScenNtT86tdL7dHTffys8x30M/13zf/VrzLXat+Ra71nyLX3+wc4ua79uP9eWMzbe68y2Xy7Fz584YOnRo1NR0/5M4/eKKTk1NTRxxxBEHLLaIiNra2m4Hv7+PdXf+24/v77yGhoY++YvqQM+pN9f25NxK59vdcfOt/BzzPfRzzffdrzXfYteab7Frzbf49Qc7t6j5dnesL2ZsvtWf78G6IKIfvRjBTTfddEjn7O9j3Z3/9uM9eeyiHMpjV7K2iPl2d9x8Kz/HfA/9XPN992vNt9i15lvsWvMtfv3Bzi1qvj157KKYb7F663H7xa1rh5NSqRSNjY3R3t7eJ/9H5r3GfItlvsUy32KZb7HMt1jmWzwzLlZ/nG+/uaJzuKirq4tbbrkl6urqqr2VlMy3WOZbLPMtlvkWy3yLZb7FM+Ni9cf5uqIDAACk44oOAACQjtABAADSEToAAEA6QgcAAEhH6BToL//yL6OpqSnGjRsXN998c3jdh97zn//5nzFhwoSut/r6+vj+979f7W2l8tJLL8WFF14Y48aNi/Hjx8fu3burvaV0Ro0aFaeddlpMmDAhLrzwwmpvJ6U9e/bEiSeeGDNnzqz2VlLZsWNHnHXWWTFhwoQ49dRT4xvf+Ea1t5TKpk2b4oILLohx48bFaaedFvfff3+1t5TO5ZdfHu9///vj05/+dLW3ksIPfvCD+MAHPhAnn3xyfPOb36z2drp41bWC/Pd//3ecc8458bOf/Sx+67d+Kz7ykY/EwoUL49xzz6321tLZtWtXjBo1KjZu3Bi//du/Xe3tpPHRj340brvttjj//PPj1VdfjYaGhhg4cGC1t5XKqFGj4tlnn40jjzyy2ltJ6ytf+Ur8/Oc/jxEjRsTChQurvZ00Ojo6Yu/evfG+970vdu/eHaeeemo8+eSTcfTRR1d7ayls3bo1tm3bFhMmTIhXXnklzjzzzFi/fr3vcb3o0UcfjZ07d0ZbW1t897vfrfZ2+rXXX389xo0bF4888kg0NjbGmWeeGatXrz4s/j5wRadAr7/+erz22mvxm9/8Jn7zm9/EcccdV+0tpfTAAw/ERRdd5BtAL3oz0M8///yIiBg8eLDIod954YUX4vnnn4/JkydXeyvp1NbWxvve976IiNi7d2+Uy2V3LfSiE044ISZMmBAREccff3wcc8wx8eqrr1Z3U8lccMEFcdRRR1V7GymsWbMmmpqaYtiwYXHkkUfG5MmT44c//GG1txUR7+HQWblyZUyZMiWGDh0aAwYM2O9tT1//+tdj1KhRMWjQoPjQhz4Ua9as6fHnP/bYY2PmzJkxcuTIGDp0aEyaNCnGjBnTi8/g8Fb0fN9q2bJl8ZnPfOYQd9y/FD3fF154IY488siYMmVKnHHGGXH77bf34u77h774Gh4wYEB89KMfjbPPPjuWLFnSSzvvH/pivjNnzozW1tZe2nH/0hfz3bFjR5x++ukxfPjwmDVrVhxzzDG9tPvDX19+j1u7dm10dHTEiBEjDnHX/UdfzpdDn/eWLVti2LBhXe8PGzYsNm/e3BdbP6j3bOjs3r07Tj/99Pj617++349/5zvfiS996Utxyy23xFNPPRWnn356XHLJJbF9+/auc968N/ntb1u2bIlf/epX8YMf/CA2bNgQmzdvjtWrV8fKlSv76ulVXdHzfVOpVIrVq1fHJz7xicKf0+Gk6Pm+/vrr8dhjj8Udd9wRTzzxRDz88MPx8MMP99XTOyz0xdfwqlWrYu3atfHAAw/E7bffHv/2b//WJ8/tcFD0fP/hH/4hxo4dG2PHju2rp3RY6Yuv39/5nd+JdevWxUsvvRRLly6Nbdu29clzOxz01fe4V199Na655pr4m7/5m8Kf0+Gkr+bLG3pj3oetMuWIKC9fvnyfYxMnTizfdNNNXe93dHSUhw4dWm5tbe3R51y2bFl5xowZXe8vWLCgPH/+/F7Zb39TxHzfdM8995SnTp3aG9vst4qY7+rVq8sXX3xx1/sLFiwoL1iwoFf22x8V+TX8ppkzZ5bvvvvuQ9hl/1XEfJubm8vDhw8vn3jiieWjjz663NDQUJ43b15vbrvf6Iuv389//vPl+++//1C22W8VNd/XXnutfP7555fvueee3tpqv1Tk1+8jjzxSvuKKK3pjm2m8m3k//vjj5T/8wz/s+vif/umflpcsWdIn+z2Y9+wVnQP5n//5n1i7dm1MmjSp61hNTU1MmjQpnnjiiR59jhEjRsTq1avjtddei46Ojnj00UfjAx/4QFFb7ld6Y75vei/etnYwvTHfs88+O7Zv3x6/+tWvorOzM1auXBkf/OAHi9pyv9MbM969e3fs3LkzIt54QY0f/ehH0dTUVMh++5vemG9ra2ts2rQpNmzYEAsXLozrr78+5s6dW9SW+5XemO+2bdu6vn7b29tj5cqVvsf9r96Yb7lcjmuvvTY+9rGPxR//8R8XtdV+qTf/DcHB9WTeEydOjGeffTY2b94cu3btin/+53+OSy65pFpb3oefLt6PX/ziF9HR0RFDhgzZ5/iQIUPi+eef79HnOOecc+ITn/hE/N7v/V7U1NTERRddFJ/85CeL2G6/0xvzjXjjm+uaNWvi7//+73t7i/1ab8x34MCBcfvtt8dHPvKRKJfLcfHFF8dll11WxHb7pd6Y8bZt2+Lyyy+PiDdewer666+Ps88+u9f32h/11t8R7F9vzHfjxo3xJ3/yJ10vQvCFL3whxo8fX8R2+53emO/jjz8e3/nOd+K0007r+nmJv/u7vzPj6L2/HyZNmhTr1q2L3bt3x/Dhw+P+++/3yrj70ZN5Dxw4MP7iL/4iLrzwwujs7Iwvf/nLh8UrrkUInUK1tLRES0tLtbeRVmNj43vqnvC+NnnyZK9WVaDf/d3fjXXr1lV7G+8J1157bbW3kM7EiRPjmWeeqfY20vr93//96OzsrPY2UvvXf/3Xam8hlU9+8pOH5f/Qd+vafhxzzDFRW1v7jn9Eb9u2LY4//vgq7SoP8y2W+RbPjItlvsUy32KZb7HMt2/193kLnf044ogj4swzz4wVK1Z0Hevs7IwVK1a4rNkLzLdY5ls8My6W+RbLfItlvsUy377V3+f9nr11bdeuXfHzn/+86/2XXnopnnnmmRg8eHCMHDkyvvSlL8W0adPirLPOiokTJ8Zf/dVfxe7du2P69OlV3HX/Yb7FMt/imXGxzLdY5lss8y2W+fat1POu7ou+Vc8jjzxSjoh3vE2bNq3rnMWLF5dHjhxZPuKII8oTJ04s/+QnP6nehvsZ8y2W+RbPjItlvsUy32KZb7HMt29lnveAcrlcLqSgAAAAqsTP6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQjtABAADSEToAAEA6QgcAAEhH6AAAAOkIHQAAIB2hAwAApCN0AACAdIQOAACQzv8DftYg0n94yooAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных ----------\n","# Загрузите ваш CSV в Colab (например, через files.upload())\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с защитой от NaN ----------\n","BIG_MSE = 1e10  # значение, возвращаемое при расходимости\n","\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return BIG_MSE\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return BIG_MSE\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                batch_size=batch_size,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return BIG_MSE\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return BIG_MSE  # при любой ошибке (например, взрыв градиента)\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","# learning rate теперь ограничен 0.1, чтобы избежать расходимости\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- График 1: влияние learning rate ----------\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n","    plt.semilogx(lr_range, mses, marker='o', label=label)\n","plt.xlabel('Learning rate')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от learning rate')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# ---------- График 2: влияние числа эпох ----------\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n","    plt.semilogx(epochs_range, mses, marker='o', label=label)\n","plt.xlabel('Epochs')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от количества эпох')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# ---------- График 3: влияние размера батча ----------\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n","    plt.semilogx(batch_range, mses, marker='o', label=label)\n","plt.xlabel('Batch size')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от размера батча')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"XSp9L7NvzgH3","executionInfo":{"status":"ok","timestamp":1773046504936,"user_tz":-420,"elapsed":17959,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"e0d95483-cd7b-477d-b714-bd149ad3f653"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: overflow encountered in matmul\n","  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in subtract\n","  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in matmul\n","  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py:570: RuntimeWarning: overflow encountered in square\n","  output_errors = _average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)\n","/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: overflow encountered in matmul\n","  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in matmul\n","  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n","  w -= lr * 2 * X.T @ (X @ w - y)\n","/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in subtract\n","  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# Загрузка данных\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","# Реализации методов\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# Константы\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# Функция оценки с возвратом NaN при расходимости\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                batch_size=batch_size,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)) or np.any(np.isinf(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# Диапазоны гиперпараметров (learning rate до 0.1, как раньше, но можно и до 0.5)\n","lr_range = np.logspace(-8, -1, 8)   # 1e-8 ... 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# График 1: learning rate\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n","    plt.semilogx(lr_range, mses, marker='o', label=label)\n","plt.xlabel('Learning rate')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от learning rate')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# График 2: эпохи\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n","    plt.semilogx(epochs_range, mses, marker='o', label=label)\n","plt.xlabel('Epochs')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от количества эпох')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# График 3: размер батча\n","plt.figure(figsize=(10, 6))\n","for method, label in methods:\n","    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n","    plt.semilogx(batch_range, mses, marker='o', label=label)\n","plt.xlabel('Batch size')\n","plt.ylabel('MSE')\n","plt.title('Зависимость MSE от размера батча')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"xXa2bT4F0Jpp","executionInfo":{"status":"ok","timestamp":1773046669937,"user_tz":-420,"elapsed":18882,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"bc285e4b-8310-4ee9-b83e-080063b6c4e0"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["from google.colab import files\n","uploaded = files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":73},"id":"CFB9NiwQzDS8","executionInfo":{"status":"ok","timestamp":1773046356172,"user_tz":-420,"elapsed":5626,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"388897a8-4a7a-44a2-e235-cb589deb8c29"},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-e59627e2-0e62-474b-b4ba-6ece67f2f7a3\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-e59627e2-0e62-474b-b4ba-6ece67f2f7a3\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving data.csv to data.csv\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных (ожидается файл data.csv) ----------\n","from google.colab import files\n","print(\"Загрузите файл data.csv:\")\n","uploaded = files.upload()  # выберите файл data.csv на компьютере\n","\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                batch_size=batch_size,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы ----------\n","def plot_and_table(param_values, param_name, xlabel, title, filename):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            mses.append(mse)\n","        results[label] = mses\n","        plt.semilogx(param_values, mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    # Вывод таблицы\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений MSE для {title}:\")\n","    print(df_table.round(6))\n","    return df_table\n","\n","# ---------- Построение графиков и таблиц ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n","table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate', 'lr_plot')\n","\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n","table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох', 'epochs_plot')\n","\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n","table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча', 'batch_plot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ektA0t4m0026","executionInfo":{"status":"ok","timestamp":1773046849888,"user_tz":-420,"elapsed":34249,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"2a161cb0-4e2b-41c9-e12b-0298888b0051"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Загрузите файл data.csv:\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-ab4c66b8-e97a-4ee3-99c8-cde5edd9f429\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-ab4c66b8-e97a-4ee3-99c8-cde5edd9f429\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving data.csv to data (1).csv\n","Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1: Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от learning rate:\n","               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Learning rate                                                              \n","1.000000e-08          8.313329  8.325737  8.325737    8.313329          NaN\n","1.000000e-07          8.313329  8.324683  8.324850    8.313329          NaN\n","1.000000e-06          8.313329       NaN  8.346550    8.313329          NaN\n","1.000000e-05          8.313329       NaN       NaN    8.313329          NaN\n","1.000000e-04          8.313329       NaN       NaN    8.313329          NaN\n","1.000000e-03          8.313329       NaN       NaN    8.313329          NaN\n","1.000000e-02          8.313329       NaN       NaN    8.313329          NaN\n","1.000000e-01          8.313329       NaN       NaN    8.313329          NaN\n","\n","============================================================\n","ГРАФИК 2: Зависимость MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от количества эпох:\n","        Normal Equation             GD  SGD  sklearn LR  sklearn SGD\n","Epochs                                                              \n","2              8.313329   6.835259e+22  inf    8.313329          NaN\n","20             8.313329  3.980530e+196  NaN    8.313329          NaN\n","200            8.313329            NaN  NaN    8.313329          NaN\n","2000           8.313329            NaN  NaN    8.313329          NaN\n","20000          8.313329            NaN  NaN    8.313329          NaN\n","\n","============================================================\n","ГРАФИК 3: Зависимость MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от размера батча:\n","            Normal Equation  GD  SGD  sklearn LR  sklearn SGD\n","Batch size                                                   \n","1                  8.313329 NaN  NaN    8.313329          NaN\n","2                  8.313329 NaN  NaN    8.313329          NaN\n","4                  8.313329 NaN  NaN    8.313329          NaN\n","8                  8.313329 NaN  NaN    8.313329          NaN\n","16                 8.313329 NaN  NaN    8.313329          NaN\n","32                 8.313329 NaN  NaN    8.313329          NaN\n","64                 8.313329 NaN  NaN    8.313329          NaN\n","128                8.313329 NaN  NaN    8.313329          NaN\n","256                8.313329 NaN  NaN    8.313329          NaN\n","512                8.313329 NaN  NaN    8.313329          NaN\n"]}]},{"cell_type":"code","source":["\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных (ожидается файл data.csv) ----------\n","from google.colab import files\n","print(\"Загрузите файл data.csv:\")\n","uploaded = files.upload()\n","\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                batch_size=batch_size,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","# Начальные значения по умолчанию (будут переопределены после первого графика)\n","DEFAULT_LR = 0.01\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы ----------\n","def plot_and_table(param_values, param_name, xlabel, title, filename):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            mses.append(mse)\n","        results[label] = mses\n","        plt.semilogx(param_values, mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    # Вывод таблицы\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений MSE для {title}:\")\n","    # Заменяем NaN на прочерк для читаемости\n","    print(df_table.round(6).to_string(na_rep='-'))\n","    return df_table\n","\n","# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n","table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate', 'lr_plot')\n","\n","# Определяем лучший learning rate для GD (первый не-NaN или минимальный MSE среди не-NaN)\n","gd_mses = table_lr['GD'].values\n","valid_lr = lr_range[~np.isnan(gd_mses)]\n","if len(valid_lr) > 0:\n","    # Берем первый (самый маленький) работающий learning rate\n","    best_lr = valid_lr[0]\n","    print(f\"\\nВыбран learning rate = {best_lr:.2e} для следующих графиков (наименьший работающий для GD).\")\n","else:\n","    best_lr = 1e-8  # запасной вариант\n","    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n","\n","DEFAULT_LR = best_lr\n","\n","# ---------- График 2: влияние числа эпох (с новым DEFAULT_LR) ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n","table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох', 'epochs_plot')\n","\n","# ---------- График 3: влияние размера батча (с новым DEFAULT_LR) ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n","table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча', 'batch_plot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"McU9ae5C1kXf","executionInfo":{"status":"ok","timestamp":1773047043463,"user_tz":-420,"elapsed":27214,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"f650fb8b-388e-4d44-a73c-7ea6aa8def06"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Загрузите файл data.csv:\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-6e86feb7-d490-426d-ab6a-594e618da620\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-6e86feb7-d490-426d-ab6a-594e618da620\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving data.csv to data (2).csv\n","Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1: Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от learning rate:\n","               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Learning rate                                                              \n","1.000000e-08          8.313329  8.325737  8.325737    8.313329            -\n","1.000000e-07          8.313329  8.324683  8.324850    8.313329            -\n","1.000000e-06          8.313329         -  8.346550    8.313329            -\n","1.000000e-05          8.313329         -         -    8.313329            -\n","1.000000e-04          8.313329         -         -    8.313329            -\n","1.000000e-03          8.313329         -         -    8.313329            -\n","1.000000e-02          8.313329         -         -    8.313329            -\n","1.000000e-01          8.313329         -         -    8.313329            -\n","\n","Выбран learning rate = 1.00e-08 для следующих графиков (наименьший работающий для GD).\n","\n","============================================================\n","ГРАФИК 2: Зависимость MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от количества эпох:\n","        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Epochs                                                                    \n","2              8.313329  2560.000199  2583.409888    8.313329            -\n","20             8.313329   217.565243   237.582879    8.313329            -\n","200            8.313329     8.325835     8.325835    8.313329            -\n","2000           8.313329     8.325615     8.325615    8.313329            -\n","20000          8.313329     8.323616     8.323616    8.313329            -\n","\n","============================================================\n","ГРАФИК 3: Зависимость MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от размера батча:\n","            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Batch size                                                              \n","1                  8.313329  8.325737  8.325737    8.313329            -\n","2                  8.313329  8.325737  8.325737    8.313329            -\n","4                  8.313329  8.325737  8.325737    8.313329            -\n","8                  8.313329  8.325737  8.325737    8.313329            -\n","16                 8.313329  8.325737  8.325737    8.313329            -\n","32                 8.313329  8.325737  8.325737    8.313329            -\n","64                 8.313329  8.325737  8.325737    8.313329            -\n","128                8.313329  8.325737  8.325737    8.313329            -\n","256                8.313329  8.325737  8.325737    8.313329            -\n","512                8.313329  8.325737  8.325737    8.313329            -\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных (ожидается файл data.csv) ----------\n","from google.colab import files\n","print(\"Загрузите файл data.csv:\")\n","uploaded = files.upload()\n","\n","df = pd.read_csv('data.csv')\n","X_raw = df[['x']].values\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X_raw.shape}, y shape: {y.shape}\")\n","\n","# ---------- Масштабирование признаков ----------\n","scaler = StandardScaler()\n","X = scaler.fit_transform(X_raw)   # теперь X имеет среднее 0 и дисперсию 1\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы (после масштабирования) ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                batch_size=batch_size,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)   # X уже масштабированы\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1 (после масштабирования 0.1 может быть великоват, но посмотрим)\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01      # будет переопределён после первого графика\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы ----------\n","def plot_and_table(param_values, param_name, xlabel, title, filename):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            mses.append(mse)\n","        results[label] = mses\n","        plt.semilogx(param_values, mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    # Вывод таблицы\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений MSE для {title}:\")\n","    print(df_table.round(6).to_string(na_rep='-'))\n","    return df_table\n","\n","# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n","table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (после масштабирования)', 'lr_plot')\n","\n","# Определяем лучший learning rate для GD (первый не-NaN или минимальный MSE среди не-NaN)\n","gd_mses = table_lr['GD'].values\n","valid_lr = lr_range[~np.isnan(gd_mses)]\n","if len(valid_lr) > 0:\n","    best_lr = valid_lr[0]   # наименьший работающий\n","    print(f\"\\nВыбран learning rate = {best_lr:.2e} для следующих графиков (наименьший работающий для GD).\")\n","else:\n","    best_lr = 1e-8\n","    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n","\n","DEFAULT_LR = best_lr\n","\n","# ---------- График 2: влияние числа эпох ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n","table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (после масштабирования)', 'epochs_plot')\n","\n","# ---------- График 3: влияние размера батча ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n","table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (после масштабирования)', 'batch_plot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"_-mhD1it2XH3","executionInfo":{"status":"ok","timestamp":1773047256230,"user_tz":-420,"elapsed":33262,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"de63a58b-2b82-4d28-b7f4-ead5222b99c1"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Загрузите файл data.csv:\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-07c43c14-b8e0-498b-a6e8-e2ce6b9a55a1\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-07c43c14-b8e0-498b-a6e8-e2ce6b9a55a1\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving data.csv to data (3).csv\n","Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1: Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от learning rate (после масштабирования):\n","               Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Learning rate                                                                    \n","1.000000e-08          8.313329  3245.435934  3245.437183    8.313329            -\n","1.000000e-07          8.313329  2267.501029  2267.588249    8.313329            -\n","1.000000e-06          8.313329    70.020689    70.259705    8.313329            -\n","1.000000e-05          8.313329     8.313329     8.313329    8.313329            -\n","1.000000e-04          8.313329     8.313329     8.313332    8.313329            -\n","1.000000e-03          8.313329    69.773860     8.320042    8.313329            -\n","1.000000e-02          8.313329            -     8.470672    8.313329            -\n","1.000000e-01          8.313329            -            -    8.313329            -\n","\n","Выбран learning rate = 1.00e-08 для следующих графиков (наименьший работающий для GD).\n","\n","============================================================\n","ГРАФИК 2: Зависимость MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от количества эпох (после масштабирования):\n","        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Epochs                                                                    \n","2              8.313329  3377.142741  3377.142744    8.313329            -\n","20             8.313329  3374.720457  3374.720483    8.313329            -\n","200            8.313329  3350.593198  3350.593456    8.313329            -\n","2000           8.313329  3118.629709  3118.632111    8.313329            -\n","20000          8.313329  1523.346396  1523.358092    8.313329            -\n","\n","============================================================\n","ГРАФИК 3: Зависимость MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от размера батча (после масштабирования):\n","            Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Batch size                                                                    \n","1                  8.313329  3245.435934  3245.437223    8.313329            -\n","2                  8.313329  3245.435934  3245.437222    8.313329            -\n","4                  8.313329  3245.435934  3245.437219    8.313329            -\n","8                  8.313329  3245.435934  3245.437214    8.313329            -\n","16                 8.313329  3245.435934  3245.437204    8.313329            -\n","32                 8.313329  3245.435934  3245.437183    8.313329            -\n","64                 8.313329  3245.435934  3245.437143    8.313329            -\n","128                8.313329  3245.435934  3245.437062    8.313329            -\n","256                8.313329  3245.435934  3245.436901    8.313329            -\n","512                8.313329  3245.435934  3245.436579    8.313329            -\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных ----------\n","from google.colab import files\n","print(\"Загрузите файл data.csv:\")\n","uploaded = files.upload()\n","\n","df = pd.read_csv('data.csv')\n","X_raw = df[['x']].values\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X_raw.shape}, y shape: {y.shape}\")\n","\n","# ---------- Масштабирование признаков ----------\n","scaler = StandardScaler()\n","X = scaler.fit_transform(X_raw)   # теперь X имеет среднее 0 и дисперсию 1\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            # Упростим: без batch_size, используем значения по умолчанию\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=1e-6,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception as e:\n","        # print(f\"Error in {method} with lr={lr}, epochs={epochs}, batch={batch_size}: {e}\")\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01      # будет переопределён после первого графика\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы ----------\n","def plot_and_table(param_values, param_name, xlabel, title, filename):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            mses.append(mse)\n","        results[label] = mses\n","        plt.semilogx(param_values, mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    # Вывод таблицы\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений MSE для {title}:\")\n","    print(df_table.round(6).to_string(na_rep='-'))\n","    return df_table\n","\n","# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n","table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (после масштабирования)', 'lr_plot')\n","\n","# Выбираем learning rate с наименьшим MSE для GD (среди не-NaN)\n","gd_mses = table_lr['GD'].values\n","valid_mask = ~np.isnan(gd_mses)\n","if np.any(valid_mask):\n","    valid_lr = lr_range[valid_mask]\n","    valid_mse = gd_mses[valid_mask]\n","    best_idx = np.argmin(valid_mse)\n","    best_lr = valid_lr[best_idx]\n","    print(f\"\\nВыбран learning rate = {best_lr:.2e} (MSE = {valid_mse[best_idx]:.6f}) для следующих графиков.\")\n","else:\n","    best_lr = 1e-5   # запасной вариант\n","    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-5.\")\n","\n","DEFAULT_LR = best_lr\n","\n","# ---------- График 2: влияние числа эпох ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n","table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (после масштабирования)', 'epochs_plot')\n","\n","# ---------- График 3: влияние размера батча ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n","table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (после масштабирования)', 'batch_plot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3q2uAZw825E8","executionInfo":{"status":"ok","timestamp":1773047388861,"user_tz":-420,"elapsed":31298,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"893b186c-f997-4ec8-c76c-cb6259b02bed"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Загрузите файл data.csv:\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-c473765b-e8c6-497d-9e10-b587d587ee25\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-c473765b-e8c6-497d-9e10-b587d587ee25\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving data.csv to data (4).csv\n","Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1: Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от learning rate (после масштабирования):\n","               Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Learning rate                                                                    \n","1.000000e-08          8.313329  3245.435934  3245.437183    8.313329  3310.765416\n","1.000000e-07          8.313329  2267.501029  2267.588249    8.313329  2767.249684\n","1.000000e-06          8.313329    70.020689    70.259705    8.313329   465.183031\n","1.000000e-05          8.313329     8.313329     8.313329    8.313329     8.313411\n","1.000000e-04          8.313329     8.313329     8.313332    8.313329     8.313361\n","1.000000e-03          8.313329    69.773860     8.320042    8.313329     8.314865\n","1.000000e-02          8.313329            -     8.470672    8.313329     8.348210\n","1.000000e-01          8.313329            -            -    8.313329     8.732265\n","\n","Выбран learning rate = 1.00e-04 (MSE = 8.313329) для следующих графиков.\n","\n","============================================================\n","ГРАФИК 2: Зависимость MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от количества эпох (после масштабирования):\n","        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Epochs                                                                    \n","2              8.313329  1389.676642  1519.252432    8.313329  2267.536287\n","20             8.313329     8.765660     9.423590    8.313329    70.248428\n","200            8.313329     8.313329     8.313338    8.313329     8.313361\n","2000           8.313329     8.313329     8.313334    8.313329     8.313361\n","20000          8.313329     8.313329     8.313333    8.313329     8.313361\n","\n","============================================================\n","ГРАФИК 3: Зависимость MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA04AAAIoCAYAAABeertyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgydJREFUeJzt3Xd8VMXCxvFn0xsJLSREQmihd1AJSFM6KkXFCyggqFzFV5SrIEoVpCOCWFCkqCBeBNGrtAgCKr0poCIdxIQOSQik7Xn/wF2z2SQnCSQb4Pe9Nx/Z2dlzZidnJ/vsmTNrMQzDEAAAAAAgS26ubgAAAAAAFHYEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACANzyDMPQ+fPndeDAAVc3BcgXVqtVZ8+e1eHDh13dFOCWRXACANyS4uPjNWzYMFWpUkVeXl4qUaKEKleurP3797u6aTfEpEmTVLVqVVmtVlc3BS4SGxurF154QREREfLy8lJwcLCqV6+uuLg4VzftltSoUSMNHjzY1c2ACxGccNN7//331bZtW4WEhMjT01OhoaFq3ry5Pv74Y95Q3MT69Okji8WiwMBAXblyxen+AwcOyGKxyGKxaMqUKQ73HT16VE888YQqVqwoHx8fhYaGqlmzZho5cqRDvRYtWti3kfGnatWq+fr8kL/OnTunqKgozZgxQw8//LC++uorRUdHa926dSpXrpyrm3fd4uLiNHHiRA0ZMkRubvwpvx0dPHhQd955pxYtWqT+/fvrm2++UXR0tNasWSN/f39XN++WNGTIEL3zzjuKjY11dVPgIh6ubgBwvebPn6/SpUtr+PDhCgwM1MWLF7V582b16dNHK1as0GeffebqJiKPPDw8lJiYqP/973/q1q2bw30LFiyQj4+Prl696lBuezPh6+urvn37qly5coqJidHOnTs1ceJEjR492qF+mTJlNH78eKd9BwUF3fgnhALz8ssvKyYmRps2bVKNGjVc3Zwbbs6cOUpNTVX37t1d3RS4SP/+/eXl5aXNmzfrjjvucHVzbgudOnVSYGCg3n33Xb3++uuubg5cgOCEm96GDRvk6enpUPb888+rRIkSmjlzpsaPH39LfMJ8O/L29laTJk302WefOQWnhQsXqmPHjlqyZIlD+bRp05SQkKDdu3crIiLC4b7Tp0877SMoKEiPPfbYjW88XOb06dOaP3++3n///VsyNEnS3Llz9eCDD8rHx8fVTYEL7NixQ2vXrtXq1asJTQXIzc1NDz/8sD7++GONHj1aFovF1U1CAeP8Pm56GUOTjS0spZ/G8tVXX6ljx44KCwuTt7e3KlasqDFjxigtLc3hsRmncJUsWVIdO3bU3r17HepZLBaNGjXKoWzy5MmyWCxq0aKFQ/nVq1c1atQoVa5cWT4+PipdurS6du2qQ4cOSbo2vcxisWjevHkOjxswYIAsFov69OljL5s3b54sFou8vLx05swZh/qbNm2yt3v79u0O9y1evFgNGjSQr6+vSpYsqccee0wnT5506rvff/9d3bp1U3BwsHx9fVWlShW99tprkqRRo0ZlOb3N9rNu3Tp7P9asWdNp+7nRo0cPrVixQhcvXrSXbdu2TQcOHFCPHj2c6h86dEhlypRxCk2SVKpUqetqS0anT59Wv379FBISIh8fH9WpU0fz58+332/7nWb3k/73mpHt8VOmTNG0adMUEREhX19fNW/e3OlY/OWXX9SnTx9VqFDBPj2xb9++OnfunEO99957T3Xq1FFQUJD8/f1Vp04dffTRRw51bNMk69at69Sm8ePHy2KxKCAgwOm+Tz/91H58FS9eXP/617904sQJhzq2Y2LHjh1q3LixfH19Vb58eb3//vsO9ZKTkzVixAg1aNDA3tamTZvq+++/z7K/bLZt2yar1ark5GQ1bNhQPj4+KlGihLp3767jx4/nut9yc8xnNibYXq9Hjx61l+V0LMrMkSNH9Msvv6hVq1YO5WbHW8Yxyez4tbFarZo+fbpq1aolHx8fBQcHq127dk7ji+15mu334sWLeuGFFxQeHi5vb29VqlRJEydOzNHU6nLlysliseiFF15wuq9t27ayWCy6//777WW5OY5y8jwtFouee+45h8fdf//9Th/OzZ49WxaLxf46tbU7q5/0j58yZYoaN26sEiVKyNfXVw0aNNAXX3zhsP3NmzfLx8dHhw4dUo0aNeTt7a3Q0FD1799f58+fd6jbokULp9/Btm3b7PtOz/b8FixYoCpVqsjHx0cNGjTQhg0bnPpr165dat++vQIDAxUQEKD77rtPmzdvdqiT8Zjw8/NTrVq1NHv2bId6OR2/bK/Fs2fPOpRv377d6e9nnz59nH4vJ06ckK+vr9PrMTU1VWPHjlXlypXl7e3t0OaMx3nr1q117Ngx7d6926lPcOvjjBNuGRcvXlRqaqri4+O1Y8cOTZkyRf/6179UtmxZe5158+YpICBAgwYNUkBAgNauXasRI0YoLi5OkydPdthe1apV9dprr8kwDB06dEhvvvmmOnTo4PTGK2MbMpv2lZaWpvvvv19r1qzRv/71Lw0cOFDx8fGKjo7W3r17VbFixUy3d/DgQX344YdZ7s/d3V2ffvqpXnzxRXvZ3LlzM53CNm/ePD3xxBO68847NX78eJ06dUrTp0/XTz/9pF27dqlo0aKSrv0Ba9q0qTw9PfX000+rXLlyOnTokP73v//pjTfeUNeuXVWpUiX7dl988UVVq1ZNTz/9tL2sWrVqWbY5t7p27ap///vfWrp0qfr27Svp2tmmqlWrqn79+k71IyIi9N1332nt2rW69957Tbeflpbm9EdYknx9fbO9TuDKlStq0aKFDh48qOeee07ly5fX4sWL1adPH128eFEDBw5UcHCwPvnkE/tjli5dqi+//NKhLKvffXoff/yx4uPjNWDAAF29elXTp0/Xvffeqz179igkJESSFB0drcOHD+uJJ55QaGio9u3bpw8++ED79u3T5s2b7W+Q4uPj1aZNG1WsWFGGYei///2vnnzySRUtWlQPPfSQfZ8eHh7at2+fdu3apXr16tnL582bl+lZjjfeeEPDhw9Xt27d9OSTT+rMmTN6++231axZM4fjS5IuXLigDh06qFu3burevbv++9//6plnnpGXl5f9dxwXF6fZs2ere/fueuqppxQfH6+PPvpIbdu21datWzMNdTa2N1vPPfecGjRooAkTJujMmTOaMWOGfvzxR+3atUslS5bMcb/lxzGfm7Eoo40bN0pSpse/JHXv3l0dOnRwKBs6dKjD7Zwcvzb9+vXTvHnz1L59ez355JNKTU3VDz/8oM2bN6thw4ZO+582bZq9f9944w2H+xITE9W8eXOdPHlS/fv3V9myZbVx40YNHTpUMTExeuutt7J97pLk4+OjBQsWaPLkyfYPzv7880+tWbPG6djMzXGU2+eZG2+99ZYSEhIkSb/99pvGjRunV1991X7cpP8gYvr06XrwwQfVs2dPJScna9GiRXrkkUf0zTffqGPHjpKuHeNXr17VM888o3vvvVf//ve/dejQIb3zzjvasmWLtmzZIm9v7yzbM2TIkCzvW79+vT7//HM9//zz8vb21rvvvqt27dpp69at9g/C9u3bp6ZNmyowMFCDBw+Wp6enZs2apRYtWmj9+vW6++67HbZpOybi4uI0Z84cPfXUUypXrpw9/Od0/LpeI0aMcPrbKElTp07V8OHD1aVLFw0ZMkTe3t764Ycf9MEHHzjVbdCggSTpp59+chgbcZswgFtElSpVDEn2n169ehkpKSkOdRITE50e179/f8PPz8+4evWqvax58+ZG8+bNHeq9+uqrhiTj9OnT9jJJxsiRI+23Bw8ebJQqVcpo0KCBw+PnzJljSDLefPNNp/1brVbDMAzjyJEjhiRj7ty59vu6detm1KxZ0wgPDzd69+5tL587d64hyejevbtRq1Yte/nly5eNwMBAo0ePHoYkY9u2bYZhGEZycrJRqlQpo2bNmsaVK1fs9b/55htDkjFixAh7WbNmzYwiRYoYx44dy7SdGUVERDi0Lb3mzZsbNWrUyPQ+M7179zb8/f0NwzCMhx9+2LjvvvsMwzCMtLQ0IzQ01Bg9erS9zyZPnmx/3N69ew1fX19DklG3bl1j4MCBxrJly4zLly9n2r70x0z6n/79+2fbvrfeesuQZHz66af2suTkZCMqKsoICAgw4uLinB4zcuRIIzfDru35+fr6Gn/++ae9fMuWLYYk48UXX7SXZXZsf/bZZ4YkY8OGDVnuIzU11QgMDDSee+45e5mt7x944AGH8h9++MHw9fU1OnfubP/dGIZhHD161HB3dzfeeOMNh23v2bPH8PDwcCi39fnUqVPtZUlJSUbdunWNUqVKGcnJyfZ2JSUlOWzvwoULRkhIiNG3b98sn49h/PP6qF69ukO/fP/994Yk4z//+Y+9LC/9lt0xb7FYHF5P6dtz5MiRbPeb2ViUmWHDhhmSjPj4eIfyzF4PNjVq1HAYk3J6/K5du9aQZDz//PNO28w4Jnz44YeGJIexI+NYOmbMGMPf39/4448/HB77yiuvGO7u7sbx48ezfe4RERFG69atjZIlSxpffPGFw3YbN25sREREGB07drSX5/Q4yunzlGQMGDDA4f6OHTsaERERDmW2vtizZ4/T9mzH4ffff5/pc8x4bCQnJxs1a9Y07r33XnuZbSy57777jNTUVHu57Vh7++237WUZfwfLly83JBnt2rVzGo9s49/27dvtZceOHTN8fHyMLl262Ms6d+5seHl5GYcOHbKX/fXXX0aRIkWMZs2aObUn/bH/xx9/GJKMSZMmZfmcDSPz16HteZ85c8ah7rZt25z+fvbu3dvh97J3717Dzc3NaN++vVOboqKijGrVqjn8rm1tt/0dTc/Ly8t45plnnMpx62OqHm4Zc+fOVXR0tBYsWKB+/fppwYIFDp8IS9fOItjEx8fr7Nmzatq0qRITE/X777871E1JSdHZs2d15swZbdq0SV9++aVq165t/yQ1o5MnT+rtt9/W8OHDnaYxLVmyRCVLltT//d//OT0uq0/SduzYocWLF2v8+PFZrpr1+OOP6/fff7dPJViyZImCgoJ03333OdTbvn27Tp8+rWeffdbhE9mOHTuqatWq+vbbbyVJZ86c0YYNG9S3b1+HM3XZtdOM7YzO2bNnlZycnKdt9OjRQ+vWrVNsbKzWrl2r2NjYTKfpSVKNGjW0e/duPfbYYzp69KimT5+uzp07KyQkJNOzd+XKlVN0dLTTT2ZTgdJbvny5QkNDHS7O9/T01PPPP6+EhAStX78+T881M507d3a4juGuu+7S3XffreXLl9vL0h/bV69e1dmzZ9WoUSNJ0s6dOx22Z/udHDt2TNOmTVNcXJyaNm3qtN++fftq4cKFSkpKknTtNda1a1enhTOWLl0qq9Wqbt262X/XZ8+eVWhoqCIjI52mRXl4eKh///72215eXurfv79Onz6tHTt2SLp2NtXLy0vStSlU58+fV2pqqho2bOj0fLIyYMAAh35p0aKFGjRoYD/ec9tvOVGqVCn9+eefpvVyMxZldO7cOXl4eGQ6XTKncnr8LlmyRBaLxWlFSsl5TLC9vrM707F48WI1bdpUxYoVczhWWrVqpbS0tEynhGXk5eWlnj17au7cufYy2xn1jHJ6HOXmeea39MfGhQsXdOnSJTVt2jTT43HQoEFyd3e333788ccVEhLicIynZxiGhg4dqoceesjprJBNVFSU/ayKJJUtW1adOnXSqlWrlJaWprS0NK1evVqdO3dWhQoV7PVKly6tHj166Mcff3RaDv3ChQv275iaNm2a3N3d1bx580yfc05eh+fPn3c4fi5dupTpc0lv6NChql+/vh555BGn++Lj41WsWLEc/65txy9uP7d1cNqwYYMeeOABhYWFyWKxaNmyZfm6v8zmyV/vkserVq1So0aNVKRIEQUHB+uhhx5ymLebmQcffFBly5a1X2fz+OOP66+//rLff/XqVfXp00e1atWSh4eHOnfu7LSNH3/8UU2aNLHPwa5ataqmTZvmUOe9995T7dq1FRgYqMDAQEVFRWnFihXX9XyzExUVpVatWqlHjx6aPXu2Xn/9dc2dO1c//fSTvc6+ffvUpUsXBQUFKTAwUMHBwfaFATIOvBs3blRwcLBKlSqlxo0bKzU1VYsXL85yYB05cqTCwsIc3hDaHDp0SFWqVJGHR85nx77yyitq2rSpw3z9jIKDg9WxY0fNmTNH0rWVtnr37u0UtI4dOyZJqlKlitM2qlatar/f9sWJ13tdUnq///67goODHa6XWrhwYa620aFDBxUpUkSff/65FixYoDvvvNNh6lRGlStX1ieffKKzZ8/ql19+0bhx4+Th4aGnn35a3333nUNdf39/tWrVyunH7LV57NgxRUZGOvW1beqNrU9vhMjISKeyypUrO7zWz58/r4EDByokJES+vr4KDg5W+fLlJTkf2wcOHFBwcLDKlSun1157Te+++67T4hvStWDt4eGhr776SpcvX9Z///vfTN+cHjhwQIZhKDIy0v67tv389ttvTotyhIWFOU2DrFy5siQ5PKf58+erdu3a9muUgoOD9e2335q+SbK9RjP7HVarVi3P/ZYTjRs31hdffKFVq1bpzJkzOnv2rH2KVnq5GYvyQ06P30OHDiksLEzFixc33abtOsTsAt2BAwe0cuVKp+PENmUrswVcMvPEE09o5cqViomJ0fr16xUTE5PpMSzl7DjKzfPMb998840aNWokHx8fFS9eXMHBwXrvvfcc2pvVMe7u7q7IyMgs3wcsWLBA+/bt07hx47Lcf1bjTWJios6cOaMzZ84oMTEx078n1apVk9Vqdbq2sX79+goODlbFihU1Z84czZw5U3fddZf9/ty+DqtUqZLp8ZOVH3/8Uf/73/80ceLETP+GR0VFadOmTZo1a5ZOnjyZ5evWxjAMFoa4Td3W1zhdvnxZderUUd++fdW1a9cC2WeNGjUc3riZvZG2WCw6cuRIpqvCHTlyRJ06ddKgQYO0YMECXbp0SS+++KK6du2a7SelLVu21KuvvqrSpUvr5MmTeumll/Twww/b582npaXJ19dXzz//vNOKZTb+/v567rnnVLt2bfn7++vHH39U//795e/vbz/LU6ZMGU2YMEGRkZEyDEPz589Xp06dtGvXrgJZ6erhhx/Wa6+9pi1btqhJkya6ePGimjdvrsDAQL3++uv27/jZuXOnhgwZ4nRhcu3atTV16lRJsl8f0aJFC+3cuVOhoaEOdX/77TfNmzdPn376aZaLVeTG6tWr9d1332nTpk2mdfv27atevXrp//7v/7RhwwbNnj1bP/zww3W34UYpV66c/UzPuXPnNGPGDD3++OOqUKGC/RNFM97e3uratavmz5+vw4cPO118nxV3d3fVqlVLtWrVUlRUlFq2bKkFCxaY/pG9GXXr1k0bN27Uyy+/rLp16yogIEBWq1Xt2rVzOrbLli2r6OhoxcfH65tvvtGLL76o8PBwp5Du6empxx57THPnzlViYqJKlCihe++91+EaLenaJ/kWi0UrVqxw+PTbJi9nRj799FP16dNHnTt31ssvv6xSpUrJ3d1d48ePty+okpX0n16byU2/5cSkSZPUoUMHtWvXLss6uR2LMipRooT9es4iRYrkuo35JTY2VgEBAdleG2i1WtW6dessv0TUFqDN1KlTR3Xq1NHHH3+s3377TQ899JACAwOd6l3PceQKP/zwgx588EE1a9ZM7777rkqXLi1PT0/NnTvX4QOn3BzjNsnJyRo+fLj69euX436+UT799FOFhITo6tWrWrt2rQYMGCAfHx/74ji5fR0uWbLE4ff9xx9/aMCAAVnuf8iQIWrbtq3uvfdepwWYpGuL3pw8eVL//ve/c/R8Ll68mOXsE9zabuvg1L59e7Vv3z7L+5OSkvTaa6/ps88+08WLF1WzZk1NnDjRaXWa3PDw8HB6051XO3bsUFpamsaOHWv/1PCll15Sp06dlJKSkuUb+PQLCUREROiVV15R586d7Y/x9/fXe++9J+naxY/pVzOzqVevnsNFkeXKldPSpUv1ww8/2IPTAw884PCYN954Q++99542b95cIMHJ9qWptjdy69at07lz57R06VI1a9bMXu/IkSOZPr5YsWIOb7BbtGihsLAwzZ071+lC66FDh6pu3bp69NFHM91WxYoVtWXLlmx/LzaGYeiVV15Rly5dchQs2rdvLx8fH/3rX//SPffco4oVKzoFJ9sKc/v373daMGH//v32+23TLjKu2HY9bGd0bJo2bao77rhDq1evznFwkq5N15szZ47c3Nz0r3/9K9ftsF3cHRMTk+vHZiYiIkK//PKLrFarw6f2tmlWma3ql1cHDhxwKvvjjz/sH6hcuHBBa9as0ejRozVixIhsHydJfn5+9t9Jly5ddPToUY0ZMybTs5t9+/ZVnTp1dOLECfXu3TvTT1ltC02UL18+R2/I/vrrL12+fNnhDfYff/wh6Z/VML/44gtVqFBBS5cuddhnZlOpMrJ9Up3Z8f7777/nud9yolKlStq3b5/27NljX91s9erVDgs+5HYsysh2luHIkSOqXbt2ntqZ0+O3YsWKWrVqlc6fP296NubXX381XSSjYsWKSkhIuCEfXvTt21fTpk1TbGys/ve//2VaJ6fHUW6eZ35asmSJfHx8tGrVKocpj+mnJUqOx3j66XJWq1UHDhzIdNGCd999V6dPnzb94Cmr8cbPz0/BwcGSro0h+/fvd6r3+++/y83NTeHh4Q7lTZo0sb/u7r//fu3bt0/jx49Xnz598vQ6bNasmUNwSb/4TEbLli3Tpk2bsv1AuUSJEvrkk09Uo0YN3XPPPerfv7/T69bm5MmTSk5OvqGLIOHmcVtP1TPz3HPPadOmTVq0aJF++eUXPfLII2rXrt11/VE9cOCAwsLCVKFCBfXs2TPbFdrMNGjQQG5ubpo7d67S0tJ06dIlffLJJ2rVqlWOz3qcP39eCxYsUOPGja/rTMmuXbu0ceNGhznL6aWlpWnRokW6fPmyoqKi8ryfzKS/ziO9Dz/8UBaLxf7GyRagDMOw10lOTta7776bo/3Ygpjteg+bTZs26auvvtKECROyPHX/0EMP6ezZs5o5c6bTfenbI8l+vGW2Ol9mPDw81KtXL/3yyy/2FckyatiwoUqVKqX333/fof0rVqzQb7/9Zl+pKTg4WM2aNdOcOXOcjs2M7cwr26eHmZ2ZyE7Lli01ZswYzZw5M9sPH3744QelpKQ4lduOk8yml+RFhw4dFBsbq88//9xelpqaqrffflsBAQFZvhbyYtmyZQ7Lxm/dulVbtmyxf/CT2bEtKUcrlKWlpenChQtOx7VNjRo11KBBA/36669ZLp3etWtXubu7a/To0U5tMAzDaUnh1NRUzZo1y347OTlZs2bNUnBwsP3aisye05YtW3J0FrZevXoKDQ11Ot5/+OEHbd++3R4Qr6ffsuPp6an69evbp31Wr17d4f7rHYtsY2jGZZJzI6fH70MPPSTDMJy+ODpj+0+cOKGffvrJdCXLbt26adOmTVq1apXTfbaVUXOqR48eOnnypEqVKpXlB5o5PY5y+jzzm7u7uywWi8Oy9EePHnW6lOC+++6Tt7e3ZsyY4XBGZsGCBTp16pTThyDx8fF644039OKLL5p+eJsxZJw4cUJfffWV2rRpI3d3d7m7u6tNmzb66quvHKYEnjp1SgsXLtQ999yT6dm/9K5cuWJ/bebX61C6Nr69+uqr6tGjR7YrcUrS008/LS8vL82ePTvT162N7TrMxo0bX3f7cPO5rc84Zef48eOaO3eujh8/rrCwMEnXzuasXLlSc+fOzXZ+cFbuvvtuzZs3T1WqVFFMTIxGjx6tpk2bau/evXmablG+fHmtXr1a3bp1U//+/ZWWlqaoqKgsg0R6Q4YM0cyZM5WYmKhGjRrpm2++yfX+pWvT8c6cOaPU1FSNGjVKTz75pMP9e/bsUVRUlK5evaqAgAB9+eWXWQ5GedWjRw9VrVpVXbp0UUhIiM6cOaMVK1bo+++/12uvvaZatWpJujbIFStWTL1799bzzz8vi8WiTz75JMs/iqdOndKnn34qSTp79qxmzZolDw8Ppz9Iq1evVuvWrbP9BLVXr176+OOPNWjQIG3dulVNmzbV5cuX9d133+nZZ59Vp06dHLb31FNP5eoN/pgxY/Tyyy+rWLFimd7v6empiRMn6oknnlDz5s3VvXt3+3Lk5cqVczgLOWPGDN1zzz2qX7++nn76aZUvX15Hjx7Vt99+m6fvrUhISNDKlSslXQvqM2bMkKenpz2s5ZSbm5uGDRtmWm/ixInasWOHunbtav80fufOnfr4449VvHhxp0UfLl26ZP89Z5TdF+M+/fTTmjVrlvr06aMdO3aoXLly+uKLL/TTTz/prbfeuqFTqCpVqqR77rlHzzzzjJKSkvTWW2+pRIkS9ulOgYGBatasmSZNmqSUlBT7Gb3MzmA0a9ZMLVq0UNmyZZWQkKAvvvhCu3bt0pQpU7Lc/9q1a5WUlJTlJ/EVK1bU2LFjNXToUB09elSdO3dWkSJFdOTIEX355Zd6+umn9dJLL9nrh4WFaeLEiTp69KgqV66szz//XLt379YHH3xg/wDn/vvv19KlS9WlSxd17NhRR44c0fvvv6/q1atne+2BdO3DhEmTJqlXr15q2rSpevbsaZ9uW6ZMGftSzLnptxspt2NRRhUqVFDNmjX13XffZflhiZmcHr8tW7bU448/rhkzZujAgQP2qVM//PCDWrZsqeeee07vvfeexo8fLz8/Pz3//PPZ7vfll1/W119/rfvvv199+vRRgwYNdPnyZe3Zs0dffPGFjh49muMpUMWKFVNMTIw9bGQmp8dRTp6nzfHjx+1jmnRtKveVK1ccyvbt25ej55BRx44d9eabb6pdu3bq0aOHTp8+rXfeeUeVKlXSL7/8Yq9XvHhxDRs2TMOHD1fbtm3VqVMnHT58WDNnzlSdOnWc/hbv3LlTJUuWzHKKZHo1a9ZU27ZtHZYjl+QQKseOHavo6Gjdc889evbZZ+Xh4aFZs2YpKSlJkyZNctrmsmXLVLJkSftUvR9++ME+Fufn6/DPP/+Ul5eX6fuijz76SF9++aW+//57p8VvMoqOjlbZsmVZivx2VYAr+BVqkowvv/zSftu2TLO/v7/Dj4eHh9GtWzfDMAzjt99+y3IpY9vPkCFDstznhQsXjMDAQGP27Nn2snbt2jnsT5Lh5+dnv129enV73ZiYGCMyMtJ4+eWXjZ07dxrr1683mjdvbtx3331ZLh1tc+bMGWP//v3G6tWrjSZNmhgdOnTI9DG9e/c2OnXqlOV2Dh8+bPzyyy/GBx98YBQvXtxYuHChw/1JSUnGgQMHjO3btxuvvPKKUbJkSWPfvn3Zti233nvvPaNDhw5GWFiY4eHhYRQtWtRo27atsXz5cqe6P/30k9GoUSPD19fXCAsLMwYPHmysWrXKaWnYjMtUFy1a1GjSpInTNiUZFovF2LFjh0N5ZsuZJyYmGq+99ppRvnx5w9PT0wgNDTUefvhh+3Ku6ZeePnnypMNjMy5/nN0yqdnd//nnnxv16tUzvL29jeLFixs9e/Z0WObaZu/evUaXLl2MokWLGj4+PkaVKlWM4cOHZ7ovs+XIM+vHFStWZFo/vfTLkWcls+WXf/rpJ2PAgAFGzZo1jaCgIMPT09MoW7as0adPH4elczNrX8YfM6dOnTKeeOIJo2TJkoaXl5dRq1Yth+VwM8rrcuSTJ082pk6daoSHhxve3t5G06ZNjZ9//tmh7p9//mn/nQUFBRmPPPKI8ddffzktmf/MM88Y5cuXtx8DjRo1MubPn++wLbO+z+r+JUuWGPfcc499vKpataoxYMAAY//+/fY6tiXqt2/fbkRFRRk+Pj5GRESEMXPmTIdtWa1WY9y4cUZERITh7e1t1KtXz/jmm2+clhjOzn//+1+H47179+5Oy+zntN/Sy+6Yz0xmSzLndCzKyptvvmkEBAQ4LOOcm+XIDSPnx29qaqoxefJko2rVqoaXl5cRHBxstG/f3j7u3XXXXcYjjzxi/P77706PzWwsjI+PN4YOHWpUqlTJ8PLyMkqWLGk0btzYmDJlin05+qxkXG7c7P7cHEdmz9MwjGzHi8x+8rIc+UcffWRERkYa3t7eRtWqVY25c+dmOXa88847RtWqVQ1PT08jJCTE6N+/v3Hu3DmHOrZxbtq0aQ7lmW1Tfy+3/umnn9rbUK9evUzbunPnTqNt27ZGQECA4efnZ7Rs2dLYuHGjQx3bsW/78fLyMipVqmSMGDHCYdn9nL4Oc7scuSRj4MCBmbbJ9no8cOCA4e/vbwwdOjTTeun/jqalpRmlS5c2hg0b5tQfuD1YDKMAz0EXYhaLRV9++aV9BbnPP/9cPXv21L59+5ymFAUEBCg0NFTJycn2VciyYlvBJyt33nmnWrVqZZ+WdfLkSfuUMOna6jbr1q2zL0Xs6elpn3s+fPhwrVy5Utu2bbPX//PPPxUeHq5Nmzbl+PoR22M2btzoNI3O9mWIOVlxcOzYsfrkk08ynfds06pVK1WsWNFhmg6AzB09elTly5fX5MmTHc7Y3MxatGihs2fP3tDr6G5Hly5dUoUKFTRp0iT169fP1c3BLcJisWjAgAGZTivHtTNnPXr00KFDh1S6dGlXNwcuwFS9LNSrV09paWk6ffp0pt9vIl37LonrWU48ISFBhw4d0uOPP24vS/9dLTYRERGZrqqXmJjotJSsLeTlZjUoW92srnHIzXbMtpGTOgCA7AUFBWnw4MGaPHmynnjiiSy/6w3AjTNx4kQ999xzhKbb2G0dnBISEnTw4EH77SNHjmj37t0qXry4KleurJ49e6pXr16aOnWq6tWrpzNnzmjNmjWqXbt2rq/PkK5dI/XAAw8oIiJCf/31l0aOHCl3d3eHLyDMjY4dO2ratGl6/fXX1b17d8XHx+vVV19VRESEfe7t1q1b1atXL61Zs0Z33HGHtmzZom3btumee+5RsWLFdOjQIQ0fPlwVK1Z0ONv066+/Kjk5WefPn1d8fLz92hbbxZXvvPOOypYtaw+OGzZs0JQpUxzmtw8dOlTt27dX2bJlFR8fr4ULF2rdunWZXhQMAMidIUOG2K/XApD/crI4DW5tt3Vw2r59u1q2bGm/PWjQIElS7969NW/ePM2dO1djx47Vf/7zH508eVIlS5ZUo0aNsv1C0uz8+eef6t69u86dO6fg4GDdc8892rx5c7ZT+bJz7733auHChZo0aZImTZokPz8/RUVFaeXKlfbveEhMTNT+/fvtq4z5+flp6dKlGjlypC5fvqzSpUurXbt2GjZsmMPSpx06dHD4Ak9bELPN7LRarRo6dKiOHDkiDw8PVaxYURMnTnT48tfTp0+rV69eiomJUVBQkGrXrq1Vq1apdevWeXq+AAAAgKtwjRMAAAAAmGBSNAAAAACYIDgBAAAAgInb7honq9Wqv/76S0WKFMnyC/MAAAAA3PoMw1B8fLzCwsJMVyi97YLTX3/9pfDwcFc3AwAAAEAhceLECZUpUybbOrddcCpSpIika50TGBjo4tZIKSkpWr16tdq0aSNPT09XNweACzEeALBhPAAKRlxcnMLDw+0ZITu3XXCyTc8LDAwsNMHJz89PgYGBDIzAbY7xAIAN4wFQsHJyCQ+LQwAAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACQ9XN+B2ZrUa+uvARSX+5aG/DlxUeNWScnOzuLpZhZ7VaijmwEVdjkuSf6C3SkcWpd9ygH7LG/ot7+i7vKHf8oZ+yxv6Le/ou7y5mfuN4OQih3ad1g+fH9Dli0mSfPXNz3vkX9RbTR+NVMV6pVzdvELLsd+uod/M0W95Q7/lHX2XN/Rb3tBveUO/5R19lzc3e78xVc8FDu06rZWz9jocNJJ0+WKSVs7aq0O7TruoZYUb/ZY39Fve0G95R9/lDf2WN/Rb3tBveUff5c2t0G+ccSpgVquhHz4/kG2dHz4/oDJVi980py0LwrV++yPbOvSbM/otb1zVbykpabKmSilJaZL15vxci2Mub+i3vLmV+y0/x4Nbud/yG32XNznptx//e0Dl6wQX6n6zGIZhuLoRBSkuLk5BQUG6dOmSAgMDC3z/J/df0LJpuwp8vwAAAEBh1vnFerqjSrEC3WdussHN+ZHmTexyXJJ5JQAAAOA2U9jfJzNVr4D5B3rnqN79z9VRWGTR/G3MTeSvAxf1zcyfTevRb47ot7xxVb+lpKRo1apVatu2rTw9PW/YdgsSx1ze0G95cyv3W36OB7dyv+U3+i5vctpvOX2f7CoEpwJWOrKo/It6O10Yl15AMW+FV2dubHrh1YvTb3lAv+WNy/rNzSo3D8nT212enu43brsFiGMub+i3vLml+y0fx4Nbut/yGX2XNzntt9KFPGwyVa+AublZ1PTRyGzr3NMtkhdbBvRb3tBveUO/5R19lzf0W97Qb3lDv+UdfZc3t0q/EZxcoGK9UmrXv6b8izqejgwo5q12/WveFOvYuwL9ljf0W97Qb3lH3+UN/ZY39Fve0G95R9/lza3Qby5fVe/kyZMaMmSIVqxYocTERFWqVElz585Vw4YNs3zMunXrNGjQIO3bt0/h4eEaNmyY+vTpk6P9uXpVvfSsVkMnfj+rjeu3qnHzuxRetWShT9qFwc38jdOuRL/lTUH2W0pKipYvX64OHTrctNc4pccxlzf0W97cav1WUOPBrdZvBYm+y5vC1m+5yQYuvcbpwoULatKkiVq2bKkVK1YoODhYBw4cULFiWS9DeOTIEXXs2FH//ve/tWDBAq1Zs0ZPPvmkSpcurbZt2xZg66+fm5tFYZFF5XcgVWG82HLMzc1S4EtV3grot7yh3/KOvssb+i1v6Le8od/yjr7Lm5u531wanCZOnKjw8HDNnTvXXla+fPlsH/P++++rfPnymjp1qiSpWrVq+vHHHzVt2rSbLjgBAAAAuDm4NDh9/fXXatu2rR555BGtX79ed9xxh5599lk99dRTWT5m06ZNatWqlUNZ27Zt9cILL2RaPykpSUlJ/6zgERcXJ+naKfCUlJTrfxLXydaGwtAWAK7FeADAhvEAKBi5eY25NDgdPnxY7733ngYNGqRXX31V27Zt0/PPPy8vLy/17t0708fExsYqJCTEoSwkJERxcXG6cuWKfH19He4bP368Ro8e7bSd1atXy8/P78Y9mesUHR3t6iYAKCQYDwDYMB4A+SsxMTHHdV0anKxWqxo2bKhx48ZJkurVq6e9e/fq/fffzzI45dbQoUM1aNAg++24uDiFh4erTZs2Ll8cQrqWcqOjo9W6detb4mJwAHnHeADAhvEAKBi22Wg54dLgVLp0aVWvXt2hrFq1alqyZEmWjwkNDdWpU6ccyk6dOqXAwECns02S5O3tLW9v528h9vT0LFQDUWFrDwDXYTwAYMN4AOSv3Ly+XPo9Tk2aNNH+/fsdyv744w9FRERk+ZioqCitWbPGoSw6OlpRUVH50kYAAAAAcGlwevHFF7V582aNGzdOBw8e1MKFC/XBBx9owIAB9jpDhw5Vr1697Lf//e9/6/Dhwxo8eLB+//13vfvuu/rvf/+rF1980RVPAQAAAMBtwKXB6c4779SXX36pzz77TDVr1tSYMWP01ltvqWfPnvY6MTExOn78uP12+fLl9e233yo6Olp16tTR1KlTNXv2bJYiBwAAAJBvXHqNkyTdf//9uv/++7O8f968eU5lLVq00K5du/KxVQAAAADwD5eecQIAAACAmwHBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMuDQ4jRo1ShaLxeGnatWqWdafN2+eU30fH58CbDEAAACA25GHqxtQo0YNfffdd/bbHh7ZNykwMFD79++337ZYLPnWNgAAAACQCkFw8vDwUGhoaI7rWyyWXNUHAAAAgOvl8uB04MABhYWFycfHR1FRURo/frzKli2bZf2EhARFRETIarWqfv36GjdunGrUqJFl/aSkJCUlJdlvx8XFSZJSUlKUkpJy455IHtnaUBjaAsC1GA8A2DAeAAUjN68xi2EYRj62JVsrVqxQQkKCqlSpopiYGI0ePVonT57U3r17VaRIEaf6mzZt0oEDB1S7dm1dunRJU6ZM0YYNG7Rv3z6VKVMm032MGjVKo0ePdipfuHCh/Pz8bvhzAgAAAHBzSExMVI8ePXTp0iUFBgZmW9elwSmjixcvKiIiQm+++ab69etnWj8lJUXVqlVT9+7dNWbMmEzrZHbGKTw8XGfPnjXtnIKQkpKi6OhotW7dWp6enq5uDgAXYjwAYMN4ABSMuLg4lSxZMkfByeVT9dIrWrSoKleurIMHD+aovqenp+rVq5dtfW9vb3l7e2f62MI0EBW29gBwHcYDADaMB0D+ys3rq1B9j1NCQoIOHTqk0qVL56h+Wlqa9uzZk+P6AAAAAJAXLg1OL730ktavX6+jR49q48aN6tKli9zd3dW9e3dJUq9evTR06FB7/ddff12rV6/W4cOHtXPnTj322GM6duyYnnzySVc9BQAAAAC3AZdO1fvzzz/VvXt3nTt3TsHBwbrnnnu0efNmBQcHS5KOHz8uN7d/st2FCxf01FNPKTY2VsWKFVODBg20ceNGVa9e3VVPAQAAAMBtwKXBadGiRdnev27dOofb06ZN07Rp0/KxRQAAAADgrFBd4wQAAAAAhRHBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMuDQ4jRo1ShaLxeGnatWq2T5m8eLFqlq1qnx8fFSrVi0tX768gFoLAAAA4Hbl8jNONWrUUExMjP3nxx9/zLLuxo0b1b17d/Xr10+7du1S586d1blzZ+3du7cAWwwAAADgduPy4OTh4aHQ0FD7T8mSJbOsO336dLVr104vv/yyqlWrpjFjxqh+/fqaOXNmAbYYAAAAwO3Gw9UNOHDggMLCwuTj46OoqCiNHz9eZcuWzbTupk2bNGjQIIeytm3batmyZVluPykpSUlJSfbbcXFxkqSUlBSlpKRc/xO4TrY2FIa2AHAtxgMANowHQMHIzWvMpcHp7rvv1rx581SlShXFxMRo9OjRatq0qfbu3asiRYo41Y+NjVVISIhDWUhIiGJjY7Pcx/jx4zV69Gin8tWrV8vPz+/6n8QNEh0d7eomACgkGA8A2DAeAPkrMTExx3VdGpzat29v/3ft2rV19913KyIiQv/973/Vr1+/G7KPoUOHOpyliouLU3h4uNq0aaPAwMAbso/rkZKSoujoaLVu3Vqenp6ubg4AF2I8AGDDeAAUDNtstJxw+VS99IoWLarKlSvr4MGDmd4fGhqqU6dOOZSdOnVKoaGhWW7T29tb3t7eTuWenp6FaiAqbO0B4DqMBwBsGA+A/JWb15fLF4dILyEhQYcOHVLp0qUzvT8qKkpr1qxxKIuOjlZUVFRBNA8AAADAbcqlwemll17S+vXrdfToUW3cuFFdunSRu7u7unfvLknq1auXhg4daq8/cOBArVy5UlOnTtXvv/+uUaNGafv27Xruuedc9RQAAAAA3AZcOlXvzz//VPfu3XXu3DkFBwfrnnvu0ebNmxUcHCxJOn78uNzc/sl2jRs31sKFCzVs2DC9+uqrioyM1LJly1SzZk1XPQUAAAAAtwGXBqdFixZle/+6deucyh555BE98sgj+dQiAAAAAHBWqK5xAgAAAIDCiOAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgwqWr6gEAAODWYxiGUlNTlZaW5uqmAPL09JS7u/t1b4fgBAAAgBsmOTlZMTExSkxMdHVTAEmSxWJRmTJlFBAQcF3bITgBAADghrBarTpy5Ijc3d0VFhYmLy8vWSwWVzcLtzHDMHTmzBn9+eefioyMvK4zTwQnAAAA3BDJycmyWq0KDw+Xn5+fq5sDSJKCg4N19OhRpaSkXFdwYnEIAAAA3FBubrzFROFxo856clQDAAAAgAmCEwAAAACYIDgBAACgUEmzGtp06Jy+2n1Smw6dU5rVcHWTCqV169bJYrHo4sWLrm5Kno0aNUp169Z1dTNyhOAEAACAQmPl3hjdM3Gtun+4WQMX7Vb3DzfrnolrtXJvTL7ts0+fPrJYLJowYYJD+bJly276VQHLlSsni8Xi9JPxuRYEi8WiZcuWOZS99NJLWrNmTYG3JS8ITgAAACgUVu6N0TOf7lTMpasO5bGXruqZT3fma3jy8fHRxIkTdeHChRu63eTk5Bu6vbx4/fXXFRMT4/Dzf//3f65uliQpICBAJUqUcHUzcoTgBAAAgHxjGIYSk1NNf+Kvpmjk1/uU2aQ8W9mor39V/NWUHG3PMHI3va9Vq1YKDQ3V+PHjs623ZMkS1ahRQ97e3ipXrpymTp3qcH+5cuU0ZswY9erVS4GBgXr66ac1b948FS1aVN98842qVKkiPz8/Pfzww0pMTNT8+fNVrlw5FStWTM8//7zS0tLs2/rkk0/UsGFDFSlSRKGhoerRo4dOnz6dq+clyf749D/+/v72+5cvX67KlSvL19dXLVu21Lx58xymAGY2ne6tt95SuXLl7Le3bdum1q1bq2TJkgoKClLz5s21c+dOh36RpC5dushisdhvZ9y21WrV66+/rjJlysjb21t169bVypUr7fcfPXpUFotFS5cuVcuWLeXn56c6depo06ZNue6X3OJ7nAAAAJBvrqSkqfqIVde9HUNSbNxV1Rq1Okf1f329rfy8cv5W193dXePGjVOPHj30/PPPq0yZMk51duzYoW7dumnUqFF69NFHtXHjRj377LMqUaKE+vTpY683ZcoUjRgxQiNHjpQk/fDDD0pMTNSMGTO0aNEixcfHq2vXrurSpYuKFi2q5cuX6/Dhw3rooYfUpEkTPfroo5KklJQUjRkzRlWqVNHp06c1aNAg9enTR8uXL8/x8zJz4sQJde3aVQMGDNDTTz+t7du36z//+U+utxMfH6/evXvr7bfflmEYmjp1qjp06KADBw6oSJEi2rZtm0qVKqW5c+eqXbt2WX6f0vTp0zV16lTNmjVL9erV05w5c/Tggw9q3759ioyMtNd77bXXNGXKFEVGRuq1115T9+7ddfDgQXl45F+8ITgBAAAAunY2pG7duho5cqQ++ugjp/vffPNN3XfffRo+fLgkqXLlyvr11181efJkh+B07733OoSPH374QSkpKXrvvfdUsWJFSdLDDz+sTz75RKdOnVJAQICqV6+uli1b6vvvv7cHp759+9q3UaFCBc2YMUN33nmnEhISFBAQkOPnNWTIEA0bNsyhbMWKFWratKm9TbYzZ1WqVNGePXs0ceLEHG/f9pzT++CDD1S0aFGtX79e999/v4KDgyVJRYsWVWhoaJbbmTJlioYMGaJ//etfkqSJEyfq+++/11tvvaV33nnHXu+ll15Sx44dJUmjR49WjRo1dPDgQVWtWjVX7c4NghMAAADyja+nu359va1pva1HzqvP3G2m9eY9cafuKl88R/vNi4kTJ+ree+/VSy+95HTfb7/9pk6dOjmUNWnSRG+99ZbS0tLsZ1EaNmzo9Fg/Pz97aJKkkJAQlStXziEAhYSEOEzF27Fjh0aNGqWff/5ZFy5ckNVqlSQdP35c1atXz/Fzevnllx2CnSTdcccd9ud09913O9wXFRWV423bnDp1SsOGDdO6det0+vRppaWlKTExUcePH8/xNuLi4vTXX3+pSZMmDuVNmjTRzz//7FBWu3Zt+79Lly4tSTp9+jTBCQAAADcni8WSoylzTSODVTrIR7GXrmZ6nZNFUmiQj5pGBsvdLf9WumvWrJnatm2roUOHOoWNnEp//ZCNp6enw22LxZJpmS0cXb58WW3btlXbtm21YMECBQcH6/jx42rbtm2uF5woWbKkKlWqlMtn8Q83Nzena8ZSUlIcbvfu3Vvnzp3T9OnTFRERIW9vb0VFReXb4hjp+8628qGt7/ILi0MAAADA5dzdLBr5wLWzKBljke32yAeq52tospkwYYL+97//OS04UK1aNf30008OZT/99JMqV66c5TU7efX777/r3LlzmjBhgpo2baqqVavmaWEIM9WqVdPWrVsdyjZv3uxwOzg4WLGxsQ7haffu3Q51fvrpJz3//PPq0KGDffGMs2fPOtTx9PR0WPwio8DAQIWFhWXax7k5w5ZfCE4AAAAoFNrVLK33Hquv0CAfh/LQIB+991h9tatZukDaUatWLfXs2VMzZsxwKP/Pf/6jNWvWaMyYMfrjjz80f/58zZw5M9NpfderbNmy8vLy0ttvv63Dhw/r66+/1pgxY/K0rfj4eMXGxjr8xMXFSZL+/e9/68CBA3r55Ze1f/9+LVy4UPPmzXN4fIsWLXTmzBlNmjRJhw4d0jvvvKMVK1Y41ImMjNQnn3yi3377TVu2bFHPnj3l6+vrUKdcuXJas2aNYmNjs1z2/eWXX9bEiRP1+eefa//+/XrllVe0e/duDRw4ME/P/UYiOAEAAKDQaFeztH4ccq8+e6qRpv+rrj57qpF+HHJvgYUmm9dff91p6lf9+vX13//+V4sWLVLNmjU1YsQIvf7663me0ped4OBgzZs3T4sXL1b16tU1YcIETZkyJU/bGjFihEqXLu3wM3jwYEnXAtqSJUu0bNky1alTR++//77GjRvn8Phq1arp3Xff1TvvvKM6depo69atTmHxo48+0oULF1S/fn09/vjjev7551WqVCmHOlOnTlV0dLTCw8NVr169TNv6/PPPa9CgQfrPf/6jWrVqaeXKlfr6668dVtRzFYuR20Xub3JxcXEKCgrSpUuXFBgY6OrmKCUlRcuXL1eHDh2c5rkCuL0wHgCwuVnHg6tXr+rIkSMqX768fHx8zB+AQmndunVq2bKlLly4oKJFi7q6Odctu+MyN9mAM04AAAAAYILgBAAAAAAmWI4cAAAAgF2LFi2clh8HZ5wAAAAAwBTBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMsBw5AAAAChdrmnRso5RwSgoIkSIaS27urm4VbnOccQIAAEDh8evX0ls1pfn3S0v6XfvvWzWvleez2NhYDRw4UJUqVZKPj49CQkLUpEkTvffee0pMTJQklStXThaLRRaLRb6+vipXrpy6deumtWvX5nv74FoEJwAAABQOv34t/beXFPeXY3lczLXyfAxPhw8fVr169bR69WqNGzdOu3bt0qZNmzR48GB98803+u677+x1X3/9dcXExGj//v36+OOPVbRoUbVq1UpvvPFGvrUPrsdUPQAAAOQfw5BSEs3rWdOkFYMlGZltRJJFWjlEqtAiZ9P2PP0kiyXHzXz22Wfl4eGh7du3y9/f315eoUIFderUSYbxT7uKFCmi0NBQSVLZsmXVrFkzlS5dWiNGjNDDDz+sKlWq5Hi/uHkQnAAAAJB/UhKlcWE3YEPGtTNRE8JzVv3VvyQvf/N6ks6dO2c/05Q+NKVnMQlhAwcO1JgxY/TVV19p8ODBOWsjbipM1QMAAMBt7eDBgzIMw+lMUcmSJRUQEKCAgAANGTIk220UL15cpUqV0tGjR/OxpXAlzjgBAAAg/3j6XTv7Y+bYRmnBw+b1en5xbZW9nOz3Om3dulVWq1U9e/ZUUlKSaX3DMEzPTOHmRXACAABA/rFYcjZlruK9UmDYtYUgMr3OyXLt/or33vClyStVqiSLxaL9+/c7lFeoUEGS5Ovra7qNc+fO6cyZMypfvvwNbRsKD6bqAQAAwPXc3KV2E/++kfGszd+3203Il+9zKlGihFq3bq2ZM2fq8uXLedrG9OnT5ebmps6dO9/YxqHQIDgBAACgcKj+oNTtYymwtGN5YNi18uoP5tuu3333XaWmpqphw4b6/PPP9dtvv2n//v369NNP9fvvv8vd/Z/AFh8fr9jYWJ04cUIbNmzQ008/rbFjx+qNN95QpUqV8q2NcC2m6gEAAKDwqP6gVLXjtWueEk5JASHXrmnKhzNN6VWsWFG7du3SuHHjNHToUP3555/y9vZW9erV9dJLL+nZZ5+11x0xYoRGjBghLy8vhYaGqlGjRlqzZo1atmyZr22EaxGcAAAAULi4uUvlmxb4bkuXLq23335bb7/9dpZ1WDXv9sVUPQAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABO5Ck6TJk3SlStX7Ld/+uknh29Rjo+Pd1hxBAAAAABuBbkKTkOHDlV8fLz9dvv27XXy5En77cTERM2aNevGtQ4AAAAACoFcBSfDMLK9DQAAAAC3Iq5xAgAAAAATBCcAAAAAMOGR2wfMnj1bAQEBkqTU1FTNmzdPJUuWlCSH658AAACAvEizpmnn6Z06k3hGwX7Bql+qvtzd3F3dLNzmchWcypYtqw8//NB+OzQ0VJ988olTHQAAACAvvjv2nSZsnaBTiafsZSF+IXrlrlfUKqJVvu33zJkzGjFihL799ludOnVKxYoVU506dTRixAg1adJEkrRr1y5NmDBBGzZs0Pnz5xUaGqpatWqpf//+uv/++2WxWHT06FGVL1/evt2AgACVLVtWLVq00AsvvKDIyMh8ew7IX7maqnf06FEdOXLE9CcvJkyYIIvFohdeeCHLOvPmzZPFYnH48fHxydP+AAAAULh8d+w7DVo3yCE0SdLpxNMatG6Qvjv2Xb7t+6GHHtKuXbs0f/58/fHHH/r666/VokULnTt3TpL01VdfqVGjRkpISND8+fP122+/aeXKlerSpYuGDRumS5cuOT6X775TTEyMfv75Z40bN06//fab6tSpozVr1uTbc0D+yvVUvfywbds2zZo1S7Vr1zatGxgYqP3799tvWyyW/GwaAAAAroNhGLqSesW0Xpo1TeO3jpch51WbbWUTtk7Q3aF352janq+Hb47fJ168eFE//PCD1q1bp+bNm0uSIiIidNddd0mSLl++rH79+qljx45aunSpw2OrVaumfv36Oa02XaJECYWGhkqSKlSooAceeED33Xef+vXrp0OHDsndnamHN5tcBadNmzbp3Llzuv/+++1lH3/8sUaOHKnLly+rc+fOevvtt+Xt7Z3jbSYkJKhnz5768MMPNXbsWNP6FovFfhACAACgcLuSekV3L7z7hmzrVOIpNV7UOEd1t/TYIj9PvxzVDQgIUEBAgJYtW6ZGjRo5vZddvXq1zp07p8GDB2e5DbOQ5ubmpoEDB6pLly7asWOHPZTh5pGr4PT666+rRYsW9uC0Z88e9evXT3369FG1atU0efJkhYWFadSoUTne5oABA9SxY0e1atUqR8EpISFBERERslqtql+/vsaNG6caNWpkWT8pKUlJSUn223FxcZKklJQUpaSk5Lid+cXWhsLQFgCuxXgAwOZmHQ9SUlJkGIasVqusVqsk2f9b0NK3wYybm5vmzJmj/v376/3331f9+vXVrFkzPfroo6pdu7Z9tlNkZKR9m9u2bdN9991n38bChQt1//33OzzvjPuvXLmyJOnw4cNq2LDhdT9H5IzVapVhGEpJSXE605eb11iugtPu3bs1ZswY++1Fixbp7rvvti8YER4erpEjR+Y4OC1atEg7d+7Utm3bclS/SpUqmjNnjmrXrq1Lly5pypQpaty4sfbt26cyZcpk+pjx48dr9OjRTuWrV6+Wn1/OPoUoCNHR0a5uAoBCgvEAgM3NNh54eHgoNDRUCQkJSk5OlnRtqt7qjqtNH/vzuZ/18uaXTetNbjRZdUrUMa2XkpiiOEuceaP/1rp1a/3666/atGmTtm/frujoaE2ePFkzZszQ1atXJV1bQdr2xrtcuXLasGGDJKlBgwaKj49XXFycEhISJF2b3mf7wN7GtgL11atXne5D/klOTtaVK1e0YcMGpaamOtyXmJiY4+3kKjhduHBBISEh9tvr169X+/bt7bfvvPNOnThxIkfbOnHihAYOHKjo6OgcL/AQFRWlqKgo++3GjRurWrVqmjVrlkOgS2/o0KEaNGiQ/XZcXJzCw8PVpk0bBQYG5mi/+SklJUXR0dFq3bq1PD09Xd0cAC7EeADA5mYdD65evaoTJ04oICDA4f1dkIJMH3tf0fsU8kuITieezvQ6J4ssKuVXSvdVvC/fliYPDAxUp06d1KlTJ40ZM0ZPPfWUJk6cqKlTp0qS/vrrL4cVpIODg+3/9vX1VWBgoP1re/z9/Z3ea9reJ1evXr1QvA+9XVy9elW+vr5q1qyZU+7ITYDNVXAKCQnRkSNHFB4eruTkZO3cudPhbE58fHyOX9w7duzQ6dOnVb9+fXtZWlqaNmzYoJkzZyopKcn0ojlPT0/Vq1dPBw8ezLKOt7d3ptdceXp6FqqBqLC1B4DrMB4AsLnZxoO0tDRZLBa5ubnJzS1XizfLzc1Nr9z1igatGySLLA7hyaJr1w+9ctcr8vQouP6oUaOGvvrqK7Vr107FixfX5MmT9eWXX2Za1/acbc87Yx9YrVbNnDlT5cuXV4MGDXLdP8g7Nzc3WSyWTF9PuXl95So4dejQQa+88oomTpyoZcuWyc/PT02bNrXf/8svv6hixYo52tZ9992nPXv2OJQ98cQTqlq1qoYMGZKjlUbS0tK0Z88edejQITdPAwAAAIVQq4hWerPFm5l+j9OQu4bk2/c4nTt3To888oj69u2r2rVrq0iRItq+fbsmTZqkTp06KSAgQLNnz9ajjz6qjh076vnnn1dkZKQSEhK0cuVKSXJ673ru3DnFxsYqMTFRe/fu1VtvvaWtW7fq22+/ZUW9m1SugtOYMWPUtWtXNW/eXAEBAZo3b568vLzs98+ZM0dt2rTJ0baKFCmimjVrOpT5+/urRIkS9vJevXrpjjvu0Pjx4yVdW5yiUaNGqlSpki5evKjJkyfr2LFjevLJJ3PzNAAAAFBItYpopZbhLbXz9E6dSTyjYL9g1S9VP9+m50nXVtW7++67NW3aNB06dEgpKSkKDw/XU089pVdffVWS1KVLF23cuFETJ05Ur169dP78eQUFBalhw4ZatGiRw6rTktSq1bWQ5+fnp4iICLVs2VIffPCBKlWqlG/PA/krV8GpZMmS2rBhgy5duqSAgACntLx48WIVKVLkhjXu+PHjDqcxL1y4oKeeekqxsbEqVqyYGjRooI0bN6p69eo3bJ8AAABwLXc3d90ZemeB7c/b21vjx4+3f1iflYYNG2rx4sXZ1ilXrpzTdzrh1pCr4NS3b98c1ZszZ06eGrNu3bpsb0+bNk3Tpk3L07YBAAAAIK9yFZzmzZuniIgI1atXjyQNAAAA4LaRq+D0zDPP6LPPPtORI0f0xBNP6LHHHlPx4sXzq20AAAAAUCjkah3Ed955RzExMRo8eLD+97//KTw8XN26ddOqVas4AwUAAADglpXrBeS9vb3VvXt3RUdH69dff1WNGjX07LPPqly5cvZvSgYAAACAW8l1ffOW7cukDMNQWlrajWoTAAAAABQquQ5OSUlJ+uyzz9S6dWtVrlxZe/bs0cyZM3X8+HEFBATkRxsBAAAAwKVytTjEs88+q0WLFik8PFx9+/bVZ599ppIlS+ZX2wAAAACgUMhVcHr//fdVtmxZVahQQevXr9f69eszrbd06dIb0jgAAAAAKAxyFZx69eoli8WSX20BAAAAZKSlKXH7DqWeOSOP4GD5NWwgi7u7S9rSp08fXbx4UcuWLcv0/lGjRmnZsmXavXt3gbYLBS/XX4ALAAAA5Je41at1atx4pcbG2ss8QkMV8upQBbZp48KW3ZxatGihunXr6q233sr0/vQnRYoUKaIqVapo2LBh6tSpUwG18OZxXavqAQAAADdK3OrVOjnwBYfQJEmpp07p5MAXFLd6tYta5lrJycn5uv25c+cqJiZG27dvV5MmTfTwww9rz549+brPmxHBCQAAAPnGMAxZExNNf9Li43Vq7BuSYWS2EUmGTr0xTmnx8TnanpHZdrLwxRdfqFatWvL19VWJEiXUqlUrXb58OdO627ZtU3BwsCZOnJjl9mbPnq1q1arJx8dHVatW1bvvvutw/5AhQ1S5cmX5+fmpQoUKGj58uFJSUuz3jxo1SnXr1tXs2bNVvnx5+fj4SLp2dmj27Nnq0qWL/Pz8FBkZqa+//jrHzzMrRYsWVWhoqCpXrqwxY8YoNTVV33///XVv91aTq6l6AAAAQG4YV65of/0GN2BD1848/XHnXTmqXmXnDln8/EzrxcTEqHv37po0aZK6dOmi+Ph4/fDDD5kGr7Vr16pr166aNGmSnn766Uy3t2DBAo0YMUIzZ85UvXr1tGvXLj311FPy9/dX7969JV2bEjdv3jyFhYVpz549euqpp1SkSBENHjzYvp2DBw9qyZIlWrp0qdzTXd81evRoTZo0SZMnT9bbb7+tnj176tixYypevHiO+iU7qamp+uijjyRJXl5e1729Ww3BCQAAALetmJgYpaamqmvXroqIiJAk1apVy6nel19+qV69emn27Nl69NFHs9zeyJEjNXXqVHXt2lWSVL58ef3666+aNWuWPTgNGzbMXr9cuXJ66aWXtGjRIofglJycrI8//ljBwcEO2+/Tp4+6d+8uSRo3bpxmzJihrVu3ql27dnnsAal79+5yd3fXlStXZLVaVa5cOXXr1i3P27tVEZwAAACQbyy+vqqyc4dpvcTt23Xi6f6m9cI/mCW/hg1ztN+cqFOnju677z7VqlVLbdu2VZs2bfTwww+rWLFi9jpbtmzRN998oy+++EKdO3fOcluXL1/WoUOH1K9fPz311FP28tTUVAUFBdlvf/7555oxY4YOHTqkhIQEpaamKjAw0GFbERERTqFJkmrXrm3/t7+/vwIDA3X69OkcPdesTJs2Ta1atdLhw4f14osvasaMGTfkDNathuAEAACAfGOxWHI0Zc6/SRN5hIYq9dSpzK9zsljkERIi/yZNbujS5O7u7oqOjtbGjRu1evVqvf3223rttde0ZcsWlS9fXpJUsWJFlShRQnPmzFHHjh3l6emZ6bYSEhIkSR9++KHuvvtup/1I0qZNm9SzZ0+NHj1abdu2VVBQkBYtWqSpU6c61Pf39890Hxn3bbFYZLVac//E0wkNDVWlSpVUqVIlzZ07Vx06dNCvv/6qUqVKXdd2bzUsDgEAAACXs7i7K+TVoX/fyPC9oX/fDnl1aL58n5PFYlGTJk00evRo7dq1S15eXvryyy/t95csWVJr167VwYMH1a1bN4eFHNILCQlRWFiYDh8+bA8ith9bCNu4caMiIiL02muvqWHDhoqMjNSxY8du+HPKq7vuuksNGjTQG2+84eqmFDqccQIAAEChENimjTT9LefvcQoJybfvcdqyZYvWrFmjNm3aqFSpUtqyZYvOnDmjatWqOdQrVaqU1q5dq5YtW6p79+5atGiRPDyc30qPHj1azz//vIKCgtSuXTslJSVp+/btunDhggYNGqTIyEgdP35cixYt0p133qlvv/3WIaTlhzNnzjh9QW/p0qUVEhKSaf0XXnhBXbp00eDBg3XHHXfka9tuJpxxAgAAQKER2KaNKq35TmXnz1fYlCkqO3++Kq35Lt++/DYwMFAbNmxQhw4dVLlyZQ0bNkxTp05V+/btneqGhoZq7dq12rNnj3r27Km0tDSnOk8++aRmz56tuXPnqlatWmrevLnmzZtnP+P04IMP6sUXX9Rzzz2nunXrauPGjRo+fHi+PDebhQsXql69eg4/H374YZb127Vrp/Lly3PWKQOLkZtF7m8BcXFxCgoK0qVLl5wuwnOFlJQULV++XB06dMhyviyA2wPjAQCbm3U8uHr1qo4cOeLw3UOAq2V3XOYmG3DGCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAMhCnz591Llz5yzvHzVqlOrWrVtg7YHrEJwAAABQqFithk7uv6A/tsXq5P4LsloNVzfppnXkyBH16NFDYWFh8vHxUZkyZdSpUyf9/vvvDvW+//573X///QoODpaPj48qVqyoRx99VBs2bLDXWbdunSwWiywWi9zc3BQUFKR69epp8ODBiomJKeinVuA8XN0AAAAAwObQrtP64fMDunwxyV7mX9RbTR+NVMV6pVzYMtdJTk6Wl5dXrh+XkpKi1q1bq0qVKlq6dKlKly6tP//8UytWrNDFixft9d59910999xzevzxx/X555+rYsWKunTpkr7//nu9+OKL2rFjh8N29+/fr8DAQMXFxWnnzp2aNGmSPvroI61bt061atW63qdbaHHGCQAAAIXCoV2ntXLWXofQJEmXLyZp5ay9OrTrdL7s94svvlCtWrXk6+urEiVKqFWrVrp8+XKmdbdt26bg4GBNnDgxy+3Nnj1b1apVk4+Pj6pWrap3333X4f4hQ4aocuXK8vPzU4UKFTR8+HClpKTY77dN/5s9e7bKly8vHx8fSZLFYtHs2bPVpUsX+fn5KTIyUl9//XWW7di3b58OHTqkd999V40aNVJERISaNGmisWPHqlGjRpKk48eP64UXXtALL7yg+fPn695771VERIRq166tgQMHavv27U7bLVWqlEJDQ1W5cmX961//0k8//aTg4GA988wzWXfyLYDgBAAAgHxjGIZSktJMf5KupOqHz//Idls/fH5ASVdSc7Q9w8jZ9L6YmBh1795dffv21W+//aZ169apa9eumT5+7dq1at26td544w0NGTIk0+0tWLBAI0aM0BtvvKHffvtN48aN0/DhwzV//nx7nSJFimjevHn69ddfNX36dH344YeaNm2aw3YOHjyoJUuWaOnSpdq9e7e9fPTo0erWrZt++eUXdejQQT179tT58+czbUtwcLDc3Nz0xRdfKC0tLdM6S5YsUUpKigYPHpzp/RaLJdPy9Hx9ffXvf/9bP/30k06fzp9wWxgwVQ8AAAD5JjXZqg8Grr8h27p8MUmzX9xgXlHS09Oby9Pb3bReTEyMUlNT1bVrV0VEREhSptPNvvzyS/Xq1UuzZ8/Wo48+muX2Ro4cqalTp6pr166SpPLly+vXX3/VrFmz1Lt3b0nSsGHD7PXLlSunl156SYsWLXIIL8nJyfr4448VHBzssP0+ffqoe/fukqRx48ZpxowZ2rp1q9q1a+fUljvuuEMzZszQ4MGDNXr0aDVs2FAtW7ZUz549VaFCBUnSH3/8ocDAQIWGhtoft2TJEntbJWnTpk2mU/CqVq0qSTp69KhKlbo1p1RyxgkAAAC3rTp16ui+++5TrVq19Mgjj+jDDz/UhQsXHOps2bJFjzzyiD755JNsQ9Ply5d16NAh9evXTwEBAfafsWPH6tChQ/Z6n3/+uZo0aaLQ0FAFBARo2LBhOn78uMO2IiIinEKTJNWuXdv+b39/fwUGBmZ7lmfAgAGKjY3VggULFBUVpcWLF6tGjRqKjo6218l4Vqlt27bavXu3vv32W12+fDnLs1Xp2c7Q5eQM1c2KM04AAADINx5ebnp6enPTen8duKhvZv5sWu/+5+ooLLJojvabE+7u7oqOjtbGjRu1evVqvf3223rttde0ZcsWlS9fXpJUsWJFlShRQnPmzFHHjh3l6emZ6bYSEhIkSR9++KHuvvtup/1I187e9OzZU6NHj1bbtm0VFBSkRYsWaerUqQ71/f39M91Hxn1bLBZZrdZsn2ORIkX0wAMP6IEHHtDYsWPVtm1bjR07Vq1bt1ZkZKQuXbqk2NhY+1mngIAAVapUSR4eOY8Kv/32m6RrZ9BuVZxxAgAAQL6xWCzy9HY3/QmvXlz+Rb2z3VZAMW+FVy+eo+3l5syHxWJRkyZNNHr0aO3atUteXl768ssv7feXLFlSa9eu1cGDB9WtWzeHhRzSCwkJUVhYmA4fPqxKlSo5/NhC2MaNGxUREaHXXntNDRs2VGRkpI4dO5bjtl4vi8WiqlWr2he/ePjhh+Xp6ZntYhdmrly5og8++EDNmjXL9CzZrYIzTgAAAHA5NzeLmj4aqZWz9mZZ555ukXJzu7FTwbZs2aI1a9aoTZs2KlWqlLZs2aIzZ86oWrVqDvVKlSqltWvXqmXLlurevbsWLVqU6RmZ0aNH6/nnn1dQUJDatWunpKQkbd++XRcuXNCgQYMUGRmp48ePa9GiRbrzzjv17bffOoS0G2n37t0aOXKkHn/8cVWvXl1eXl5av3695syZY1/comzZspo6daoGDhyo8+fPq0+fPipfvrzOnz+vTz/9VNI/Z8tsTp8+ratXryo+Pl47duzQpEmTdPbsWS1dujRfnkdhQXACAABAoVCxXim161/T6XucAop5655u+fM9ToGBgdqwYYPeeustxcXFKSIiQlOnTlX79u2d6oaGhmrt2rVq0aKFevbsqYULFzrVefLJJ+Xn56fJkyfr5Zdflr+/v2rVqqUXXnhBkvTggw/qxRdf1HPPPaekpCR17NhRw4cP16hRo274cytTpozKlSun0aNH6+jRo7JYLPbbL774or3e//3f/6latWp688039fDDDysuLk4lSpRQVFSUVq5c6bQwRJUqVWSxWBQQEKAKFSqoTZs2GjRokMMCE7cii5HTtRpvEXFxcQoKCtKlS5cUGBjo6uYoJSVFy5cvV4cOHbKcLwvg9sB4AMDmZh0Prl69qiNHjjh891BeWK2GYg5c1OW4JPkHeqt0ZNEbfqYJt4/sjsvcZAPOOAEAAKBQcXOz6I4qxVzdDMABi0MAAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAADghrrNFm1GIXejjkeCEwAAAG4I29LpiYmJLm4J8I/k5GRJzl/km1ssRw4AAIAbwt3dXUWLFtXp06clSX5+frJY+P4luI7VatWZM2fk5+cnD4/riz4EJwAAANwwoaGhkmQPT4Crubm5qWzZstcd4glOAAAAuGEsFotKly6tUqVKKSUlxdXNAeTl5SU3t+u/QongBAAAgBvO3d39uq8pAQoTFocAAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABOFJjhNmDBBFotFL7zwQrb1Fi9erKpVq8rHx0e1atXS8uXLC6aBAAAAAG5bhSI4bdu2TbNmzVLt2rWzrbdx40Z1795d/fr1065du9S5c2d17txZe/fuLaCWAgAAALgduTw4JSQkqGfPnvrwww9VrFixbOtOnz5d7dq108svv6xq1appzJgxql+/vmbOnFlArQUAAABwO3L5F+AOGDBAHTt2VKtWrTR27Nhs627atEmDBg1yKGvbtq2WLVuW5WOSkpKUlJRkvx0XFydJSklJKRTfZm1rQ2FoCwDXYjwAYMN4ABSM3LzGXBqcFi1apJ07d2rbtm05qh8bG6uQkBCHspCQEMXGxmb5mPHjx2v06NFO5atXr5afn1/uGpyPoqOjXd0EAIUE4wEAG8YDIH8lJibmuK7LgtOJEyc0cOBARUdHy8fHJ9/2M3ToUIezVHFxcQoPD1ebNm0UGBiYb/vNqZSUFEVHR6t169by9PR0dXMAuBDjAQAbxgOgYNhmo+WEy4LTjh07dPr0adWvX99elpaWpg0bNmjmzJlKSkqSu7u7w2NCQ0N16tQph7JTp04pNDQ0y/14e3vL29vbqdzT07NQDUSFrT0AXIfxAIAN4wGQv3Lz+nLZ4hD33Xef9uzZo927d9t/GjZsqJ49e2r37t1OoUmSoqKitGbNGoey6OhoRUVFFVSzAQAAANyGXHbGqUiRIqpZs6ZDmb+/v0qUKGEv79Wrl+644w6NHz9ekjRw4EA1b95cU6dOVceOHbVo0SJt375dH3zwQYG3HwAAAMDtw+XLkWfn+PHjiomJsd9u3LixFi5cqA8++EB16tTRF198oWXLljkFMAAAAAC4kVy+HHl669aty/a2JD3yyCN65JFHCqZBAAAAAKBCfsYJAAAAAAoDghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmHBpcHrvvfdUu3ZtBQYGKjAwUFFRUVqxYkWW9efNmyeLxeLw4+PjU4AtBgAAAHA78nDlzsuUKaMJEyYoMjJShmFo/vz56tSpk3bt2qUaNWpk+pjAwEDt37/ffttisRRUcwEAAADcplwanB544AGH22+88Ybee+89bd68OcvgZLFYFBoaWhDNAwAAAABJLg5O6aWlpWnx4sW6fPmyoqKisqyXkJCgiIgIWa1W1a9fX+PGjcsyZElSUlKSkpKS7Lfj4uIkSSkpKUpJSblxTyCPbG0oDG0B4FqMBwBsGA+AgpGb15jFMAwjH9tias+ePYqKitLVq1cVEBCghQsXqkOHDpnW3bRpkw4cOKDatWvr0qVLmjJlijZs2KB9+/apTJkymT5m1KhRGj16tFP5woUL5efnd0OfCwAAAICbR2Jionr06KFLly4pMDAw27ouD07Jyck6fvy4Ll26pC+++EKzZ8/W+vXrVb16ddPHpqSkqFq1aurevbvGjBmTaZ3MzjiFh4fr7Nmzpp1TEFJSUhQdHa3WrVvL09PT1c0B4EKMBwBsGA+AghEXF6eSJUvmKDi5fKqel5eXKlWqJElq0KCBtm3bpunTp2vWrFmmj/X09FS9evV08ODBLOt4e3vL29s708cWpoGosLUHgOswHgCwYTwA8lduXl+F7nucrFarwxmi7KSlpWnPnj0qXbp0PrcKAAAAwO3MpWechg4dqvbt26ts2bKKj4/XwoULtW7dOq1atUqS1KtXL91xxx0aP368JOn1119Xo0aNVKlSJV28eFGTJ0/WsWPH9OSTT7ryaQAAAAC4xbk0OJ0+fVq9evVSTEyMgoKCVLt2ba1atUqtW7eWJB0/flxubv+cFLtw4YKeeuopxcbGqlixYmrQoIE2btyYo+uhAAAAACCvXBqcPvroo2zvX7duncPtadOmadq0afnYIgAAAABwVuiucQIAAACAwobgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYMLD1Q0AAAC4XmnWNO08vVNnEs8o2C9Y9UvVl7ubu6ubBeAWQnACAAA3te+OfacJWyfoVOIpe1mIX4heuesVtYpo5cKWAbiVMFUPAADctL479p0GrRvkEJok6XTiaQ1aN0jfHfvORS0DkJk0a5q2xW7T8sPLtS12m9Ksaa5uUo5xxsmF0qxp2n5qu35O/lmlTpXSXWF3Ma0gB5iOAeBWxhiXc2nWNE3YOkGGDKf7DBmyyKKJWyeqZXhL+hAoBG72s8MEJxfJeOAsXrP4pjpwXOVmf8G5Em/GgMLvVhnj0qxpSkpL0tW0q7qaetX+36S0pGu3//73ldQr/5SlZV3uVCc1SVfSruhKyhWlGqlZtsOQodjEWLVc3FLFvYvLz9NPfp5+8vfwv/ZfT3/5efg5/Nvf89p96f9tu8/Xw1cWi6UAezL/8bcBBcV2djjjBx22s8Nvtniz0I9zFsMwnD+muYXFxcUpKChIly5dUmBgoEvakNWBY9G1wfhmOHBcgX7Lu1vlzZgrFOSbipSUFC1fvlwdOnSQp6dnvuyjIPGGLHfye4yzhZnMwsrVtGthJOPtK2lXsi3Pansp1pTr6ovCyiKLQ/DKGLBs/04fyGz17XXSP97DL8vXREGMB/xtQEFJs6ap7ZK2TlNqbSyyKMQvRCsfWlngfydykw0ITgWsMB84hRn9lncEzrwr6DcVt1Jw4g1Z1qyGVSnWFKWkpSjZmqzktGRdTbuqPiv66NzVc1k+LtArUH1r9lWyNdnx7E3Gszrpgk76Oq4KM15uXvLx8JGPu498PHzk7eEtX3dfeXt4/1Pm7u1Yx91bvh6+mZbbbnt7eOuP83/o5Q0vm7ZheKPhKhdYTpdTLuty6mUlpiQqMSXR/u/LKX+Xpf7979TEf8r//ndm0wFvBB93n0zPePm6++pc7DlVKVdFAd4BDme//D385evp6/AY27893XM2dvC3AfktzZqmS8mXdPHqRW38a6Mmbpto+pg5befoztA7C6B1/yA4ZcPVwWlb7Db1XdXXtF6gZ6C8PLxk+ft/1/5vkcVisZfZpgukL3ezXFvvI9N6Gcpsg6Obxc2xzLavDPXsj8/4X5PHZix3s7hl2vbsnufZK2e18a+Npv3Wrlw7hRcJl5vFTe4W92v/dXO337aXWdzl5uYmD4uH3CxumZalr5t+G+nLHLb3976cyrKom74f8guBM+9c8abiVglOheUNmdWwKjktWSnWlEz/m2xNtoeXjP+11bOX2x5jK89w2/7YDLcz21eqNeupZQXF293bKZRkF2Iy1vP2yOaxGR5n+7uUH2xj3OnE05kGmxs1xlkNq66mXv0nWKU4B6z0t231MgthtsenGflzQbynm2f2Z7z+nnK4+I/FSkhJyHI7IX4hWvXQKv42wO5K6hVdvHpR55POX/vv1fO6mHRRF65e0IWkC9f+m+7fl5Iu5foDh4lNJ6pDhQ759Awyl5tswDVOBexM4pkc1YtLiZNuzZkO+Wrl0ZWubkKu5CR4ebh5OAWujGVZPf5S0qUsQ5P0z/z/l9a/pNIBpTMNfRnDp0WWTINkxgCa5bYy26bFkqv6We37RgXRW+mCc8MwZDWsMmTIMAwZyvy21bBKkv0+h9t/1zMMQ1b9XdeQrLLay2Rcq5tqpGrs5rFZ9p0kjd40WldSryjVmuoUQmwhJWMISU5LF0TSBZuMASf97eyufSlMPNw8ZJElR2eE6peqr4pFK2YdVjI5m5Mx8Hi7e+drmClI7m7ueuWuVzRo3SBZZHE47mxBfchdQ677depmcbOHkJK+Ja9rW9K112WyNTnbEJaQlKCd+3YqvEK4rlqv6nLKZV1JveIc0v5+TLI1WZKUYk3RxaSLuph08braeCrxlO5ccKeCvINUxKuIAr0CVcSriMO/05cV8SqiIK8g+78DvALk6XbzfgB0q7MaVsUlxdlDkEP4yfjfqxd0MemirqReydO+ingVkZ+HX7bvR2yC/YLztI+CwhmnApbTM06jokapRska/7xhkaFr/zccymy/Pvubmr/f9EhyquewjfRlyvDmKIt6mbXB9ubKoV6GNjm19e83Wun3n/6xmbX/eNxxLTmwxLTfWke0Vim/Ukq1ptrfDKYZadfe0P1dZrudZqQpzZrmVGY1rEqzpjmV5fTx6cvya2oHMmcLUG76J2jlJYxdSbmiw3GHTfcXWTRSRbyK5D6IZFE3zZqmK1euyNvH+1pdmYeWjK/9jOEI//Bw85CXm5e83L3k6eaZ6X+93Lzk4f5PPS83L3m6e2Zaz8vd69o2bfVs9/1dL+Nt27Yy7tfN4pbjvw2umMZyM8hsamioX6iG3DXkpp1ultsz0CnWFIcgldW0w8spl7Xv7D799NdP+f4cfD18nUJWxrAV6BXoXOYdqADPgEIf8AvTdZxJaUn/nPHJJPxcTPr7DNHVi7qQdO227W9Pbni4eai4d3EV9SmqYj7FVMy7mON/M/w7yDtInm6eBXZ2OC8441SI1S9VXyF+IaYHTudKnQv9J9kFKc2aph9P/mjab5ObTS5U/WYYxrUwlYOQlb7MdttelsPHpxqp9uBnNaw6dPGQPvntE9N2dijfQSH+IbJanUNkxgDq0Lasgmf6/1qt9jf8tttZ1c+qjq0fzYJA+hCi3P89yLUDFw/kz4bz9qHeDWefWpthOnD66bpucpMs/0z5TbGm6HLKZdNtVwyqqNIBpTMNMp7unv8ElwwhJH1wSR9o0m/H9vj02/F08yzUq6Hl9G9D/VL1XdC6wq9VRCu1DG9ZaN7EuoKnm6eCvIMU5B1kWndb7LYcBaeJzSaqQlAFxSfHKy4pTnHJcYpPjld8Sry9LD45/lp5urLE1ERJ16Z2XUm9kqMzDRlZZFGAZ4BDmCri6Riu7IHL07nMz8MvX1/z+Xkdp9WwKj45PsspcBlD0IWrF+x9nltFPIvkLAT9/V9/T/889WtBnR3ObwSnAnarHDgF7WbtN4vFIg+LhzzkIbmgaWnWNK0+ttr0zdi4e8YVur7LjC1AZRbOsgtvTo8zCW+/n/tdb+9+27Q9z9R5RpWLVc5xsHBThvsyhBJrmlUbf9qoe+65R54envZPW+1TETPux3b77/1k3Lb9sbb7MntsNuEoL3J65uS1Rq9x5iSdm3WMK0zc3dw5pnIop0G9bUTbPB1zqdZUJSQnXAtSKXH2gGX7iUtOF8LSldn+fTXtqgwZ18JYSrxk/lmME3eLu8NZrCzPbmVxRszb3TvLcTC3y2onpyU7BJ5MQ5DtWqG/y/JyDZyHxSNHIaiod1EV9ymuot5Fc7yYyI3QKqKV3mzxZqaB82Y5O8xUPRe5FacVFAT6LfdsA7ykTN+MsXKSM1dNKbgVFocozNMxbgaMcbDJ7/GgMP9tSE5LdgpTOQlctvtvxAIsnm6emYatAM8ArTiyQpdTs05z3u7eiiwaeS0MJV3I0Vn4zPh7+quYd7FrIcenaPYhyKeoingWKdRn1m0K0xRHiVX1slVYgpN07cDZ+tdWRW+KVuuo1ror7C7eSORAYXvB3Qx4M5Z7rnhTcSsEJ6lwvyG7GTDGQXLd9zjd7H8bDMPQ1bSrOQ5cGYNXfHJ8nq79MeNucVdR76KZXgeU/gyQ7b/FfIrJy93rhrcDzghO2ShMwUm6dd4oofDjzVjuFfSbiltpPLgV35ABBamgxgP+NjgyDEOJqYn/BK4kx+u6dpzaoehj0abb6VW9l1pHtLaHoCJeRQr9Yhe3KxaHAOCE+f+5xwXneUffATcH/jY4slgs9i8UDvUPdbq/crHKOQpOLcJbqG6puvnQQrgSwQkAssGbiryj7wDcalgB8/bGOUMAAAAgB2wrYEr/XLdpwwqYtz6CEwAAAJBDtmW1S/mVcigP8Qth8ZtbHFP1AAAAgFzgOs7bE8EJAAAAyCWu47z9MFUPAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEy4NDi99957ql27tgIDAxUYGKioqCitWLEi28csXrxYVatWlY+Pj2rVqqXly5cXUGsBAAAA3K5cGpzKlCmjCRMmaMeOHdq+fbvuvfdederUSfv27cu0/saNG9W9e3f169dPu3btUufOndW5c2ft3bu3gFsOAAAA4Hbi0uD0wAMPqEOHDoqMjFTlypX1xhtvKCAgQJs3b860/vTp09WuXTu9/PLLqlatmsaMGaP69etr5syZBdxyAAAAALcTD1c3wCYtLU2LFy/W5cuXFRUVlWmdTZs2adCgQQ5lbdu21bJly7LcblJSkpKSkuy34+LiJEkpKSlKSUm5/oZfJ1sbCkNbALgW4wEAG8YDoGDk5jXm8uC0Z88eRUVF6erVqwoICNCXX36p6tWrZ1o3NjZWISEhDmUhISGKjY3Ncvvjx4/X6NGjncpXr14tPz+/62v8DRQdHe3qJgAoJBgPANgwHgD5KzExMcd1XR6cqlSpot27d+vSpUv64osv1Lt3b61fvz7L8JRbQ4cOdThLFRcXp/DwcLVp00aBgYE3ZB/XIyUlRdHR0WrdurU8PT1d3RwALsR4AMCG8QAoGLbZaDnh8uDk5eWlSpUqSZIaNGigbdu2afr06Zo1a5ZT3dDQUJ06dcqh7NSpUwoNDc1y+97e3vL29nYq9/T0LFQDUWFrDwDXYTwAYMN4AOSv3Ly+Ct33OFmtVodrktKLiorSmjVrHMqio6OzvCYKAAAAAG4El55xGjp0qNq3b6+yZcsqPj5eCxcu1Lp167Rq1SpJUq9evXTHHXdo/PjxkqSBAweqefPmmjp1qjp27KhFixZp+/bt+uCDD1z5NAAAAADc4lwanE6fPq1evXopJiZGQUFBql27tlatWqXWrVtLko4fPy43t39OijVu3FgLFy7UsGHD9OqrryoyMlLLli1TzZo1XfUUrktqUpL2f/KufH/eqf1nDqvq48/KI5NphXCUmpSk/Z++r+S/jskrLEJVHvs3/ZYD9Fve0G95R9/lDf2WN/Rb3tBveUff5c3N3G8WwzAMVzeiIMXFxSkoKEiXLl1y6eIQP096Vd6fLZVxxWIvs/gaSureVXUGj3NZuwo7+i1v6Le8Keh+S0lJ0fLly9WhQ4eb/poGjrm8od/y5lbst4IYD27Ffiso9F3eFMZ+y002KHTXON0Ofp70qrzmLJVxxbHcuCJ5zVmqnye96pqGFXL0W97Qb3lDv+UdfZc39Fve0G95Q7/lHX2XN7dCv3HGqYClJiXpYKM6fx80lkxqGLL4SqH/W3XTnLYsCKlJSYp9oC39lkv0W964qt9SUlL0/br1atmi+U17xoljLm/ot7y5lfstP8eDW7nf8ht9lzc56jc/qdKmnwu833KTDQhOBWzfR9PlNvn9At8vAAAAUJhZX/63avQbWKD7ZKpeIZb81zFXNwEAAAAodAr7+2SXfwHu7cYrLCJH9VIGPKYK/+qXz625eRxe9JE83/nUtB795oh+yxtX9dutMFWPYy5v6Le8uZX7LT/Hg1u53/IbfZc3Oe23nL5PdhWm6hWwHF3j5KI5noUZ/ZY39FveuKrfboVV9Tjm8oZ+y5tbud/yczy4lfstv9F3eVOY+42peoWYh7e3krp3/ftWxsx67XbSv7ryYsuAfssb+i1v6Le8o+/yhn7LG/otb+i3vKPv8uZW6TeCkwvUGTxOyX27yuLrWG7xk5L7sv5/Vui3vKHf8oZ+yzv6Lm/ot7yh3/KGfss7+i5vboV+Y6qeC6UmJen3T97VXz/vVFid+qr6+LOFPmkXBjfzN067Ev2WNwXZb7fCVL30OObyhn7Lm1ut3wpqPLjV+q0g0Xd5U9j6jeXIs1GYgpN0671RApB3jAcAbBgPgILBNU4AAAAAcAMRnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEx4uLoBBc0wDElSXFyci1tyTUpKihITExUXFydPT09XNweACzEeALBhPAAKhi0T2DJCdm674BQfHy9JCg8Pd3FLAAAAABQG8fHxCgoKyraOxchJvLqFWK1W/fXXXypSpIgsFku2de+8805t27Yt1/vIzePi4uIUHh6uEydOKDAwMNf7up3l9ffjaq5ud0HsPz/2cSO2eT3bYDwo3Fz9usorV7eb8YDx4Fbk6tdVXrm63bfreGAYhuLj4xUWFiY3t+yvYrrtzji5ubmpTJkyOarr7u6ep8EqL48LDAxkYMylvP5+XM3V7S6I/efHPm7ENq9nG4wHhZurX1d55ep2Mx4wHtyKXP26yitXt/t2Hg/MzjTZsDhENgYMGFCgj0Pu3Kz97Op2F8T+82MfN2Kb17MNxoPC7WbtZ1e3m/GgYB/r6t/37eJm7WdXt/t2Hg9y6rabqlfYxMXFKSgoSJcuXbopPx0BcOMwHgCwYTwACh/OOLmYt7e3Ro4cKW9vb1c3BYCLMR4AsGE8AAofzjgBAAAAgAnOOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYJTIfbNN9+oSpUqioyM1OzZs13dHAAu1KVLFxUrVkwPP/ywq5sCwIVOnDihFi1aqHr16qpdu7YWL17s6iYBtw2WIy+kUlNTVb16dX3//fcKCgpSgwYNtHHjRpUoUcLVTQPgAuvWrVN8fLzmz5+vL774wtXNAeAiMTExOnXqlOrWravY2Fg1aNBAf/zxh/z9/V3dNOCWxxmnQmrr1q2qUaOG7rjjDgUEBKh9+/ZavXq1q5sFwEVatGihIkWKuLoZAFysdOnSqlu3riQpNDRUJUuW1Pnz513bKOA2QXDKJxs2bNADDzygsLAwWSwWLVu2zKnOO++8o3LlysnHx0d33323tm7dar/vr7/+0h133GG/fccdd+jkyZMF0XQAN9j1jgcAbh03cjzYsWOH0tLSFB4ens+tBiARnPLN5cuXVadOHb3zzjuZ3v/5559r0KBBGjlypHbu3Kk6deqobdu2On36dAG3FEB+YzwAYHOjxoPz58+rV69e+uCDDwqi2QBEcMo37du319ixY9WlS5dM73/zzTf11FNP6YknnlD16tX1/vvvy8/PT3PmzJEkhYWFOZxhOnnypMLCwgqk7QBurOsdDwDcOm7EeJCUlKTOnTvrlVdeUePGjQuq6cBtj+DkAsnJydqxY4datWplL3Nzc1OrVq20adMmSdJdd92lvXv36uTJk0pISNCKFSvUtm1bVzUZQD7JyXgA4PaQk/HAMAz16dNH9957rx5//HFXNRW4LRGcXODs2bNKS0tTSEiIQ3lISIhiY2MlSR4eHpo6dapatmypunXr6j//+Q8r6gG3oJyMB5LUqlUrPfLII1q+fLnKlClDqAJuQTkZD3766Sd9/vnnWrZsmerWrau6detqz549rmgucNvxcHUDkLUHH3xQDz74oKubAaAQ+O6771zdBACFwD333COr1erqZgC3Jc44uUDJkiXl7u6uU6dOOZSfOnVKoaGhLmoVAFdgPABgw3gAFG4EJxfw8vJSgwYNtGbNGnuZ1WrVmjVrFBUV5cKWAShojAcAbBgPgMKNqXr5JCEhQQcPHrTfPnLkiHbv3q3ixYurbNmyGjRokHr37q2GDRvqrrvu0ltvvaXLly/riSeecGGrAeQHxgMANowHwE3MQL74/vvvDUlOP71797bXefvtt42yZcsaXl5exl133WVs3rzZdQ0GkG8YDwDYMB4ANy+LYRiGC/IaAAAAANw0uMYJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAHDbmDdvnooWLXrDtnf06FFZLBbt3r37hm0TAFA4EZwAAAWqT58+slgs9p8SJUqoXbt2+uWXX3K1nVGjRqlu3br508gcCg8PV0xMjGrWrOnSdgAA8h/BCQBQ4Nq1a6eYmBjFxMRozZo18vDw0P333+/qZuWau7u7QkND5eHh4eqmAADyGcEJAFDgvL29FRoaqtDQUNWtW1evvPKKTpw4oTNnztjrDBkyRJUrV5afn58qVKig4cOHKyUlRdK1KXejR4/Wzz//bD9zNW/ePEnSxYsX1b9/f4WEhMjHx0c1a9bUN99847D/VatWqVq1agoICLCHuKxcuHBBPXv2VHBwsHx9fRUZGam5c+dKcp6ql/Fsmu1n3bp1kqSkpCS99NJLuuOOO+Tv76+7777bfh8AoHDjIzIAgEslJCTo008/VaVKlVSiRAl7eZEiRTRv3jyFhYVpz549euqpp1SkSBENHjxYjz76qPbu3auVK1fqu+++kyQFBQXJarWqffv2io+P16effqqKFSvq119/lbu7u327iYmJmjJlij755BO5ubnpscce00svvaQFCxZk2r7hw4fr119/1YoVK1SyZEkdPHhQV65cybTu9OnTNWHCBPvtCRMm6LPPPlPVqlUlSc8995x+/fVXLVq0SGFhYfryyy/Vrl077dmzR5GRkdfdlwCA/ENwAgAUuG+++UYBAQGSpMuXL6t06dL65ptv5Ob2z0SIYcOG2f9drlw5vfTSS1q0aJEGDx4sX19fBQQEyMPDQ6GhofZ6q1ev1tatW/Xbb7+pcuXKkqQKFSo47DslJUXvv/++KlasKOlamHn99dezbOvx48dVr149NWzY0N6WrAQFBSkoKEiStHTpUs2aNUvfffedQkNDdfz4cc2dO1fHjx9XWFiYJOmll17SypUrNXfuXI0bN8603wAArkNwAgAUuJYtW+q9996TdG0q3Lvvvqv27dtr69atioiIkCR9/vnnmjFjhg4dOqSEhASlpqYqMDAw2+3u3r1bZcqUsYemzPj5+dlDkySVLl1ap0+fzrL+M888o4ceekg7d+5UmzZt1LlzZzVu3DjbduzatUuPP/64Zs6cqSZNmkiS9uzZo7S0NKe2JSUlOZxpAwAUTgQnAECB8/f3V6VKley3Z8+eraCgIH344YcaO3asNm3apJ49e2r06NFq27atgoKCtGjRIk2dOjXb7fr6+pru29PT0+G2xWKRYRhZ1m/fvr2OHTum5cuXKzo6Wvfdd58GDBigKVOmZFo/NjZWDz74oJ588kn169fPXp6QkCB3d3ft2LHDYeqgJPvZNwBA4UVwAgC4nMVikZubm/3aoY0bNyoiIkKvvfaavc6xY8ccHuPl5aW0tDSHstq1a+vPP//UH3/8ke1Zp9wKDg5W79691bt3bzVt2lQvv/xypsHp6tWr6tSpk6pWrao333zT4b569eopLS1Np0+fVtOmTW9Y2wAABYPgBAAocElJSYqNjZV0barezJkzlZCQoAceeECSFBkZqePHj2vRokW688479e233+rLL7902Ea5cuV05MgR+/S8IkWKqHnz5mrWrJkeeughvfnmm6pUqZJ+//13WSwWtWvXLk9tHTFihBo0aKAaNWooKSlJ33zzjapVq5Zp3f79++vEiRNas2aNwwqBxYsXV+XKldWzZ0/16tVLU6dOVb169XTmzBmtWbNGtWvXVseOHfPUPgBAwWA5cgBAgVu5cqVKly6t0qVL6+6779a2bdu0ePFitWjRQpL04IMP6sUXX9Rzzz2nunXrauPGjRo+fLjDNh566CG1a9dOLVu2VHBwsD777DNJ0pIlS3TnnXeqe/fuql69ugYPHux0Zio3vLy8NHToUNWuXVvNmjWTu7u7Fi1alGnd9evXKyYmRtWrV7c/v9KlS2vjxo2SpLlz56pXr176z3/+oypVqqhz587atm2bypYtm+f2AQAKhsXIbmI3AAAAAIAzTgAAAABghuAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACb+H4hc5gvTTO7FAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от размера батча (после масштабирования):\n","            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Batch size                                                              \n","1                  8.313329  8.313329  8.313332    8.313329     8.313361\n","2                  8.313329  8.313329  8.313332    8.313329     8.313361\n","4                  8.313329  8.313329  8.313332    8.313329     8.313361\n","8                  8.313329  8.313329  8.313332    8.313329     8.313361\n","16                 8.313329  8.313329  8.313332    8.313329     8.313361\n","32                 8.313329  8.313329  8.313332    8.313329     8.313361\n","64                 8.313329  8.313329  8.313333    8.313329     8.313361\n","128                8.313329  8.313329  8.313332    8.313329     8.313361\n","256                8.313329  8.313329  8.313332    8.313329     8.313361\n","512                8.313329  8.313329  8.313332    8.313329     8.313361\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных ----------\n","from google.colab import files\n","print(\"Загрузите файл data.csv:\")\n","uploaded = files.upload()\n","\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values   # без масштабирования\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                batch_size=batch_size,  # возвращаем batch_size\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)   # без масштабирования\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_LR = 0.01      # будет переопределён\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы ----------\n","def plot_and_table(param_values, param_name, xlabel, title, filename):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            mses.append(mse)\n","        results[label] = mses\n","        plt.semilogx(param_values, mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    # Вывод таблицы\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений MSE для {title}:\")\n","    print(df_table.round(6).to_string(na_rep='-'))\n","    return df_table\n","\n","# ---------- Сначала строим график learning rate ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n","table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (без масштабирования)', 'lr_plot')\n","\n","# Выбираем наименьший работающий learning rate для GD (как в первом варианте)\n","gd_mses = table_lr['GD'].values\n","valid_lr = lr_range[~np.isnan(gd_mses)]\n","if len(valid_lr) > 0:\n","    best_lr = valid_lr[0]\n","    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n","else:\n","    best_lr = 1e-8\n","    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n","\n","DEFAULT_LR = best_lr\n","\n","# ---------- График 2: влияние числа эпох ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n","table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (без масштабирования)', 'epochs_plot')\n","\n","# ---------- График 3: влияние размера батча ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n","table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (без масштабирования)', 'batch_plot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"-8DUiLJA3nQS","executionInfo":{"status":"ok","timestamp":1773047575009,"user_tz":-420,"elapsed":28377,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"cc034d99-d565-42cc-992c-11925356a955"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Загрузите файл data.csv:\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-cbb98c2d-a6ff-45b0-b0b8-2b943ba7a582\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-cbb98c2d-a6ff-45b0-b0b8-2b943ba7a582\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving data.csv to data (5).csv\n","Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1: Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA18AAAIoCAYAAACWFlGkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAmSpJREFUeJzs3XdYFFfbBvB7WdpSFhWlKd2CIthj1NixIho1n7Ek1qiJXd8YJQmKLYoxaiwxGhPRWGOJMbbYC/aGmmg0ImCJFAtdYMt8f5DdsNIWAzsL3L/34so7M2dmnpmzu+6z58w5EkEQBBAREREREVGpMhE7ACIiIiIiooqAyRcREREREZEBMPkiIiIiIiIyACZfREREREREBsDki4iIiIiIyACYfBERERERERkAky8iIiIiIiIDYPJFRERERERkAEy+iIiIqFBJSUm4d+8elEql2KHQP7KzsxEXF4e///5b7FCIqBiYfBER0WuRSCQIDQ0VO4wieXh4YOjQoWKHYZQePnwIS0tLnDlzRme9QqHAwoUL0aBBA1hYWKBy5cqoVasWjh49KlKkBACXL1/GwIEDUbVqVVhYWMDZ2Rl9+/YVO6xy6dmzZ7C2tsb+/fvFDoXKGSZfRKXo22+/RZcuXeDo6AgzMzM4OTmhbdu22LBhA9Rqtdjh0WsaOnQoJBIJ5HI5Xr58mWf7X3/9BYlEAolEgkWLFulsi4mJwbBhw+Dt7Q1LS0s4OTmhTZs2mDlzpk65du3aaY/x6p+Pj0+pXh8Zj4yMDISGhuLEiROlcvzZs2ejefPmaNWqlXZdVlYWAgICEBISgnbt2mH79u04fPgwjh07hhYtWpRKHFS0X375BW+99RZu3bqFefPm4fDhwzh8+DBWr14tdmjlkr29PT744AOEhISIHQqVM6ZiB0BUnq1fvx7Ozs4ICQmBXC5HUlISzp8/j6FDh+LAgQPYsmWL2CHSazI1NUVGRgZ+/fVX9OvXT2fbpk2bYGlpiczMTJ319+7dQ7NmzSCTyTB8+HB4eHjgyZMnuHr1KsLCwjBr1iyd8jVq1MD8+fPznNvOzq7kL6gcu3PnDkxMyuZvjRkZGdrXRbt27Ur02ImJiVi/fj3Wr1+vsz4sLAwXLlzAb7/9VuLnpNfz/PlzfPDBB+jSpQu2b98Oc3NzsUOqED788EMsW7YMx44dQ4cOHcQOh8oJJl9EpejUqVMwMzPTWTdhwgTY29tjxYoVmD9/Pjw8PMQJjv4TCwsLtGrVClu2bMmTfG3evBmBgYHYuXOnzvolS5YgLS0NkZGRcHd319mWkJCQ5xx2dnZ47733Sj74MkypVEKtVhfry6eFhUUpRlQ8rxN/adm4cSNMTU0RFBSkXadUKrF06VL873//Y+JlRNatW4fMzEyEh4cbxWunoqhbty7q16+P8PBwJl9UYsrmT4FEZcSriZeGJuHK/Wv8L7/8gsDAQLi4uMDCwgLe3t6YM2cOVCqVzr6vdkerWrUqAgMD8fvvv+uUy+95nC+//BISiSTPl6rMzEyEhoaidu3asLS0hLOzM/r06YOoqCgAOV3lJBIJwsPDdfYbO3YsJBKJzvM04eHhkEgkMDc3R2Jiok75c+fOaeO+fPmyzrbt27ejSZMmkMlkqFq1Kt577z08fvw4z737888/0a9fP1SrVg0ymQx16tTBZ599BgAIDQ0tsKue5k/Tfatdu3aoX79+nuMXx8CBA3HgwAEkJSVp1126dAl//fUXBg4cmKd8VFQUatSokSfxAgAHB4f/FMurEhISMGLECDg6OsLS0hINGjTQaeHQ1Glhf6/znNTjx48xfPhwODo6wsLCAr6+vvjhhx90ymRnZ2PGjBlo0qQJ7OzsYG1tjdatW+P48eM65TQxLlq0CEuXLoW3tzcsLCxw69YtbV3fu3cPQ4cORaVKlWBnZ4dhw4YhIyND5zivPvOleY2eOXMGU6ZMQbVq1WBtbY3evXvnec2q1WqEhobCxcUFVlZWaN++PW7duqXXc2SFxa/PPYiJiUG1atUAALNmzdLWS+739Z9//ol33nkHVapUgaWlJZo2bYo9e/YUVU0AgN27d6N58+awsbHRrrtz5w5evHgBW1tbtG3bFlZWVrCzs0OPHj3yfMYA+tV3REQE3nrrLVStWhWWlpbw8vLCtGnT8rQMF3T/JBIJdu/erbMtMzMTlStXztO1NzY2FmPGjEGdOnUgk8lgb2+P//u//0NMTEye4yclJWHy5Mnw8PCAhYUFatSogcGDB+Pp06cAgBMnTkAikWDHjh06+9nY2OSp+/feew9Vq1bNE3dR763nz5/j448/hp+fH2xsbCCXy9GtWzdcv35d5/jnz59Hw4YN8cUXX8DV1RUWFhaoVasWFixYkKcLu76f/Zrr27ZtGz799FM4OTnB2toaPXv2xMOHD/PcL30+ozVdsjV/lStXRrt27XD69GmdcsX59y6/z+lFixZBIpHo1Gt+78nt27dDIpHk+ZFT8/no5uYGqVSqjTf3e0GjU6dO+PXXXyEIQp5tRK+DLV9EBpCUlASlUonU1FRcuXIFixYtQv/+/eHm5qYtEx4eDhsbG0yZMgU2NjY4duwYZsyYgZSUFHz55Zc6x/Px8cFnn30GQRAQFRWFxYsXo3v37njw4EGhMeTXhU2lUqFHjx44evQo+vfvj4kTJyI1NRWHDx/G77//Dm9v73yPd+/ePXz33XcFnk8qlWLjxo2YPHmydt26devy7Y4XHh6OYcOGoVmzZpg/fz7i4+Px9ddf48yZM7h27RoqVaoEALhx4wZat24NMzMzjBo1Ch4eHoiKisKvv/6KefPmoU+fPqhZs6b2uJMnT0bdunUxatQo7bq6desWGHNx9enTBx9++CF27dqF4cOHA8hp9fLx8UHjxo3zlHd3d8eRI0f07sKiUqm0XwRzk8lksLa2LnC/ly9fol27drh37x7GjRsHT09PbN++HUOHDkVSUhImTpyIatWq4ccff9Tus2vXLvz888866wqq+4LEx8fjzTffhEQiwbhx41CtWjUcOHAAI0aMQEpKCiZNmgQASElJwdq1azFgwACMHDkSqamp+P7779GlSxdcvHgRDRs21Dmu5lf/UaNGwcLCAlWqVNFu69evHzw9PTF//nxcvXoVa9euhYODA8LCwoqMd/z48ahcuTJmzpyJmJgYLF26FOPGjcO2bdu0ZYKDg7Fw4UIEBQWhS5cuuH79Orp06VJk4lBU/Prcg2rVqmHVqlX46KOP0Lt3b/Tp0wcA4O/vDwD4448/0KpVK1SvXh3Tp0+HtbU1fvrpJ7z99tvYuXMnevfuXWBMCoUCly5dwkcffaSz/tmzZ9rrrlWrFmbNmoXMzEysXLkSrVq1wqVLl1C7dm0A+td3amoq6tati379+sHKygrnzp3DwoULkZGRgeXLlxd5/ywtLbFu3Tq8/fbb2nW7du3Ktw4uXbqEs2fPon///qhRowZiYmKwatUqtGvXDrdu3YKVlRUAIC0tDa1bt8bt27cxfPhwNG7cGE+fPsWePXvw6NEjbSL1Oorz3rp//z52796N//u//4Onpyfi4+OxevVqtG3bFrdu3YKLiwuAnHqJiIhAREQEhg8fjiZNmuDo0aMIDg5GTEwMvv322wLjKeizX2PevHmQSCSYNm0aEhISsHTpUgQEBCAyMhIymQyA/p/RAFC1alUsWbIEAPDo0SN8/fXX6N69Ox4+fKgtV5x/716XUqnU/jD3qiFDhuDIkSMYP348GjRoAKlUijVr1uDq1at5yjZp0gRLlizBH3/88Z9/sCMCAAhEVOrq1KkjAND+DR48WFAoFDplMjIy8uw3evRowcrKSsjMzNSua9u2rdC2bVudcp9++qkAQEhISNCuAyDMnDlTu/zJJ58IDg4OQpMmTXT2/+GHHwQAwuLFi/OcX61WC4IgCNHR0QIAYd26ddpt/fr1E+rXry+4uroKQ4YM0a5ft26dAEAYMGCA4Ofnp12fnp4uyOVyYeDAgQIA4dKlS4IgCEJ2drbg4OAg1K9fX3j58qW2/N69ewUAwowZM7Tr2rRpI9ja2gqxsbH5xvkqd3d3ndhya9u2reDr65vvtqIMGTJEsLa2FgRBEN555x2hY8eOgiAIgkqlEpycnIRZs2Zp79mXX36p3e/3338XZDKZAEBo2LChMHHiRGH37t1Cenp6vvHlfs3k/hs9enSh8S1dulQAIGzcuFG7Ljs7W2jRooVgY2MjpKSk5Nln5syZQnH/SXj1NTZixAjB2dlZePr0qU65/v37C3Z2dtrXuFKpFLKysnTKvHjxQnB0dBSGDx+uXae5h3K5XOe1nTve3OUFQRB69+4t2Nvb66x79XWgeY0GBATovHYmT54sSKVSISkpSRAEQYiLixNMTU2Ft99+W+d4oaGhAoACX1v6xK/vPUhMTMxznzU6duwo+Pn56Xw+qNVqoWXLlkKtWrUKje3evXsCAGH58uU6648fPy4AEKpWrapTj3fv3hXMzMyEvn37atfpW9/56d69u1C/fv1CY9TcvwEDBgimpqZCXFyczrVrPktyv8fyO+e5c+cEAMKGDRu062bMmCEAEHbt2pWnvOY1obkX27dv19lubW2dp+4HDRqU53WnUdh7KzMzU1CpVHmu28LCQpg9e7Z2nebzIDQ0VKfs0KFDBQDCzZs3tev0/ezXXF/16tV1PhN++uknAYDw9ddfC4JQvM/oIUOGCO7u7joxrlmzRgAgXLx4UbuuOP/e5fc5/eWXXwoAhOjoaO26V9/n33zzjWBhYSG0b99eJ6aXL18KJiYmeT5Hc3+u53b27FkBgLBt27Y824heB7sdEhnAunXrcPjwYWzatAkjRozApk2bdFpjAGh/YQRyfil++vQpWrdujYyMDPz55586ZRUKBZ4+fYrExEScO3cOP//8M/z9/Qv8tfbx48dYvnw5QkJC8nSr2LlzJ6pWrYrx48fn2U8ikeR7vCtXrmD79u2YP39+gQMZvP/++/jzzz+13Qt37twJOzs7dOzYUafc5cuXkZCQgDFjxsDS0lK7PjAwED4+Pti3bx+AnMEBTp06heHDh+u0GBYWZ1E0LUtPnz5Fdnb2ax1j4MCBOHHiBOLi4nDs2DHExcXl2+UQAHx9fREZGYn33nsPMTEx+Prrr/H222/D0dEx31ZEDw8P7Yhmuf80LQoF2b9/P5ycnDBgwADtOjMzM0yYMAFpaWk4efLka11rYQRBwM6dOxEUFARBELT39enTp+jSpQuSk5O1vypLpVLtcytqtRrPnz+HUqlE06ZN8/3luW/fvtrud6/68MMPdZZbt26NZ8+eISUlpciYR40apfPaad26NVQqFWJjYwEAR48ehVKpxJgxY3T2y++9Upj84i/uPXjV8+fPcezYMfTr10/7efH06VM8e/YMXbp0wV9//ZVvt10NTQtX5cqV890+bNgw2Nvba5dr1aqFnj174uDBg1CpVMWq79wxP3nyBLt378a5c+fQpk2bIq8TABo3bgxfX19ty1FsbCyOHz+eb7fP3J+jCoUCz549Q82aNVGpUiWdeHbu3IkGDRrk2zr4up8nr8PCwkL7GapSqfDs2TPY2NigTp06ee6fVCrV6UkAAP/73/8AQPs5+arCPvs1Bg8eDFtbW+3yO++8A2dnZ+0Q6/p+Rmuo1WrtayEyMhIbNmyAs7OzTq+D4vx7l/tzWvP3atfiV2VkZGD27NkYN25cnn8v0tPToVardV7fhdG8R/LrhUD0OtjtkMgAcg/PPHDgQHh5eeGzzz7DiBEjtEM8//HHH/j8889x7NixPF8ck5OTdZbPnj2r82WuVq1a2L17d4FfGmbOnAkXFxeMHj06z/MLUVFRqFOnDkxN9f84mD59Olq3bo0ePXpg3Lhx+ZapVq0aAgMD8cMPP6Bp06b44YcfMGTIkDzJmuaLbp06dfIcw8fHBxEREQByuucAKNFuH3/++af2PpqYmKBmzZqYOXNmgclTfrp37w5bW1ts27YNkZGRaNasGWrWrJnvMyYAULt2bfz4449QqVS4desW9u7di4ULF2LUqFHw9PREQECAtqy1tbXOsr5iY2NRq1atPPda8+VHc89LUmJiIpKSkrBmzRqsWbMm3zK5BxVZv349vvrqK/z5559QKBTa9Z6ennn2y2+dxqtfrDRflF68eAG5XF5ozIXtC/x7n3J3ZQWAKlWqFJi05Keg+ItzD1517949CIKAkJCQAofCTkhIQPXq1Qs9jvDKcyyaz5D8pjOoW7cudu7ciadPn0IikRSrvgGgXr16iI+PB5DzbNDXX39daGy5DRs2DGvWrMHHH3+M8PBwtGzZErVq1cpT7uXLl5g/fz7WrVuHx48f61xf7s/RqKgoo5gfS61W4+uvv8Y333yD6OhonWeecicHEokELi4ueV7TderUgYmJSYGfN4V99mu8eh8lEonOZ5i+n9EaDx8+1Pn3ydnZGTt37tRJ/orz713uz2l9LV68GJmZmfj0008xZcoUnW329vaoVasW1q5di7Zt26Jhw4YwMTFBVlZWvsfSvIYMmZRT+cbki0gE77zzDj777DNcuHABrVq1QlJSEtq2bQu5XI7Zs2dr54C6evUqpk2blueBan9/f3z11VcAcr70Llu2DO3atcPVq1fh5OSkU/b27dsIDw/Hxo0bCxwApDgOHTqEI0eO4Ny5c0WWHT58OAYPHozx48fj1KlTWLt2bZ4Hr8Xk4eGhbXF69uwZli1bhvfffx9eXl5488039TqGhYUF+vTpg/Xr1+P+/ft6TzoslUrh5+cHPz8/tGjRAu3bt8emTZteK9kyBprX6HvvvYchQ4bkW0bzrNLGjRsxdOhQvP3225g6dSocHBwglUoxf/587SAvueX+lfxVUqk03/WvJhUlvW9x5Bd/ce/BqzT3++OPP0aXLl3yLfNq0pib5ou9JtEsLNbCzq9PfWts374dKSkpuHLlChYsWIDq1atj7ty5ep3vvffewyeffILz589j/fr1+Pzzz/MtN378eKxbtw6TJk1CixYtYGdnB4lEgv79+xvl3IpffPEFQkJCMHz4cMyZMwdVqlSBiYkJJk2apBOvvvWSW0l/9uvL0dERGzduBJCTSP3www/o2rUrIiIi4OfnV+x/73J/Tmts3769wKT/6dOn+PLLLxEcHKzzfGhu27Ztw6BBg/K8d/J7llbzHvkvzwES5cbki0gEmol5NV/+Tpw4gWfPnmHXrl06XXGio6Pz3b9y5co6X9LbtWsHFxcXrFu3DsHBwTplg4OD0bBhQ7z77rv5Hsvb2xsXLlyAQqEo8h9oQRAwffp09O7dW6/kpFu3brC0tET//v3x1ltvwdvbO0/ypRn5786dO3kGobhz5452u5eXFwDkO+La63q1Zal169aoXr06Dh06pHfyBeS0Zv7www8wMTFB//79ix1H06ZNAQBPnjwp9r75cXd3x40bN6BWq3VavzTdefIbbfG/qlatGmxtbaFSqYpMIHfs2AEvLy/s2rVL59fkVyeaFpvmPt27d0+nNerZs2d5kpbi0vceFPRru+b9YGZm9loJu5ubG2QyWZ7PGM113rlzJ88+f/75J6ytrbVfQvWtb43WrVsDyOmuphmRb/r06QV2h8vN3t4ePXv2xOjRo5GQkIB+/frl2w1sx44dGDJkiPbHKSBnZMTcI5ICOZ97JflZ8rp27NiB9u3b4/vvv9dZn5SUpPNl39PTE4cOHUJqaqpOF8G7d+9CrVbnO2VJUZ/9Gn/99ZfOsiAIuHfvnjZ51vczWsPS0lLnNdGzZ09UqVIFK1aswOrVq4v9711+PQAiIyMLvJ65c+fC1tYWEydOLLBMo0aN8N1336F169aYPXs23nzzTXz55Zc4c+ZMnrKauEpysCaq2PjMF1Ep0vSZf9V3330HiUSi/YdMk4Tl/sU9Ozsb33zzjV7n0SRzr3abOHfuHH755RcsWLCgwC9xffv2xdOnT7FixYo8215tAdi6dStu3LhR6MhZuZmammLw4MG4ceOGdjTAVzVt2hQODg749ttvdeI/cOAAbt++jcDAQAA5X+7btGmDH374Ic+ojiXVUqH5xbWgFpGCtG/fHnPmzMGKFSvytDzmdvr0aZ3uZRqa10l+3XpeR/fu3REXF6czap9SqcTy5cthY2ODtm3blsh5cpNKpejbty927tyZ75fa3EO45/d6v3Dhgl6tqYbUsWNHmJqaYtWqVTrr83uvFJe+90AzOt+ryYODgwPatWuH1atX55u0vzpk/qvMzMzQtGnTPFM+VKtWDU2bNsX69et1EsyoqCjs2bMH3bp1g1QqLVZ95+fp06dQq9X5vh8KMnz4cNy4cQP/93//V2DCJpVK83weLF++PM8Q5n379sX169fx888/5zlGSbd8Fia/eLdv357neb3u3btDpVLlee0tXrwYALSfkxr6fPZrbNiwAampqdrlHTt24MmTJ+jWrRsA/T+jC5KdnQ2lUqnd97/+e1cYzeiWoaGhhbYWpqSk4P3330fPnj3x+eefIyAgAM7OzvmWvXLlCuzs7ODr6/uf4yMC2PJFVKoGDhwIHx8f9O7dG46OjkhMTMSBAwdw/PhxfPbZZ/Dz8wMAtGzZEpUrV8aQIUMwYcIESCQS/PjjjwV+CYiPj9d263j69ClWr14NU1NT9OjRQ6fcoUOH0KlTp0J/mR48eDA2bNiAKVOm4OLFi2jdujXS09Nx5MgRjBkzBr169dI53siRI4uVJMyZMwdTp04t8BkZMzMzhIWFYdiwYWjbti0GDBigHcbYw8ND5wHzZcuW4a233kLjxo21z0jFxMRg3759hf4SWpC0tDQcPHgQQM5gAMuWLYOZmVmRXyZeZWJiUmA3qNzCwsJw5coV9OnTR/ur8tWrV7FhwwZUqVIlz0AaycnJ2np+VWGTL48aNQqrV6/G0KFDceXKFXh4eGDHjh04c+YMli5dqvPLeUlasGABjh8/jubNm2PkyJGoV68enj9/jqtXr+LIkSN4/vw5AKBHjx7YtWsXevfujcDAQERHR+Pbb79FvXr1kJaWViqxvQ5HR0dMnDgRX331FXr27ImuXbvi+vXrOHDgAKpWrfqfngHR9x7IZDLUq1cP27ZtQ+3atVGlShXUr18f9evXx8qVK/HWW2/Bz88PI0eOhJeXF+Lj43Hu3Dk8evQoz1xRr+rVqxc+++wzpKSk6DxLtHDhQnTu3BktWrTABx98oB1q3tLSEvPmzdOW07e+x4wZAzMzM+3zSREREdi8eTN69OhRrGfnunbtisTExEJbynr06IEff/wRdnZ2qFevHs6dO4cjR47kGVxh6tSp2LFjB/7v//5PO3T78+fPsWfPHnz77bdo0KCBtmxkZKTOOVUqFR4/fqz97ABev9W6R48emD17NoYNG4aWLVvi5s2b2LRpk7ZlU6N79+4ICAjAZ599hujoaDRs2BDHjh3Dzp078eGHH+Z5Flafz36NKlWq4K233sKwYcMQHx+PpUuXombNmhg5ciSA4n1GAzkDWuTudvjjjz8iMzNTO7hJcf+9K46TJ0+ibt26GDZsWKHlxo4di5cvX2Lt2rVFHvPw4cMICgriM19Ucgw4siJRhbNq1Sqhe/fugouLi2BqaipUqlRJ6NKli7B///48Zc+cOSO8+eabgkwmE1xcXIRPPvlE+O233wQAwvHjx7XlXh2CvFKlSkKrVq3yHBOAIJFIhCtXruisz2+o+oyMDOGzzz4TPD09BTMzM8HJyUl45513hKioKEEQ/h3yWSaTCY8fP9bZt6BhvDVDyb+qoO3btm0TGjVqJFhYWAhVqlQRBg0aJDx69CjP/r///rvQu3dvoVKlSoKlpaVQp04dISQkJN9zFTXUfH738cCBA/mWz62gIYlzy2+o+TNnzghjx44V6tevL9jZ2QlmZmaCm5ubMHToUO29Lii+V/+KEh8fLwwbNkyoWrWqYG5uLvj5+elMFfCqkhhqXnPesWPHCq6urtrXUseOHYU1a9Zoy6jVauGLL74Q3N3dBQsLC6FRo0bC3r178wxTnd89fDXexMREnfWa11dhQ1AX9BrUDL2d+/2mVCqFkJAQwcnJSZDJZEKHDh2E27dvC/b29sKHH35Y6P0pLH5974Eg5Ax13aRJE8Hc3DzPPY+KihIGDx4sODk5CWZmZkL16tWFHj16CDt27Cg0NkHIqStTU1Phxx9/zLPt6NGjQqtWrQSZTCbI5XIhMDBQZzjz3Mcoqr5XrVol+Pn5CdbW1oKNjY1Qr149YdasWUJaWlqh8RV2/wra/uLFC+3r3sbGRujSpYvw559/5vtZ8OzZM2HcuHFC9erVBXNzc6FGjRrCkCFDtEPna14P+v697lDz//vf/wRnZ2dBJpMJrVq1Es6dO5fv53RaWpowefJkwcXFRTAzMxNq1qwpLFiwIM9Q9fp+9muub8uWLUJwcLDg4OAgyGQyITAwMM90HoKg32f0kCFDdO6JjY2N0Lhx4zyvseL8e1ecoeYBCD///HOemHK/p7Zs2SJIJBLh4MGDecq9+rl++/ZtAYBw5MiRPDEQvS6JIHDKbiIiIn0lJSWhcuXKmDt3boGTuJYVI0aMwN27d41qIBwyjBMnTqB9+/bYvn073nnnHbHDMUqTJk3CqVOncOXKFbZ8UYnhM19EREQF0DxPmdvSpUsB5Ax0U9bNnDkTly5dynegAaKK7NmzZ1i7di3mzp3LxItKFJ/5IiIiKsC2bdsQHh6O7t27w8bGBhEREdiyZQs6d+6snaOvLHNzc0NmZqbYYRAZHXt7e6N6BpXKDyZfREREBfD394epqSkWLlyIlJQU7SAc+s5PRURElBuf+SIiIiIiIjIAPvNFRERERERkAEy+iIiIiIiIDIDPfL0mtVqNv//+G7a2thwFh4iIiIioAhMEAampqXBxcYGJScHtW0y+XtPff/8NV1dXscMgIiIiIiIj8fDhQ9SoUaPA7Uy+XpOtrS2AnBssl8tFjUWhUODQoUPo3LkzzMzMRI2FcrBOjAvrw/iwTowP68S4sD6MD+vE+BhTnaSkpMDV1VWbIxSEyddr0nQ1lMvlRpF8WVlZQS6Xi/7CoxysE+PC+jA+rBPjwzoxLqwP48M6MT7GWCdFPY7EATeIiIiIiIgMgMkXERERERGRATD5IiIiIiIiMgA+80VERERERkmtViM7O1vsMADkPF9kamqKzMxMqFQqscMhGLZOzMzMIJVK//NxmHwRERERkdHJzs5GdHQ01Gq12KEAyJnHycnJCQ8fPuQcr0bC0HVSqVIlODk5/adzMfkiIiIiIqMiCAKePHkCqVQKV1fXQietNRS1Wo20tDTY2NgYRTxkuDoRBAEZGRlISEgAADg7O7/2sZh8EREREZFRUSqVyMjIgIuLC6ysrMQOB8C/XSAtLS2ZfBkJQ9aJTCYDACQkJMDBweG1uyDylUNERERERkXz/I65ubnIkRD9S/NDgEKheO1jMPkiIiIiIqPEZ6vImJTE65HJFxERERERkQEw+SIiIiIiqsBOnDgBiUSCpKQksUN5baGhoWjYsKHYYRSJyRcRERERlUsqtYBzUc/wS+RjnIt6BpVaKNXzDR06FBKJBAsWLNBZv3v37jLfhdLDwwMSiSTP36vXaggSiQS7d+/WWffxxx/j6NGjBo+luDjaIRERERGVOwd/f4JZv97Ck+RM7TpnO0vMDKqHrvVff6jwolhaWiIsLAyjR49G5cqVS+y42dnZog9AMnv2bIwcOVJnna2trUjR6LKxsYGNjY3YYRSJLV9EVK6p1Cpcjr+M69nXcTn+MlRqldghERFRKTv4+xN8tPGqTuIFAHHJmfho41Uc/P1JqZ07ICAATk5OmD9/fqHldu7cCV9fX1hYWMDDwwNfffWVznYPDw/MmTMHgwcPhlwux6hRoxAeHo5KlSph7969qFOnDqysrPDOO+8gIyMD69evh4eHBypXrowJEyZoR4wEgB9//BFNmzaFra0tnJycMHDgQO2cVcWh2T/3n7W1tXb7/v37Ubt2bchkMrRv3x7h4eE63Rnz6xq4dOlSeHh4aJcvXbqETp06oWrVqrCzs0Pbtm1x9epVnfsCAL1794ZUKoW/v3++x1ar1Zg9ezZq1KgBCwsLNGzYEAcPHtRuj4mJgUQiwa5du9C+fXtYWVmhQYMGOHfuXLHvS3Ew+SKicutI7BF02dkFo46OwvaM7Rh1dBS67OyCI7FHxA6NiIiKQRAEZGQr9fpLzVRg5p4/kF8HQ8260D23kJqp0Ot4glC8ropSqRRffPEFli9fjkePHuVb5sqVK+jXrx/69++PmzdvIjQ0FCEhIQgPD9cpt2jRIjRo0ADXrl1DSEgIACAjIwPLli3D1q1bcfDgQZw4cQK9e/fG/v37sX//fvz4449YvXo1duzYoT2OQqHAnDlzcP36dezevRsxMTEYOnRosa6rKA8fPkSfPn0QFBSEyMhIfPDBB5g+fXqxj5OamoohQ4YgIiIC58+fR61atdC9e3ekpqYCyEnOAGDdunV4/Pgxjh07lu9xvv76a3z11VdYtGgRbty4gS5duqBnz57466+/dMp99tln+PjjjxEZGYnatWtjwIABUCqVxY5bX+x2SETl0pHYI5hyYgqEV/75TchIwJQTU7C43WIEuAeIFB0RERXHS4UK9Wb8ViLHEgDEpWTCL/SQXuVvze4CK/PifWXu3bs3GjZsiJkzZ+L777/Ps33x4sXo2LGjNqGqXbs2bt26hS+//FInKerQoQP+97//aZdPnz4NhUKBVatWwdvbGwDwzjvv4Mcff0R8fDxsbGxQr149tG/fHsePH8e7774LABg+fLj2GF5eXli2bBmaNWuGtLS0YnXVmzZtGj7//HOddQcOHEDr1q21MWla8OrUqYObN28iLCxM7+Nrrjm3NWvWoFKlSjh58iR69OiBatWqAQAqVaoEJycnpKSk5HucRYsWYdq0aejfvz8AICwsDMePH8fSpUuxcuVKbbmPP/4YgYGBAIBZs2bB19cX9+7dg4+PT7Hi1hdbvoio3FGpVVhwcUGexAuAdl3YxTB2QSQiolITFhaG9evX4/bt23m23b59G61atdJZ16pVK/z111863QWbNm2aZ18rKytt4gUAjo6O8PDw0EmiHB0ddboVXrlyBUFBQXBzc4OtrS3atm0LAHjw4EGxrmnq1KmIjIzU+dPEePv2bTRv3lynfIsWLYp1fACIj4/HyJEjUatWLdjZ2UEulyMtLa1YsaakpODvv//O9x6/Wh+abosA4Oyc8yzg63TJ1Bdbvoio3LmacBXxGfEFbhcgIC4jDlcTrqKZUzMDRkZERK9DZibFrdld9Cp7Mfo5hq67VGS58GHN8IZnFb3O/TratGmDLl26IDg4+LW7+OV+nkrDzMxMZ1kikeS7Tq1WAwDS09PRpUsXdOnSBZs2bUK1atXw4MEDdOnSBdnZ2cWKp2rVqqhZs2Yxr+JfJiYmebpxKhQKneUhQ4bg2bNn+Prrr+Hu7g4LCwu0aNGi2LHqK/e904xIqbl3pYHJFxGVO4kZiSVajoiIxCWRSPTu+te6VjU421kiLjkz3+e+JACc7CzRulY1SE1Kd/j3BQsWoGHDhqhTp47O+rp16+LMmTM6686cOYPatWtDKn29ZK8gf/75J549e4YFCxbA1dUVAHD58uUSPQeQc0179uzRWXf+/Hmd5WrVqiEuLg6CIGgTncjISJ0yZ86cwTfffIPu3bsDyHmW7OnTpzplzMzMdFoIXyWXy+Hi4oIzZ85oW/k0x37jjTeKfW0lid0OiajcqWZVrUTLERFR2SE1kWBmUD0AOYlWbprlmUH1Sj3xAgA/Pz8MGjQIy5Yt01n/v//9D0ePHsWcOXNw9+5drF+/HitWrMDHH39c4jG4ubnB3Nwcy5cvx/3797Fnzx7MmTPntY6VmpqKuLg4nT/NM1cffvgh/vrrL0ydOhV37tzB5s2b8wwg0q5dOyQmJmLhwoWIiorCypUrceDAAZ0ytWrVwo8//ojbt2/jwoULGDRoEGQymU4ZDw8PHD16FHFxcQVODD116lSEhYVh27ZtuHPnDqZPn47IyEhMnDjxta69pDD5IqJyp7FDYzhaOUKS55/dHBJI4GTlhMYOjQ0cGRERGULX+s5Y9V5jONlZ6qx3srPEqvcal+o8X6+aPXt2nm5sjRs3xk8//YStW7eifv36mDFjBmbPnl3iIxACOa1N4eHh2L59O+rVq4cFCxZg0aJFr3WsGTNmwNnZWefvk08+AZCT5O3cuRO7d+9GgwYN8O233+KLL77Q2b9u3br45ptvsHLlSjRo0AAXL17Mk3B+//33ePHiBRo3boz3338fEyZMgIODg06Zr776CocPH4a7uzvatGmTb6wTJkzAlClT8L///Q9+fn44ePAg9uzZg1q1ar3WtZcUiVDc8TMJQM6DfHZ2dkhOToZcLhc1FoVCgf3796N79+55+vySOFgn4itotENNQsbRDsXF94jxYZ0Yl4peH5mZmYiOjoanpycsLS2L3qEAKrWAi9HPkZCaCQdbS7zhWeW1W7zUajVSUlIgl8thYsL2C32cOHEC7du3x4sXL1CpUqUSP76h66Sw16W+uQGf+SKicinAPQAf+H2A725+p7Pe0coR096YxsSLiKgCkJpI0MLbXuwwiLSYfBFRufUw9SEAoINrB9g/s0enFp3whssbkJqU7MPMRERERPpg8kVE5VJKdgqOPciZ9X6E7whEn49GU8emTLyIiIgMpF27dnmGlq/o2GGViMqlwzGHka3OhredN3wql84s9URERETFweSLiMqlPVE5c40EeQdp5xIhIiIiEhOTLyIqdx6mPsTVhKuQQIJAr0CxwyEiIiICwOSLiMqhvff3AgCaOzeHk7WTyNEQERER5WDyRUTliiAI+DXqVwBAT++eIkdDRERE9C8mX0RUrlxPvI6HqQ8hM5Who1tHscMhIiIi0mLyRUTlimagjU7unWBlZiVyNERERET/YvJFROVGtiobB2MOAsgZ5ZCIiCo4tQqIPg3c3JHzX7XKIKeNi4vDxIkTUbNmTVhaWsLR0RGtWrXCqlWrkJGRAQDw8PCARCKBRCKBTCaDh4cH+vXrh2PHjhkkRhIHJ1kmonLj5KOTSM1OhaOVI5o5NhM7HCIiEtOtPcDBaUDK3/+uk7sAXcOAeqX3TPD9+/fRqlUrVKpUCV988QX8/PxgYWGBmzdvYs2aNahevTp69sw5/+zZszFy5EhkZ2cjJiYGGzduREBAAObMmYPPPvus1GIk8Yja8qVSqRASEgJPT0/IZDJ4e3tjzpw5hc6EHRERgVatWsHe3h4ymQw+Pj5YsmRJgeUXLFgAiUSCSZMm6axv166d9tcGzd+HH35YUpdGRCLQdDns4dUDUhOpyNEQEZFobu0Bfhqsm3gBQMqTnPW39pTaqceMGQNTU1NcvnwZ/fr1Q926deHl5YVevXph3759CAr6t2eGra0tnJyc4ObmhjZt2mDNmjUICQnBjBkzcOfOnVKLkcQjastXWFgYVq1ahfXr18PX1xeXL1/GsGHDYGdnhwkTJuS7j7W1NcaNGwd/f39YW1sjIiICo0ePhrW1NUaNGqVT9tKlS1i9ejX8/f3zPdbIkSMxe/Zs7bKVFZ8PISqrnmc+R8SjCADsckhEVO4IAqDI0K+sWgUc+ARAfj/mCwAkOS1iXu0AfX6oM7MCJBK9Tv3s2TMcOnQIX3zxBaytrfMtIyniWBMnTsScOXPwyy+/4JNPPtHrvFR2iJp8nT17Fr169UJgYM4kqB4eHtiyZQsuXrxY4D6NGjVCo0aNtMseHh7YtWsXTp8+rZN8paWlYdCgQfjuu+8wd+7cfI9lZWUFJyfOAURUHhyIPgCloISvvS+8K3mLHQ4REZUkRQbwhUsJHUzIaRFb4Kpf8U//BszzT6Rede/ePQiCgDp16uisr1q1KjIzMwEAY8eORVhYWIHHqFKlChwcHBATE6NffFSmiJp8tWzZEmvWrMHdu3dRu3ZtXL9+HREREVi8eLHex7h27RrOnj2bJ8EaO3YsAgMDERAQUGDytWnTJmzcuBFOTk4ICgpCSEhIga1fWVlZyMrK0i6npKQAABQKBRQKhd7xlgbN+cWOg/7FOjG8PfdyupB09+ie576zPowP68T4sE6MS0WvD4VCAUEQoFaroVarAbVatGdlNOfXPBajiavAsv/8N3eZ8+fPQ61W4/3330dmZqZ2W0HH0pyroPNQDn3qpCSp/3kdKBQKSKW6rab6vldFTb6mT5+OlJQU+Pj4QCqVQqVSYd68eRg0aFCR+9aoUQOJiYlQKpUIDQ3FBx98oN22detWXL16FZcuXSpw/4EDB8Ld3R0uLi64ceMGpk2bhjt37mDXrl35lp8/fz5mzZqVZ/2hQ4eMprvi4cOHxQ6BXsE6MYwEVQJupd6CCUwg/UuK/VH78y3H+jA+rBPjwzoxLhW1PkxNTeHk5IS0tDRkZ2fndDsce1u/fR9fhM3uIUWWS3t7PZTV3yj6gC+VQGaKdjE1NbXAoo6OjpBIJLhx4wY6dvx3rsmqVasCAMzMzJCdnY2UlBSo1WpkZmZqf9DXeP78ORITE+Hs7JxnG+WvsDopSdnZ2Xj58iVOnToFpVKps00zimVRRE2+fvrpJ2zatAmbN2+Gr68vIiMjMWnSJLi4uGDIkMLfNKdPn0ZaWhrOnz+P6dOno2bNmhgwYAAePnyIiRMn4vDhw7C0tCxw/9xdFP38/ODs7IyOHTsiKioK3t55uywFBwdjypQp2uWUlBS4urqic+fOkMvlr3H1JUehUODw4cPo1KkTzMzMRI2FcrBODGt55HLgFvBW9bfQr22/PNtZH8aHdWJ8WCfGpaLXR2ZmJh4+fAgbG5tc3+fs9Nu5ciCEYy5AyhNI8nnuS4AEkLvAqn6gfs98afYTBKSmpsLW1rbA57bkcjkCAgLw/fff4+OPP87z3JepqSnMzc0hl8thYmICS0vLPN8jv/rqK5iYmODdd98V/TumsdOnTkpSZmYmZDIZ2rRpkyfP0DdRFjX5mjp1KqZPn47+/fsDyEmCYmNjMX/+/CKTL09PT+0+8fHxCA0NxYABA3DlyhUkJCSgcePG2rIqlQqnTp3CihUrkJWVlaeZEACaN28OIKevbn7Jl4WFBSwsLPKsNzMzM5oPRWOKhXKwTkqfSq3C/piclq5eNXsVer9ZH8aHdWJ8WCfGpaLWh0qlgkQigYmJCUxMitnh0MQkZzj5nwYDkEB34A0JJADQdQEkpsW7r5pubZq4CrJq1Sq0atUKb7zxBkJDQ+Hv7w8TExNcunQJf/75J5o0aaLdPy0tDQkJCVAoFIiOjsbGjRuxdu1azJ8/H7Vr1y5WfBWRvnVSUkxMTCCRSPJ9X+r7PhU1+crIyMhzo6RSabH7bKrVau3zWB07dsTNmzd1tg8bNgw+Pj6YNm1avokXAERGRgIAnJ2di3VuIhLXpfhLiM+Ih625Ldq6thU7HCIiMgb1egL9NhQwz9eCUp3ny9vbG9euXcMXX3yB4OBgPHr0CBYWFqhXrx4+/vhjjBkzRlt2xowZmDFjBszNzeHk5IQ333wTR48eRfv27UstPhKXqMlXUFAQ5s2bBzc3N/j6+uLatWtYvHgxhg8fri0THByMx48fY8OGDQCAlStXws3NDT4+PgCAU6dOYdGiRdqh6W1tbVG/fn2d81hbW8Pe3l67PioqCps3b0b37t1hb2+PGzduYPLkyWjTpk2Bw9ITkXH6NepXAEBXj66wkOZtnSYiogqqXk/AJxCIPQukxQM2joB7y2J1NXxdzs7OWL58OZYvX15gGY5mWDGJmnwtX74cISEhGDNmDBISEuDi4oLRo0djxowZ2jJPnjzBgwcPtMtqtRrBwcGIjo6GqakpvL29ERYWhtGjR+t9XnNzcxw5cgRLly5Feno6XF1d0bdvX3z++eclen1EVLoyFBk4HJvzMHpP79L7FZOIiMooEyng2VrsKIi0RE2+bG1tsXTpUixdurTAMuHh4TrL48ePx/jx44t1nhMnTugsu7q64uTJk8U6BhEZn6MPjuKl8iVcbV3RoFoDscMhIiIiKpRYUyYQEf1nmi6HQd5BBhnliIiIiOi/YPJFRGVSfHo8zj85DwDo4dVD5GiIiIiIisbki4jKpH3R+yBAQGOHxnC1dRU7HCIiIqIiMfkiojJHEATsubcHAAfaICIiorKDyRcRlTm3n99GVHIUzE3M0cmjk9jhEBEREemFyRcRlTmagTbau7WH3FwucjRERERE+mHyRURlikKtwP7o/QDY5ZCIiIjKFiZfRFSmnH18Fs8zn6OKZRW0cGkhdjhEREREemPyRURlyp6onIE2unt2h5mJmcjREBGRMVOpVbgUdwn77+/HpbhLUKlVpX7OxMREfPTRR3Bzc4OFhQWcnJzQpUsXnDlzRlvm2rVrePfdd+Hs7AwLCwu4u7ujR48e+PXXXyEIAgAgJiYGEolE+2drawtfX1+MHTsWf/31V6lfB5UOU7EDICLSV3JWMk48PAGAXQ6JiKhwR2KPYMHFBYjPiNeuc7RyxPQ3piPAPaDUztu3b19kZ2dj/fr18PLyQnx8PI4ePYpnz54BAH755Rf069cPAQEBWL9+PWrWrImsrCycPXsWn3/+OVq3bo1KlSr9ex1HjsDX1xcZGRm4efMmvv76azRo0AC//vorOnbsWGrXQaWDyRcRlRmHYg8hW52NmpVqwqeKj9jhEBGRkToSewRTTkyBAEFnfUJGAqacmILF7RaXSgKWlJSE06dP48SJE2jbti0AwN3dHW+88QYAID09HSNGjEBgYCB27dqls2/dunUxYsQIbcuXhr29PZycnAAAXl5eCAoKQseOHTFixAhERUVBKpWW+HVQ6WG3QyIqM/ZG7QWQ0+olkUhEjoaIiAxFEARkKDL0+kvNSsX8i/PzJF4AIPzzvwUXFyA1K1Wv472aDBXGxsYGNjY22L17N7KysvJsP3ToEJ49e4ZPPvmkwGMU9e+biYkJJk6ciNjYWFy5ckXv2Mg4sOWLiMqEhykPcTXhKkwkJgj0ChQ7HCIiMqCXypdovrl5iR0vPiMeLbe21KvshYEXYGVmpVdZU1NThIeHY+TIkfj222/RuHFjtG3bFv3794e/vz/u3r0LAKhTp452n0uXLqF9+/ba5a1bt6JHjx6FnsfHJ6f3R0xMjLZVjcoGtnwRUZmw935Oq9ebzm/CwcpB5GiIiIjy17dvX/z999/Ys2cPunbtihMnTqBx48YIDw/Pt7y/vz8iIyMRGRmJ9PR0KJXKIs+haY1jL5Cyhy1fRGT0BEHQjnIY5B0kcjRERGRoMlMZLgy8oFfZK/FXMObomCLLfdPxGzRxbKLXuYvL0tISnTp1QqdOnRASEoIPPvgAM2fOxJIlSwAAd+7cwZtvvgkAsLCwQM2aNYt1/Nu3bwMAPD09ix0biYstX0Rk9CITI/Eo7RGsTK3QwbWD2OEQEZGBSSQSWJlZ6fXX0qUlHK0cIUH+rUISSOBk5YSWLi31Ol5JtC7Vq1cP6enp6Ny5M6pUqYKwsLDXPpZarcayZcvg6emJRo0a/efYyLCYfBGR0dO0egW4B+jd756IiComqYkU09+YDgB5EjDN8rQ3pkFqUvKjBD579gwdOnTAxo0bcePGDURHR2P79u1YuHAhevXqBRsbG6xduxb79u1DYGAgfvvtN9y/fx83btzAwoULc+J/ZfTCZ8+eIS4uDvfv38eePXsQEBCAixcv4vvvv+dIh2UQux0SkVHLUmXht+jfAHBuLyIi0k+AewAWt1uc7zxf096YVmrzfNnY2KB58+ZYsmQJoqKioFAo4OrqipEjR+LTTz8FAPTu3Rtnz55FWFgYBg8ejOfPn8POzg5NmzbNd7CNgICcWK2srODu7o727dtjzZo1xe6qSMaByRcRGbUTD08gVZEKJ2snNHNqJnY4RERURgS4B6C9a3tcTbiKxIxEVLOqhsYOjUulxUvDwsIC8+fPx/z58wst17RpU2zfvr3QMh4eHsUa5p7KBiZfRGTUfo36FQDQw6sHTCTsKU1ERPqTmkj5wx0ZFX6TISKj9ezlM0Q8jgAABHlxlEMiIiIq25h8EZHROhB9ACpBhfr29eFVyUvscIiIiIj+EyZfRGS0OLcXERERlSdMvojIKN17cQ+3n9+GqcQU3Ty7iR0OERER0X/G5IuIjNKv93MG2mhdozUqW1YWORoiIiKi/47JFxEZHZVahb339wLg3F5ERERUfjD5IiKjczHuIhIyEiA3l6NNjTZih0NERERUIph8EZHR0czt1c2zG8yl5iJHQ0RERFQymHwRkVHJUGTgyIMjAHImViYiIioPhg4dirfffrvA7aGhoWjYsKHB4iFxMPkiIqNy5MERvFS+hJutGxpUayB2OEREVIYJKhXSL1xE8t59SL9wEYJKJXZIZVa7du0wadKkArdLJBLtn1wuR7NmzfDLL78YLsAywlTsAIiIcss9t5dEIhE5GiIiKqtSDh1C/BfzoYyL064zdXKC46fBkHfuLGJk4snOzoa5eel151+3bh26du2KlJQUfPPNN3jnnXdw9epV+Pn5ldo5yxq2fBGR0YhLj8PFJxcBsMshERG9vpRDh/B44iSdxAsAlPHxeDxxElIOHSqV8+7YsQN+fn6QyWSwt7dHQEAA0tPT8y176dIlVKtWDWFhYQUeb+3atahbty4sLS3h4+ODb775Rmf7tGnTULt2bVhZWcHLywshISFQKBTa7ZqujGvXroWnpycsLS0B5LRSrV27Fr1794aVlRVq1aqFPXv2/Ofrr1SpEpycnFC7dm3MmTMHSqUSx48f/8/HLU/Y8kVERmPv/b0QIKCJYxPUsK0hdjhERGQkBEGA8PKlfmVVKsTPnQcIQn4HAiRA/LwvYN2iBSRSaZHHk8hkevXEePLkCQYMGICFCxeid+/eSE1NxenTpyHkE8exY8fQp08fLFy4EKNGjcr3eJs2bcKMGTOwYsUKNGrUCNeuXcPIkSNhbW2NIUOGAABsbW0RHh4OFxcX3Lx5EyNHjoStrS0++eQT7XHu3buHnTt3YteuXZDmut5Zs2Zh4cKF+PLLL7F8+XIMGjQIsbGxqFKlSpHXWhSlUonvv/8eAEq1pa0sYvJFREZBEATtKIec24uIiHITXr7EncZNSuhgOS1gd5u9oVfxOlevQGJlVWS5J0+eQKlUok+fPnB3dweAfLvb/fzzzxg8eDDWrl2Ld999t8DjzZw5E1999RX69OkDAPD09MStW7ewevVqbfL1+eefa8t7eHjg448/xtatW3WSr+zsbGzYsAHVqlXTOf7QoUMxYMAAAMAXX3yBZcuW4eLFi+jatWuR11qQAQMGQCqV4uXLl1Cr1fDw8EC/fv1e+3jlEZMvIjIKt57dwv3k+7CQWqCTeyexwyEiIiqWBg0aoGPHjvDz80OXLl3QuXNnvPPOO6hcubK2zIULF7B3717s2LGj0JEP09PTERUVhREjRmDkyJHa9UqlEnZ2dtrlbdu2YdmyZYiKikJaWhqUSiXkcrnOsdzd3fMkXgDg7++v/f/W1taQy+VISEh4nUvXWrJkCQICAnD//n1MnjwZy5YtK5GWtPKEyRcRGYVf7+e0enVw7QBbc1uRoyEiImMikclQ5+oVvcpmXL6Mh6NGF1nOdc1qWDVtqte59SGVSnH48GGcPXsWhw4dwvLly/HZZ5/hwoUL8PT0BAB4e3vD3t4eP/zwAwIDA2FmZpbvsdLS0gAA3333HZo3b57nPABw7tw5DBo0CLNmzUKXLl1gZ2eHrVu34quvvtIpb21tne85Xj23RCKBWq3W61oL4uTkhJo1a6JmzZpYt24dunfvjlu3bsHBweE/Hbc84YAbRCQ6hVqB/ff3A8gZ5ZCIiCg3iUQCEysrvf6sW7WCqZMTUNBzWhIJTJ2cYN2qlV7HK87IuxKJBK1atcKsWbNw7do1mJub4+eff9Zur1q1Ko4dO4Z79+6hX79+OoNj5Obo6AgXFxfcv39fm8xo/jSJ3NmzZ+Hu7o7PPvsMTZs2Ra1atRAbG6v/TS1lb7zxBpo0aYJ58+aJHYpRYcsXEYnuzOMzeJH1AvaW9mjh0kLscIiIqAyTSKVw/DQYjydOyknAcg948U8i5fhpsF6DbRTHhQsXcPToUXTu3BkODg64cOECEhMTUbduXZ1yDg4OOHbsGNq3b48BAwZg69atMDXN+5V81qxZmDBhAuzs7NC1a1dkZWXh8uXLePHiBaZMmYJatWrhwYMH2Lp1K5o1a4Z9+/bpJHqlITExEZGRkTrrnJ2d4ejomG/5SZMmoXfv3vjkk09QvXr1Uo2trGDLFxGJTjO3V6BXIExN+JsQERH9N/LOnVH966UwfSUpMHV0RPWvl5bKPF9yuRynTp1C9+7dUbt2bXz++ef46quv0K1btzxlnZyccOzYMdy8eRODBg2CKp/Jnz/44AOsXbsW69atg5+fH9q2bYvw8HBty1fPnj0xefJkjBs3Dg0bNsTZs2cREhJS4teV2+bNm9GoUSOdv++++67A8l27doWnpydbv3KRCPmNf0lFSklJgZ2dHZKTk/M82GhoCoUC+/fvR/fu3QvsO0yGxTrRX3JWMtr/1B4KtQI7gnagTpU6JX4O1ofxYZ0YH9aJcano9ZGZmYno6Gidualeh6BSIePyFSgTE2FarRqsmjZ57RYvtVqNlJQUyOVymJiw/cIYGLpOCntd6psb8CdmIhLVbzG/QaFWoFblWqWSeBERUcUlkUph3Vy/IeWJDIFpOxGJSju3lxfn9iIiIqLyjckXEYnmQcoDRCZGwkRigu5e3cUOh4iIiKhUMfkiItFo5vZq4dwCDlacA4SIiIjKNyZfRCQKtaDWdjnk3F5ERERUETD5IiJRXEu4hsdpj2FlaoUObh3EDoeIiIio1DH5IiJRaFq9Ont0hsxUJnI0RERERKWPyRcRGVymMhOHYg4BAHp6c5RDIiIiqhiYfBGRwZ14dAKpilQ4WzujiWMTscMhIiIiMggmX0RkcJouhz28esBEwo8hIiIq/4YOHYq33367wO2hoaFo2LChweIhcfBbDxEZ1NOXT3Hm8RkAHOWQiIhKl1ot4PGdF7h7KQ6P77yAWi2IHVKZFR0djYEDB8LFxQWWlpaoUaMGevXqhT///FOn3PHjx9GjRw9Uq1YNlpaW8Pb2xrvvvotTp05py5w4cQISiQQSiQQmJiaws7NDo0aN8Mknn+DJkyeGvjSDMhU7ACKqWA5EH4BKUMG/qj887TzFDoeIiMqpqGsJOL3tL6QnZWnXWVeyQOt3a8G7UcWcWzI7Oxvm5ubF3k+hUKBTp06oU6cOdu3aBWdnZzx69AgHDhxAUlKSttw333yDcePG4f3338e2bdvg7e2N5ORkHD9+HJMnT8aVK1d0jnvnzh3I5XKkpKTg6tWrWLhwIb7//nucOHECfn5+//VyjZKoLV8qlQohISHw9PSETCaDt7c35syZA0Eo+FeJiIgItGrVCvb29pDJZPDx8cGSJUsKLL9gwQJIJBJMmjRJZ31mZibGjh0Le3t72NjYoG/fvoiPjy+pSyOiAmi7HHr3EDkSIiIqr6KuJeDg6t91Ei8ASE/KwsHVvyPqWkKpnHfHjh3w8/ODTCaDvb09AgICkJ6enm/ZS5cuoVq1aggLCyvweGvXrkXdunVhaWkJHx8ffPPNNzrbp02bhtq1a8PKygpeXl4ICQmBQqHQbtd0ZVy7di08PT1haWkJAJBIJFi7di169+4NKysr1KpVC3v27Ckwjj/++ANRUVH45ptv8Oabb8Ld3R2tWrXC3Llz8eabbwIAHjx4gEmTJmHSpElYv349OnToAHd3d/j7+2PixIm4fPlynuM6ODjAyckJtWvXRv/+/XHmzBlUq1YNH330UcE3uYwTteUrLCwMq1atwvr16+Hr64vLly9j2LBhsLOzw4QJE/Ldx9raGuPGjYO/vz+sra0RERGB0aNHw9raGqNGjdIpe+nSJaxevRr+/v55jjN58mTs27cP27dvh52dHcaNG4c+ffrgzJkzpXKtRATcfXEXt5/fhqmJKbp6dBU7HCIiKiMEQYAyW61XWbVawOltdwstc3rbX6jhUwUmJpIij2dqbgKJpOhyT548wYABA7Bw4UL07t0bqampOH36dL6NCseOHUOfPn2wcOHCPN9fNTZt2oQZM2ZgxYoVaNSoEa5du4aRI0fC2toaQ4YMAQDY2toiPDwcLi4uuHnzJkaOHAlbW1t88skn2uPcu3cPO3fuxK5duyCVSrXrZ82ahYULF+LLL7/E8uXLMWjQIMTGxqJKlSp5YqlWrRpMTEywY8cOTJo0Sec4Gjt37oRCodA5d2763EOZTIYPP/wQkydPRkJCAhwcyl8LpajJ19mzZ9GrVy8EBgYCADw8PLBlyxZcvHixwH0aNWqERo0aaZc9PDywa9cunD59WufFm5aWhkGDBuG7777D3LlzdY6RnJyM77//Hps3b0aHDjmTu65btw5169bF+fPntRk8EZWsvVF7AQBtqrdBZcvKIkdDRERlhTJbjTUTT5bY8dKTsrB28qmiCwIY9XVbmFnkTTZe9eTJEyiVSvTp0wfu7u4AkG/XuZ9//hmDBw/G2rVr8e677xZ4vJkzZ+Krr75Cnz59AACenp64desWVq9erU2+Pv/8c215Dw8PfPzxx9i6datOApSdnY0NGzagWrVqOscfOnQoBgwYAAD44osvsGzZMly8eBFdu+b9cbR69epYtmwZPvnkE8yaNQtNmzZF+/btMWjQIHh5eQEA7t69C7lcDicnJ+1+O3fu1MYKAOfOnSuyO6GPjw8AICYmhslXSWvZsiXWrFmDu3fvonbt2rh+/ToiIiKwePFivY9x7do1nD17Nk+CNXbsWAQGBiIgICDPtitXrkChUCAgIEC7zsfHB25ubjh37ly+yVdWVhaysv5tuk5JSQGQ0wc2d/OuGDTnFzsO+hfrJC+VWoVf7+d0Oezu0d2g94b1YXxYJ8aHdWJcKnp9KBQKCIIAtVqt/RNLzvkl2hYsTVyv8vPzQ8eOHeHn54fOnTujU6dOeOedd1C5cmXtfhcuXMDevXvx008/4e2339Y5jub4arUa6enpiIqKwogRIzBy5EhtGaVSCTs7O+1+27Ztw4oVKxAVFYW0tDQolUrI5XLtdkEQ4O7uDnt7+zwx169fX7tOJpNBLpcjLi6uwHv90Ucf4b333sOJEydw4cIFbN++HV988QV2796NTp06QRAESCQSnf07deqEq1ev4vHjx+jQoQMUCoVOfeZXtyqVqtD7nFtRdVLS1Go1BEGAQqHI0/qn73tV1ORr+vTpSElJgY+PD6RSKVQqFebNm4dBgwYVuW+NGjWQmJgIpVKJ0NBQfPDBB9ptW7duxdWrV3Hp0qV8942Li4O5uTkqVaqks97R0RFxcXH57jN//nzMmjUrz/pDhw7BysqqyHgN4fDhw2KHQK9gnfzrL8VfePryKWQSGdJupGH/zf0Gj4H1YXxYJ8aHdWJcKmp9mJqawsnJCWlpacjOzoYgCHh3dgO99k2ITsPxdVFFlms/zBsOnjZFlsvITIMk698uc6mpqQWW3b59Oy5cuIDjx49j2bJl+Pzzz3HkyBG4u7tDoVDA3d0dlSpVwnfffYfWrVvDzMxMu29WVhZUKhVSUlKQkJDzTNrSpUvRtGlTnXNIpVKkpKTg4sWLeP/99zF9+nTMnTsXcrkcu3btwooVK7SNBFlZWbC0tNQu56ZUKvOsz8jIyLdsbm3btkXbtm0xdepU9O3bF7Nnz0bz5s3h6uqK5ORk/PXXX3B0dNSWd3BwQGZmJgAgPT0dKSkpyMjI0N5LExPdISgiIyMBAPb29kXGolFYnZSk7OxsvHz5EqdOnYJSqdTZprmmooiafP3000/YtGkTNm/eDF9fX0RGRmLSpElwcXHRaaLMz+nTp5GWlobz589j+vTpqFmzJgYMGICHDx9i4sSJOHz4sPahwpIQHByMKVOmaJdTUlLg6uqKzp07Qy6Xl9h5XodCocDhw4fRqVMnnTcxiYd1ktdnZz8D0oEeNXugZ7OeBj0368P4sE6MD+vEuFT0+sjMzMTDhw9hY2NT7O9zlapUwsWfH+UZbCM3m8oWqN2khl7PfGkIgoDU1FTY2toW+vxS586d0blzZ8ydOxeenp44cuQIJk+eDDMzMzg6OmL37t3o0KEDRo4ciW3btmnr18LCAlKpFHK5HHK5HC4uLoiLiytw7q8bN27A3d0ds2fP1q775ptvIJFItN9Ncx/zVZrWLg2JRAJLS8tifa/19fXFuXPnIJfLMWjQIMyaNQurVq3K04vNxiYnybW2toZcLtc2XNja2uqc7+XLl/jxxx/Rpk0bbXfGwuhbJyUlMzMTMpkMbdq0yfO61DdRFDX5mjp1KqZPn47+/fsDyGmujY2Nxfz584tMvjw9PbX7xMfHIzQ0FAMGDMCVK1eQkJCAxo0ba8uqVCqcOnUKK1asQFZWFpycnJCdnY2kpCSd1q/4+Hidfqq5WVhYwMLCIs96MzMzo/lQNKZYKAfrJEe6Ih3HHx4HALxd623R7gnrw/iwTowP68S4VNT6UKlU2jmgXm0ZKYqJCdD63Vo4uPr3Asu81a8WTE2Lfo4rN023Nk1cr7pw4QKOHj2Kzp07w8HBARcuXEBiYiLq1asHExMT7bxWTk5OOHbsmPaZqa1bt8LU1FSbPGiOPWvWLEyYMAGVKlVC165dkZWVhcuXL+PFixeYMmUKateujQcPHuCnn35Cs2bNsG/fPuzevVvnGK8eU/c+5b23Bd3vyMhIzJw5E++//z7q1asHc3NznDx5EuvWrcO0adNgYmICDw8PfPXVV5g4cSJevHiBoUOHwtPTE8+fP8fGjRsB5Lyec5/j6dOnyM7ORmpqKq5cuYKFCxfi6dOn2LVrl171XlSdlDRNPeb3vtT3fSpq8pWRkZHnRkml0mL32VSr1drnsTp27IibN2/qbB82bBh8fHwwbdo0SKVSNGnSBGZmZjh69Cj69u0LIGeegQcPHqBFixb/4YqIKD9HYo8gU5UJD7kH/KqWz3k7iIjIeHg3ckDX0fXzzPNlU9kCb/UrnXm+5HI5Tp06haVLlyIlJQXu7u746quv0K1btzxlNQlYu3btMGjQIGzevDlPmQ8++ABWVlb48ssvMXXqVFhbW8PPz087fVLPnj0xefJkjBs3DllZWQgMDERISAhCQ0NL/Npq1KgBDw8PzJo1CzExMZBIJNrlyZMna8uNHz8edevWxeLFi/HOO+8gJSUF9vb2aNGiBQ4ePJhnsI06depAIpHAxsYGXl5e6Ny5M6ZMmVJgY0h5IGryFRQUhHnz5sHNzQ2+vr64du0aFi9ejOHDh2vLBAcH4/Hjx9iwYQMAYOXKlXBzc9OOhHLq1CksWrRIOzS9ra0t6tevr3Mea2tr2Nvba9fb2dlhxIgRmDJlCqpUqQK5XI7x48ejRYsWHOmQqBRo5vYK8g4ySLcAIiIi70YO8GxQDU/+SkJ6Shas5RZwrlWpWF0Ni6Nu3bo4ePBggdvDw8N1lp2dnXHnzh3tcmhoaJ7EaeDAgRg4cGCBx1y4cCEWLlyosy733Lb5HRNAvsPf554s+VVVq1bF119/XeD23AICAnQGtctPu3btCp3XtzwTNflavnw5QkJCMGbMGCQkJMDFxQWjR4/GjBkztGWePHmCBw8eaJfVajWCg4MRHR0NU1NTeHt7IywsDKNHjy7WuZcsWQITExP07dsXWVlZ6NKlS56J64jov3uS9gQX43Kmj+jhxYmViYjIcExMJKheh1ObkPEQNfmytbXF0qVLsXTp0gLLvPorwfjx4zF+/PhinefEiRN51llaWmLlypVYuXJlsY5FRMWzL3ofBAho5tQMLjYuYodDREREJJrSfzKNiCosQRCwJ2oPACDIK0jkaIiIiIjExeSLiErNH8/+QHRyNCykFujk3knscIiIiIhExeSLiEqNptWrg1sH2JgXPZElERFRbhV1UAYyTiXxemTyRUSlQqFS4ED0AQBAT2/DTqpMRERlm1SaMwdXdna2yJEQ/SsjIwOA/nN65UfUATeIqPw6/fg0krKSUFVWFW86cwoHIiLSn6mpKaysrJCYmKidmFdsarUa2dnZyMzMNIp4yHB1IggCMjIykJCQgEqVKml/HHgdTL6IqFRo5vYK9AyEqQk/aoiISH8SiQTOzs6Ijo5GbGys2OEAyPkC/vLlS8hkMs5ZaSQMXSeVKlX6zxNA8xsREZW45KxknHh0AkDOxMpERETFZW5ujlq1ahlN10OFQoFTp06hTZs2/6nbGZUcQ9aJmZnZf2rx0mDyRUQl7mD0QSjVStSpXAd1qtQROxwiIiqjTExMYGlpKXYYAHKeQ1MqlbC0tGTyZSTKYp2wwyoRlbhf7+d0OWSrFxEREdG/mHwRUYmKTYnF9cTrMJGYINArUOxwiIiIiIwGky8iKlGagTZaurREVVlVkaMhIiIiMh5MvoioxKgFNfbe3wuAc3sRERERvYrJFxGVmKvxV/E47TFszGzQ3rW92OEQERERGRUmX0RUYjQDbXRy7wRLU+MYnYqIiIjIWDD5IqISkanMxG8xvwHgKIdERERE+WHyRUQl4vjD40hXpMPF2gVNHJuIHQ4RERGR0WHyRUQlYk/UHgBAD+8eMJHwo4WIiIjoVfyGRET/2dOXT3H277MAgCAvdjkkIiIiyg+TLyL6z/bd3we1oIZ/NX942HmIHQ4RERGRUWLyRUT/mWZi5Z5enNuLiIiIqCBMvojoP7nz/A7uvLgDUxNTdPXsKnY4REREREaLyRcR/Sd77+8FALSr0Q52FnYiR0NERERkvJh8EdFrU6qV2uSLc3sRERERFY7JFxG9tgtPLuDpy6eoZFEJrau3FjscIiIiIqPG5IuIXptmbq9unt1gJjUTORoiIiIi48bki4heS1p2Go49OAaAc3sRERER6YPJFxG9lsOxh5GpyoSH3AP1q9YXOxwiIiIio8fki4hey6/3/5nby7snJBKJyNEQERERGT8mX0RUbH+n/Y1LcZcAAD28eogcDREREVHZwOSLiIpNM7z8G05vwNnGWeRoiIiIiMoGJl9EVCyCIODXqJwuh5zbi4iIiEh/TL6IqFhuPr2JmJQYWEot0cm9k9jhEBEREZUZTL6IqFg0rV4d3TvC2sxa5GiIiIiIyg4mX0SkN4VKgQMxBwAAPb16ihwNERERUdnC5IuI9Hbq8SkkZyWjmqwamjs3FzscIiIiojKFyRcR6U3T5bCHVw9ITaQiR0NERERUtjD5IiK9JGUm4eSjkwA4yiERERHR62DyRUR6ORhzEEq1Ej5VfFCrci2xwyEiIiIqc5h8EZFetHN7ebHVi4iIiOh1MPkioiJFJ0fjxtMbkEqk6O7VXexwiIiIiMokJl9EVCRNq1dLl5aoKqsqcjREREREZROTLyIqlFpQY+/9vQCAnt6c24uIiIjodTH5IqJCXYm/gifpT2BjZoN2ru3EDoeIiIiozGLyRUSF2hO1BwDQxaMLLE0tRY6GiIiIqOxi8kVEBXqpfInDsYcBcG4vIiIiov+KyRcRFej4g+NIV6Sjuk11NHJoJHY4RERERGUaky8iKtCe+zldDoO8g2Ai4ccFERER0X/Bb1NElK/EjESc+/scAE6sTERERFQSmHwRUb72R++HWlCjYbWGcJO7iR0OERERUZnH5IuI8qUZ5ZADbRARERGVDCZfRJTHned3cPfFXZiZmKGLRxexwyEiIiIqF5h8EVEemlavdq7tYGdhJ3I0REREROWDqMmXSqVCSEgIPD09IZPJ4O3tjTlz5kAQhAL3iYiIQKtWrWBvbw+ZTAYfHx8sWbJEp8yqVavg7+8PuVwOuVyOFi1a4MCBAzpl2rVrB4lEovP34Ycflsp1EpUlSrUS++7vA8CBNoiIiIhKkqmYJw8LC8OqVauwfv16+Pr64vLlyxg2bBjs7OwwYcKEfPextrbGuHHj4O/vD2tra0RERGD06NGwtrbGqFGjAAA1atTAggULUKtWLQiCgPXr16NXr164du0afH19tccaOXIkZs+erV22srIq3QsmKgPO/X0OzzKfobJFZbxV/S2xwyEiIiIqN0RNvs6ePYtevXohMDAQAODh4YEtW7bg4sWLBe7TqFEjNGr072SvHh4e2LVrF06fPq1NvoKCdH+tnzdvHlatWoXz58/rJF9WVlZwcnIqyUsiKvN+jfoVANDNsxvMpGYiR0NERERUfoiafLVs2RJr1qzB3bt3Ubt2bVy/fh0RERFYvHix3se4du0azp49i7lz5+a7XaVSYfv27UhPT0eLFi10tm3atAkbN26Ek5MTgoKCEBISUmDrV1ZWFrKysrTLKSkpAACFQgGFQqF3vKVBc36x46B/ldU6Sc1OxbGHxwAA3d27l7n4C1JW66M8Y50YH9aJcWF9GB/WifExpjrRNwaJUNgDVqVMrVbj008/xcKFCyGVSqFSqTBv3jwEBwcXuW+NGjWQmJgIpVKJ0NBQhISE6Gy/efMmWrRogczMTNjY2GDz5s3o3r27dvuaNWvg7u4OFxcX3LhxA9OmTcMbb7yBXbt25Xu+0NBQzJo1K8/6zZs3s7silRtXsq7g55c/o5pJNUywnQCJRCJ2SERERERGLyMjAwMHDkRycjLkcnmB5URNvrZu3YqpU6fiyy+/hK+vLyIjIzFp0iQsXrwYQ4YMKXTf6OhopKWl4fz585g+fTpWrFiBAQMGaLdnZ2fjwYMHSE5Oxo4dO7B27VqcPHkS9erVy/d4x44dQ8eOHXHv3j14e3vn2Z5fy5erqyuePn1a6A02BIVCgcOHD6NTp04wM2M3MWNQVutk5JGRuJJwBeMbjMcw32Fih1Niymp9lGesE+PDOjEurA/jwzoxPsZUJykpKahatWqRyZeo3Q6nTp2K6dOno3///gAAPz8/xMbGYv78+UUmX56entp94uPjERoaqpN8mZubo2bNmgCAJk2a4NKlS/j666+xevXqfI/XvHlzACgw+bKwsICFhUWe9WZmZqJXtoYxxUI5ylKdPE57jCsJVyCBBD1r9SwzcRdHWaqPioJ1YnxYJ8aF9WF8WCfGxxjqRN/zizrUfEZGBkxMdEOQSqVQq9XFOo5ardZplXqdMpGRkQAAZ2fnYp2bqLzYG7UXAPCG8xtwsuZANEREREQlTdSWr6CgIMybNw9ubm7w9fXFtWvXsHjxYgwfPlxbJjg4GI8fP8aGDRsAACtXroSbmxt8fHwAAKdOncKiRYt0hqYPDg5Gt27d4ObmhtTUVGzevBknTpzAb7/9BgCIiorSPgNmb2+PGzduYPLkyWjTpg38/f0NeAeIjIMgCPj1fs4ohz29e4ocDREREVH5JGrytXz5coSEhGDMmDFISEiAi4sLRo8ejRkzZmjLPHnyBA8ePNAuq9VqBAcHIzo6GqampvD29kZYWBhGjx6tLZOQkIDBgwfjyZMnsLOzg7+/P3777Td06tQJQE6XxCNHjmDp0qVIT0+Hq6sr+vbti88//9xwF09kRG48vYHYlFjITGUIcAsQOxwiIiKicknU5MvW1hZLly7F0qVLCywTHh6uszx+/HiMHz++0ON+//33hW53dXXFyZMn9Q2TqNzTzO3V0a0jrMw4eicRERFRaRD1mS8iEl+2KhsHog8AAIK8g4ooTURERESvi8kXUQV36tEppGSnwEHmgOZOzcUOh4iIiKjcYvJFVMHtidoDAAj0DoTURCpyNERERETlF5MvogrsReYLnH50GgAQ5MUuh0RERESlickXUQV2IPoAlIISdavURa3KtcQOh4iIiKhcY/JFVIHtvZ8zsTLn9iIiIiIqfUy+iCqo+8n3cfPpTUglUnTz7CZ2OERERETlHpMvogpqb1ROq9db1d+Cvcxe5GiIiIiIyj8mX0QVkFpQ49f7ORMrc24vIiIiIsNg8kVUAV2Ou4y49DjYmtminWs7scMhIiIiqhCYfBFVQJq5vTp7dIaF1ELkaIiIiIgqBiZfRBVMhiIDh2MPA+Aoh0RERESGxOSLqII59vAYMpQZqG5THY0cGokdDhEREVGFweSLqIL5NSpnoI2e3j0hkUhEjoaIiIio4mDyRVSBxKfH4/yT8wCAIC+OckhERERkSEy+iCqQ/dH7oRbUaOTQCK5yV7HDISIiIqpQmHwRVRCCIGhHOeTcXkRERESGx+SLqIK48+IO7iXdg7mJOTq7dxY7HCIiIqIKh8kXUQWhafVq59oOdhZ2IkdDREREVPEw+SKqAJRqJfbd3weAc3sRERERiYXJF1EFcPbvs3ie+RxVLKugZfWWYodDREREVCEx+SKqADRze3X37A4zEzORoyEiIiKqmJh8EZVzKdkpOPbgGACgh3cPkaMhIiIiqriYfBGVc4djDiNbnQ1vO2/Uq1JP7HCIiIiIKiwmX0TlXO65vSQSicjREBEREVVcTL6IyrGHqQ9xNeEqJJAg0CtQ7HCIiIiIKjQmX0Tl2N77ewEAzZ2bw8naSeRoiIiIiCo2Jl9E5ZQgCNpRDjm3FxEREZH4mHwRlVPXE6/jYepDyExl6OjWUexwiIiIiCo8Jl9E5ZSm1auTeydYmVmJHA0RERERMfkiKoeyVdk4EHMAQM4oh0REREQkPiZfROXQyUcnkZqdCkcrRzRzbCZ2OEREREQEJl9E5ZJmbq8eXj0gNZGKHA0RERERAUy+iMqd55nPEfEoAgC7HBIREREZEyZfROXMgegDUApK1LOvB+9K3mKHQ0RERET/YPJFVM5wbi8iIiIi48Tki6gciUqKwh/P/oCpxBRdPbqKHQ4RERER5cLki6gc0bR6vVX9LdjL7EWOhoiIiIhyY/JFVE6o1Crsvb8XAAfaICIiIjJGTL6IyolL8ZcQnxEPW3NbtHVtK3Y4RERERPQKJl9E5YSmy2FXj66wkFqIHA0RERERvYrJF1E5kKHIwOHYwwA4yiERERGRsWLyRVQOHH1wFC+VL+Fq64oG1RqIHQ4RERER5YPJF1E5oOlyGOQdBIlEInI0RERERJQfJl9EZVx8ejzOPzkPAOjh1UPkaIiIiIioIEy+iMq4fdH7IEBAY4fGcLV1FTscIiIiIipAsZKvhQsX4uXLl9rlM2fOICsrS7ucmpqKMWPGlFx0RFQoQRCw594eAJzbi4iIiMjYFSv5Cg4ORmpqqna5W7duePz4sXY5IyMDq1evLrnoiKhQt5/fRlRyFMxNzNHZo7PY4RARERFRIYqVfAmCUOgyERmWZqCN9m7tITeXixwNERERERWGz3wRlVEKtQL7o/cD4NxeRERERGUBky+iMurs47N4nvkcVSyroIVLC7HDISIiIqIimBZ3h7Vr18LGxgYAoFQqER4ejqpVqwKAzvNgRFS69kTlDLTR3bM7zEzMRI6GiIiIiIpSrJYvNzc3fPfdd1iyZAmWLFkCJycn/Pjjj9rltWvXws3NTe/jqVQqhISEwNPTEzKZDN7e3pgzZ06hz5JFRESgVatWsLe3h0wmg4+PD5YsWaJTZtWqVfD394dcLodcLkeLFi1w4MABnTKZmZkYO3Ys7O3tYWNjg759+yI+Pr44t4NINCnZKTjx8AQAdjkkIiIiKiuK1fIVExNToicPCwvDqlWrsH79evj6+uLy5csYNmwY7OzsMGHChHz3sba2xrhx4+Dv7w9ra2tERERg9OjRsLa2xqhRowAANWrUwIIFC1CrVi0IgoD169ejV69euHbtGnx9fQEAkydPxr59+7B9+3bY2dlh3Lhx6NOnD86cOVOi10hUGg7FHEK2Ohs1K9WETxUfscMhIiIiIj0Uu9thSTp79ix69eqFwMBAAICHhwe2bNmCixcvFrhPo0aN0KhRI+2yh4cHdu3ahdOnT2uTr6Ag3fmO5s2bh1WrVuH8+fPw9fVFcnIyvv/+e2zevBkdOnQAAKxbtw5169bF+fPn8eabb5b0pRKVKM0ohz29e0IikYgcDRERERHpo1jJ17lz5/Ds2TP06NFDu27Dhg2YOXMm0tPT8fbbb2P58uWwsLDQ63gtW7bEmjVrcPfuXdSuXRvXr19HREQEFi9erHdM165dw9mzZzF37tx8t6tUKmzfvh3p6elo0SJnUIIrV65AoVAgICBAW87Hxwdubm44d+5cvslXVlaWzoTSKSkpAACFQgGFQqF3vKVBc36x46B/lWadPEx9iKsJV2EiMUEX1y6sdz3wPWJ8WCfGh3ViXFgfxod1YnyMqU70jaFYydfs2bPRrl07bfJ18+ZNjBgxAkOHDkXdunXx5ZdfwsXFBaGhoXodb/r06UhJSYGPjw+kUilUKhXmzZuHQYMGFblvjRo1kJiYCKVSidDQUHzwwQc622/evIkWLVogMzMTNjY2+Pnnn1GvXj0AQFxcHMzNzVGpUiWdfRwdHREXF5fv+ebPn49Zs2blWX/o0CFYWVnpdb2l7fDhw2KHQK8ojTo5lnkMAOAl9cKlE5dK/PjlGd8jxod1YnxYJ8aF9WF8WCfGxxjqJCMjQ69yxUq+IiMjMWfOHO3y1q1b0bx5c3z33XcAAFdXV8ycOVPv5Ounn37Cpk2bsHnzZvj6+iIyMhKTJk2Ci4sLhgwZUui+p0+fRlpaGs6fP4/p06ejZs2aGDBggHZ7nTp1EBkZieTkZOzYsQNDhgzByZMntQlYcQUHB2PKlCna5ZSUFLi6uqJz586Qy8Wd3FahUODw4cPo1KkTzMw46p0xKK06EQQB3/76LQBgaLOh6O7ZvcSOXZ7xPWJ8WCfGh3ViXFgfxod1YnyMqU40veKKUqzk68WLF3B0dNQunzx5Et26ddMuN2vWDA8fPtT7eFOnTsX06dPRv39/AICfnx9iY2Mxf/78IpMvT09P7T7x8fEIDQ3VSb7Mzc1Rs2ZNAECTJk1w6dIlfP3111i9ejWcnJyQnZ2NpKQkndav+Ph4ODk55Xs+CwuLfLtTmpmZiV7ZGsYUC+Uo6Tq5lnANj9IewcrUCp08xf+gKWv4HjE+rBPjwzoxLqwP48M6MT7GUCf6nr9YQ807OjoiOjoaAJCdnY2rV6/qPB+VmpparAvPyMiAiYluCFKpFGq1ujhhQa1W6zyPVVSZJk2awMzMDEePHtVuv3PnDh48eKB9LozIGGnm9gpwD4CVmXF0dyUiIiIi/RSr5at79+6YPn06wsLCsHv3blhZWaF169ba7Tdu3IC3t7fexwsKCsK8efPg5uYGX19fXLt2DYsXL8bw4cO1ZYKDg/H48WNs2LABALBy5Uq4ubnBxydneO1Tp05h0aJFOkPTBwcHo1u3bnBzc0Nqaio2b96MEydO4LfffgMA2NnZYcSIEZgyZQqqVKkCuVyO8ePHo0WLFhzpkIxWlioLv0XnvIY5txcRERFR2VOs5GvOnDno06cP2rZtCxsbG4SHh8Pc3Fy7/YcffkDnzp31Pt7y5csREhKCMWPGICEhAS4uLhg9ejRmzJihLfPkyRM8ePBAu6xWqxEcHIzo6GiYmprC29sbYWFhGD16tLZMQkICBg8ejCdPnsDOzg7+/v747bff0KlTJ22ZJUuWwMTEBH379kVWVha6dOmCb775pji3g8igTjw8gVRFKpysndDMqZnY4RARERFRMRUr+apatSpOnTqF5ORk2NjYQCqV6mzfvn07bG1t9T6era0tli5diqVLlxZYJjw8XGd5/PjxGD9+fKHH/f7774s8t6WlJVauXImVK1fqEyqR6DRze/Xw6gETSbF6DBMRERGREShW8pW7O2Bhfvjhh9cKhojy9+zlM0Q8jgAABHkFFVGaiIiIiIxRsZKv8PBwuLu7o1GjRhAEobRiIqJXHIg+AJWgQn37+vCq5CV2OERERET0GoqVfH300UfYsmULoqOjMWzYMLz33nuoUqVKacVGRP/49X5Ol8Mgb7Z6EREREZVVxXpwZOXKlXjy5Ak++eQT/Prrr3B1dUW/fv3w22+/sSWMqJTce3EPt57dgqnEFN08uxW9AxEREREZpWI/tW9hYYEBAwbg8OHDuHXrFnx9fTFmzBh4eHggLS2tNGIkqtA0rV6ta7RGZcvKIkdDRERERK/rPw2ZZmJiAolEAkEQoFKpSiomIvqHSq3C3vt7AXBuLyIiIqKyrtjJV1ZWFrZs2YJOnTqhdu3auHnzJlasWIEHDx7AxsamNGIkqrAuxl1EQkYC5OZytKnRRuxwiIiIiOg/KNaAG2PGjMHWrVvh6uqK4cOHY8uWLahatWppxUZU4Wnm9urm2Q3mUvMiShMRERGRMStW8vXtt9/Czc0NXl5eOHnyJE6ePJlvuV27dpVIcEQVWYYiA0ceHAGQM7EyEREREZVtxUq+Bg8eDIlEUlqxEFEuRx4cwUvlS7jZuqFBtQZih0NERERE/1GxJ1kmIsPYE7UHQM7cXvzRg4iIiKjs+0+jHRJR6YhLj8PFJxcBsMshERERUXnB5IvICO29vxcCBDRxbIIatjXEDoeIiIiISgCTLyIjIwiCdpRDzu1FREREVH4w+SIyMree38L95PuwkFqgk3snscMhIiIiohLC5IvIyGhavTq4doCtua3I0RARERFRSWHyRWREFGoF9t/fDyBnlEMiIiIiKj+YfBEZkTOPz+BF1gvYW9qjhUsLscMhIiIiohLE5IvIiGjm9gr0CoSpSbGm4SMiIiIiI8fki8hIJGcl48TDEwA4yiERERFRecTki8hI/BbzGxRqBWpVroU6VeqIHQ4RERERlTAmX0RGQju3lxdbvYiIiIjKIyZfREbgQcoDRCZGwkRigu5e3cUOh4iIiIhKAZMvIiPw6/2cVq8Wzi3gYOUgcjREREREVBqYfBGJTC2otV0OObcXERERUfnF5ItIZNcSruFx2mNYmVqhg1sHscMhIiIiolLC5ItIZJpWr84enSEzlYkcDRERERGVFiZfRCLKVGbiUMwhAJzbi4iIiKi8Y/JFJKITj04gVZEKZ2tnNHFsInY4RERERFSKmHwRiUjT5bCHVw+YSPh2JCIiIirP+G2PSCRPXz7FmcdnAHCUQyIiIqKKgMkXkUgORB+ASlDBv6o/PO08xQ6HiIiIiEoZky8ikWi7HHr3EDkSIiIiIjIEJl9EIrj74i5uP78NUxNTdPXoKnY4RERERGQATL6IRLA3ai8AoE31NqhsWVnkaIiIiIjIEJh8ERmYSq3C3vs5yRfn9iIiIiKqOJh8ERnYhScXkPgyEXYWdmhdo7XY4RARERGRgTD5IjKwPff3AAC6enSFudRc5GiIiIiIyFCYfBEZULoiHcceHAPALodEREREFQ2TLyIDOhJ7BC+VL+Eh94BfVT+xwyEiIiIiA2LyRWRAmrm9gryDIJFIRI6GiIiIiAyJyReRgTxJe4KLcRcBAD28OLEyERERUUXD5KuMUymzceXGOjx8vgNXbqyDSpktdkhUgH3R+yBAQDOnZnCxcRE7HCIiIiIyMFOxA6DXdyRiPhbc3YR4qQQwAVb/HgnH68swvfYgBLwVLHZ4lIsgCNgTlTPKYZBXkMjREBEREZEY2PJVRh2JmI8p9zYh/pUaTDABptzbhCMR88UJjPL1x7M/EJ0cDQupBTq5dxI7HCIiIiISAZOvMkilzMaCu5sgAMArgzYI/yyH3d3ELohGRNPq1cGtA2zMbUSOhoiIiIjEwOSrDLp688ecroYFjJYnSCSIk0rw85m5eP7yGQRBMHCElJtCpcCB6AMAOLcXERERUUXGZ77KoMSUB3qVmxXzM2bF/AxzAXCUmMPJzBaOsqpwsq0Bx0pecLL3gaPcFU7WTqhkUYlDn5eS049PIykrCVVlVfGm85tih0NEREREImHyVQZVk7vpVU6uUiFFKkW2BHiIbDxUPAMUz4CUO8DjozplLWACR1NrOFpWgZO1MxztPOBU2Tvn/1s7wsnKCXYWdkzQXoNmbq9Az0CYmvAtR0RERFRR8ZtgGdTY7304XluCBJN/n/HKTSIIcFQDBwechzr1EeLjbyD+2Z+IS4pGfNrfiMt8hnhlOuJMBMRLTfHMVIosqPFAmYoHaalAWiwQfz7PcS0lpnC0qARHK0c4yd3haFsdTtZOOcv//JcJmq7krGSceHQCQM7EykRERERUcTH5KoOkpuaYXnsQptzbBIkg6CRgkn+e75pWexCkMjmksnqo4VAPNV49iCAAGc+BFzHIfvYX4p/eRnxSFOJSHyH+5TPEKdMQLzVBnKkp4k2leC6VIlNQIjbzKWIznwLP/8g3NksTMzjKHOBkWx2OuRKz3Ema3FxeYRK0ww8OQ6lWok7lOqhTpY7Y4RARERGRiJh8lVEBbwVjMfDPPF//rndU5yReRc7zJZEA1vaAtT3MazSBKwDX3NuV2UDKI+BFDPAiBlnPo5Dw/B7iUh4gPiMBcUI24k2liJNKEZ87QVMrEJv+GLHpjws8tUxqmZOYWTvqtJqVxwRtb/ReAGz1IiIiIiImX2VawFvBaP/m/3Dpejgu3ziNpv6t0azBUEhNzf/7wU3NgSpeOX8ALADdBO3li38Ss9hcCdp9JKTEIi4jAXEmQLypKeJMpYh/JUF7qcpETEoMYlJiCjy9zFQGRytH7fNmjtZ5kzRjT9Ceqp7iRtINmEhMEOgVKHY4RERERCQyUZMvlUqF0NBQbNy4EXFxcXBxccHQoUPx+eefF/ilOiIiAtOmTcOff/6JjIwMuLu7Y/To0Zg8ebK2zPz587Fr1y78+eefkMlkaNmyJcLCwlCnzr/dvtq1a4eTJ0/qHHv06NH49ttvS+diS4nU1BxN/Ich/pEjmvh3h9TUzDAnllXO+XNppF2lTdBUSiDlsTYpw4sYICknSct6GoP47OScVjNTKeKl/yZomi6OL6RSvFS+1DtB00nK/knWNP/f1szW4AmaSq3C5fjL+O3lbwCAFs4tUFVW1aAxEBEREZHxETX5CgsLw6pVq7B+/Xr4+vri8uXLGDZsGOzs7DBhwoR897G2tsa4cePg7+8Pa2trREREYPTo0bC2tsaoUaMAACdPnsTYsWPRrFkzKJVKfPrpp+jcuTNu3boFa2tr7bFGjhyJ2bNna5etrKxK94IrCqkpUNk95w9tdTZZAHDLTIFbUqxucqZpQYuPRaZagQTpP8mZqRRxUtN/kjXTnCTNzBRJJiZ6JWhWplaFtp45WTvBxsymxBK0I7FHsODiAsRnxGvX/f70dxyJPYIA94ASOQcRERERlU2iJl9nz55Fr169EBiY0yXLw8MDW7ZswcWLFwvcp1GjRmjU6N/WFg8PD+zatQunT5/WJl8HDx7U2Sc8PBwODg64cuUK2rRpo11vZWUFJyenkrwk0oelHHDyy/l7lVoNy9QncHsRAzed5Cwmp/UsLQ4AkCmR/NOdMVdSZprTvTHOXIZ4qQmSJGpkKDMQnRyN6OToAsOxMrXKt/Us939tzW2LvKwjsUcw5cQUCNCd1Do5OxlTTkzB4naLmYARERERVWCiJl8tW7bEmjVrcPfuXdSuXRvXr19HREQEFi9erPcxrl27hrNnz2Lu3LkFlklOTgYAVKlSRWf9pk2bsHHjRjg5OSEoKAghISEFtn5lZWUhKytLu5ySkgIAUCgUUCgUesdbGjTnFzuOEmPlkPNX/Y2827LTgeSHMH0RgxpJsaiRFAvJixhIkmKBpFhIlJnaoi/zS9AsLBFvYY04UzPES1RIFhTIUGbgfvJ93E++X2BI1qbWcLBy0CZoDjIHnf/ay+wx/+L8PIlXbgsuLsBbTm9BaiItsAyVjnL3HikHWCfGh3ViXFgfxod1YnyMqU70jUEiCELB3xZLmVqtxqeffoqFCxdCKpVCpVJh3rx5CA4uYqQ+ADVq1EBiYiKUSiVCQ0MREhJS4Dl69uyJpKQkREREaNevWbMG7u7ucHFxwY0bNzBt2jS88cYb2LVrV77HCQ0NxaxZs/Ks37x5M7srGgtBgIUyGVZZibDOTsjzX5niRZ5dMiQSbRfHOFNTPDK3wd/mVnhiaoZEqQRPJUqkS0ruDT3ceji8zLxK7HhEREREJL6MjAwMHDgQycnJkMvlBZYTNfnaunUrpk6dii+//BK+vr6IjIzEpEmTsHjxYgwZMqTQfaOjo5GWlobz589j+vTpWLFiBQYMGJCn3EcffYQDBw4gIiICNWrkme1K69ixY+jYsSPu3bsHb2/vPNvza/lydXXF06dPC73BhqBQKHD48GF06tQJZmYGGnCjLFJmAkkPIUmKgSTpAZAUA8mL2H9azWIgyU7Pd7cMiSSn9czcCvFyB8TJ5DktaCYmiBeyEa9IRYoy/31f9UXLL9DVo2tJXhXpge8R48M6MT6sE+PC+jA+rBPjY0x1kpKSgqpVqxaZfIna7XDq1KmYPn06+vfvDwDw8/NDbGws5s+fX2Ty5enpqd0nPj4eoaGheZKvcePGYe/evTh16lShiRcANG/eHAAKTL4sLCxgYWGRZ72ZmZnola1hTLEYJTMzQFYPcK6Xd5sgABnPcj1jFq0dBMTqRSw8Ux7BU5ECpKfke+jTMkuMcXIoMgQn62qsIxHxPWJ8WCfGh3ViXFgfxod1YnyMoU70Pb+oyVdGRgZMTEx01kmlUqjV6mIdR61W67RKCYKA8ePH4+eff8aJEye0iVphIiMjAQDOzs7FOjeVExIJYF01569G07zbldlA8sNXRmiMyRkE5HkMWr5MhqNSiQSpFEI+IydKBAGOKhUaZ2bl2UZEREREFYOoyVdQUBDmzZsHNzc3+Pr64tq1a1i8eDGGDx+uLRMcHIzHjx9jw4YNAICVK1fCzc0NPj4+AIBTp05h0aJFOkPTjx07Fps3b8Yvv/wCW1tbxMXljJBnZ2cHmUyGqKgobN68Gd27d4e9vT1u3LiByZMno02bNvD39zfgHaAyw9QcsPfO+cuH9Eo4ph+djikOVSERBJ0ETPJPz95pz15Amp5okHCJiIiIyPiImnwtX74cISEhGDNmDBISEuDi4oLRo0djxowZ2jJPnjzBgwcPtMtqtRrBwcGIjo6GqakpvL29ERYWhtGjR2vLrFq1CkDORMq5rVu3DkOHDoW5uTmOHDmCpUuXIj09Ha6urujbty8+//zz0r1gKr+qeCMg4yUWJzzFAvvKiDf9963lqFJh2rMXCMh4Cdg4ihgkEREREYlJ1OTL1tYWS5cuxdKlSwssEx4errM8fvx4jB8/vtDjFjWGiKurK06ePKlvmERFc28JyF0QkPIE7TP+xlVLCyRKpaj2T1dDKSSAvHpOOSIiIiKqkEyKLkJERTKRAl3DAABSSNAsMwvd0zPQTJN4AUDXBTnliIiIiKhCYvJFVFLq9QT6bQDkrwzaInfJWV+vpzhxEREREZFRELXbIVG5U68n4BMI5f1TiDz9Gxq27gJTrzZs8SIiIiIitnwRlTgTKQT3t/C4SgsI7m8x8SIiIiIiAEy+iIiIiIiIDILJFxERERERkQEw+SIiIiIiIjIAJl9EREREREQGwOSLiIiIiIjIAJh8ERERERERGQCTLyIiIiIiIgNg8kVERERERGQATL6IiIiIiIgMgMkXERERERGRATD5IiIiIiIiMgAmX0RERERERAbA5IuIiIiIiMgAmHwREREREREZAJMvIiIiIiIiA2DyRUREREREZABMvoiIiIiIiAyAyRcREREREZEBMPkiIiIiIiIyACZfREREREREBsDki4iIiIiIyACYfBERERERERkAky8iIiIiIiIDYPJFRERERERkAEy+iIiIiIiIDIDJFxERERERkQEw+SIiIiIiIjIAJl9EREREREQGwOSLiIiIiIjIAJh8ERERERERGQCTLyIiIiIiIgNg8kVERERERGQATL6IiIiIiIgMgMkXERERERGRATD5IiIiIiIiMgAmX0RERERERAbA5IuIiIiIiMgAmHwREREREREZAJMvIiIiIiIiA2DyRUREREREZABMvoiIiIiIiAyAyRcREREREZEBMPkiIiIiIiIyACZfREREREREBsDki4iIiIiIyACYfBERERERERkAky8iIiIiIiIDYPJFRERERERkAKImXyqVCiEhIfD09IRMJoO3tzfmzJkDQRAK3CciIgKtWrWCvb09ZDIZfHx8sGTJEp0y8+fPR7NmzWBrawsHBwe8/fbbuHPnjk6ZzMxMjB07Fvb29rCxsUHfvn0RHx9fKtdJRERERERkKubJw8LCsGrVKqxfvx6+vr64fPkyhg0bBjs7O0yYMCHffaytrTFu3Dj4+/vD2toaERERGD16NKytrTFq1CgAwMmTJzF27Fg0a9YMSqUSn376KTp37oxbt27B2toaADB58mTs27cP27dvh52dHcaNG4c+ffrgzJkzBrt+IiIiIiKqOERNvs6ePYtevXohMDAQAODh4YEtW7bg4sWLBe7TqFEjNGrUSLvs4eGBXbt24fTp09rk6+DBgzr7hIeHw8HBAVeuXEGbNm2QnJyM77//Hps3b0aHDh0AAOvWrUPdunVx/vx5vPnmmyV9qUREREREVMGJmny1bNkSa9aswd27d1G7dm1cv34dERERWLx4sd7HuHbtGs6ePYu5c+cWWCY5ORkAUKVKFQDAlStXoFAoEBAQoC3j4+MDNzc3nDt3Lt/kKysrC1lZWdrllJQUAIBCoYBCodA73tKgOb/YcdC/WCfGhfVhfFgnxod1YlxYH8aHdWJ8jKlO9I1BIhT2gFUpU6vV+PTTT7Fw4UJIpVKoVCrMmzcPwcHBRe5bo0YNJCYmQqlUIjQ0FCEhIQWeo2fPnkhKSkJERAQAYPPmzRg2bJhOMgUAb7zxBtq3b4+wsLA8xwkNDcWsWbPyrN+8eTOsrKz0uVwiIiIiIiqHMjIyMHDgQCQnJ0MulxdYTtSWr59++gmbNm3C5s2b4evri8jISEyaNAkuLi4YMmRIofuePn0aaWlpOH/+PKZPn46aNWtiwIABecqNHTsWv//+uzbxel3BwcGYMmWKdjklJQWurq7o3LlzoTfYEBQKBQ4fPoxOnTrBzMxM1FgoB+vEuLA+jA/rxPiwTowL68P4sE6MjzHViaZXXFFETb6mTp2K6dOno3///gAAPz8/xMbGYv78+UUmX56entp94uPjERoamif5GjduHPbu3YtTp06hRo0a2vVOTk7Izs5GUlISKlWqpF0fHx8PJyenfM9nYWEBCwuLPOvNzMxEr2wNY4qFcrBOjAvrw/iwTowP68S4sD6MD+vE+BhDneh7flGHms/IyICJiW4IUqkUarW6WMdRq9U6XQgFQcC4cePw888/49ixY9pETaNJkyYwMzPD0aNHtevu3LmDBw8eoEWLFq9xJURERERERIUTteUrKCgI8+bNg5ubG3x9fXHt2jUsXrwYw4cP15YJDg7G48ePsWHDBgDAypUr4ebmBh8fHwDAqVOnsGjRIp2h6ceOHYvNmzfjl19+ga2tLeLi4gAAdnZ2kMlksLOzw4gRIzBlyhRUqVIFcrkc48ePR4sWLTjSIRERERERlQpRk6/ly5cjJCQEY8aMQUJCAlxcXDB69GjMmDFDW+bJkyd48OCBdlmtViM4OBjR0dEwNTWFt7c3wsLCMHr0aG2ZVatWAQDatWunc75169Zh6NChAIAlS5bAxMQEffv2RVZWFrp06YJvvvmm9C6WiIiIiIgqNFGTL1tbWyxduhRLly4tsEx4eLjO8vjx4zF+/PhCj6vPAI6WlpZYuXIlVq5cqU+oRERERERE/4moz3wRERERERFVFEy+iIiIiIiIDIDJFxERERERkQEw+SIiIiIiIjIAJl9EREREREQGwOSLiIiIiIjIAJh8ERERERERGQCTLyIiIiIiIgNg8kVERERERGQATL6IiIiIiIgMgMkXERERERGRATD5IiIiIiIiMgAmX0RERERERAbA5IuIiIiIiMgAmHwREREREREZAJMvIiIiIiIiA2DyRUREREREZABMvoiIiIiIiAyAyRcREREREZEBMPkiIiIiIiIyACZfREREREREBsDki4iIiIiIyACYfBERERERERkAky8iIiIiIiIDYPJFRERERERkAEy+iIiIiIiIDIDJFxERERERkQEw+SIiIiIiIjIAJl9EREREREQGwOSLiIiIiIjIAJh8ERERERERGQCTLyIiIiIiIgNg8kVERERERGQATL6IiIiIiIgMgMkXERERERGRATD5IiIiIiIiMgAmX0RERERERAbA5IuIiIiIiMgAmHwREREREREZAJMvIiIiIiIiA2DyRUREREREZABMvoiIiIiIiAyAyRcREREREZEBMPkiIiIiIiIyACZfREREREREBsDki4iIiIiIyACYfBERERERERkAky8iIiIiIiIDYPJFRERERERkAEy+iIiIiIiIDIDJFxERERERkQEw+SIiIiIiIjIAUZMvlUqFkJAQeHp6QiaTwdvbG3PmzIEgCAXuExERgVatWsHe3h4ymQw+Pj5YsmSJTplTp04hKCgILi4ukEgk2L17d57jDB06FBKJROeva9euJX2JREREREREAABTMU8eFhaGVatWYf369fD19cXly5cxbNgw2NnZYcKECfnuY21tjXHjxsHf3x/W1taIiIjA6NGjYW1tjVGjRgEA0tPT0aBBAwwfPhx9+vQp8Pxdu3bFunXrtMsWFhYle4FERERERET/EDX5Onv2LHr16oXAwEAAgIeHB7Zs2YKLFy8WuE+jRo3QqFEj7bKHhwd27dqF06dPa5Ovbt26oVu3bkWe38LCAk5OTv/xKoiIiIiIiIomavLVsmVLrFmzBnfv3kXt2rVx/fp1REREYPHixXof49q1azh79izmzp1b7POfOHECDg4OqFy5Mjp06IC5c+fC3t4+37JZWVnIysrSLqekpAAAFAoFFApFsc9dkjTnFzsO+hfrxLiwPowP68T4sE6MC+vD+LBOjI8x1Ym+MUiEwh6wKmVqtRqffvopFi5cCKlUCpVKhXnz5iE4OLjIfWvUqIHExEQolUqEhoYiJCQk33ISiQT/3969B0dV3/8ff20uXEISQi4CGRICFFECSbgkokMEBEEYuYlCKaOgfh2mUCxNQwWpCD8FBkg1/Vq0A84U6OBwsRWZ/qz8EMkXDKiEloC2AqEBo2K4hFz5gkn2/P5AdgxJIIbdcz7JPh8zmeF8ztnPvk9e2ZO8OWfPvvPOO5o0aVKd8S1btigkJEQ9evTQqVOn9Pzzzys0NFQHDx5UYGBgvXmWLl2qZcuW1Rt/6623FBIS0rQdBgAAANDqXL58WT/72c9UVlam8PDwRrdztPnasmWLFixYoDVr1igxMVFHjhzR/Pnz9corr2jmzJk3fWxhYaEqKyv18ccfa+HChfrDH/6g6dOn19uusebrRv/5z3/Uq1cvffDBBxo5cmS99Q2d+YqLi9OFCxdu+g22Q3V1tXbv3q0HH3xQwcHBjtaCa8jELORhHjIxD5mYhTzMQybmMSmT8vJyRUdH37L5cvSywwULFmjhwoX66U9/Kknq37+/zpw5o5UrV96y+erRo4fnMcXFxVq6dGmDzVdT9ezZU9HR0SooKGiw+Wrbtm2DN+QIDg52POzrTKoF15CJWcjDPGRiHjIxC3mYh0zMY0ImTX1+R281f/nyZQUE1C0hMDBQbrf7R83jdrvrnJVqjq+++koXL15U165db2seAAAAAGiIo2e+xo8fr+XLlys+Pl6JiYn65z//qVdeeUVPPfWUZ5tFixbp66+/1qZNmyRJa9euVXx8vO666y5J1z7TKysrq86t6SsrK1VQUOBZLiws1JEjRxQZGan4+HhVVlZq2bJlmjJlirp06aJTp07pN7/5jX7yk59ozJgxNu09AAAAAH/iaPP12muv6YUXXtCcOXN07tw5xcbGavbs2VqyZIlnm7Nnz+rLL7/0LLvdbi1atEiFhYUKCgpSr169tGrVKs2ePduzTV5enkaMGOFZzsjIkCTNnDlTGzZsUGBgoI4ePaqNGzeqtLRUsbGxGj16tF566SU+6wsAAACATzjafIWFhSk7O1vZ2dmNbrNhw4Y6y/PmzdO8efNuOu/w4cN1s/uItG/fXrt27foxpQIAAADAbXH0PV8AAAAA4C9ovgAAAADABjRfAAAAAGADmi8AAAAAsAHNFwAAAADYgOYLAAAAAGxA8wUAAAAANqD5AgAAAAAb0HwBAAAAgA1ovlq4muoaHd65Rxf25+vwzj2qqa5xuiS/RyZmIQ/zkIl5yMQs5GEeMjFPS83EZVmW5XQRLVF5ebk6duyosrIyhYeHO1LD/7y5VUGvZyvycqlnrCQkQjVz5mvYf01zpCZ/RyZmIQ/zkIl5yMQs5GEeMjGPiZk0tTeg+Womp5uv/3lzq2KylkqSXD8Yd3+/fD5zKQcEm5GJWcjDPGRiHjIxC3mYh0zMY2omNF8+5mTzVVNdo0/uSVeny6V1fuiuc0sqDYlQ4v/7vwoKCrK1Nn9VU1Ojzx8cp07/W0YmBiAP85CJecjELORhHjIxT1MyuRQSoSGf7FdQsL2Z0Hz5mJPN16F3dit00bO2PicAAADQElSu/G+lTn7Q1udsam/ADTdaoIpvvnW6BAAAAMBIJv+tzDnSFigstkuTtitZskrJ44b7thhIkvLfy1Hk/3nultuRiT3IwzxkYh4yMQt5mIdMzNPUTJr6t7ITuOywmUx4z1fE5dIGT106eb2rvyITs5CHecjEPGRiFvIwD5mYx+RMuOywFQsKDlLNnPly6doP2Q9dv9NL7Zz5HAhsRCZmIQ/zkIl5yMQs5GEeMjFPa8iEM1/N5PSt5qWGP+PgYkiEavncCceQiVnIwzxkYh4yMQt5mIdMzGNiJtzt0MdMaL6ka6df83Z+oM8PfqrEe9M0eMIoo7t9f0AmZiEP85CJecjELORhHjIxj2mZNLU34KemhQsKDtKgCSNVHHRVg8aN5EBgADIxC3mYh0zMQyZmIQ/zkIl5WmomvOcLAAAAAGxA8wUAAAAANqD5AgAAAAAb0HwBAAAAgA1ovgAAAADABjRfAAAAAGADmi8AAAAAsAHNFwAAAADYgOYLAAAAAGxA8wUAAAAANqD5AgAAAAAb0HwBAAAAgA1ovgAAAADABkFOF9BSWZYlSSovL3e4Eqm6ulqXL19WeXm5goODnS4HIhPTkId5yMQ8ZGIW8jAPmZjHpEyu9wTXe4TG0Hw1U0VFhSQpLi7O4UoAAAAAmKCiokIdO3ZsdL3LulV7hga53W598803CgsLk8vl8oynpqbq0KFDjT7uZusbWtfY9j8cLy8vV1xcnIqKihQeHt6c3Wm2W+2vL+dpymO8nUdj4zeO+WMmTd3eiUyczKOx+uyYw9eZ8BrhuOUtHLfqj3Hcur3tOG55bx6OWw27sRbLslRRUaHY2FgFBDT+zi7OfDVTQECAunXrVm88MDDwpuHfbH1D6xrbvqHx8PBw23/wbrW/vpynKY/xdh6NjTe2rT9l0tTtnczEiTwaq8WOOXydCa8RjlvewnGr8TGOW83bjuOW9+bhuNWwhmq52Rmv67jhhpfNnTu32esbWtfY9rd6Hrt4q47mzNOUx3g7j8bGTclDci6Tpm5PJvbN4etM/DmP5s7DcathHLd+XD124LjV+vJo7jwctxrW3Fq47LAVKC8vV8eOHVVWVubI/46hPjIxC3mYh0zMQyZmIQ/zkIl5WmImnPlqBdq2basXX3xRbdu2dboUfI9MzEIe5iET85CJWcjDPGRinpaYCWe+AAAAAMAGnPkCAAAAABvQfAEAAACADWi+AAAAAMAGNF8AAAAAYAOaLwAAAACwAc2Xn3n11VeVmJiovn376tlnnxU3u3TW8ePHlZKS4vlq3769duzY4XRZfq+wsFAjRoxQ37591b9/f1VVVTldkl9LSEhQUlKSUlJSNGLECKfLwfcuX76s7t27KzMz0+lS/F5paakGDx6slJQU9evXT+vXr3e6JL9WVFSk4cOHq2/fvkpKStL27dudLgmSJk+erE6dOunRRx91tA5uNe9Hzp8/ryFDhujzzz9XcHCw7r//fmVlZenee+91ujRIqqysVEJCgs6cOaMOHTo4XY5fGzZsmF5++WWlp6erpKRE4eHhCgoKcrosv5WQkKDPPvtMoaGhTpeCH1i8eLEKCgoUFxenrKwsp8vxa7W1tbp69apCQkJUVVWlfv36KS8vT1FRUU6X5pfOnj2r4uJipaSk6Ntvv9WgQYN04sQJfrc7LCcnRxUVFdq4caPefvttx+rgzJefqamp0ZUrV1RdXa3q6mrdcccdTpeE7+3cuVMjR47k4Oyw6/85kZ6eLkmKjIyk8QJucPLkSX3xxRcaO3as06VAUmBgoEJCQiRJV69elWVZXNnioK5duyolJUWS1KVLF0VHR6ukpMTZoqDhw4crLCzM6TJovkyyb98+jR8/XrGxsXK5XA1efrZ27VolJCSoXbt2uueee/Tpp582ef6YmBhlZmYqPj5esbGxGjVqlHr16uXFPWh9fJ3JD23btk3Tpk27zYpbP19ncvLkSYWGhmr8+PEaOHCgVqxY4cXqWx87XiMul0vDhg1TamqqNm/e7KXKWy87MsnMzNTKlSu9VHHrZ0cmpaWlSk5OVrdu3bRgwQJFR0d7qfrWx87f7YcPH1Ztba3i4uJus+rWzc5MnEbzZZCqqiolJydr7dq1Da7funWrMjIy9OKLL+of//iHkpOTNWbMGJ07d86zzfXrvW/8+uabb3Tp0iX97W9/0+nTp/X111/rwIED2rdvn1271yL5OpPrysvLdeDAAY0bN87n+9TS+TqTmpoa7d+/X6+//roOHjyo3bt3a/fu3XbtXotjx2vko48+0uHDh7Vz506tWLFCR48etWXfWipfZ/Luu+/qzjvv1J133mnXLrV4drxOIiIilJ+fr8LCQr311lsqLi62Zd9aIrt+t5eUlOiJJ57QunXrfL5PLZ1dmRjBgpEkWe+8806dsbS0NGvu3Lme5draWis2NtZauXJlk+bctm2bNWfOHM/y6tWrrVWrVnmlXn/gi0yu27RpkzVjxgxvlOlXfJHJgQMHrNGjR3uWV69eba1evdor9bZ2vnyNXJeZmWn96U9/uo0q/YsvMlm4cKHVrVs3q3v37lZUVJQVHh5uLVu2zJtlt2p2vE5+/vOfW9u3b7+dMv2Gr/K4cuWKlZ6ebm3atMlbpfoNX75G9u7da02ZMsUbZTYbZ75aiO+++06HDx/WqFGjPGMBAQEaNWqUDh482KQ54uLidODAAV25ckW1tbXKyclRnz59fFVyq+eNTK7jkkPv8EYmqampOnfunC5duiS32619+/bp7rvv9lXJrZo38qiqqlJFRYWkazel+fDDD5WYmOiTev2BNzJZuXKlioqKdPr0aWVlZemZZ57RkiVLfFVyq+eNTIqLiz2vk7KyMu3bt4/f783kjTwsy9KsWbP0wAMP6PHHH/dVqX7Dm39vmYDmq4W4cOGCamtr1blz5zrjnTt31rffftukOYYMGaJx48ZpwIABSkpKUq9evTRhwgRflOsXvJGJdO0X5aeffqoxY8Z4u0S/441MgoKCtGLFCt1///1KSkpS79699fDDD/ui3FbPG3kUFxdr6NChSk5O1pAhQ/TEE08oNTXVF+X6BW8dt+A93sjkzJkzSk9PV3JystLT0zVv3jz179/fF+W2et7IIzc3V1u3btWOHTs8HyVz7NgxX5TrF7x13Bo1apQee+wxvffee+rWrZtjjRu38PIzy5cv1/Lly50uAz/QsWNHrs03zNixY7mLmyF69uyp/Px8p8tAI2bNmuV0CZCUlpamI0eOOF0Gvjd06FC53W6ny8ANPvjgA6dLkMSZrxYjOjpagYGB9f5ILy4uVpcuXRyqyr+RiXnIxCzkYR4yMQ+ZmIU8zNPaMqH5aiHatGmjQYMGac+ePZ4xt9utPXv28CHJDiET85CJWcjDPGRiHjIxC3mYp7VlwmWHBqmsrFRBQYFnubCwUEeOHFFkZKTi4+OVkZGhmTNnavDgwUpLS1N2draqqqr05JNPOlh160Ym5iETs5CHecjEPGRiFvIwj19l4ui9FlHH3r17LUn1vmbOnOnZ5rXXXrPi4+OtNm3aWGlpadbHH3/sXMF+gEzMQyZmIQ/zkIl5yMQs5GEef8rEZVmW5bvWDgAAAAAg8Z4vAAAAALAFzRcAAAAA2IDmCwAAAABsQPMFAAAAADag+QIAAAAAG9B8AQAAAIANaL4AAAAAwAY0XwAAAABgA5ovAACaICEhQdnZ2U6XAQBowVyWZVlOFwEAgCTNmjVLpaWl2rFjh9Ol1HP+/Hl16NBBISEhTpfSIJO/dwCAazjzBQDwa9XV1U3aLiYmxpHGq6n1AQDMR/MFAGgxPvvsM40dO1ahoaHq3LmzHn/8cV24cMGz/v3339fQoUMVERGhqKgoPfzwwzp16pRn/enTp+VyubR161YNGzZM7dq10+bNmzVr1ixNmjRJWVlZ6tq1q6KiojR37tw6jc+Nlx26XC69+eabmjx5skJCQtS7d2/t3LmzTr07d+5U79691a5dO40YMUIbN26Uy+VSaWlpo/vocrn0xhtvaMKECerQoYOWL1+u2tpaPf300+rRo4fat2+vPn366Pe//73nMUuXLtXGjRv17rvvyuVyyeVyKScnR5JUVFSkqVOnKiIiQpGRkZo4caJOnz7dvAAAALeF5gsA0CKUlpbqgQce0IABA5SXl6f3339fxcXFmjp1qmebqqoqZWRkKC8vT3v27FFAQIAmT54st9tdZ66FCxfql7/8pf79739rzJgxkqS9e/fq1KlT2rt3rzZu3KgNGzZow4YNN61p2bJlmjp1qo4ePapx48ZpxowZKikpkSQVFhbq0Ucf1aRJk5Sfn6/Zs2dr8eLFTdrXpUuXavLkyTp27Jieeuopud1udevWTdu3b9e//vUvLVmyRM8//7y2bdsmScrMzNTUqVP10EMP6ezZszp79qzuu+8+VVdXa8yYMQoLC9P+/fuVm5ur0NBQPfTQQ/ruu++a+q0HAHiLBQCAIWbOnGlNnDixwXUvvfSSNXr06DpjRUVFliTr+PHjDT7m/PnzliTr2LFjlmVZVmFhoSXJys7Orve83bt3t2pqajxjjz32mDVt2jTPcvfu3a1XX33VsyzJ+u1vf+tZrqystCRZf//73y3LsqznnnvO6tevX53nWbx4sSXJunTpUsPfgO/nnT9/fqPrr5s7d641ZcqUOvtw4/fuz3/+s9WnTx/L7XZ7xq5evWq1b9/e2rVr1y2fAwDgXZz5AgC0CPn5+dq7d69CQ0M9X3fddZckeS4tPHnypKZPn66ePXsqPDxcCQkJkqQvv/yyzlyDBw+uN39iYqICAwM9y127dtW5c+duWlNSUpLn3x06dFB4eLjnMcePH1dqamqd7dPS0pq0rw3Vt3btWg0aNEgxMTEKDQ3VunXr6u3XjfLz81VQUKCwsDDP9ywyMlJXrlypczkmAMAeQU4XAABAU1RWVmr8+PFatWpVvXVdu3aVJI0fP17du3fX+vXrFRsbK7fbrX79+tW7xK5Dhw715ggODq6z7HK56l2u6I3HNMWN9W3ZskWZmZn63e9+p3vvvVdhYWFas2aNPvnkk5vOU1lZqUGDBmnz5s311sXExNx2nQCAH4fmCwDQIgwcOFB/+ctflJCQoKCg+r++Ll68qOPHj2v9+vVKT0+XJH300Ud2l+nRp08fvffee3XGDh061Ky5cnNzdd9992nOnDmesRvPXLVp00a1tbV1xgYOHKitW7fqjjvuUHh4eLOeGwDgPVx2CAAwSllZmY4cOVLnq6ioSHPnzlVJSYmmT5+uQ4cO6dSpU9q1a5eefPJJ1dbWqlOnToqKitK6detUUFCgDz/8UBkZGY7tx+zZs/XFF1/oueee04kTJ7Rt2zbPDTxcLtePmqt3797Ky8vTrl27dOLECb3wwgv1GrmEhAQdPXpUx48f14ULF1RdXa0ZM2YoOjpaEydO1P79+1VYWKicnBw9++yz+uqrr7y1qwCAJqL5AgAYJScnRwMGDKjztWzZMsXGxio3N1e1tbUaPXq0+vfvr/nz5ysiIkIBAQEKCAjQli1bdPjwYfXr10+/+tWvtGbNGsf2o0ePHnr77bf117/+VUlJSXrjjTc8dzts27btj5pr9uzZeuSRRzRt2jTdc889unjxYp2zYJL0zDPPqE+fPho8eLBiYmKUm5urkJAQ7du3T/Hx8XrkkUd099136+mnn9aVK1c4EwYADnBZlmU5XQQAAP5g+fLl+uMf/6iioiKnSwEAOID3fAEA4COvv/66UlNTFRUVpdzcXK1Zs0a/+MUvnC4LAOAQmi8AAHzk5MmTevnll1VSUqL4+Hj9+te/1qJFi5wuCwDgEC47BAAAAAAbcMMNAAAAALABzRcAAAAA2IDmCwAAAABsQPMFAAAAADag+QIAAAAAG9B8AQAAAIANaL4AAAAAwAY0XwAAAABgA5ovAAAAALDB/wepimTijxNiWwAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от learning rate (без масштабирования):\n","               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Learning rate                                                              \n","1.000000e-08          8.313329  8.325737  8.325737    8.313329            -\n","1.000000e-07          8.313329  8.324683  8.324850    8.313329            -\n","1.000000e-06          8.313329         -  8.346550    8.313329            -\n","1.000000e-05          8.313329         -         -    8.313329            -\n","1.000000e-04          8.313329         -         -    8.313329            -\n","1.000000e-03          8.313329         -         -    8.313329            -\n","1.000000e-02          8.313329         -         -    8.313329            -\n","1.000000e-01          8.313329         -         -    8.313329            -\n","\n","Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n","\n","============================================================\n","ГРАФИК 2: Зависимость MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от количества эпох (без масштабирования):\n","        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Epochs                                                                    \n","2              8.313329  2560.000199  2583.409888    8.313329            -\n","20             8.313329   217.565243   237.582879    8.313329            -\n","200            8.313329     8.325835     8.325835    8.313329            -\n","2000           8.313329     8.325615     8.325615    8.313329            -\n","20000          8.313329     8.323616     8.323616    8.313329            -\n","\n","============================================================\n","ГРАФИК 3: Зависимость MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от размера батча (без масштабирования):\n","            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Batch size                                                              \n","1                  8.313329  8.325737  8.325737    8.313329            -\n","2                  8.313329  8.325737  8.325737    8.313329            -\n","4                  8.313329  8.325737  8.325737    8.313329            -\n","8                  8.313329  8.325737  8.325737    8.313329            -\n","16                 8.313329  8.325737  8.325737    8.313329            -\n","32                 8.313329  8.325737  8.325737    8.313329            -\n","64                 8.313329  8.325737  8.325737    8.313329            -\n","128                8.313329  8.325737  8.325737    8.313329            -\n","256                8.313329  8.325737  8.325737    8.313329            -\n","512                8.313329  8.325737  8.325737    8.313329            -\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных (файл data.csv должен лежать в папке с ноутбуком) ----------\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            # batch_size не передаём, используем значение по умолчанию\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы ----------\n","def plot_and_table(param_values, param_name, xlabel, title):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=best_lr, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=best_lr, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            mses.append(mse)\n","        results[label] = mses\n","        plt.semilogx(param_values, mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений MSE для {title}:\")\n","    print(df_table.round(6).to_string(na_rep='-'))\n","    return df_table\n","\n","# ---------- Определяем best_lr по первому графику ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n","table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate')\n","\n","gd_mses = table_lr['GD'].values\n","valid_lr = lr_range[~np.isnan(gd_mses)]\n","if len(valid_lr) > 0:\n","    best_lr = valid_lr[0]\n","    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n","else:\n","    best_lr = 1e-8\n","    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n","\n","# ---------- График 2: влияние числа эпох ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n","table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох')\n","\n","# ---------- График 3: влияние размера батча ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n","table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"JTCgt5Iu4AnF","executionInfo":{"status":"ok","timestamp":1773047673728,"user_tz":-420,"elapsed":22974,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"a535d63e-a80c-42f6-917b-5c0070ac391e"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1: Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от learning rate:\n","               Normal Equation        GD       SGD  sklearn LR   sklearn SGD\n","Learning rate                                                               \n","1.000000e-08          8.313329  8.325737  8.325737    8.313329  8.325798e+00\n","1.000000e-07          8.313329  8.324683  8.324850    8.313329  8.325257e+00\n","1.000000e-06          8.313329         -  8.346550    8.313329  8.321114e+00\n","1.000000e-05          8.313329         -         -    8.313329  8.315080e+00\n","1.000000e-04          8.313329         -         -    8.313329  1.340738e+01\n","1.000000e-03          8.313329         -         -    8.313329  4.890101e+20\n","1.000000e-02          8.313329         -         -    8.313329  1.932537e+26\n","1.000000e-01          8.313329         -         -    8.313329  4.280040e+24\n","\n","Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n","\n","============================================================\n","ГРАФИК 2: Зависимость MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от количества эпох:\n","        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n","Epochs                                                                    \n","2              8.313329  2560.000199  2583.409888    8.313329  2954.214186\n","20             8.313329   217.565243   237.582879    8.313329   888.456215\n","200            8.313329     8.325835     8.325835    8.313329     8.330808\n","2000           8.313329     8.325615     8.325615    8.313329     8.325737\n","20000          8.313329     8.323616     8.323616    8.313329     8.324683\n","\n","============================================================\n","ГРАФИК 3: Зависимость MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений MSE для Зависимость MSE от размера батча:\n","            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Batch size                                                              \n","1                  8.313329  8.325737  8.325737    8.313329     8.325798\n","2                  8.313329  8.325737  8.325737    8.313329     8.325798\n","4                  8.313329  8.325737  8.325737    8.313329     8.325798\n","8                  8.313329  8.325737  8.325737    8.313329     8.325798\n","16                 8.313329  8.325737  8.325737    8.313329     8.325798\n","32                 8.313329  8.325737  8.325737    8.313329     8.325798\n","64                 8.313329  8.325737  8.325737    8.313329     8.325798\n","128                8.313329  8.325737  8.325737    8.313329     8.325798\n","256                8.313329  8.325737  8.325737    8.313329     8.325798\n","512                8.313329  8.325737  8.325737    8.313329     8.325798\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from mpl_toolkits.mplot3d import Axes3D\n","import warnings\n","warnings.filterwarnings('ignore')\n","from sklearn.linear_model import LinearRegression, SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","\n","%matplotlib inline\n","\n","# ---------- Загрузка данных ----------\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n","\n","# ---------- Реализации методов ----------\n","def normal_equation(X, y, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    return np.linalg.pinv(X.T @ X) @ X.T @ y\n","\n","def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    for _ in range(epochs):\n","        w -= lr * 2 * X.T @ (X @ w - y)\n","    return w\n","\n","def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n","    if fit_intercept:\n","        X = np.hstack([np.ones((X.shape[0], 1)), X])\n","    w = np.zeros(X.shape[1])\n","    n = len(X)\n","    for _ in range(epochs):\n","        idx = np.random.permutation(n)\n","        X_s, y_s = X[idx], y[idx]\n","        for i in range(0, n, batch_size):\n","            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n","            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n","    return w\n","\n","# ---------- Константы ----------\n","Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n","w_ne = normal_equation(X, y)\n","mse_ne = mean_squared_error(y, Xwb @ w_ne)\n","lr_sk = LinearRegression().fit(X, y)\n","mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n","\n","# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n","def compute_mse(method, lr=None, epochs=None, batch_size=None):\n","    np.random.seed(42)\n","    try:\n","        if method == 'ne':\n","            return mse_ne\n","        elif method == 'gd':\n","            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'sgd':\n","            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n","            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n","                return np.nan\n","            return mean_squared_error(y, Xwb @ w)\n","        elif method == 'lr_sk':\n","            return mse_lr_sk\n","        elif method == 'sgd_sk':\n","            sgd_sk = SGDRegressor(\n","                fit_intercept=True,\n","                max_iter=epochs,\n","                eta0=lr,\n","                learning_rate='constant',\n","                penalty=None,\n","                tol=None,\n","                random_state=42\n","            )\n","            sgd_sk.fit(X, y)\n","            y_pred = sgd_sk.predict(X)\n","            if np.any(np.isnan(y_pred)):\n","                return np.nan\n","            return mean_squared_error(y, y_pred)\n","        else:\n","            raise ValueError('Unknown method')\n","    except Exception:\n","        return np.nan\n","\n","# ---------- Диапазоны гиперпараметров ----------\n","lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n","epochs_range = [2, 20, 200, 2000, 20000]\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","\n","DEFAULT_EPOCHS = 1000\n","DEFAULT_BATCH = 32\n","\n","methods = [\n","    ('ne', 'Normal Equation'),\n","    ('gd', 'GD'),\n","    ('sgd', 'SGD'),\n","    ('lr_sk', 'sklearn LR'),\n","    ('sgd_sk', 'sklearn SGD')\n","]\n","\n","# ---------- Функция для построения графика и вывода таблицы для 1/MSE ----------\n","def plot_1over_mse(param_values, param_name, xlabel, title):\n","    plt.figure(figsize=(10, 6))\n","    results = {}\n","    for method, label in methods:\n","        inv_mses = []\n","        for val in param_values:\n","            if param_name == 'lr':\n","                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'epochs':\n","                mse = compute_mse(method, lr=best_lr, epochs=val, batch_size=DEFAULT_BATCH)\n","            elif param_name == 'batch':\n","                mse = compute_mse(method, lr=best_lr, epochs=DEFAULT_EPOCHS, batch_size=val)\n","            if np.isnan(mse) or mse == 0:\n","                inv = np.nan\n","            else:\n","                inv = 1.0 / mse\n","            inv_mses.append(inv)\n","        results[label] = inv_mses\n","        plt.semilogx(param_values, inv_mses, marker='o', label=label)\n","    plt.xlabel(xlabel)\n","    plt.ylabel('1 / MSE')\n","    plt.title(title)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n","    df_table = pd.DataFrame(results, index=param_values)\n","    df_table.index.name = xlabel\n","    print(f\"\\nТаблица значений 1/MSE для {title}:\")\n","    print(df_table.round(6).to_string(na_rep='-'))\n","    return df_table\n","\n","# ---------- Определяем best_lr по первому графику ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 1 (MSE): Зависимость MSE от learning rate\")\n","table_lr = plot_1over_mse(lr_range, 'lr', 'Learning rate', 'Зависимость 1/MSE от learning rate (для определения best_lr)')\n","# Используем те же данные, но для выбора best_lr нужны MSE, поэтому пересчитаем MSE отдельно\n","mse_values = []\n","for lr in lr_range:\n","    mse_values.append(compute_mse('gd', lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH))\n","gd_mses = np.array(mse_values)\n","valid_lr = lr_range[~np.isnan(gd_mses)]\n","if len(valid_lr) > 0:\n","    best_lr = valid_lr[0]\n","    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n","else:\n","    best_lr = 1e-8\n","    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n","\n","# ---------- Графики 1/MSE ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 2: Зависимость 1/MSE от learning rate (все методы)\")\n","plot_1over_mse(lr_range, 'lr', 'Learning rate', 'Зависимость 1/MSE от learning rate')\n","\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 3: Зависимость 1/MSE от количества эпох\")\n","plot_1over_mse(epochs_range, 'epochs', 'Epochs', 'Зависимость 1/MSE от количества эпох')\n","\n","print(\"\\n\" + \"=\"*60)\n","print(\"ГРАФИК 4: Зависимость 1/MSE от размера батча\")\n","plot_1over_mse(batch_range, 'batch', 'Batch size', 'Зависимость 1/MSE от размера батча')\n","\n","# ---------- Трехмерный график: количество эпох до достижения MSE < 15 для SGD ----------\n","print(\"\\n\" + \"=\"*60)\n","print(\"Построение трехмерного графика зависимости числа эпох (до MSE<15) от lr и batch_size для SGD\")\n","\n","threshold = 15\n","max_epochs = 20000\n","# Создадим сетку эпох для поиска (логарифмическая шкала)\n","epochs_search = np.unique(np.logspace(0, np.log10(max_epochs), 30).astype(int))\n","epochs_search = epochs_search[epochs_search > 0]\n","\n","# Матрица результатов\n","Z = np.full((len(lr_range), len(batch_range)), np.nan)\n","\n","for i, lr in enumerate(lr_range):\n","    for j, bs in enumerate(batch_range):\n","        # Ищем минимальное число эпох, при котором MSE < threshold\n","        found = False\n","        for e in epochs_search:\n","            mse = compute_mse('sgd', lr=lr, epochs=e, batch_size=bs)\n","            if not np.isnan(mse) and mse < threshold:\n","                Z[i, j] = e\n","                found = True\n","                break\n","        if not found:\n","            Z[i, j] = np.nan  # не достигнуто за max_epochs\n","\n","# Построение поверхности\n","fig = plt.figure(figsize=(12, 8))\n","ax = fig.add_subplot(111, projection='3d')\n","\n","X_grid, Y_grid = np.meshgrid(lr_range, batch_range, indexing='ij')\n","# Убираем NaN для построения (можно заменить на max_epochs, но лучше маскировать)\n","mask = ~np.isnan(Z)\n","ax.scatter(X_grid[mask], Y_grid[mask], Z[mask], c=Z[mask], cmap='viridis', s=50)\n","\n","ax.set_xlabel('Learning rate')\n","ax.set_ylabel('Batch size')\n","ax.set_zlabel('Epochs to MSE < 15')\n","ax.set_title('Количество эпох до достижения MSE<15 (SGD)')\n","ax.set_xscale('log')\n","ax.set_yscale('log')\n","plt.show()\n","\n","# Таблица результатов\n","df_3d = pd.DataFrame(Z, index=lr_range, columns=batch_range)\n","df_3d.index.name = 'lr'\n","df_3d.columns.name = 'batch_size'\n","print(\"\\nТаблица количества эпох до MSE<15 (NaN — не достигнуто за 20000):\")\n","print(df_3d.round(1).to_string(na_rep='-'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"c3q8SWWS5fYV","executionInfo":{"status":"error","timestamp":1773049114481,"user_tz":-420,"elapsed":1076830,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"04cab204-3c1e-4a1c-b474-c742f5b058cc"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Данные загружены. X shape: (999, 1), y shape: (999,)\n","\n","============================================================\n","ГРАФИК 1 (MSE): Зависимость MSE от learning rate\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений 1/MSE для Зависимость 1/MSE от learning rate (для определения best_lr):\n","               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Learning rate                                                              \n","1.000000e-08          0.120289  0.120109  0.120109    0.120289     0.120109\n","1.000000e-07          0.120289  0.120125  0.120122    0.120289     0.120116\n","1.000000e-06          0.120289         -  0.119810    0.120289     0.120176\n","1.000000e-05          0.120289         -         -    0.120289     0.120263\n","1.000000e-04          0.120289         -         -    0.120289     0.074586\n","1.000000e-03          0.120289         -         -    0.120289     0.000000\n","1.000000e-02          0.120289         -         -    0.120289     0.000000\n","1.000000e-01          0.120289         -         -    0.120289     0.000000\n","\n","Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n","\n","============================================================\n","ГРАФИК 2: Зависимость 1/MSE от learning rate (все методы)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений 1/MSE для Зависимость 1/MSE от learning rate:\n","               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Learning rate                                                              \n","1.000000e-08          0.120289  0.120109  0.120109    0.120289     0.120109\n","1.000000e-07          0.120289  0.120125  0.120122    0.120289     0.120116\n","1.000000e-06          0.120289         -  0.119810    0.120289     0.120176\n","1.000000e-05          0.120289         -         -    0.120289     0.120263\n","1.000000e-04          0.120289         -         -    0.120289     0.074586\n","1.000000e-03          0.120289         -         -    0.120289     0.000000\n","1.000000e-02          0.120289         -         -    0.120289     0.000000\n","1.000000e-01          0.120289         -         -    0.120289     0.000000\n","\n","============================================================\n","ГРАФИК 3: Зависимость 1/MSE от количества эпох\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений 1/MSE для Зависимость 1/MSE от количества эпох:\n","        Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Epochs                                                              \n","2              0.120289  0.000391  0.000387    0.120289     0.000338\n","20             0.120289  0.004596  0.004209    0.120289     0.001126\n","200            0.120289  0.120108  0.120108    0.120289     0.120036\n","2000           0.120289  0.120111  0.120111    0.120289     0.120109\n","20000          0.120289  0.120140  0.120140    0.120289     0.120125\n","\n","============================================================\n","ГРАФИК 4: Зависимость 1/MSE от размера батча\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица значений 1/MSE для Зависимость 1/MSE от размера батча:\n","            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n","Batch size                                                              \n","1                  0.120289  0.120109  0.120109    0.120289     0.120109\n","2                  0.120289  0.120109  0.120109    0.120289     0.120109\n","4                  0.120289  0.120109  0.120109    0.120289     0.120109\n","8                  0.120289  0.120109  0.120109    0.120289     0.120109\n","16                 0.120289  0.120109  0.120109    0.120289     0.120109\n","32                 0.120289  0.120109  0.120109    0.120289     0.120109\n","64                 0.120289  0.120109  0.120109    0.120289     0.120109\n","128                0.120289  0.120109  0.120109    0.120289     0.120109\n","256                0.120289  0.120109  0.120109    0.120289     0.120109\n","512                0.120289  0.120109  0.120109    0.120289     0.120109\n","\n","============================================================\n","Построение трехмерного графика зависимости числа эпох (до MSE<15) от lr и batch_size для SGD\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_406/583569280.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mfound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mepochs_search\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m             \u001b[0mmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sgd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmse\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m                 \u001b[0mZ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_406/583569280.py\u001b[0m in \u001b[0;36mcompute_mse\u001b[0;34m(method, lr, epochs, batch_size)\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXwb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sgd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msgd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misinf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_406/583569280.py\u001b[0m in \u001b[0;36msgd\u001b[0;34m(X, y, lr, epochs, batch_size, fit_intercept)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0mXb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_s\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_s\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m             \u001b[0mw\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mXb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mXb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from mpl_toolkits.mplot3d import Axes3D\n","from sklearn.linear_model import SGDRegressor\n","from sklearn.metrics import mean_squared_error\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","# ---------- Загрузка данных ----------\n","df = pd.read_csv('data.csv')\n","X = df[['x']].values\n","y = df['y'].values\n","\n","# ---------- Параметры ----------\n","lr_range = np.logspace(-8, -1, 8)\n","batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n","threshold = 15\n","max_epochs = 20000\n","\n","# Для прогресса\n","from tqdm.notebook import tqdm\n","\n","Z = np.full((len(lr_range), len(batch_range)), np.nan)\n","\n","for i, lr in enumerate(tqdm(lr_range, desc=\"lr\")):\n","    for j, bs in enumerate(tqdm(batch_range, desc=\"batch\", leave=False)):\n","        # Обучаем SGD один раз и сохраняем историю ошибок\n","        model = SGDRegressor(\n","            fit_intercept=True,\n","            max_iter=1,           # Будем делать по 1 эпохе за раз\n","            eta0=lr,\n","            learning_rate='constant',\n","            penalty=None,\n","            tol=None,\n","            warm_start=True,      # Важно: сохранять веса между вызовами\n","            random_state=42\n","        )\n","\n","        mse_history = []\n","        for epoch in range(1, max_epochs + 1):\n","            model.partial_fit(X, y)   # Одна эпоха\n","            y_pred = model.predict(X)\n","            mse = mean_squared_error(y, y_pred)\n","            mse_history.append(mse)\n","\n","            if mse < threshold:\n","                Z[i, j] = epoch\n","                break\n","\n","# ---------- Построение ----------\n","fig = plt.figure(figsize=(12, 8))\n","ax = fig.add_subplot(111, projection='3d')\n","\n","X_grid, Y_grid = np.meshgrid(lr_range, batch_range, indexing='ij')\n","mask = ~np.isnan(Z)\n","sc = ax.scatter(X_grid[mask], Y_grid[mask], Z[mask],\n","                c=Z[mask], cmap='viridis', s=50)\n","\n","ax.set_xlabel('Learning rate')\n","ax.set_ylabel('Batch size')\n","ax.set_zlabel('Эпох до MSE < 15')\n","ax.set_title('SGD: минимальное число эпох для MSE < 15')\n","ax.set_xscale('log')\n","ax.set_yscale('log')\n","plt.colorbar(sc)\n","plt.show()\n","\n","# Таблица\n","df_3d = pd.DataFrame(Z, index=lr_range, columns=batch_range)\n","df_3d.index.name = 'lr'\n","df_3d.columns.name = 'batch_size'\n","print(\"\\nТаблица (число эпох):\")\n","print(df_3d.round(1).to_string(na_rep='-'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["26511a612fb240878e45b0be1b4f1aab","dc8907c29c8742be945f91c053aee93b","267de66128304dbeaf02517d855023bc","ded69f943c254be1a499dc4a462334f4","7748bdb577c9422c9cc8d3b3b5a216c1","b8002cdfb5584cc7b84ea812ee460974","ad009a7315cd4cb6817c33d1927b4c41","e6ba9cef5521425fa5aa8f8ed050fbc9","bddeb3ad8290486ea3106771e4e26001","e40191a8e4984203bed0647f11ef7f4a","b5eead6a813749c9976f56b4415c0fbb","e145436d63874a97bce53dc7e29b71bc","24474a7c66ba4f5da622dab2c4f2afba","1064448d917a417da9517e5adca120e5","58e69002306e4c3f87ef1e5ff3ea7d92","dde89e54d5d342f6b528520204657ea4","2b78d4bda1c8452caad279dd527e093a","013a5e417c8340bfaf5e35cf71561aea","8ecd1af312f545c9a4bc47ba7e8387ea","e36ec1ed388345e69a775db490afe9aa","a705a40461a14ea2914d1c41afeb4ad8","0c90285b641c483d9401f313eda143f9","7254ae5628694a73a104b99aa1cb8077","46de6bd3b22240a69ac859a5590dcc30","b0ff7eefd5a542beb6844988e7b328da","404057be731144fa8448384bd5bd5514","a489308bb89644f0ad7b98a3c41e219f","5b828dc6657e4f1c8c424e0986dcdadf","b9938872da8d4d97862462d93124367b","62e058f786d24a04b8e47a3fc7d38e9e","3f5e5f24664040edad3bf1599cca8a9f","b08300dc0653425dbe983bdc56c634dc","1d2ef1fbf7314991a1e9818c49dc0cf8","031d3bb7b1a24a6194b5d43f53e5748f","63f8c04350964d2b903f28bea7b1a082","237bb39b162c4d6ca329ee2a969617ef","2f8a3fcd5e9d4d2ea9eb13133106a32c","e8597177b14e4ced8f2f863013ca1b09","37d257fa0f564cc4af65e247876ec743","e83bc113542d46d3a84eef4bca859a5a","d738a0434ce2419a8ea58c52100ee123","7307b7a2a26f4b18b835be10bdf6d407","efd411c908d74ca1a5320f9815d3fc21","f70be4dd46f94aaf89a44d46647cb0fc","ab3edb3e720d47998ef2ae30cb286cac","62b5dc58d5d145a685fd665e1b8019d1","4351daab7af443b791e2d927dae6bb73","fd5a069b59284c2b886aacea3ce16534","b04b27f4a3d94461b7f5f5fcd0f4a9d3","ad735e3b855543f5ab9ff26dfb9313a7","351e4494c83e402db049a7c4b0c81f80","85c64386d03742e4a67e4a8496f86f40","2eaee0b62f8e4bb6868bd16ff4f2f235","c49da9dbc29c45bd81acf7e4b2daf5e7","75585ed3495c44d7b0b85bbc2b1fe046","051cfe00efec4bf08978527130603332","74e4d33cedb54836a8239b9fe273fbc2","1f125c02b0d1494e86dce61d927a5d80","9b57cd2efc3a478fbb1c30e17492e623","c29815289a744a558da61f594798f2c0","52e3ec2ebccb45e2aa560114b91f9188","4a1e6417afab4014a352dc414e309a29","d0ad16a0be6c43ebaefe3cf711cc76d1","a69cdba6b2294d94aa5206f1042be7d5","db0e58b34a5a4540b7dfce7727a3ca00","db84ce4ca3cd4e0f978a88dd4dcc1124","16d9592bb1c34bc7b5ffe9dfea26a6ea","827f6bf4f198406b801c1e0b419df66f","567160aef16a4489961e9822f8b97829","0e96c54d43104601ad9af688e72f9f5e","8217b3374c4147ee8228760cf7bec747","1c32d8a3565e42a18d4785caf3bdf478","56c1ddd51deb4f3eb3ac8b0bf4490ec6","99fa959ce4f14901b2af137675bbb741","2d62375f068a4afd805943edcf65fc2b","789b9ce8e2da4b5792f5c3e49ad01da1","1fbca0165b79445889d808c23215eadf","1149024c43c64af5bb020ca86c52877f","f7cf1c4374fe4c2d82c3e66bb6ae6480","18a850bc9f9f4ea883ffc692b353fee4","abe5f6c082394cc59e3e9dd80070b291","30ac12042af54310973009ff68ccbc5a","fb65ccaff5df43e19e3c561e29b0fdae","d944ea247484426abe3f8c7fbe066eb2","e2910d5e333045669e00452b04729b02","685a7520c2a5493a8ba33e35f3e2c546","360c977b32b34685a35b175079b3da8b","77c1aac3802a44baa0a619b021f46aab","35c8fcdd43ff458197b8a3bcd640cf22","ce92a3f149ef4e5dad97a6187607660a","1764eafebd4d450a86c79efcb5f477fa","c23ea4c554eb4bdfaab0221d817e030d","2db47c7de2e14af989f5eee18dac447c","bd2d411042d24686a0edeefd7a601429","327a8bf1af2141208f0f00c972c96779","573979e176b64750aa51313ac67b5b05","d63b0f24a3d645a19679364a90a59107","62c379887aa94436b8dd61ba1858d93d","9b65caa913554fae9550e8dc2fc744c5"]},"id":"82a-cMerBWJs","executionInfo":{"status":"ok","timestamp":1773050622961,"user_tz":-420,"elapsed":189462,"user":{"displayName":"Сергей Денисович","userId":"01081411300278477821"}},"outputId":"ac1a95fd-b596-4efd-8067-7d348c678269"},"execution_count":16,"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"26511a612fb240878e45b0be1b4f1aab","version_major":2,"version_minor":0},"text/plain":["lr:   0%|          | 0/8 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e145436d63874a97bce53dc7e29b71bc","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7254ae5628694a73a104b99aa1cb8077","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"031d3bb7b1a24a6194b5d43f53e5748f","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ab3edb3e720d47998ef2ae30cb286cac","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"051cfe00efec4bf08978527130603332","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"16d9592bb1c34bc7b5ffe9dfea26a6ea","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1149024c43c64af5bb020ca86c52877f","version_major":2,"version_minor":0},"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["batch:   0%|          | 0/10 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"35c8fcdd43ff458197b8a3bcd640cf22"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Таблица (число эпох):\n","batch_size     1     2     4     8     16    32    64    128   256   512\n","lr                                                                      \n","1.000000e-08  93.0  93.0  93.0  93.0  93.0  93.0  93.0  93.0  93.0  93.0\n","1.000000e-07  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0\n","1.000000e-06   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0\n","1.000000e-05   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0\n","1.000000e-04   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0\n","1.000000e-03     -     -     -     -     -     -     -     -     -     -\n","1.000000e-02     -     -     -     -     -     -     -     -     -     -\n","1.000000e-01     -     -     -     -     -     -     -     -     -     -\n"]}]}]}
\ No newline at end of file
diff --git "a/\320\232\320\276\320\277\320\270\321\217_\320\261\320\273\320\276\320\272\320\275\320\276\321\202\320\260_\"Untitled2_ipynb\".ipynb" "b/\320\232\320\276\320\277\320\270\321\217_\320\261\320\273\320\276\320\272\320\275\320\276\321\202\320\260_\"Untitled2_ipynb\".ipynb"
new file mode 100644
index 0000000..4d3ba7e
--- /dev/null
+++ "b/\320\232\320\276\320\277\320\270\321\217_\320\261\320\273\320\276\320\272\320\275\320\276\321\202\320\260_\"Untitled2_ipynb\".ipynb"
@@ -0,0 +1,3938 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true,
+      "mount_file_id": "1gflZGIkz8_-SFrkGwKsRes8PHpImpLQA",
+      "authorship_tag": "ABX9TyOnznCkFrCjL9pjVmDp8upy",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/denisovich14072006-afk/linear-regression-task/blob/main/%D0%9A%D0%BE%D0%BF%D0%B8%D1%8F_%D0%B1%D0%BB%D0%BE%D0%BA%D0%BD%D0%BE%D1%82%D0%B0_%22Untitled2_ipynb%22.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FUjXwauVwuYb"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Новый раздел"
+      ],
+      "metadata": {
+        "id": "Ab2caDlCwy66"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы для независимых методов ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция оценки ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    if method == 'ne':\n",
+        "        return mse_ne\n",
+        "    if method == 'gd':\n",
+        "        w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "        return mean_squared_error(y, Xwb @ w)\n",
+        "    if method == 'sgd':\n",
+        "        w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "        return mean_squared_error(y, Xwb @ w)\n",
+        "    if method == 'lr_sk':\n",
+        "        return mse_lr_sk\n",
+        "    if method == 'sgd_sk':\n",
+        "        sgd_sk = SGDRegressor(fit_intercept=True, max_iter=epochs, eta0=lr,\n",
+        "                              learning_rate='constant', penalty=None, tol=None,\n",
+        "                              batch_size=batch_size, random_state=42)\n",
+        "        sgd_sk.fit(X, y)\n",
+        "        return mean_squared_error(y, sgd_sk.predict(X))\n",
+        "    raise ValueError('Unknown method')\n",
+        "\n",
+        "# ---------- Диапазоны параметров ----------\n",
+        "lr_range = np.logspace(-8, 0, 9)\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- График 1: learning rate ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n",
+        "    plt.semilogx(lr_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Learning rate')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от learning rate')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 2: эпохи ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n",
+        "    plt.semilogx(epochs_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от количества эпох')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 3: размер батча ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n",
+        "    plt.semilogx(batch_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Batch size')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от размера батча')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        },
+        "id": "oEzoSPneyUfG",
+        "outputId": "28e789d9-70b6-4787-c8e0-907213e76edb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/3602992315.py:25: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/3602992315.py:25: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "Input contains NaN.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_406/3602992315.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmethods\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0mmses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcompute_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEFAULT_EPOCHS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEFAULT_BATCH\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlr_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Learning rate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/tmp/ipykernel_406/3602992315.py\u001b[0m in \u001b[0;36mcompute_mse\u001b[0;34m(method, lr, epochs, batch_size)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgradient_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXwb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sgd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msgd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m                     )\n\u001b[1;32m    215\u001b[0m                 ):\n\u001b[0;32m--> 216\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_namespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m     _, y_true, y_pred, sample_weight, multioutput = (\n\u001b[0;32m--> 565\u001b[0;31m         _check_reg_targets_with_floating_dtype(\n\u001b[0m\u001b[1;32m    566\u001b[0m             \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         )\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets_with_floating_dtype\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, xp)\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mdtype_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_matching_floating_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     )\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype, xp)\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1107\u001b[0;31m             _assert_all_finite(\n\u001b[0m\u001b[1;32m   1108\u001b[0m                 \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m     _assert_all_finite_element_wise(\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    167\u001b[0m                 \u001b[0;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m             )\n\u001b[0;32m--> 169\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Input contains NaN."
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "# Загрузите ваш CSV в Colab (например, через files.upload())\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с защитой от NaN ----------\n",
+        "BIG_MSE = 1e10  # значение, возвращаемое при расходимости\n",
+        "\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return BIG_MSE\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return BIG_MSE\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return BIG_MSE\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return BIG_MSE  # при любой ошибке (например, взрыв градиента)\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "# learning rate теперь ограничен 0.1, чтобы избежать расходимости\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- График 1: влияние learning rate ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n",
+        "    plt.semilogx(lr_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Learning rate')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от learning rate')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n",
+        "    plt.semilogx(epochs_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от количества эпох')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n",
+        "    plt.semilogx(batch_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Batch size')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от размера батча')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "XSp9L7NvzgH3",
+        "outputId": "e0d95483-cd7b-477d-b714-bd149ad3f653"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in subtract\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIoCAYAAABeertyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkHFJREFUeJzs3Xd4VGX6//H3pCekUNIoIaHXQOgiIqAUBXVF/dkFLLiu61r4upZVmgUEG/aGgnXByrqKLIgiLrAghACKICAktDQgpJE65/fHOEOGJCSBJOck83lx5ZqZM8+cuWfuJMydp9kMwzAQERERERGRSnmZHYCIiIiIiIjVqXASERERERGpggonERERERGRKqhwEhERERERqYIKJxERERERkSqocBIREREREamCCicREREREZEqqHASERERERGpggonERERERGRKqhwEhERsSibzcaMGTPMDqNKcXFxTJo0yewwRETqlAonEWnQXn/9dcaMGUNUVBS+vr5ER0czbNgw3nvvPex2u9nhyRmaNGkSNpuN0NBQTpw4Ue7+Xbt2YbPZsNlsPPPMM2737du3j5tvvpkOHToQEBBAdHQ0559/PtOnT3drN3z4cNc5Tv3q2rVrnb4+sY78/HxmzJjBqlWrzA5FRCzOx+wARETOxrvvvkvLli2ZOnUqoaGhZGVl8b///Y9JkybxzTff8M9//tPsEOUM+fj4kJ+fz7///W+uvvpqt/s+/PBDAgICKCgocDu+e/duBgwYQGBgILfccgtxcXEcPnyYxMRE5syZw8yZM93at2nThtmzZ5d77rCwsNp/QY3Yzp078fJqmH+Lzc/Pd31fDB8+3NxgRMTSVDiJSIO2evVqfH193Y7dfffdtGjRgpdffpnZs2cTFxdnTnByVvz9/RkyZAj//Oc/yxVOH330EePGjeOzzz5zO/7888+Tm5tLUlISsbGxbvelp6eXe46wsDBuvPHG2g++ASspKcFut+Pn51ftx/j7+9dhRDVzJvGLiFRHw/zzkIjIH04tmpycxVLZv4L/61//Yty4cbRq1Qp/f386dOjA448/TmlpqdtjTx3CFR4ezrhx4/j555/d2lU0/+Tpp5/GZrOV+8t1QUEBM2bMoHPnzgQEBNCyZUuuuOIK9uzZAziGl9lsNhYuXOj2uL/+9a/YbDa3+SMLFy7EZrPh5+dHRkaGW/t169a54t64caPbfZ988gn9+vUjMDCQ8PBwbrzxRg4ePFjuvduxYwdXX301ERERBAYG0qVLFx555BEAZsyYUenwNueXc8jT8OHD6dmzZ7nz18T111/PN998Q1ZWluvYTz/9xK5du7j++uvLtd+zZw9t2rQpVzQBREZGnlUsp0pPT+fWW28lKiqKgIAAevfuzbvvvuu635nT032dybyggwcPcssttxAVFYW/vz89evTgnXfecWtTVFTEtGnT6NevH2FhYTRp0oShQ4fy/fffu7VzxvjMM88wb948OnTogL+/P9u3b3flevfu3UyaNImmTZsSFhbGzTffTH5+vtt5Tp3j5PweXbNmDVOmTCEiIoImTZowfvz4ct+zdrudGTNm0KpVK4KCghgxYgTbt2+v1ryp08Vfnfdg3759REREADBz5kxXXsr+XO/YsYOrrrqK5s2bExAQQP/+/fnyyy+rSpOINELqcRKRRiErK4uSkhJycnLYtGkTzzzzDNdeey1t27Z1tVm4cCHBwcFMmTKF4OBgvvvuO6ZNm0Z2djZPP/202/m6du3KI488gmEY7Nmzh+eee46xY8eSkpJy2hgqGvZVWlrKJZdcwsqVK7n22mu55557yMnJYcWKFfz888906NChwvPt3r2bt956q9Ln8/b25oMPPuC+++5zHVuwYEGFQ9gWLlzIzTffzIABA5g9ezZpaWm88MILrFmzhs2bN9O0aVMAtm7dytChQ/H19eX2228nLi6OPXv28O9//5snn3ySK664go4dO7rOe99999GtWzduv/1217Fu3bpVGnNNXXHFFdxxxx18/vnn3HLLLYCjt6lr16707du3XPvY2Fi+/fZbvvvuOy644IIqz19aWkpmZma544GBgTRp0qTSx504cYLhw4eze/du7rrrLtq1a8cnn3zCpEmTyMrK4p577iEiIoL333/f9ZjPP/+cL774wu1YZbmvTFpaGueccw42m4277rqLiIgIvvnmG2699Vays7O59957AcjOzmb+/Plcd911TJ48mZycHN5++23GjBnDhg0bSEhIcDvvggULKCgo4Pbbb8ff35/mzZu77rv66qtp164ds2fPJjExkfnz5xMZGcmcOXOqjPdvf/sbzZo1Y/r06ezbt4958+Zx1113sXjxYlebhx9+mLlz53LppZcyZswYtmzZwpgxY8p9D59ORfFX5z2IiIjgtdde4y9/+Qvjx4/niiuuAKBXr14A/PLLLwwZMoTWrVvz0EMP0aRJEz7++GMuv/xyPvvsM8aPH1/tGEWkETBERBqBLl26GIDra8KECUZxcbFbm/z8/HKP+/Of/2wEBQUZBQUFrmPDhg0zhg0b5tbuH//4hwEY6enprmOAMX36dNftBx54wIiMjDT69evn9vh33nnHAIznnnuu3PPb7XbDMAxj7969BmAsWLDAdd/VV19t9OzZ04iJiTEmTpzoOr5gwQIDMK677jojPj7edTwvL88IDQ01rr/+egMwfvrpJ8MwDKOoqMiIjIw0evbsaZw4ccLV/quvvjIAY9q0aa5j559/vhESEmIkJydXGOepYmNj3WIra9iwYUaPHj0qvK8qEydONJo0aWIYhmFcddVVxoUXXmgYhmGUlpYa0dHRxsyZM13v2dNPP+163M8//2wEBgYagJGQkGDcc889xpIlS4y8vLwK4yv7PVP2689//vNp45s3b54BGB988IHrWFFRkTF48GAjODjYyM7OLveY6dOnGzX9b/fU77Fbb73VaNmypZGZmenW7tprrzXCwsJc3+MlJSVGYWGhW5tjx44ZUVFRxi233OI65nwPQ0ND3b63y8Zbtr1hGMb48eONFi1auB079fvA+T06cuRIt++d++67z/D29jaysrIMwzCM1NRUw8fHx7j88svdzjdjxgwDqPR7qzrxV/c9yMjIKPc+O1144YVGfHy82+8Hu91unHvuuUanTp1OG5uIND4aqicijcKCBQtYsWIFH374IbfeeisffvihWy8IOHoRnHJycsjMzGTo0KHk5+ezY8cOt7bFxcVkZmaSkZHBunXr+OKLL+jVqxfh4eEVPv/Bgwd56aWXmDp1KsHBwW73ffbZZ4SHh/O3v/2t3ONsNluF59u0aROffPIJs2fPrnTS/U033cSOHTtcQ/I+++wzwsLCuPDCC93abdy4kfT0dO68804CAgJcx8eNG0fXrl35+uuvAcjIyGD16tXccsstbj11p4uzKs4enczMTIqKis7oHNdffz2rVq0iNTWV7777jtTU1AqH6QH06NGDpKQkbrzxRvbt28cLL7zA5ZdfTlRUVIW9d3FxcaxYsaLcl7PnpjJLly4lOjqa6667znXM19eXu+++m9zcXH744Yczeq2nYxgGn332GZdeeimGYbje18zMTMaMGcPx48dJTEwEHL2Rzjk+drudo0ePUlJSQv/+/V1tyrryyitdQ9ZOdccdd7jdHjp0KEeOHCE7O7vKmG+//Xa3752hQ4dSWlpKcnIyACtXrqSkpIQ777zT7XEV/aycTkXx1/Q9ONXRo0f57rvvuPrqq12/LzIzMzly5Ahjxoxh165dFQ51FZHGy6MLp9WrV3PppZfSqlUrbDYbS5YsqdHjCwoKmDRpEvHx8fj4+HD55ZdX2G7VqlX07dsXf39/OnbsWG4Og4icvcGDBzNy5Eiuv/565s+fz2OPPcaCBQtYs2aNq80vv/zC+PHjCQsLIzQ0lIiICNfCAMePH3c739q1a4mIiCAyMpJzzz2XkpISPvnkk0oLiOnTp9OqVSv+/Oc/l7tvz549dOnSBR+f6o+Ofuihhxg6dCiXXHJJpW0iIiIYN26ca37LO++8w8SJE8sVWs4PqV26dCl3jq5du7ru//333wHOel5SWTt27CAiIsJtvtRHH31Uo3OMHTuWkJAQFi9ezIcffsiAAQPchgueqnPnzrz//vtkZmaydetWZs2ahY+PD7fffjvffvutW9smTZowcuTIcl9VLUeenJxMp06dyr3XzmGKzve0NmVkZJCVlcWbb77pek+dXzfffDPgvgDGu+++S69evQgICKBFixZERETw9ddfl/teB2jXrl2lz3tqEd2sWTMAjh07VmXMVT3W+T6dms/mzZu72lZHZfHX5D041e7duzEMg6lTp5Z7v51L21e04IiINF4ePccpLy+P3r17c8stt7jGNddEaWkpgYGB3H333eVWdnLau3cv48aN44477uDDDz9k5cqV3HbbbbRs2ZIxY8ac7UsQkUpcddVVPPLII6xfv54hQ4aQlZXFsGHDCA0N5bHHHnPt8ZOYmMiDDz5Ybs+nXr168eyzzwKOD6wvvvgiw4cPJzExkejoaLe2v/76KwsXLuSDDz6odLGKmli+fDnffvst69atq7LtLbfcwoQJE/jb3/7G6tWrmT9/Pj/++ONZx1Bb4uLiXD09R44c4cUXX+Smm26iffv2nHPOOdU6h7+/P1dccQXvvvsuv//+e7U3hPX29iY+Pp74+HgGDx7MiBEj+PDDDxk5cuSZvhxTOb9Hb7zxRiZOnFhhG+fcnA8++IBJkyZx+eWX8/e//53IyEi8vb2ZPXu2a0GSssr2xp7K29u7wuOGYVQZ89k8tiYqir+m78GpnO/3/fffX+n/16cr4EWk8fHowuniiy/m4osvrvT+wsJCHnnkEf75z3+SlZVFz549mTNnjmu1rCZNmvDaa68BsGbNGrdVn5xef/112rVr5/oA1q1bN/773//y/PPPq3ASqUPOTVOdH9xWrVrFkSNH+Pzzzzn//PNd7fbu3Vvh45s1a+b2AXv48OG0atWKBQsW8PDDD7u1ffjhh0lISOCaa66p8FwdOnRg/fr1FBcXV1lYGYbBQw89xPjx46tVWFx88cUEBARw7bXXct5559GhQ4dyhZNzhbmdO3eWWzBh586drvvbt28PUG71wLPh7NFxGjp0KK1bt2b58uXVLpzAMVzvnXfewcvLi2uvvbbGcfTv3x+Aw4cP1/ixFYmNjWXr1q3Y7Xa3XifnkM+KVvU7WxEREYSEhFBaWlpl8ffpp5/Svn17Pv/8c7de0lM3ATab833avXu3W6/RkSNHqtWjdTrVfQ8q60V2/jz4+vo22GJbRGqXRw/Vq8pdd93FunXrWLRoEVu3buX//b//x0UXXcSuXbuqfY5169aV+4U7ZsyYav0lWUSqtnTp0gqPv/XWW9hsNleh4Cygyv6lu6ioiFdffbVaz+MsxAoLC92Or1u3jn/961889dRTlX4Au/LKK8nMzOTll18ud9+pf3l3/r6paHW+ivj4+DBhwgS2bt3qWnXuVP379ycyMpLXX3/dLf5vvvmGX3/9lXHjxgGOD+bnn38+77zzTrnVA2urh8D5V/zKeiIqM2LECB5//HFefvnlcj1+Zf34448UFxeXO+78PqlouOKZGDt2LKmpqW6rw5WUlPDSSy8RHBzMsGHDauV5yvL29ubKK6/ks88+q7C4LbvMd0Xf7+vXr7fc/z0XXnghPj4+rj9COlX0s1JT1X0PgoKCAMr98TMyMpLhw4fzxhtvVFhwn7qsuog0fh7d43Q6KSkpLFiwgJSUFFq1agU4uuuXLVvGggULmDVrVrXOk5qaSlRUlNuxqKgosrOzOXHixGmHR4hI1a6//nq6du3K+PHjiYqKIiMjg2+++Ybvv/+eRx55hPj4eADOPfdcmjVrxsSJE7n77rux2Wy8//77lRYEaWlpfPDBBwBkZmbyxhtv4OPjU27O0fLlyxk1atRp/yI9YcIE3nvvPaZMmcKGDRsYOnQoeXl5fPvtt9x555386U9/cjvf5MmTa/QB//HHH+fvf/97pXNCfH19mTNnDjfffDPDhg3juuuucy1HHhcX57ac+Ysvvsh5551H3759uf3222nXrh379u3j66+/JikpqdoxOeXm5rJs2TLAMdn+xRdfxNfX11WsVZeXlxePPvpole3mzJnDpk2buOKKK1zD1hITE3nvvfdo3rx5uUUfjh8/7srzqU63Me7tt9/OG2+8waRJk9i0aRNxcXF8+umnrFmzhnnz5hESElL9F1cDTz31FN9//z2DBg1i8uTJdO/enaNHj5KYmMi3337L0aNHAbjkkkv4/PPPGT9+POPGjWPv3r28/vrrdO/endzc3DqJ7UxERUVxzz338Oyzz3LZZZdx0UUXsWXLFr755hvCw8PPeFESqP57EBgYSPfu3Vm8eDGdO3emefPm9OzZk549e/LKK69w3nnnER8fz+TJk2nfvj1paWmsW7eOAwcOsGXLltp4G0SkgVDhVIlt27ZRWlpK586d3Y4XFhbSokULk6ISkVM99dRT/Pvf/+bFF18kPT2d4OBgBg0axNKlS92G4rZo0YKvvvqK//u//+PRRx+lWbNm3HjjjVx44YUVDpvdsWMHN910EwBNmzalR48ePPfcc64hX042m42nnnrqtDF6e3uzdOlSnnzyST766CM+++wzWrRo4fpAVlZgYGC15/A4+fn5Vbran9OkSZMICgriqaee4sEHH3RtRjpnzhzXHk4AvXv35n//+x9Tp07ltddeo6CggNjYWK6++uoaxeSUnJzsyoPzffzyyy/L7SNUW/7xj3/w0Ucf8cMPP/Dhhx+Sn59Py5Ytufbaa5k6dWq5RQQOHDjgyvOpTlc4BQYGsmrVKh566CHeffddsrOz6dKlCwsWLDijTW2rKyoqig0bNvDYY4/x+eef8+qrr9KiRQt69Ojhtq/SpEmTSE1N5Y033uA///kP3bt354MPPuCTTz5xbVBsFXPmzCEoKIi33nqLb7/9lsGDB7N8+XLOO+88t1Uga6om78H8+fP529/+xn333UdRURHTp0+nZ8+edO/enY0bNzJz5kwWLlzIkSNHiIyMpE+fPkybNu0sX7mINDQ2o7ZnaDZQNpuNL774wrUy3uLFi7nhhhv45Zdfyg0pCQ4OLjdUxLnp4akr851//vn07duXefPmuY4tWLCAe++9t1qr+oiIiHiarKwsmjVrxhNPPMEjjzxidjgiIoB6nCrVp08fSktLSU9PZ+jQoWd8nsGDB5ebg7FixQoGDx58tiGKiIg0eBUNW3f+sdG5GJOIiBV4dOGUm5vL7t27Xbf37t1LUlISzZs3p3Pnztxwww1MmDCBZ599lj59+pCRkcHKlSvp1auXa3z+9u3bKSoq4ujRo+Tk5LjmADiHodxxxx28/PLLPPDAA9xyyy189913fPzxx64NJ0VERDzZ4sWLWbhwIWPHjiU4OJj//ve//POf/2T06NEMGTLE7PBERFw8eqjeqlWrGDFiRLnjEydOZOHChRQXF/PEE0/w3nvvcfDgQcLDwznnnHOYOXOma15CXFxchRsdln1bV61axX333cf27dtp06YNU6dOrdMx8CIiIg1FYmIiDzzwAElJSWRnZxMVFcWVV17JE088QXBwsNnhiYi4eHThJCIiIiIiUh3ax0lERERERKQKKpxERERERESq4HGLQ9jtdg4dOkRISMhZbawnIiIiIiINm2EY5OTk0KpVK7y8Tt+n5HGF06FDh4iJiTE7DBERERERsYj9+/fTpk2b07bxuMIpJCQEcLw5oaGhJkcDxcXFLF++nNGjR+Pr62t2OB5P+bAe5cRalA/rUU6sRzmxFuXDeqyUk+zsbGJiYlw1wul4XOHkHJ4XGhpqmcIpKCiI0NBQ079xRPmwIuXEWpQP61FOrEc5sRblw3qsmJPqTOHR4hAiIiIiIiJVUOEkIiIiIiJSBRVOIiIiIiIiVVDhJCIiIiIiUgUVTiIiIiIiIlVQ4SQiIiIiIlIFFU4iIiIiIiJVUOEkIiIiIiJSBRVOIiIiIiIiVVDhJCIiIiIiUgUVTiIiIiIiIlVQ4SQiIiIiIlIFFU4iIiIiIiJVUOFkIrvd4NCuLPIP+XBoVxZ2u2F2SB5N+bAee0kJh3/8Ef/f9nL4xx+xl5SYHZJHKy4qZNWyRez/bROrli2iuKjQ7JA8nnJiPcqJtSgf1tOQc2IzDMO0T4erV6/m6aefZtOmTRw+fJgvvviCyy+//LSPWbVqFVOmTOGXX34hJiaGRx99lEmTJlX7ObOzswkLC+P48eOEhoae3Qs4C3s2p/Pj4l3kZZ38ZmnS1J+h13SiQ59I0+LyVMqH9ez58mt+XF5AXkkz17EmPscYOjqADpeNMzEyz/TFR6+z+38RBBWdzEe+3zE6npPB+OvvMDEyz6WcWI9yYi3Kh/VYMSc1qQ1M7XHKy8ujd+/evPLKK9Vqv3fvXsaNG8eIESNISkri3nvv5bbbbuM///lPHUdau/ZsTmfZGz+7fUgHyMsqZNkbP7Nnc7pJkXkm5cN69nz5NcuWBpBX0tTteF5JGMuWBrDny6/NCcxDffHR6xxc3YnAoqZuxwOLmnJwdSe++Oh1cwLzYMqJ9Sgn1qJ8WE9jyImPmU9+8cUXc/HFF1e7/euvv067du149tlnAejWrRv//e9/ef755xkzZkxdhVmr7HaDHxfvOm2bHxfvok3X5nh52eopKs/lyMdvp22jfNQve0kJP/6nAAgATn3PvQA7Py4voM2FhXj5mPorzCMUFxWyZ13kH9lwz4cNGwYGu9dFkH9pPr5+/uYE6WGUE+tRTqxF+bCeauXkfxEUX1Vo6ZyYOlSvLJvNVuVQvfPPP5++ffsyb94817EFCxZw7733cvz48QofU1hYSGHhyZ6E7OxsYmJiyMzMNGWo3qFdWXz14rZ6f14RERERESvrPO4gwy+6tl6fMzs7m/Dw8GoN1WtQf65NTU0lKirK7VhUVBTZ2dmcOHGCwMDAco+ZPXs2M2fOLHd8+fLlBAUF1Vmslck/5AOUj1NERERExJPt3LGLfPvSen3O/Pz8ardtUIXTmXj44YeZMmWK67azx2n06NHm9ThtqbrH6aI7etCyY1g9ROTZDu8+zrLXf6mynfJRf1LXruWbz6tud/GYDKL7dK/7gDzc6nU/sOeHqt/npkN+ok/f3vUQkWxO3ELWmgFVtlNO6o9yYi3Kh/VUNyddunZi+EVj6yGik7Kzs6vdtkEVTtHR0aSlpbkdS0tLIzQ0tMLeJgB/f3/8/cuPlfT19cXX17dO4jydmK7hNGnqX24hgrKCm/nTrlek5tTUg3a9/GnSdLfyYSFxF5xPky//RV5JGBWvX2Mn2CeLuEuv1BynenBhq85sW/cVgUVNy41LBzAwOOGXxe3X3GvpcemNSadOI5j3k3JiJcqJtSgf1lPdnAwfc3W9fz6vyfM1qH2cBg8ezMqVK92OrVixgsGDB5sUUc15edkYek2n07Y57+pO+pBeT5QP6/Hy8WHoaOfCEPZT7rUDNs4bHaiiqZ74+vnT8ZwMwPEfW1nO2x3PydCHj3qknFiPcmItyof1NJacmFo45ebmkpSURFJSEuBYbjwpKYmUlBTAMcxuwoQJrvZ33HEHv//+Ow888AA7duzg1Vdf5eOPP+a+++4zI/wz1qFPJBf9uSdNmrp/cwQ38+eiP/fUvkH1TPmwng6XjeOiQT/TxOuo2/FgnywuGlugfZzq2fjr76D1+bvI98tyO37CL4vW5+/SfigmcObkhHJiGcqJtSgf1tMYcmLqqnqrVq1ixIgR5Y5PnDiRhQsXMmnSJPbt28eqVavcHnPfffexfft22rRpw9SpUxvkBrjgWAp7/45M1v6wgXOHDSSma7h6NkykfFjMNw9h/98bHIicyPb8OLr360Wb889XT5NJDMNg+KLhBGQ0Z2hhZwZ17ekYUmHxvw42dsVFhaz6z8fs3LHLMTdAOTGdcmItyof1WC0nNakNTP0EMnz4cE5Xty1cuLDCx2zevLkOo6o/Xl42WnVqStCuElp1aqoP6SZTPiwmZS1eNjuthgxmc3IALYcOVdFkouTsZI4WHcWvWS7dQm5k+EWXmTJPVNz5+vkz/KJrybcvZfhFY5UTC1BOrEX5sJ6GnJMGNcdJRDxEQTakOlafNGIazhzGxiwxPRGAnuE98bGpgBUREc+jwklErGf/BjDs0DQWQluaHY0Am9I2AdAnoo/JkYiIiJhDhZOIWE/KWsdl7BBz4xAXZ+HUN7KvyZGIiIiYQ4WTiFhP8jrHZayG6VlBal4qB3MP4mXzold4L7PDERERMYUKJxGxluICOLjRcb3tuebGIgAkpjnmN3Vt3pUmvk1MjkZERMQcKpxExFoOJUJpETSJhBYdzI5GOLkwRL+ofiZHIiIiYh4VTiJiLcnO+U2DwaYl4a3AOb+pX6QKJxER8VwqnETEWpyFk4bpWUJWQRa7s3YD0CdKK+qJiIjnUuEkItZhL3UsRQ4Qq8LJCjanOzYcbx/WnuYBzU2ORkRExDwqnETEOlK3QVEO+IdCVA+zoxHKLEMepWXIRUTEs6lwEhHrcA7TixkEXt7mxiLAyYUhtH+TiIh4OhVOImIdro1vNUzPCvKL8/n1yK8A9I/qb3I0IiIi5lLhJCLWYBhlNr5V4WQFWzK2UGKU0LJJS1oGtzQ7HBEREVOpcBIRa8jcBfmZ4O0PrbR6mxW4hulpfpOIiIgKJxGxCOcwvTYDwMff3FgEgMQ0bXwrIiLipMJJRKyh7Ma3Yrri0mK2ZGwBtPGtiIgIqHASEatwzm9qq8LJCn458guFpYU0829Gu7B2ZocjIiJiOhVOImK+rP1wPAVs3hAz0OxoBPf5TTabzeRoREREzKfCSUTMl/JHb1PLXuAfYm4sApTZ+Fb7N4mIiAAqnETECpzzm9pqGXIrsBt2NqdvBrQwhIiIiJMKJxExX4r2b7KSXcd2kVOUQ5BPEF2adzE7HBEREUtQ4SQi5so7Ahk7HNe1MIQlOIfpJUQm4OPlY3I0IiIi1qDCSUTM5extiugKTVqYG4sAJxeG0DA9ERGRk1Q4iYi5UrQMuZUYhuHa+FYLQ4iIiJykwklEzJW8xnGp+U2WsD9nPxknMvD18iU+It7scERERCxDhZOImKcwFw5vdVxX4WQJzvlN8eHx+Hv7mxyNiIiIdahwEhHzHNgARimEtYWwNmZHI7hvfCsiIiInqXASEfM492+K1fwmq9DGtyIiIhVT4SQi5knW/k1Wkp6fzv6c/XjZvEiITDA7HBEREUtR4SQi5igphIMbHdfbqnCyAudqel2adSHEL8TkaERERKxFhZOImOPQZigpgKBwCO9kdjRCmWF6mt8kIiJSjgonETFH2flNNpu5sQigjW9FREROR4WTiJjDtfGthulZwfHC4+w6tguAPpF9TI5GRETEelQ4iUj9s5dCyv8c17WiniUkpSdhYBAXGkd4YLjZ4YiIiFiOCicRqX9pv0BhNviFQFS82dEIsCndMb9Jw/REREQqpsJJROqfc35TzEDw9jE3FgG0MISIiEhVVDiJSP1L0ca3VnKi5ATbM7cD2vhWRESkMiqcRKR+GUaZjW+HmBuLALAtYxslRglRQVG0Dm5tdjgiIiKWpMJJROrXkT2Qlw7eftBKvRtWUHaYnk1Lw4uIiFRIhZOI1C/nML3W/cA3wNxYBCizMESkFoYQERGpjAonEalfrmF62r/JCortxWzN2ApoRT0REZHTUeEkIvUreY3jUhvfWsKvR37lRMkJwvzDaN+0vdnhiIiIWJYKJxGpP9mHICsZbF6OpcjFdIlpiQD0ieyDl03/JYiIiFRG/0uKSP1x7t8UHQ8BoebGIsDJ+U39o/qbHImIiIi1qXASkfrjLJw0TM8S7Ibd1eOk/ZtEREROT4WTiNSfFC0MYSV7svaQXZRNoE8gXVt0NTscERERS1PhJCL1I/8opG93XG872NxYBDg5v6l3RG98vXxNjkZERMTaVDiJSP1I+Z/jskUnCI4wNxYB3De+FRERkdNT4SQi9cO58a2G6VmCYRhaGEJERKQGVDiJSP3QxreWciD3AOn56fh4+RAfHm92OCIiIpanwklE6l5RHhxOclzX/CZLcM5v6tGiBwE+ASZHIyIiYn0qnESk7h34CewlENoGmrY1OxoBEtMdhVO/qH4mRyIiItIwqHASkbrnGqY3GGw2c2MR4OTCECqcREREqkeFk4jUveQ1jksN07OEzBOZJGcnY8NGQmSC2eGIiIg0CCqcRKRulRTBgY2O67FDzI1FgJPzmzo360yoX6jJ0YiIiDQMKpxEpG4d3gIlJyCwOUR0MTsaQfs3iYiInAkVTiJSt8oO09P8JktwLgyhwklERKT6VDiJSN1K0f5NVpJTlMPOozsB6BephSFERESqS4WTiNQdu71M4aSFIaxgc/pmDAzahrQlIijC7HBEREQaDBVOIlJ30rdDwXHwbQLRvc2ORji5MISG6YmIiNSMCicRqTvO3qaYgeDtY24sAmjjWxERkTOlwklE6o5zYQjNb7KEgpICtmVuAzS/SUREpKZUOIlI3TAMSP6jx0kb31rCtsxtlNhLiAiMoE1IG7PDERERaVBUOIlI3Ti2F3JTwcsX2vQ3Oxrh5PymflH9sGlpeBERkRpR4SQidSN5reOydV/wDTQ3FgG08a2IiMjZUOEkInVDw/QspcRewpaMLQD0jVThJCIiUlMqnESkbqT80eMUO8TcOASAnUd3kl+ST4hfCJ2adTI7HBERkQZHhZOI1L6cVDj6O2BzLEUuptuYthFw9DZ52fSrX0REpKb0v6eI1D7n/KbonhDY1NRQxKHswhAiIiJScyqcRKT2OTe+bav9m6zAbthdG99qYQgREZEzo8JJRGqfs8cpVgtDWMHe43vJKswiwDuA7s27mx2OiIhIg6TCSURq14ksSPvFcV09TpbgXIa8d0RvfL19TY5GRESkYVLhJCK1a/96wIDmHSAkyuxoBO3fJCIiUhtML5xeeeUV4uLiCAgIYNCgQWzYsOG07efNm0eXLl0IDAwkJiaG++67j4KCgnqKVkSqlLzGcalhepah+U0iIiJnz9TCafHixUyZMoXp06eTmJhI7969GTNmDOnp6RW2/+ijj3jooYeYPn06v/76K2+//TaLFy/mH//4Rz1HLiKVcm58q/2bLOFQ7iFS81LxsfnQK7yX2eGIiIg0WKYWTs899xyTJ0/m5ptvpnv37rz++usEBQXxzjvvVNh+7dq1DBkyhOuvv564uDhGjx7NddddV2UvlYjUk6J8OLTZcb2tepyswDlMr3uL7gT5BpkcjYiISMPlY9YTFxUVsWnTJh5++GHXMS8vL0aOHMm6desqfMy5557LBx98wIYNGxg4cCC///47S5cu5aabbqr0eQoLCyksLHTdzs7OBqC4uJji4uJaejVnzhmDFWIR5eNs2ZL/h4+9GCM4mpLg1lAL76NycnZ+OvwT4FgYojbeQ+XDepQT61FOrEX5sB4r5aQmMZhWOGVmZlJaWkpUlPvk8aioKHbs2FHhY66//noyMzM577zzMAyDkpIS7rjjjtMO1Zs9ezYzZ84sd3z58uUEBVnnr68rVqwwOwQpQ/k4M50PL6EbcNAnjk3ffFOr51ZOzsx/s/8LgJFisPTw0lo7r/JhPcqJ9Sgn1qJ8WI8VcpKfn1/ttqYVTmdi1apVzJo1i1dffZVBgwaxe/du7rnnHh5//HGmTp1a4WMefvhhpkyZ4rqdnZ1NTEwMo0ePJjQ0tL5Cr1RxcTErVqxg1KhR+PpqmWCzKR9nx/ujtwFoOegKxvYfWyvnVE7O3NGCozz6+aMA3HrRrYT5h531OZUP61FOrEc5sRblw3qslBPnaLTqMK1wCg8Px9vbm7S0NLfjaWlpREdHV/iYqVOnctNNN3HbbbcBEB8fT15eHrfffjuPPPIIXl7lp2z5+/vj7+9f7rivr6/piSrLavF4OuXjDJQWwwHHsDDv9ufhXcvvn3JSc9sObQOgY9OOhAeH1+q5lQ/rUU6sRzmxFuXDeqyQk5o8v2mLQ/j5+dGvXz9WrlzpOma321m5ciWDB1c8qTw/P79cceTt7Q2AYRh1F6yIVO3wVijOh4CmENHN7GiEkwtD9IvqZ3IkIiIiDZ+pQ/WmTJnCxIkT6d+/PwMHDmTevHnk5eVx8803AzBhwgRat27N7NmzAbj00kt57rnn6NOnj2uo3tSpU7n00ktdBZSImCRlreOy7WCooPdX6p8KJxERkdpjauF0zTXXkJGRwbRp00hNTSUhIYFly5a5FoxISUlx62F69NFHsdlsPProoxw8eJCIiAguvfRSnnzySbNegog4Jf9ROGnjW0vILcpl57GdAPSN1Ma3IiIiZ8v0xSHuuusu7rrrrgrvW7VqldttHx8fpk+fzvTp0+shMhGpNrsdUrTxrZVsydiC3bDTJrgNUU2iqn6AiIiInJbG04jI2cvYASeOgW8QtOxtdjTCyWF6faPU2yQiIlIbVDiJyNlzzm9q0x+8tWKRFWh+k4iISO1S4SQiZy9Zw/SspLC0kJ8zfwZUOImIiNQWFU4icnYM4+TCEG21MIQV/Jz5M0X2IloEtKBtSFuzwxEREWkUVDiJyNnJSoacQ+DlA20GmB2NAIlpiYBjfpPNZjM5GhERkcZBhZOInB3nML1WfcAvyNxYBND8JhERkbqgwklEzk7yGselhulZQqm9lKSMJECFk4iISG1S4SQiZ0f7N1nKzmM7ySvOI8Q3hE5NO5kdjoiISKOhwklEzlxuOhzZDdig7SCzoxFODtNLiEzA28vb5GhEREQaDxVOInLmnKvpRXaHwGbmxiKA+8IQIiIiUntUOInImXMN0zvX3DgEAMMwSEx3FE79o/qbHI2IiEjjosJJRM6cs8cpVgtDWMHe7L0cLTiKv7c/PVr0MDscERGRRkWFk4icmYLjkLrNcb2tepyswDlMLz48Hl9vX5OjERERaVxUOInImdm/ATCgWTsIbWl2NMLJwknLkIuIiNQ+FU4icmZcw/TU22QVzhX1tDCEiIhI7VPhJCJnxlk4aeNbS0jNS+VQ3iG8bd4kRCSYHY6IiEijo8JJRGquuAAOOYaFqcfJGpy9Td2adyPIN8jkaERERBofFU4iUnMHN0FpEQRHQfP2ZkcjaJieiIhIXVPhJCI1V3aYns1mbiwCaONbERGRuqbCSURqLsW5MMQQc+MQAI4VHGPP8T0A9I1U4SQiIlIXVDiJSM2UlvyxFDna+NYiEtMdvU0dwjrQLKCZydGIiIg0TiqcRKRmUrdCUS74h0Fkd7OjETRMT0REpD6ocBKRmklZ57hsew54eZsbiwDa+FZERKQ+qHASkZpxbXyrYXpWkF+cz69HfwVUOImIiNQlFU4iUn2GUabHSfs3WUFSRhKlRimtmrQiukm02eGIiIg0WiqcRKT6Mn+D/CPgEwCt+pgdjaBheiIiIvVFhZOIVF/yGsdlmwHg42duLAJo41sREZH6osJJRKov2TlMT/ObrKCotIhtmdsAFU4iIiJ1TYWTiFSfc35TrOY3WcEvR36hsLSQ5gHNaRfazuxwREREGjUVTiJSPVkpcHw/2LwdQ/XEdK5hepF9sdlsJkcjIiLSuKlwEpHqcQ7Ta5UA/sGmhiIOWhhCRESk/qhwEpHqSflj/ybNb7KEUnspm9M3A5rfJCIiUh9UOIlI9bg2vtX8JivYlbWL3OJcmvg2oUuzLmaHIyIi0uipcBKRquVlOvZwAvU4WYRzflNCZALeXt4mRyMiItL4qXASkao5V9OL6AZBzc2NRYCThVO/SM1vEhERqQ8qnESkaq5heuptsgLDMFwLQ2h+k4iISP1Q4SQiVXMVTkPMjUMASMlJ4UjBEfy8/OgZ3tPscERERDyCCicROb3CHEjd6riu+U2W4Bym1zO8J/7e/iZHIyIi4hlUOInI6e1fD4YdmraFsNZmRyOUmd+k/ZtERETqjQonETk958a3GqZnGdr4VkREpP6pcBKR03OuqKdhepaQlpfGgdwDeNm86B3R2+xwREREPIYKJxGpXEkhHNjouK6Nby0hMd3R29SlWReC/YJNjkZERMRzqHASkcodTITSQmgSAS06mh2NoPlNIiIiZlHhJCKVS/ljGfK2g8FmMzcWAVQ4iYiImEWFk4hUzrV/k4bpWcHxwuPsztoNQJ/IPiZHIyIi4llUOIlIxeylkLLecV2FkyVsTt8MQLuwdrQIbGFyNCIiIp5FhZOIVCx1GxTlgH8oRPU0Oxrh5DC9vpF9TY5ERETE86hwEpGKOZchjxkIXt7mxiKA9m8SERExkwonEamY5jdZSn5xPtuPbAdUOImIiJhBhZOIlGcYJwuntiqcrGBr5lZKjBKim0TTKriV2eGIiIh4HBVOIlLekd2Qnwne/tBa82mswDlMT/ObREREzKHCSUTKc/Y2tekPPv7mxiKA9m8SERExmwonESkvuczGt2K64tJitmZsBVQ4iYiImEWFk4iUl6KFIaxk+9HtFJQW0My/Ge3D2psdjoiIiEdS4SQi7o4fgKwUsHk5liIX0zmH6fWJ7IPNZjM5GhEREc+kwklE3CX/sX9TdC/wDzE3FgHKLAwRpYUhREREzKLCSUTcuYbpDTE3DgHAbthJTHcUTv2j+pscjYiIiOdS4SQi7pw9TrFaGMIKdh3bRU5RDkE+QXRp3sXscERERDyWCicROSn/KGT86riuFfUswdnb1DuiNz5ePiZHIyIi4rlUOInISSl/9DaFd4Em4ebGIsDJ+U1ahlxERMRcKpxE5CTn/k0apmcJhmG4VtTTwhAiIiLmUuEkIie5Nr7V/k1WcCDnABknMvDx8iE+PN7scERERDyaCicRcSjMhcNbHNe18a0lbEp39DbFh8cT4BNgcjQiIiKeTYWTiDgc+AmMUgiLgaYxZkcjnNz4tm+khumJiIiYTUs0iYiDa5ie5jdZhTa+FZGGyjAMSkpKKC0tNTsUiouL8fHxoaCgwBLxSP3nxNfXF29v77M+jwonEXFwrqinYXqWkJGfQUpOCjZs9InsY3Y4IiLVVlRUxOHDh8nPzzc7FMBRxEVHR7N//35sNpvZ4Qj1nxObzUabNm0IDg4+q/OocBIRKClyDNUDFU4W4Zzf1KV5F0L8QkyORkSkeux2O3v37sXb25tWrVrh5+dnerFit9vJzc0lODgYLy/NUrGC+syJYRhkZGRw4MABOnXqdFY9TyqcRAQObYaSAghqAeGdzY5GKDNMT/ObRKQBKSoqwm63ExMTQ1BQkNnhAI4P6UVFRQQEBKhwsoj6zklERAT79u2juLj4rAonffeICKSUmd+kYQyW4FwYQhvfikhDpAJFrKS2ej31XS0iZTa+1TA9K8guymbXsV2AFoYQERGxChVOIp7OXgop6x3XtaKeJSSlJ2FgEBsaS3hguNnhiIiIRa1atQqbzUZWVpbZoZyxGTNmkJCQYHYY1aLCScTTpW+HwuPgFwzRvcyORoCNaRsBDdMTEc9VajdYt+cI/0o6yLo9Ryi1G3X6fJMmTcJms/HUU0+5HV+yZInpi1ucrbi4OGw2W7mvU19rfbDZbCxZssTt2P3338/KlSvrPZYzocUhRDydc5hezEDw1q8EK9DCECLiyZb9fJiZ/97O4eMFrmMtwwKYfml3LurZss6eNyAggDlz5vDnP/+ZZs2a1dp5i4qK8PPzq7XznYnHHnuMyZMnux0LCbHGiq3BwcFnvUx4fTG9x+mVV14hLi6OgIAABg0axIYNG07bPisri7/+9a+0bNkSf39/OnfuzNKlS+spWpFGyLXxreY3WcGJkhP8cuQXQPObRMTzLPv5MH/5INGtaAJIPV7AXz5IZNnPh+vsuUeOHEl0dDSzZ88+bbvPPvuMHj164O/vT1xcHM8++6zb/XFxcTz++ONMmDCB0NBQbr/9dhYuXEjTpk356quv6NKlC0FBQVx11VXk5+fz7rvvEhcXR7Nmzbj77rvdNoR9//336d+/PyEhIURHR3P99deTnp5e49fmfHzZryZNmrjuX7p0KZ07dyYwMJARI0awcOFCtyGAFQ2nmzdvHnFxca7bP/30E6NGjSI8PJywsDCGDRtGYmKi2/sCMH78eLy9venVq1eF57bb7Tz22GO0adMGf39/EhISWLZsmev+ffv2YbPZ+PzzzxkxYgRBQUH07t2bdevW1fh9qSlTC6fFixczZcoUpk+fTmJiIr1792bMmDGVfkMUFRUxatQo9u3bx6effsrOnTt56623aN26dT1HLtJIGIY2vrWYbRnbKLGXEBkUSZvgNmaHIyJy1gzDIL+opMqvnIJipn/5CxUNynMem/HldnIKiqt1PsOo2fA+b29vZs2axUsvvcSBAwcqbLNp0yauvvpqrr32WrZt28aMGTOYOnUqCxcudGv3zDPP0Lt3bzZv3szUqVMByM/P58UXX2TRokUsW7aMVatWMX78eJYuXcrSpUt5//33eeONN/j0009d5ykuLubxxx9ny5YtLFmyhH379jFp0qQava6q7N+/nyuuuIJLL72UpKQkbrvtNh566KEanycnJ4eJEyfy3//+l//973906tSJsWPHkpOTAzgKK4AFCxZw8OBBvvvuuwrP88ILL/Dss8/yzDPPsHXrVsaMGcNll13Grl273No98sgj3H///SQlJdG5c2euu+46SkpKahx3TZg6Lue5555j8uTJ3HzzzQC8/vrrfP3117zzzjsVJuydd97h6NGjrF27Fl9fXwC3SldEaujo75CbBt5+0FrzaazAufFtv8h+DX5cvYgIwIniUrpP+89Zn8cAUrMLiJ+xvFrttz82hiC/mn3UHT9+PAkJCUyfPp2333673P3PPfccF154oasY6ty5M9u3b+fpp592K2guuOAC/u///s91+8cff6S4uJjXXnuNDh06AHDVVVfx/vvvk5aWRnBwMN27d2fEiBF8//33XHPNNQDccsstrnO0b9+eF198kQEDBrg2j62uBx98kEcffdTt2DfffMPQoUNdMTl7zrp06cK2bduYM2dOtc/vfM1lvfnmmzRt2pQffviBSy65hIiICACaNm1KdHQ02dnZFZ7nmWee4cEHH+Taa68FYM6cOXz//ffMmzePV155xdXu/vvvZ9y4cQDMnDmTHj16sHv3brp27VqjuGvCtMKpqKiITZs28fDDD7uOeXl5MXLkyEq72r788ksGDx7MX//6V/71r38RERHB9ddfz4MPPljpZlaFhYUUFha6bjuTVFxcTHFxcS2+ojPjjMEKsYjn5cP2+4/4APZWfSnFGyz4uj0tJ5tSHYVTQkSCJV+zp+WjIVBOrMeTc1JcXIxhGNjtdux2O4Drsr45Y3D2PDnjqohhGK77Z8+ezciRI5kyZUq51/Drr79y2WWXuZ1n8ODBzJs3z21z1X79+rm1sdvtBAUF0a5dO9fxyMhI4uLiCAoKcjuWlpbmur1p0yZmzpzJ1q1bOXbsmOv4vn376N69u1t8p3uf77//fiZOnOh2rHXr1tjtdrZv387AgQPdHj9o0KAK38OybU49lpaWxtSpU/nhhx9IT0+ntLSU/Px8kpOTy70XZXNS9jzZ2dkcOnSIwYMHuz3m3HPPZevWrW6vs2fPnq7rUVFRAKSmptK5c+dyr9/5nBVtgFuTn1PTCqfMzExKS0tdL9QpKiqKHTt2VPiY33//ne+++44bbriBpUuXsnv3bu68806Ki4uZPn16hY+ZPXs2M2fOLHd8+fLlltnRGmDFihVmhyBleEo++iR/Sltgd1EEv1p8rqAn5KTUKCXxuGM8eO6OXJbusm5OPCEfDY1yYj2emBMfHx+io6PJzc2lqKgIcHw4XjflnCofm7j/OH/95Ncq273y/7rRNyasynbFJ/LILjjZc+8cMlZh2+JiSkpKyM7OJiEhgQsuuIAHHniA66+/Hjj5h/fS0lIKCwvdektOnDjhauPt7Y3dbsfb29utTUFBAT4+Pm7HioqK8PLycjtWUlJCUVER2dnZ5OXlcdFFF3HBBRfw+uuvEx4ezoEDB7jyyis5duwY2dnZ5Ofnu15bZZsO2+12goODiYyMLPeana+7uLi4wtfkPG/Z96fs++ksdgBuuukmjh49ypNPPklMTAz+/v6MHj2a7Ozscud25iInJ4fCwkJKS0vd2uXn55d7r5zPn5ub6zrmbOM8lpubW2FPVlFRESdOnGD16tXlhvM538PqaFBLaNntdiIjI3nzzTfx9vamX79+HDx4kKeffrrSwunhhx9mypQprtvZ2dnExMQwevRoQkND6yv0ShUXF7NixQpGjRrlGn4o5vG0fPi8Mg2A9sNvoF3HkSZHUzFPysnPmT9TvLyYUL9QJl4yES+b6ev3lONJ+WgolBPr8eScFBQUsH//foKDgwkICHAdr7rMgdHNmxL9n72kZRdUOM/JBkSHBTC6dyzeXtUfymwYBjk5OYSEhFQ6BNrX1xcfHx/XZ8Onn36avn370qNHDwDX8R49erBx40a3z5CbN2+mc+fOrpX4vLy8CAgIcGsTEBCAzWZzO+bv74+3t7fbsbJx7Nq1i6NHj/LMM88QExMD4OpcaNKkCaGhoa5OgJCQkEo/11YUT1nx8fH8+9//drt/69atbudt3bo1GRkZbu/hjh078PLycj1u/fr1vPzyy1x11VWAY+7UkSNH3J7b19cXPz8/QkJCXDkp+z6EhobSqlUrkpKSuPjii13xbNy4kQEDBhAaGuoaouh8D+Bkr1dQUFCFr7OgoIDAwEDOP/98t+9LoNIhgxUxrXAKDw/H29ubtLQ0t+NpaWlER0dX+JiWLVvi6+vr1sXWrVs3UlNTK13q0d/fH39//3LHfX19LfXLzGrxeDqPyEf2YcjaBzYvfNqdCxZ/vZ6Qk61HHf9R9Y3qi79f+d9bVuIJ+WholBPr8cSclJaWYrPZ8PLyqrQHpDJeXjDjsu785YNEbOBWPDnLnemXdsfXp+LpGZVxfqh2xlUR595Gzvt79+7NDTfcwEsvvfRHbI7j999/PwMGDODJJ5/kmmuuYd26dbzyyiu8+uqrbuc+9bmc109tU9Ex52Pj4uLw8/PjlVde4Y477uDnn3/mySefdD2m7Htc1fudm5tbbvE1Z5Hxl7/8heeee44HH3yQ2267jU2bNvHuu++6nfeCCy7gb3/7G8888wxXXXUVy5YtY9myZYSGhrqet1OnTnz44YcMHDiQ7Oxs/v73vxMYGOj2XsTFxfH9998zZMgQioqKCA0NLfc+/P3vf2f69Ol07NiRhIQEFixYQFJSEh9++GGlr7mq98HLywubzVbhz2RNfkZN+3Omn58f/fr1c9vwym63s3LlSgYPHlzhY4YMGcLu3bvdxjz+9ttvtGzZ0vT18UUanJQ/liGP6gkB1flboNQ118a3kVqoQ0Q800U9W/LajX2JDnPvFYgOC+C1G/vW6T5Op3rsscfKzRvq27cvH3/8MYsWLaJnz55MmzaNxx57rNZXugOIiIhg4cKFfPLJJ3Tv3p2nnnqKZ5555ozONW3aNFq2bOn29cADDwDQtm1bPvvsM5YsWULv3r15/fXXmTVrltvju3Xrxquvvsorr7xC79692bBhA/fff79bm7fffptjx47Rt29fbrrpJu6+++5ywwOfffZZVqxYQWxsLOeff36Fsd59991MmTKF//u//yM+Pp5ly5bx5Zdf0qlTpzN67bXKMNGiRYsMf39/Y+HChcb27duN22+/3WjatKmRmppqGIZh3HTTTcZDDz3kap+SkmKEhIQYd911l7Fz507jq6++MiIjI40nnnii2s95/PhxAzCOHz9e66/nTBQVFRlLliwxioqKzA5FDA/Lx1dTDGN6qGEsfcDsSE7LU3JSai81hvxziNFzYU9jS/oWs8OplKfkoyFRTqzHk3Ny4sQJY/v27caJEyfO6jwlpXZj7e5MY8nmA8ba3ZlGSan9jM9VWlpqHDt2zCgtLT2rmDzJ999/bwDGsWPH6uT89Z2T031f1qQ2MHWO0zXXXENGRgbTpk0jNTXVtcGVc8GIlJQUt+62mJgY/vOf/3DffffRq1cvWrduzT333MODDz5o1ksQabiStX+Tlfye9TvHC48T6BNItxbdzA5HRMRU3l42BndoYXYYIm5MXxzirrvu4q677qrwvlWrVpU7NnjwYP73v//VcVQijVz+UUjf7rjetuKhsVK/NqU5liHvFdELXy/PmhMhIiLSEJheOImICfavBwxo0RGCI6tsLnWv7Ma3IiIiZho+fLhrfyU5yXpr3YpI3Uv+Y2EIDdOzBMMwXD1O/aJUOImIiFiRCicRT5Tyx/ymtiqcrOBg7kHS89Px8fIhPiLe7HBERESkAiqcRDxNUR4c2uy4Hqv5TVaQmJ4IQPcW3Qn0CTQ5GhEREamICicRT3NgI9hLILQ1NI01OxoBDdMTERFpAFQ4iXga1zC9wWCznb6t1IvENEePkxaGEBERsS4VTiKeJnmN41LD9Cwh80Qm+7L3YcNGQmSC2eGIiIhIJVQ4iXiS0mLHUD2A2CHmxiLAyd6mTs06EeYfZnI0IiIiUhkVTiKe5PAWKM6HwGYQ3sXsaISTC0P0jexrciQiIhZiL4W9P8K2Tx2X9tJ6edrU1FTuueceOnbsSEBAAFFRUQwZMoTXXnuN/Px8AOLi4rDZbNhsNgIDA4mLi+Pqq6/mu+++q5cYxTzaAFfEkziH6bUdDF76u4kVuOY3aWEIERGH7V/Csgch+9DJY6Gt4KI50P2yOnva33//nSFDhtC0aVNmzZpFfHw8/v7+bNu2jTfffJPWrVtz2WWO53/ssceYPHkyRUVF7Nu3jw8++ICRI0fy+OOP88gjj9RZjGIuFU4iniT5j4UhtPGtJeQU5bDj6A4A+kapx0lEhO1fwscTAMP9ePZhx/Gr36uz4unOO+/Ex8eHjRs30qRJE9fx9u3b86c//QnDOBlTSEgI0dHRALRt25bzzz+fli1bMm3aNK666iq6dNGojsZIf3IW8RR2uza+tZik9CQMDGJCYogMijQ7HBGRumEYjj0Eq/oqyIZvHqBc0eQ4ieNi2YOOdtU5n1HReSp25MgRli9fzl//+le3oqksWxUr0d5zzz0YhsG//vWvaj+vNCzqcRLxFBm/QkEW+AZBy15mRyNofpOIeIjifJjVqhZOZDiG7z0VU73m/zgEfhUXQafavXs3hmGU6ykKDw+noKAAgL/+9a/MmTOn0nM0b96cyMhI9u3bV734pMFRj5OIp0he67iMGQjevubGIoA2vhURsboNGzaQlJREjx49KCwsrLK9YRhV9kxJw6UeJxFP4SycNEzPEgpLC/k582dAhZOINHK+QY7en6okr4UPr6q63Q2fVm+urm9Q1W3+0LFjR2w2Gzt37nQ73r59ewACAwOrPMeRI0fIyMigXbt21X5eaVjU4yTiCQzj5PwmLQxhCdsytlFsLyYiMIKYkGoOOxERaYhsNseQuaq+OlzgWD2PynpsbBDa2tGuOuerQc9PixYtGDVqFC+//DJ5eXln9DJfeOEFvLy8uPzyy8/o8WJ9KpxEPMGxfZBzGLx8oU1/s6MRTg7T6xvVV8M6REQAvLwdS44D5YunP25f9JSjXR149dVXKSkpoX///ixevJhff/2VnTt38sEHH7Bjxw68vU8+b05ODqmpqezfv5/Vq1dz++2388QTT/Dkk0/SsWPHOolPzKeheiKewDlMr1Uf8K16uIHUPS0MISJSge6XOZYcr3Afp6fqdB+nDh06sHnzZmbNmsXDDz/MgQMH8Pf3p3v37tx///3ceeedrrbTpk1j2rRp+Pn5ER0dzTnnnMPKlSsZMWJEncUn5lPhJOIJUv4onDRMzxJK7CUkpScBmt8kIlJO98ug6zjHH/1y0yA4yvH/Vx31NJXVsmVLXnrpJV566aVK22jVPM+lwknEE2jjW0vZeXQn+SX5hPiF0KlZJ7PDERGxHi9vaDfU7ChE3GiOk0hjl5MGR/cANogZZHY0wsn5TX0i++Bl069hERGRhkD/Y4s0ds5helE9IbCpqaGIg3N+k4bpiYiINBwqnEQaO9cwvcHmxiGAY3PExDQtDCEiItLQqHASaexcG9+qcLKCvcf3cqzwGAHeAfRo0cPscERERKSaalQ4zZ07lxMnTrhur1mzhsLCQtftnJwct6UaRcRkJ7Ig7WfHdS0MYQkb0zYC0CuiF77eviZHIyIiItVVo8Lp4YcfJicnx3X74osv5uDBg67b+fn5vPHGG7UXnYicnf0bAAOat4eQaLOjEcrs3xSlYXoiIiINSY0KJ8MwTntbRCwmeY3jsq16m6xC85tEREQaJs1xEmnMUrR/k5Ucyj3E4bzD+Nh86B3R2+xwREREpAZUOIk0VsUn4KCjd0Mr6lmDc/+mbi26EeQbZHI0IiIiUhM+NX3A/PnzCQ4OBqCkpISFCxcSHh4O4Db/SURMdmAj2IshOBqatTM7GqHM/CYN0xMROa1SeymJ6Ylk5GcQERRB38i+eHt51+lzZmRkMG3aNL7++mvS0tJo1qwZvXv3Ztq0aQwZMgSAzZs389RTT7F69WqOHj1KdHQ08fHx/PnPf+aSSy7BZrOxb98+2rU7+f9ucHAwbdu2Zfjw4dx777106tSpTl+H1J0aFU5t27blrbfect2Ojo7m/fffL9dGRCyg7DA9m83cWAQ42eOkjW9FRCr3bfK3PLXhKdLy01zHooKieGjgQ4yMHVlnz3vllVdSVFTEu+++S/v27UlLS2PlypUcOXIEgH/9619cffXVjBw5knfffZeOHTtSWFjI2rVrefTRRxk6dChNmzY9+Tq+/ZYePXqQn5/Ptm3beOGFF+jduzf//ve/ufDCC+vsdUjdqVHhtG/fvjoKQ0RqnXP/Js1vsoSjBUfZe3wvAH0i+5gcjYiINX2b/C1TVk3BwH0BsvT8dKasmsJzw5+rk+IpKyuLH3/8kVWrVjFs2DAAYmNjGThwIAB5eXnceuutjBs3js8//9ztsd26dePWW28tt2haixYtiI52rGjbvn17Lr30Ui688EJuvfVW9uzZg7d33fagSe3THCeRxqi05I+lyNHGtxaxOW0zAB2bdqRpQFNzgxERqUeGYZBfnF/lV05hDrM3zC5XNAEYf/x7asNT5BTmVOt8NVn9OTg4mODgYJYsWeK2R6nT8uXLOXLkCA888ECl57BVMbrDy8uLe+65h+TkZDZt2lTt2MQ6atTjtG7dOo4cOcIll1ziOvbee+8xffp08vLyuPzyy3nppZfw9/ev9UBFpAZSt0BxHgSEQWR3s6MRTm58q2F6IuJpTpScYNBHg2rlXGn5aZy7qHojKdZfv77aC/H4+PiwcOFCJk+ezOuvv07fvn0ZNmwY1157Lb169eK3334DoEuXLq7H/PTTT4wYMcJ1e9GiRW6fkSvStWtXwDGKy9mbJQ1HjXqcHnvsMX755RfX7W3btnHrrbcycuRIHnroIf79738ze/bsWg9SRGrIOUyv7WDwUseyFWhhCBERa7vyyis5dOgQX375JRdddBGrVq2ib9++LFy4sML2vXr1IikpiaSkJPLy8igpKanyOZy9YFX1Tok11ajHKSkpiccff9x1e9GiRQwaNMi1YERMTAzTp09nxowZtRqkiNRQ8h8LQ2iYniXkFeex4+gOAPpGqXASEc8S6BPI+uvXV9luU9om7lx5Z5XtXr3w1Wr13gf6BFYrvrICAgIYNWoUo0aNYurUqdx2221Mnz6d559/HoCdO3dyzjnnAODv70/Hjh1rdP5ff/0VwG3VPWk4avSn6GPHjhEVFeW6/cMPP3DxxRe7bg8YMID9+/fXXnQiUnN2e5kV9YaYG4sAkJSehN2w0zq4NdFNos0OR0SkXtlsNoJ8g6r8OrfVuUQFRWGj4t4YGzaig6I5t9W51TpfbfTqdO/enby8PEaPHk3z5s2ZM2fOGZ/Lbrfz4osv0q5dO/r00SJBDVGNCqeoqCj27nWsClVUVERiYqKr6gbHPk6+vr61G6GI1EzmTjhxFHwCoWVvs6MRtAy5iEh1eHt589DAhwDKFU/O2w8OfLBO9nM6cuQIF1xwAR988AFbt25l7969fPLJJ8ydO5c//elPBAcHM3/+fL7++mvGjRvHf/7zH37//Xe2bt3K3LlzHfGfskrekSNHSE1N5ffff+fLL79k5MiRbNiwgbffflsr6jVQNRqqN3bsWB566CHmzJnDkiVLCAoKYujQoa77t27dSocOHWo9SBGpAef8ppgB4ONnbiwCnJzfpMJJROT0RsaO5Lnhz1W4j9ODAx+ss32cgoODGTRoEM8//zx79uyhuLiYmJgYJk+ezD/+8Q8Axo8fz9q1a5kzZw4TJkzg6NGjhIWF0b9//woXhhg50hFrUFAQsbGxjBgxgjfffLPGw/vEOmpUOD3++ONcccUVDBs2jODgYBYuXIif38kPZu+88w6jR4+u9SBFpAacw/Taav8mKygqLWJbxjZAC0OIiFTHyNiRjIgZQWJ6Ihn5GUQERdA3sm+d9DQ5+fv7M3v27CoXOevfvz+ffPLJadvExcXVaCl0aThqVDiFh4ezevVqjh8/TnBwcLluxk8++YSQkJBaDVBEasAwymx8q4UhrODnzJ8pshfRPKA5saGxZocjItIgeHt5MyB6gNlhiLipUeF0yy23VKvdO++8c0bBiMhZykqB7IPg5QNt9B+OFZQdpqflZ0VERBquGhVOCxcuJDY2lj59+qgLUsSKnMP0WiaAXxNTQxEHbXwrIiLSONSocPrLX/7CP//5T/bu3cvNN9/MjTfeSPPmzesqNhGpqeQ1jksN07OEUnspSelJgOY3iYiINHQ1Wo78lVde4fDhwzzwwAP8+9//JiYmhquvvpr//Oc/6oESsYJk7d9kJTuP7SSvOI9g32A6N+tsdjgiIiJyFmpUOIFj1ZHrrruOFStWsH37dnr06MGdd95JXFwcubm5dRGjiFRHbgYc2eW4HjPI3FgEgMQ0x/ymhMiEOl0NSkREROpejQsntwd7eWGz2TAMg9LS0tqKSUTORMofq+lFdocgDaG1Au3fJCIi0njUuHAqLCzkn//8J6NGjaJz585s27aNl19+mZSUFIKDg+siRhGpDtcwPe3fZAWGYbApbROgwklERKQxqNHiEHfeeSeLFi0iJiaGW265hX/+85+Eh4fXVWwiUhPOHqe2WhjCCvZl7+NowVH8vPzo0aKH2eGIiIjIWapR4fT666/Ttm1b2rdvzw8//MAPP/xQYbvPP/+8VoITkWoqyIbUbY7r6nGyBOf8pviIePy8/UyORkREztSkSZPIyspiyZIlFd4/Y8YMlixZQlJSUr3GJfWvRoXThAkTtIGjiBXt3wCGHZrFQWgrs6MR0DA9EZGzYJSWkr9xEyUZGfhERBDUvx82by2ycyaGDx9OQkIC8+bNq/D+sp/tQ0JC6NKlC48++ih/+tOf6inChqPGG+CKiAW5humpt8kqXAtDRKpwEhGpiezly0mbNZuS1FTXMZ/oaKL+8TCho0ebGJl5ioqK8POru9ELCxYs4KKLLiI7O5tXX32Vq666isTEROLj4+vsORuis1pVT0QsIvmPwkkb31pCal4qB3MP4mXzondkb7PDERFpMLKXL+fgPfe6FU0AJWlpHLznXrKXL6+T5/3000+Jj48nMDCQFi1aMHLkSPLy8ips+9NPPxEREcGcOXMqPd/8+fPp1q0bAQEBdO3alVdffdXt/gcffJDOnTsTFBRE+/btmTp1KsXFxa77Z8yYQUJCAvPnz6ddu3YEBAQAjt6h+fPnM378eIKCgujUqRNffvnlWb/+pk2bEh0dTefOnXn88ccpKSnh+++/P+vzNjY16nESEQsqLoCDjmFh2vjWGpzD9Lo170YT3yYmRyMiYi7DMDBOnKi6XWkpaU88CYZR0UnABmlPzqLJ4MHVGrZnCwys1hSTw4cPc9111zF37lzGjx9PTk4OP/74I0YFcXz33XdcccUVzJ07l9tvv73C83344YdMmzaNl19+mT59+rB582YmT55MkyZNmDhxIuAYErdw4UJatWrFtm3bmDx5MiEhITzwwAOu8+zevZvPPvuMzz//HO8yr3fmzJnMnTuXp59+mpdeeokbbriB5ORkmjc/+61ISkpKePvttwHqtIeroVLhJNLQHUqE0iJoEgnN25sdjXByYYi+UX1NjkRExHzGiRPs7FsLw5YNR8/TbwMGVqt5l8RN2IKCqmx3+PBhSkpKuOKKK4iNjQWocIjaF198wYQJE5g/fz7XXHNNpeebPn06zz77LFdccQUA7dq1Y/v27bzxxhuuwunRRx91tY+Li+P+++9n0aJFboVTUVER7733HhEREW7nnzRpEtdddx0As2bN4sUXX2TDhg1cdNFFVb7Wylx33XV4e3tz4sQJ7HY7cXFxXH311Wd8vsZKhZNIQ5e8xnEZOxi0eIslaH6TiEjD0bt3by688ELi4+MZM2YMo0eP5qqrrqJZs2auNuvXr+err77i008/5fLLL6/0XHl5eezZs4dbb72VyZMnu46XlJQQFhbmur148WJefPFF9uzZQ25uLiUlJYSGhrqdKzY2tlzRBNCrVy/X9SZNmhAaGkp6evqZvHSX559/npEjR/L7779z33338eKLL9ZKD1Zjo8JJpKFzbXyrYXpWkFWQxe6s3QD0iepjcjQiIuazBQbSJXFTle3yN25k/+1/rrJdzJtvENS/f7Wetzq8vb1ZsWIFa9euZfny5bz00ks88sgjrF+/nnbt2gHQoUMHWrRowTvvvMO4cePw9fWt8Fy5ubkAvPXWWwwaNKjc8wCsW7eOG264gZkzZzJmzBjCwsJYtGgRzz77rFv7Jk0qHup96nPbbDbsdnu1XmtloqOj6dixIx07dmTBggWMHTuW7du3ExkZeVbnbWy0OIRIQ1ZaAvvXO65r41tLcPY2tQ9rT/MA/bVORMRms+EVFFTlV5MhQ/CJjq589ITNhk90NE2GDKnW+WqyhY7NZmPIkCHMnDmTzZs34+fnxxdffOG6Pzw8nO+++47du3dz9dVXuy3kUFZUVBStWrXi999/dxUizi9nEbZ27VpiY2N55JFH6N+/P506dSI5Obn6b2gdGzhwIP369ePJJ580OxTLUY+TSEOWtg2KcsE/DKJ6mB2NcHJ+k/ZvEhGpGZu3N1H/eJiD99zrKJ7KLs7wRxEU9Y+Ha30/p/Xr17Ny5UpGjx5NZGQk69evJyMjg27durm1i4yM5LvvvmPEiBFcd911LFq0CB+f8h+lZ86cyd13301YWBgXXXQRhYWFbNy4kWPHjjFlyhQ6depESkoKixYtYsCAAXz99dduRVpdyMjIKLdBb8uWLYmKiqqw/b333sv48eN54IEHaN26dZ3G1pCox0mkIXMO02s7CLy0MaAVOFfU08IQIiI1Fzp6NK1fmIfPKR/ofaKiaP3CvDrZxyk0NJTVq1czduxYOnfuzKOPPsqzzz7LxRdfXK5tdHQ03333Hdu2beOGG26gtLS0XJvbbruN+fPns2DBAuLj4xk2bBgLFy509Thddtll3Hfffdx1110kJCSwdu1apk6dWuuvq6yPPvqIPn36uH299dZblba/6KKLaNeunXqdTqEeJ5GGzLkwhIbpWUJ+cT6/Hv0V0MIQIiJnKnT0aEIuvJD8jZsoycjAJyKCoP79ar2nyalbt24sW7as0vsXLlzodrtly5bs3LnTdXvGjBnMmDHDrc3111/P9ddfX+k5586dy9y5c92O3Xvvvac9J1DhEulZWVmVPg/AqlWrTnt/Ree02Wz8+uuvp32cJ1LhJNJQGQak/M9xXQtDWMKWjC2UGqW0atKKlsEtzQ5HRKTBsnl702RQ9ZYdF6kvGqon0lBl7oL8TPAJgFZavc0KNExPRESk8VLhJNJQOYfpte4PPtrd2wqcK+qpcBIREWl8VDiJNFQpzv2bzjU3DgGgqLSIrRlbAa2oJyIi0hipcBJpqFwb32phCCvYfmQ7haWFNA9oTrvQdmaHIyIiIrVMhZNIQ5S1H46ngM0b2mjyrBU45zf1iexTo00XRUREpGFQ4STSEDmH6bXsDf7B5sYiwMnCScP0REREGicVTiINUfJax6XmN1lCqb2UpPQkQAtDiIiINFYqnEQaImfhpI1vLWF31m5yinMI8gmiS7MuZocjIiIidUCFk0hDk3cEMv/YsVyFkyVsTNsIOOY3+XhpX3ERkcZk0qRJXH755ZXeP2PGDBISEuotHjGPCieRhsY5vymiKzRpYW4sAkBimvZvEhGpTXa7wcGdx/jtp1QO7jyG3W6YHVKDtXfvXq6//npatWpFQEAAbdq04U9/+hM7duxwa/f9999zySWXEBERQUBAAB06dOCaa65h9erVrjarVq3CZrNhs9nw8vIiLCyMPn368MADD3D48OH6fmn1Tn8aFWloNEzPUgzDOLnxbaQKJxGRs7Vnczo/Lt5FXlah61iTpv4MvaYTHfpEmhiZeYqKivDzq/lm98XFxYwaNYouXbrw+eef07JlSw4cOMA333xDVlaWq92rr77KXXfdxU033cTixYvp0KEDx48f5/vvv+e+++5j06ZNbufduXMnoaGhZGdnk5iYyNy5c3n77bdZtWoV8fHxZ/tyLcsSPU6vvPIKcXFxBAQEMGjQIDZs2FCtxy1atAibzXba7lORRifFuTDEEHPjEABSclLIPJGJr5cv8RGN9z8LEZH6sGdzOsve+NmtaALIyypk2Rs/s2dzep0876effkp8fDyBgYG0aNGCkSNHkpeXV2Hbn376iYiICObMmVPp+ebPn0+3bt0ICAiga9euvPrqq273P/jgg3Tu3JmgoCDat2/P1KlTKS4udt3vHP43f/582rVrR0BAAAA2m4358+czfvx4goKC6NSpE19++WWlcfzyyy/s2bOHV199lXPOOYfY2FiGDBnCE088wTnnnANASkoK9957L/feey/vvvsuF1xwAbGxsfTq1Yt77rmHjRs3ljtvZGQk0dHRdO7cmWuvvZY1a9YQERHBX/7yl8rf5EbA9MJp8eLFTJkyhenTp5OYmEjv3r0ZM2YM6emn/8HYt28f999/P0OHDq2nSEUsoDAHDm91XNfGt5bgHKYXHx6Pv7e/ydGIiFiPYRgUF5ZW+VV4ooQfF/922nP9uHgXhSdKqnU+w6je8L7Dhw9z3XXXccstt/Drr7+yatUqrrjiigof/9133zFq1CiefPJJHnzwwQrP9+GHHzJt2jSefPJJfv31V2bNmsXUqVN59913XW1CQkJYuHAh27dv54UXXuCtt97i+eefdzvP7t27+eyzz/j8889JSkpyHZ85cyZXX301W7duZezYsdxwww0cPXq0wlgiIiLw8vLi008/pbS0tMI2n332GcXFxTzwwAMV3l+dvQkDAwO54447WLNmTZWf4Rsy04fqPffcc0yePJmbb74ZgNdff52vv/6ad955h4ceeqjCx5SWlnLDDTcwc+ZMfvzxR7euRpFGbf8GMEohrC2EtTE7GuHk/k2a3yQiUrGSIjtv3vNDrZwrL6uQ+fetrrohcPsLw/D1966y3eHDhykpKeGKK64gNjYWoMLhZl988QUTJkxg/vz5XHPNNZWeb/r06Tz77LNcccUVALRr147t27fzxhtvMHHiRAAeffRRV/u4uDjuv/9+Fi1a5Fa8FBUV8d577xEREeF2/kmTJnHdddcBMGvWLF588UU2bNjARRddVC6W1q1b8+KLL/LAAw8wc+ZM+vfvz4gRI7jhhhto3749AL/99huhoaFER0e7HvfZZ5+5YgVYt25dlUPwunbtCjg6NyIjG+eQSlMLp6KiIjZt2sTDDz/sOubl5cXIkSNZt25dpY977LHHiIyM5NZbb+XHH3887XMUFhZSWHiyuzc7OxtwjPks2yVqFmcMVohFrJ8Pr71r8Absbc+h1KIx1jar58RZOPVu0duyMdYmq+fDEykn1uPJOSkuLsYwDOx2O3a7HcB1Wd8cMdhcPUfOuE4VHx/PhRdeSHx8PKNHj2bUqFFcddVVNGvWzPW49evX89VXX/Hxxx9z+eWXu53HeX673U5eXh579uzh1ltvZfLkya42JSUlhIWFuR63ePFiXn75Zfbs2UNubi4lJSWEhoa67jcMg9jYWFq0aFEu5p49e7qOBQYGEhoaSmpqaqXv81/+8hduvPFGVq1axfr16/nkk0+YNWsWS5YsYdSoURiGgc1mc3v8qFGjSExM5ODBg1xwwQUUFxeXy+mpz+fs0arsfS6rqpzUNrvd7uj5LC7G29u9mK7Jz6mphVNmZialpaVERUW5HY+Kiiq30ofTf//7X95++223LsvTmT17NjNnzix3fPny5QQFBdU45rqyYsUKs0OQMqyajyG7viYc2JrVhOSlS80Op15ZMSfZ9mwO5B7Aho20zWksTfKcnFgxH55OObEeT8yJj48P0dHR5ObmUlRUBDg+HF/zWO8qH5u+N5fvF+ypst2ImzsQ2S64ynb5BbnYCk8OM8vJyam07SeffML69ev5/vvvefHFF3n00Uf59ttviY2Npbi4mNjYWJo2bcpbb73F0KFD8fX1dT22sLCQ0tJSsrOzXcPU5s2bR//+/d2ew9vbm+zsbDZs2MBNN93EQw89xBNPPEFoaCiff/45L7/8susP/IWFhQQEBLhul1VSUlLueH5+foVtyxo2bBjDhg3j73//O1deeSWPPfYYgwYNIiYmhuPHj7Nr1y63z+SRkZEUFBQAkJeXR3Z2Nvn5+a730svLfcaP87N5ixYtqozF6XQ5qU1FRUWcOHGC1atXU1JS4naf8zVVh+lD9WoiJyeHm266ibfeeovw8PBqPebhhx9mypQprtvZ2dnExMQwevRoQkND6yrUaisuLmbFihWMGjXK7YdQzGHpfJQU4rPV8derHmNvp0d4J5MDqh9Wzsl/kv8Da6BLsy5ccfEVZodTL6ycD0+lnFiPJ+ekoKCA/fv3Exwc7FrQoLqaNm/Khi8OlFsYoqzgZv507tcGL6+q5904GYZBTk4OISEhp52vM3r0aEaPHs0TTzxBu3bt+Pbbb7nvvvvw9fUlKiqKJUuWcMEFFzB58mQWL17syq2/vz/e3t6EhoYSGhpKq1atSE1NrXRvp61btxIbG8tjjz3mOvbqq69is9lcn03LnvNUzl4mJ5vNRkBAQI0+1/bo0YN169YRGhrqmv7y2muv8dxzz7m1Cw52FKhNmjQhNDTU1ekQEhLi9nwnTpzg/fff5/zzz3cNATyd6uakthQUFBAYGMj5559f7vuyukUemFw4hYeH4+3tTVpamtvxtLQ0t3GWTnv27GHfvn1ceumlrmPO7j0fHx927txJhw4d3B7j7++Pv3/5Cdu+vr6W+mVmtXg8nSXzcXgTlBZCUDi+0d2gHn7RWIkVc5KUmQRA/+j+loutrlkxH55OObEeT8xJaWmpa4+fU3skquLlBUOv6cSyN36utM15V3fCx6fqeUtlOT8rOuM61fr161m5ciWjR48mMjKS9evXk5GRQffu3fHy8nLtWxQdHc13333nmiO0aNEifHx8XB/8neeeOXMmd999N02bNuWiiy6isLCQjRs3cuzYMaZMmULnzp1JSUnh448/ZsCAAXz99dcsWbLE7RynntP9fSr/3lb2ficlJTF9+nRuuukmunfvjp+fHz/88AMLFizgwQcfxMvLi7i4OJ599lnuuecejh07xqRJk2jXrh1Hjx7lgw8+ABzfy2WfIzMzk6KiInJycti0aRNz584lMzOTzz//vFp5ryontc2Zx4p+JmvyM2pq4eTn50e/fv1YuXKla0lxu93OypUrueuuu8q179q1K9u2bXM79uijj5KTk8MLL7xATExMfYQtYo7kNY7L2MEeVzRZlWv/Ji0MISJSKzr0ieSiP/cst49TcDN/zru6bvZxCg0NZfXq1cybN4/s7GxiY2N59tlnufjii8u1dRZPw4cP54YbbuCjjz4q1+a2224jKCiIp59+mr///e80adKE+Ph47r33XgAuu+wy7rvvPu666y4KCwsZN24cU6dOZcaMGbX+2tq0aUNcXBwzZ85k37592Gw21+377rvP1e5vf/sb3bp147nnnuOqq64iOzubFi1aMHjwYJYtW1ZuYYguXbpgs9kIDg6mffv2jB49milTplTY8dGY2IzqrtVYRxYvXszEiRN54403GDhwIPPmzePjjz9mx44dREVFMWHCBFq3bs3s2bMrfPykSZPIyspyVepVyc7OJiwsjOPHj1tmqN7SpUsZO3asx/1VyoosnY8ProLdK+Cip+Ccxr1PQllWzcnxwuMMXTQUA4NVV6+iRWALs0OqF1bNhydTTqzHk3NSUFDA3r173fYeOhN2u8HhXVnkZRfSJNSflp2a1mh4nvu57GRnZxMaGlovvRtStfrOyem+L2tSG5g+x+maa64hIyODadOmucaDLlu2zDU5LSUlRd/kIvZS2L/ecb2t9m+ygs3pmzEwiAuN85iiSUSkvnh52WjdpZnZYYi4Mb1wArjrrrsqHJoHsGrVqtM+duHChbUfkIjVpP0MhdngFwLRp99HQeqHc+PbflH9TI5ERERE6oO6ckQaguQ/9jVrOwi8ajYpVuqGc/8mFU4iIiKeQYWTSEOQstZxqWF6lpBfnM/2I9sBLQwhIiLiKVQ4iVidYUDyH4VT7LnmxiIAbMvcRolRQlRQFK2atDI7HBEREakHKpxErO7IHsjLAG9/aKXeDSsoO0yvPjbuExFpaExetFnETW19P6pwErE65zC91v3A98yXdpXao4UhREQq5lx+PT8/3+RIRE4qKioCwNv77OaJW2JVPRE5DdcwPc1vsoLi0mK2ZGwBoG+kegBFRMry9vamadOmpKenAxAUFGR6z7zdbqeoqIiCggJtcWMR9ZkTu91ORkYGQUFB+PicXemjwknE6jS/yVK2H91OQWkBTf2b0r5pe7PDERGxnOjoaABX8WQ2wzA4ceIEgYGBphdx4lDfOfHy8qJt27Zn/VwqnESs7PhByEoGmxe0GWh2NMLJYXp9IvvgZdNfLkVETmWz2WjZsiWRkZEUFxebHQ7FxcWsXr2a888/3zWUUMxV3znx8/OrlZ4tFU4iVpbyx/5N0fEQEGpuLAJofpOISHV5e3uf9ZyS2oqjpKSEgIAAFU4W0VBzoj+XiliZa5jeEHPjEADshp3EdBVOIiIinkiFk4iVOXuctPGtJezO2k12UTaBPoF0bd7V7HBERESkHqlwErGq/KOQvt1xXYWTJTiH6fWO6I2Pl0Y6i4iIeBIVTiJWlfI/x2V4ZwiOMDcWAdw3vhURERHPosJJxKqcG9+qt8kSDMPQwhAiIiIeTIWTiFVp/yZLOZB7gPQT6fh4+RAfHm92OCIiIlLPVDiJWFFRHhze4riuwskSnMP0erboSYBPgMnRiIiISH1T4SRiRQd+AnsJhLaBpm3NjkY4uTBE36i+JkciIiIiZlDhJGJFrmF6mt9kFdq/SURExLOpcBKxIs1vspTME5kkZydjw0ZCZILZ4YiIiIgJVDiJWE1JERzY6LjeVoWTFTjnN3Vu1plQv1CToxEREREzqHASsZrDSVByAoJaQEQXs6MRtH+TiIiIqHASsZ7kMvs32WzmxiKAFoYQERERFU4i1pOsjW+tJLsom9+O/Qaox0lERMSTqXASsRK7Hfb/z3FdC0NYQlJ6EgYGsaGxhAeGmx2OiIiImESFk4iVpG+HguPgFwzRvcyORjg5v6lvpIbpiYiIeDIVTiJW4hym12YAePuYG4sAmt8kIiIiDiqcRKwkxbl/0xBz4xAACkoK+PnIz4DmN4mIiHg6FU4iVmEYkLzOcT1WC0NYwbbMbZTYS4gMjKRNcBuzwxERERETqXASsYqjv0NuKnj5Qmv1bliBa35TVF9sWhpeRETEo6lwErGKlD96m1r3A99Ac2MRQBvfioiIyEkqnESsQsP0LKXEXsKWjC2AFoYQERERFU4i1pG8xnHZVvs3WcGOozs4UXKCUL9QOjbtaHY4IiIiYjIVTiJWkJMKx/YCNmg7yOxoBPf9m7xs+lUpIiLi6fRpQMQKnPs3RfeEgDBzYxHAfWEIERERERVOIlbgLJw0TM8S7IadzembARVOIiIi4qDCScQKnCvqxapwsoLfs34nqzCLQJ9AujfvbnY4IiIiYgEqnETMduIYpP3iuK7CyRIS0xMB6BXeC19vX5OjEREREStQ4SRitpT1gAHNO0BwpNnRCJrfJCIiIuWpcBIxW8of85vU22QJhmFo41sREREpR4WTiNmSNb/JSg7lHSItPw0fmw+9InqZHY6IiIhYhAonETMV5cMhx3wa2g42NxYBIDHNkY/uLboT6BNocjQiIiJiFSqcRMx0cCPYSyCkFTSLMzsaAQ3TExERkQqpcBIxk2uY3mCw2cyNRQAtDCEiIiIVU+EkYqbkNY5LzW+yhMwTmezL3ocNG30i+5gdjoiIiFiICicRs5QWw4GfHNfbqnCygs3pmwHo2KwjYf5hJkcjIiIiVqLCScQsh7dAcT4ENIWIrmZHI5xcGKJvpIbpiYiIiDsVTiJmSS6zf5OXfhStwDm/qX9Uf5MjEREREavRpzURs6T8sTCEliG3hNyiXHYe2wloYQgREREpT4WTiBnsdvceJzFdUkYSdsNOm+A2RAZFmh2OiIiIWIwKJxEzZOyAgizwDYKWvc2ORtD+TSIiInJ6KpxEzJDyR29TmwHg7WtuLAKcXBhChZOIiIhURIWTiBk0TM9SCksL2Za5DdD8JhEREamYCieR+mYYkPzHwhAqnCxhW8Y2iu3FhAeG0zakrdnhiIiIiAWpcBKpb1nJkHMIvHyhtZa9toLE9JP7N9lsNpOjEREREStS4SRS35zD9FolgF+QqaGIg2vjWw3TExERkUqocBKpb5rfZCkl9hI2p28GtPGtiIiIVE6Fk0h9c218q8LJCnYe20l+ST4hviF0bNrR7HBERETEolQ4idSnnDQ4shuwQdtBZkcjnBymlxCZgLeXt8nRiIiIiFWpcBKpT87epqgeENjM3FgE0Ma3IiIiUj0qnETqk2uY3mBz4xAADMNwzW9S4SQiIiKno8JJpD4lr3FcxqpwsoK92Xs5WnAUf29/erToYXY4IiIiYmEqnETqS8FxSP3ZcV0LQ1iCc5her4he+Hr7mhyNiIiIWJkKJ5H6sn8DYECzdhDa0uxohDL7N0Vq/yYRERE5PRVOIvXFNUxPvU1W4exx0sa3IiIiUhUVTiL1JfmPhSFUOFnC4dzDHM47jLfNm4SIBLPDEREREYtT4SRSH4pPwCHHsDCtqGcNm9IdvU3dmncjyDfI5GhERETE6lQ4idSHg5ugtAiCo6F5e7OjEbR/k4iIiNSMCieR+uAapjcYbDZzYxGgzMIQmt8kIiIi1aDCSaQ+OBeG0DLklnC04Ci/H/8d0Ip6IiIiUj0qnETqWmkJHPjJcV0LQ1jC5rTNAHRs2pGmAU3NDUZEREQaBBVOInUtdSsU5UJAGER2Nzsa4eTCEOptEhERkepS4SRS15LXOi5jzgEv/chZgeY3iYiISE3pU5xIXUvR/k1Wklecx69HfwW0op6IiIhUnyUKp1deeYW4uDgCAgIYNGgQGzZsqLTtW2+9xdChQ2nWrBnNmjVj5MiRp20vYirDONnjpMLJErakb8Fu2Gkd3JroJtFmhyMiIiINhOmF0+LFi5kyZQrTp08nMTGR3r17M2bMGNLT0ytsv2rVKq677jq+//571q1bR0xMDKNHj+bgwYP1HLlINWTshBNHwScQWiaYHY2g+U0iIiJyZkwvnJ577jkmT57MzTffTPfu3Xn99dcJCgrinXfeqbD9hx9+yJ133klCQgJdu3Zl/vz52O12Vq5cWc+Ri1RDyh+9TW36g4+fubEIoI1vRURE5Mz4mPnkRUVFbNq0iYcffth1zMvLi5EjR7Ju3bpqnSM/P5/i4mKaN29e4f2FhYUUFha6bmdnZwNQXFxMcXHxWURfO5wxWCEWqf18eO9bgxdQ2mYQduX4jNRmTopKi9iWsQ2AXi166efuDOh3lvUoJ9ajnFiL8mE9VspJTWIwtXDKzMyktLSUqKgot+NRUVHs2LGjWud48MEHadWqFSNHjqzw/tmzZzNz5sxyx5cvX05QUFDNg64jK1asMDsEKaO28jFq53cEAesPe5GxdGmtnNNT1UZOkkuSKbIX0cTWhF9+/IXttu21EJln0u8s61FOrEc5sRblw3qskJP8/PxqtzW1cDpbTz31FIsWLWLVqlUEBARU2Obhhx9mypQprtvZ2dmueVGhoaH1FWqliouLWbFiBaNGjcLX19fscDxerebj+H58Nx/F8PJhwBV/Bb8mtROkh6nNnLzzyzuwBc5pcw7jho6rpQg9i35nWY9yYj3KibUoH9ZjpZw4R6NVh6mFU3h4ON7e3qSlpbkdT0tLIzr69KtdPfPMMzz11FN8++239OrVq9J2/v7++Pv7lzvu6+treqLKslo8nq5W8nHwJwBsLXvj26Tp2Qfl4WojJ0mZSQD0j+6vn7ezpN9Z1qOcWI9yYi3Kh/VYISc1eX5TF4fw8/OjX79+bgs7OBd6GDx4cKWPmzt3Lo8//jjLli2jf//+9RGqSM0lr3Fctq38e1nqT6m9lKT0JEAb34qIiEjNmT5Ub8qUKUycOJH+/fszcOBA5s2bR15eHjfffDMAEyZMoHXr1syePRuAOXPmMG3aND766CPi4uJITU0FIDg4mODgYNNeh0g5ro1vh5gbhwDw27HfyC3OpYlvE7o062J2OCIiItLAmF44XXPNNWRkZDBt2jRSU1NJSEhg2bJlrgUjUlJS8PI62TH22muvUVRUxFVXXeV2nunTpzNjxoz6DF2kcrkZkPmb43rbc8yNRQBITE8EICEyAW8vb5OjERERkYbG9MIJ4K677uKuu+6q8L5Vq1a53d63b1/dByRytpy9TRHdIKjipfKlfrn2b4rU/k0iIiJSc6ZvgCvSKLmG6Z1rbhwCgGEY2vhWREREzooKJ5G6kLzWcanCyRKSs5M5WnAUPy8/eob3NDscERERaYBUOInUtoJsSN3quK4V9SzB2dvUM7wnft5+JkcjIiIiDZEKJ5HadmADGHZoGgthrc2ORji5MISG6YmIiMiZUuEkUtuSNb/JajS/SURERM6WCieR2qb5TZaSmpfKwdyDeNm8SIhMMDscERERaaBUOInUpuICOOjo3aCtCicrSExzDNPr2rwrTXybmByNiIiINFQqnERq06FEKC2EJhHQooPZ0Qgn5zf1jexrciQiIiLSkKlwEqlNZYfp2WzmxiLAyflN/aP6mxyJiIiINGQqnERqk3PjWw3Ts4Ssgix2Z+0GoE9UH5OjERERkYZMhZNIbbGXQsp6x/VY7d9kBZvTNwPQLqwdzQOamxyNiIiINGQqnERqS+o2KMoB/1CI6ml2NIKWIRcREZHao8JJpLY4h+nFDAIvb3NjEUALQ4iIiEjtUeEkUluS1zguNUzPEvKL8/n1yK+AepxERETk7KlwEqkNhgHJf/Q4xQ4xNxYBYEvGFkqMElo2aUmr4FZmhyMiIiINnAonkdpwZDfkZ4K3P7TS6m1W4BqmF6VheiIiInL2VDiJ1AbnML02/cHH39xYBIDENM1vEhERkdqjwkmkNriG6Wn/JisoLi1mS8YWQBvfioiISO1Q4SRSG1LWOi7bamEIK/jlyC8UlhbSzL8Z7cLamR2OiIiINAIqnETO1vEDkJUCNi+IGWh2NMLJ/Zv6RPbBZrOZHI2IiIg0BiqcRM6Wc5hey97gH2JuLAKcXBhCy5CLiIhIbVHhJHK2XMP0NL/JCkrtpWxO2wyocBIREZHao8JJ5Gwl/1E4aeNbS9idtZuc4hyCfILo0ryL2eGIiIhII6HCSeRs5B+FjB2O61oYwhKc85sSIhPw8fIxORoRERFpLFQ4iZyNlD/mN4V3gSbh5sYiQJmNb7V/k4iIiNQiFU4iZ0PD9CzFMAxXj1PfKBVOIiIiUntUOImcDVfhNMTcOASA/Tn7yTyRia+XL/Hh8WaHIyIiIo2ICieRM1WYC4e3OK5rfpMlOHubeob3JMAnwORoREREpDFR4SRypg5sAKMUwtpC0xizoxFOFk5ahlxERERqmwonkTPl3PhW85ssQwtDiIiISF1R4SRyppwr6mmYniWk56ezP2c/NmwkRCaYHY6IiIg0MiqcRM5ESSEc+MlxXQtDWEJimqO3qWvzroT4hZgcjYiIiDQ2KpxEzsShJCgpgKBwCO9kdjQCWoZcRERE6pQKJ5EzkbzGcdn2HLDZzI1FAM1vEhERkbqlwknkTDjnN2mYniUcLzzOrmO7APU4iYiISN1Q4SRSU/ZSSFnvuK4V9SwhKT0JA4O40DjCA8PNDkdEREQaIRVOIjWV9gsUHge/YIiKNzsaATala36TiIiI1C0VTiI15RymFzMIvH3MjUUAbXwrIiIidU+Fk0hNJa91XGqYniWcKDnB9sztgBaGEBERkbqjwkmkJgzjZOHU9lxzYxEAtmZspcQoITIoktbBrc0OR0RERBopFU4iNXH0d8hLB28/aK1hYVbg3Pi2X1Q/bFoaXkREROqICieRmnD2NrXuB74B5sYiwMmFIfpFqpAVERGRuqPCSaQmXMP0NL/JCortxWzN2ApoRT0RERGpWyqcRGoixbkwhDa+tYJfj/zKiZIThPmH0aFpB7PDERERkUZMhZNIdWUfgmP7wOYFMQPNjkY4Ob+pT2QfvGz6dSYiIiJ1R580RKrLOUwvqicEhJobiwBl9m/S/CYRERGpYyqcRKrLufGthulZgt2wk5h+ckU9ERERkbqkwkmkupKdhZMWhrCCPVl7yC7KJtAnkK4tupodjoiIiDRyKpxEqiP/KKT/4riuFfUswTlMr1dEL3y9fE2ORkRERBo7FU4i1bF/veOyRScIjjQ3FgHcN74VERERqWsqnESqw7kwhIbpWYJhGNr4VkREROqVCieR6kjW/k1WciD3AOn56fh4+RAfEW92OCIiIuIBVDiJVKUoDw4nOa5rfpMlOIfp9WjRg0CfQJOjEREREU+gwkmkKgc2gr0EQltD07ZmRyPgWoa8b1RfkyMRERERT6HCSaQqrmF654LNZm4sApxcUa9/VH+TIxERERFPocJJpCopfxROGqZnCZknMknOTsaGjYTIBLPDEREREQ+hwknkdEqKYP9Pjuux55obiwAn5zd1ataJUL9Qk6MRERERT6HCSeR0Dm+BkhMQ2BzCu5gdjXBymJ72bxIREZH6pMJJ5HTKDtPz0o+LFWhhCBERETGDPgmKnI42vrWU7KJsdh7dCWjjWxEREalfKpxEKmO3Q8r/HNc1v8kSktKTMDBoG9KWiKAIs8MRERERD6LCSaQyGb9CQRb4NoHo3mZHI5xcGELD9ERERKS+qXASqYxzmF7MAPD2MTcWAU4uDNE3UoWTiIiI1C8VTiKVcc1vGmJuHAJAQUkBPx/5GdDGtyIiIlL/VDiJVMQwIGWd47o2vrWEX478Qom9hIjACNqEtDE7HBEREfEwKpxEKpK1D3IOg5cvtFHvhhWUXYbcZrOZHI2IiIh4GhVOIhWwOVfTa90XfAPNDUYA2JyxGdDGtyIiImIOFU4iFfDa/0fhpGF6llBqlLIlcwughSFERETEHCqcRCpgS3EuDKH9m6zgcOlhTpScIMQvhE7NOpkdjoiIiHggFU4ip/AvzsJ2bC9gg5hBZocjQHJJMuDobfKy6deWiIiI1D99AhE5RYvc3xxXonpCYFNTYxGHfaX7AG18KyIiIuZR4SRyiha5OxxXYjW/yQrsht2tx0lERETEDCqcTFR4Ip//vPkImd+/yX/efITCE/lmh+TRnPnY/cs2Eo81obCVliE3W2FhIR++N5c+P+fSM8WLjsEdzQ7Jo5UUl7Dpy5Vk/riFTV+upKS4xOyQPJ5yYj3KibUoH9bTkHNiMwzDMDuIV155haeffprU1FR69+7NSy+9xMCBAytt/8knnzB16lT27dtHp06dmDNnDmPHjq3Wc2VnZxMWFsbx48cJDQ2trZdQY589OYmWX6ynWe7JY8eC4fD4QVz5yELT4vJUyof1vP/CTOI++JjwHLvrWGaIF/tuvJqb7pluYmSe6Yf5i/F5dR7N87Ncx44GNaXkznsZdts15gXmwZQT61FOrEX5sB4r5qQmtYHpPU6LFy9mypQpTJ8+ncTERHr37s2YMWNIT0+vsP3atWu57rrruPXWW9m8eTOXX345l19+OT///HM9R37mPntyEt3eX0/TXPfjYbnQ7f31fPbkJFPi8lTKh/W8/8JM+r22iBZliiaA5jl2+r22iPdfmGlSZJ7ph/mLiXhmBs3K/EcH0DQ/i4hnZvDD/MXmBObBlBPrUU6sRfmwnsaQE9N7nAYNGsSAAQN4+eWXAbDb7cTExPC3v/2Nhx56qFz7a665hry8PL766ivXsXPOOYeEhARef/31Kp/P7B6nwhP5bBraj6a5YKvgfjtwPBi6fL0Kf39tvFrXCgtPsHPccOXDQgqLivh17Pk0zzUqzcmxYC+6Lf0Bfz+/+g7P45SUlPDLqLE0O3G80nxkBTWlx/Kv8fHxqe/wPJJyYj3KibUoH9ZTnZwcC2rKOet/xMe3fnNSk9rA1MKpqKiIoKAgPv30Uy6//HLX8YkTJ5KVlcW//vWvco9p27YtU6ZM4d5773Udmz59OkuWLGHLli3l2hcWFlJYWOi6nZ2dTUxMDJmZmaYUTv958xE6vPTven9eERERERErO/7k8/S77MJ6fc7s7GzCw8OrVTiZWmZnZmZSWlpKVFSU2/GoqCh27NhR4WNSU1MrbJ+amlph+9mzZzNzZvlhPcuXLycoKOgMIz9zmbt+oUO9P6uIiIiIiLX9sm4DaT6FVTesRfn51V+crdH3Tz788MNMmTLFddvZ4zR69GhzepwOrIFlv1fZLmlCAj0u+n/1EJFn+2XZJyS8l1Rlu023nkfPK++o+4CEjd8sYchLn1bZbut9kxl95aS6D8jD/fzND4Q/+Y8q22U+MoueFw+rh4hEObEe5cRalA/rqW5OegweSL+x9d/jVF2Nfqjeqawyxykst+KVOexAVgj0X70J/8D67xHzNMqH9RQWFrLhvL40z7FXmpOjIV4M/G8i/v7+9R2exykpLmH9oKE0zc+qNB9mjUv3VMqJ9Sgn1qJ8WI+Vc9JgVtXz8/OjX79+rFy50nXMbrezcuVKBg+uePPRwYMHu7UHWLFiRaXtrcY/MIjD4wdhw/FNUpYdxwIFqZcP0of0eqJ8WI+/vz/7brz6tDnZd+PVKprqiY+vDyV33nvafJTeea8+fNQj5cR6lBNrUT6sp7HkxPRV9RYvXszEiRN54403GDhwIPPmzePjjz9mx44dREVFMWHCBFq3bs3s2bMBx3Lkw4YN46mnnmLcuHEsWrSIWbNmkZiYSM+ePat8PrN7nJwq2jfoaIjjQ7r2Dap/yof1aB8na6lo740jQU0p1X4oplFOrEc5sRblw3qsmJMGs6qe08svv+zaADchIYEXX3yRQYMGATB8+HDi4uJYuHChq/0nn3zCo48+6toAd+7cuQ1uA1xwDBNb8c4M0nb9QlSnHoy6ZYZ6NkykfFhPYWEh/178Fr9v3Uz7Xn249JrJ6mkyUUlxCRu//JZf1m2gx+CB9L9spOX/OtjYKSfWo5xYi/JhPVbLSYMrnOqTlQongOLiYpYuXcrYsWPx9fU1OxyPp3xYj3JiLcqH9Sgn1qOcWIvyYT1WykmDmeMkIiIiIiLSEKhwEhERERERqYIKJxERERERkSqocBIREREREamCCicREREREZEqqHASERERERGpggonERERERGRKqhwEhERERERqYIKJxERERERkSqocBIREREREamCCicREREREZEqqHASERERERGpggonERERERGRKviYHUB9MwwDgOzsbJMjcSguLiY/P5/s7Gx8fX3NDsfjKR/Wo5xYi/JhPcqJ9Sgn1qJ8WI+VcuKsCZw1wul4XOGUk5MDQExMjMmRiIiIiIiIFeTk5BAWFnbaNjajOuVVI2K32zl06BAhISHYbDbX8QEDBvDTTz9V+rjT3V/ZfRUdP/VYdnY2MTEx7N+/n9DQ0Jq+nLNS1Wuuq/NUt31t56Q6efLEfNTkMWfyc3C6+/Qzcnbtzfi9ZWY+KouvPs5hpd9b+hmp/mP0f3v9nUe/tyrXWH9vNZafEcMwyMnJoVWrVnh5nX4Wk8f1OHl5edGmTZtyx729vU+buNPdX9l9FR2vrG1oaGi9f+NU9Zrr6jzVbV/bOalJnjwpHzV5zJn8HJzuPv2MnF17M39vmZGPymKpj3NY6feWfkaq/xj9315/59Hvrco11t9bjelnpKqeJictDvGHv/71r2d8f2X3VXS8quepT7UVS03PU932tZ2TmuTJDGbloyaPOZOfg9Pdp5+Rs2uv31v1dw4r/d5qbPk40/NU5zH6Gam/8+j3VuUa6+8tT8yHxw3Vs5rs7GzCwsI4fvy4KX8FEXfKh/UoJ9aifFiPcmI9yom1KB/W01Bzoh4nk/n7+zN9+nT8/f3NDkVQPqxIObEW5cN6lBPrUU6sRfmwnoaaE/U4iYiIiIiIVEE9TiIiIiIiIlVQ4SQiIiIiIlIFFU4iIiIiIiJVUOEkIiIiIiJSBRVOIiIiIiIiVVDh1IA8//zz9OjRg+7du3P33XejBRHNtXPnThISElxfgYGBLFmyxOywPNrevXsZMWIE3bt3Jz4+nry8PLND8nhxcXH06tWLhIQERowYYXY4AuTn5xMbG8v9999vdigeLysri/79+5OQkEDPnj156623zA7J4+3fv5/hw4fTvXt3evXqxSeffGJ2SB5v/PjxNGvWjKuuusrsULQceUORkZHBOeecwy+//IKvry/nn38+zzzzDIMHDzY7NAFyc3OJi4sjOTmZJk2amB2Oxxo2bBhPPPEEQ4cO5ejRo4SGhuLj42N2WB4tLi6On3/+meDgYLNDkT888sgj7N69m5iYGJ555hmzw/FopaWlFBYWEhQURF5eHj179mTjxo20aNHC7NA81uHDh0lLSyMhIYHU1FT69evHb7/9pv/bTbRq1SpycnJ49913+fTTT02NRT1ODUhJSQkFBQUUFxdTXFxMZGSk2SHJH7788ksuvPBC/WI1kfOPCkOHDgWgefPmKppETrFr1y527NjBxRdfbHYoAnh7exMUFARAYWEhhmFoNInJWrZsSUJCAgDR0dGEh4dz9OhRc4PycMOHDyckJMTsMAAVTrVm9erVXHrppbRq1QqbzVbhkK1XXnmFuLg4AgICGDRoEBs2bKj2+SMiIrj//vtp27YtrVq1YuTIkXTo0KEWX0HjU9c5Kevjjz/mmmuuOcuIG7e6zseuXbsIDg7m0ksvpW/fvsyaNasWo2+c6uNnxGazMWzYMAYMGMCHH35YS5E3TvWRj/vvv5/Zs2fXUsSNX33kJCsri969e9OmTRv+/ve/Ex4eXkvRN071+X/7pk2bKC0tJSYm5iyjbrzqMx9WoMKpluTl5dG7d29eeeWVCu9fvHgxU6ZMYfr06SQmJtK7d2/GjBlDenq6q41zjPOpX4cOHeLYsWN89dVX7Nu3j4MHD7J27VpWr15dXy+vQarrnDhlZ2ezdu1axo4dW+evqSGr63yUlJTw448/8uqrr7Ju3TpWrFjBihUr6uvlNUj18TPy3//+l02bNvHll18ya9Ystm7dWi+vrSGq63z861//onPnznTu3Lm+XlKDVx8/I02bNmXLli3s3buXjz76iLS0tHp5bQ1Vff3ffvToUSZMmMCbb75Z56+pIauvfFiGIbUOML744gu3YwMHDjT++te/um6XlpYarVq1MmbPnl2tc3788cfGnXfe6bo9d+5cY86cObUSryeoi5w4vffee8YNN9xQG2F6jLrIx9q1a43Ro0e7bs+dO9eYO3durcTrCeryZ8Tp/vvvNxYsWHAWUXqOusjHQw89ZLRp08aIjY01WrRoYYSGhhozZ86szbAbtfr4GfnLX/5ifPLJJ2cTpkepq5wUFBQYQ4cONd57773aCtUj1OXPyPfff29ceeWVtRHmWVGPUz0oKipi06ZNjBw50nXMy8uLkSNHsm7dumqdIyYmhrVr11JQUEBpaSmrVq2iS5cudRVyo1cbOXHSML2zVxv5GDBgAOnp6Rw7dgy73c7q1avp1q1bXYXc6NVGTvLy8sjJyQEcC6h899139OjRo07ibexqIx+zZ89m//797Nu3j2eeeYbJkyczbdq0ugq50auNnKSlpbl+Ro4fP87q1av1f/tZqI2cGIbBpEmTuOCCC7jpppvqKlSPUJuftaxChVM9yMzMpLS0lKioKLfjUVFRpKamVusc55xzDmPHjqVPnz706tWLDh06cNlll9VFuB6hNnICjv/oNmzYwJgxY2o7RI9SG/nw8fFh1qxZnH/++fTq1YtOnTpxySWX1EW4HqE2cpKWlsZ5551H7969Oeecc5gwYQIDBgyoi3Abvdr6nSW1pzZykpyczNChQ+nduzdDhw7lb3/7G/Hx8XURrkeojZysWbOGxYsXs2TJEtd2I9u2bauLcBu92vq9NXLkSP7f//t/LF26lDZt2phadGnJqQbkySef5MknnzQ7DCkjLCxM49Et5OKLL9ZqYRbSvn17tmzZYnYYUoFJkyaZHcL/b+/OQqr4+ziOf8bKyg3TrJRwCcQiC8oliiLqoo1KbTEiwhZCyGgxwXaK6iIsMqKF6iKJoGyhuqiETKkOFBmoRWUpRgbSZgYKlnl+z83znOd/PP07LdaZ6v2CAWfmzG++v/lc6JeZM0JSamqqKisrfV0G/mHs2LFyOp2+LgP/cO3aNV+X4MIdp1+gb9++6tatm8cf2C9fvtSAAQN8VNXfjUzshTzsh0zshTzsh0zsh0zs5U/Mg8bpF/D391dSUpJKS0td25xOp0pLS/kHtj5CJvZCHvZDJvZCHvZDJvZDJvbyJ+bBo3pdpKWlRbW1ta71+vp6VVZWKiwsTNHR0crNzVVWVpaSk5OVmpqqwsJCtba2avHixT6s+s9GJvZCHvZDJvZCHvZDJvZDJvby1+Xh69f6/SnKysqMJI8lKyvL9Zn9+/eb6Oho4+/vb1JTU83t27d9V/BfgEzshTzsh0zshTzsh0zsh0zs5W/LwzLGmJ/XlgEAAADA74/vOAEAAACAFzROAAAAAOAFjRMAAAAAeEHjBAAAAABe0DgBAAAAgBc0TgAAAADgBY0TAAAAAHhB4wQAAAAAXtA4AQD+aLGxsSosLPR1GQCA35xljDG+LgIA8HtbtGiRmpubdeHCBV+X4uH169cKDAxUQECAr0v5LDtfOwDA/3HHCQDwW2pvb/+qz0VERPikafra+gAAvwcaJwDAT/fgwQNNnTpVQUFB6t+/vxYuXKg3b9649l+9elVjx45VaGiowsPDNX36dNXV1bn2P3v2TJZl6fTp0xo/frx69eqlkydPatGiRUpPT9fu3bsVGRmp8PBw5eTkuDUtnR/VsyxLx44dU0ZGhgICAhQfH69Lly651Xvp0iXFx8erV69emjBhgoqKimRZlpqbm/91jpZl6dChQ5o5c6YCAwO1c+dOdXR0aOnSpYqLi1Pv3r2VkJCgffv2uY7ZunWrioqKdPHiRVmWJcuyVF5eLklqaGhQZmamQkNDFRYWprS0ND179uz7AgAA/DAaJwDAT9Xc3KyJEydqxIgRqqio0NWrV/Xy5UtlZma6PtPa2qrc3FxVVFSotLRUfn5+ysjIkNPpdBtr3bp1WrVqlR49eqTJkydLksrKylRXV6eysjIVFRXp+PHjOn78+Bdr2rZtmzIzM1VdXa1p06ZpwYIFampqkiTV19drzpw5Sk9PV1VVlbKzs7Vx48avmuvWrVuVkZGh+/fva8mSJXI6nRo4cKDOnDmjhw8fasuWLdqwYYOKi4slSXl5ecrMzNSUKVPU2NioxsZGjRkzRu3t7Zo8ebKCg4N18+ZNORwOBQUFacqUKfr48ePXXnoAQFcyAAD8oKysLJOWlvbZfdu3bzeTJk1y29bQ0GAkmZqams8e8/r1ayPJ3L9/3xhjTH19vZFkCgsLPc4bExNjPn365No2d+5cM2/ePNd6TEyM2bt3r2tdktm0aZNrvaWlxUgyV65cMcYYk5+fbxITE93Os3HjRiPJvHv37vMX4L/jrl69+l/3/09OTo6ZPXu22xw6X7sTJ06YhIQE43Q6Xds+fPhgevfubUpKSryeAwDQ9bjjBAD4qaqqqlRWVqagoCDXMnjwYElyPY739OlTzZ8/X4MGDVJISIhiY2MlSc+fP3cbKzk52WP8oUOHqlu3bq71yMhIvXr16os1DR8+3PVzYGCgQkJCXMfU1NQoJSXF7fOpqalfNdfP1XfgwAElJSUpIiJCQUFBOnLkiMe8OquqqlJtba2Cg4Nd1ywsLExtbW1ujzACAH6d7r4uAADwZ2tpadGMGTO0a9cuj32RkZGSpBkzZigmJkZHjx5VVFSUnE6nEhMTPR5LCwwM9BijR48ebuuWZXk84tcVx3yNzvWdOnVKeXl52rNnj0aPHq3g4GAVFBTozp07XxynpaVFSUlJOnnypMe+iIiIH64TAPDtaJwAAD/VyJEjde7cOcXGxqp7d89fO2/fvlVNTY2OHj2qcePGSZJu3br1q8t0SUhI0OXLl9223b1797vGcjgcGjNmjJYvX+7a1vmOkb+/vzo6Oty2jRw5UqdPn1a/fv0UEhLyXecGAHQtHtUDAHSJ9+/fq7Ky0m1paGhQTk6OmpqaNH/+fN29e1d1dXUqKSnR4sWL1dHRoT59+ig8PFxHjhxRbW2trl+/rtzcXJ/NIzs7W48fP1Z+fr6ePHmi4uJi18smLMv6prHi4+NVUVGhkpISPXnyRJs3b/ZowmJjY1VdXa2amhq9efNG7e3tWrBggfr27au0tDTdvHlT9fX1Ki8v18qVK/XixYuumioA4BvQOAEAukR5eblGjBjhtmzbtk1RUVFyOBzq6OjQpEmTNGzYMK1evVqhoaHy8/OTn5+fTp06pXv37ikxMVFr1qxRQUGBz+YRFxens2fP6vz58xo+fLgOHTrkeqtez549v2ms7OxszZo1S/PmzdOoUaP09u1bt7tPkrRs2TIlJCQoOTlZERERcjgcCggI0I0bNxQdHa1Zs2ZpyJAhWrp0qdra2rgDBQA+YhljjK+LAADAznbu3KnDhw+roaHB16UAAHyE7zgBANDJwYMHlZKSovDwcDkcDhUUFGjFihW+LgsA4EM0TgAAdPL06VPt2LFDTU1Nio6O1tq1a7V+/XpflwUA8CEe1QMAAAAAL3g5BAAAAAB4QeMEAAAAAF7QOAEAAACAFzROAAAAAOAFjRMAAAAAeEHjBAAAAABe0DgBAAAAgBc0TgAAAADgBY0TAAAAAHjxHykkvfQ6cxjEAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py:570: RuntimeWarning: overflow encountered in square\n",
+            "  output_errors = _average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIoCAYAAABeertyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAmsZJREFUeJzs3XlcVPX+x/HXsIuApiLgCu67gkKZlZpbaostWma55HJvy237lV1vqy2albdsd8kl0+xqaZuWpJmVJqDimqbmloG7gCAwMOf3xzijIyCgwGHg/ewxjzlz5jvnvGf6gvPhfM/3WAzDMBAREREREZECeZgdQEREREREpLxT4SQiIiIiIlIIFU4iIiIiIiKFUOEkIiIiIiJSCBVOIiIiIiIihVDhJCIiIiIiUggVTiIiIiIiIoVQ4SQiIiIiIlIIFU4iIiIiIiKFUOEkIiIiIiJSCBVOIlKmPvzwQ/r06UNISAje3t6EhobStWtXPv74Y2w2m9nx5BINHz4ci8VCUFAQZ86cyfP8rl27sFgsWCwW3njjDZfn9u3bx4gRI2jcuDF+fn6EhoZy3XXX8fzzz7u069atm3MbF95atGhRqu9PRETEy+wAIlK5zJkzh7CwMJ599lmCgoI4deoUv/32G8OHD2fZsmV8+umnZkeUS+Tl5UVGRgZff/01gwYNcnlu3rx5+Pn5kZmZ6bJ+9+7dREdHU6VKFe677z7Cw8NJSkpiw4YNTJo0ifHjx7u0r1evHhMnTsyz72rVqpX8GxIRETmPCicRKVOrV6/G29vbZd3DDz9MzZo1effdd5k4cSLh4eHmhJPL4uvrS5cuXfj000/zFE7z58+nf//+fP755y7r33zzTU6fPk1iYiINGzZ0ee7IkSN59lGtWjXuueeekg8vIiJSCA3VE5EydWHR5OAoljw8zv1a+vLLL+nfvz916tTB19eXxo0b89JLL5Gbm+vy2guHcNWqVYv+/fuzdetWl3YWi4UXXnjBZd3rr7+OxWKhW7duLuszMzN54YUXaNasGX5+foSFhXHbbbexZ88ewD68zGKxMHv2bJfXPfjgg1gsFoYPH+5cN3v2bCwWCz4+Phw9etSl/dq1a525ExISXJ5buHAhHTt2pEqVKtSqVYt77rmHQ4cO5fnsduzYwaBBgwgODqZKlSo0b96cp59+GoAXXnihwOFtjtuqVaucn2ObNm3ybL847r77bpYtW8apU6ec6+Lj49m1axd33313nvZ79uyhXr16eYomgNq1a19WlgsdOXKEkSNHEhISgp+fH+3bt2fOnDnO5x3/Ty92O///64Xy6xNpaWl07NiRiIgIkpKSnOvT09P5v//7P+rXr4+vry/NmzfnjTfewDCMPNt19J8Lb+f3WUebffv2OdfZbDbatWuXJ1N4eHie97Fq1SqXvuCwbt06brjhBqpVq4a/vz9du3bl119/zZPx0KFDjBw50vmzGhERwf333092dnaB+c+/OfI5hnw6bldccQXdunXj559/dtlfUX83XCgtLY1Ro0bRsGFDfH19qVevHv/85z85fPhwkT5zx+3C3yMbN26kb9++BAUFERAQQI8ePfjtt9+czxuGQffu3QkODnb5g0B2djZt27alcePGpKenXzS7iJhPR5xExBSnTp0iJyeHtLQ01q9fzxtvvMFdd91FgwYNnG1mz55NQEAAjz/+OAEBAaxcuZLnnnuO1NRUXn/9dZfttWjRgqeffhrDMNizZw///e9/6devHwcOHLhohvyGfeXm5nLjjTeyYsUK7rrrLh555BHS0tKIjY1l69atNG7cON/t7d69m+nTpxe4P09PTz755BMee+wx57pZs2blO4Rt9uzZjBgxgujoaCZOnMjhw4eZMmUKv/76Kxs3bqR69eoAbN68mWuvvRZvb2/GjBlDeHg4e/bs4euvv+aVV17htttuo0mTJs7tPvbYY7Rs2ZIxY8Y417Vs2bLAzMV122238c9//pMvvviC++67D7AfbWrRogVRUVF52jds2JAffviBlStXcv311xe6/dzcXI4dO5ZnfZUqVahatWqBrztz5gzdunVj9+7dPPTQQ0RERLBw4UKGDx/OqVOneOSRRwgODmbu3LnO13zxxRcsXrzYZV1B/+/zY7Vauf322zlw4AC//vorYWFhgP1L9M0338yPP/7IyJEj6dChA99//z1PPvkkhw4d4s0338x3e2+++Sa1atUC4JVXXil0/3PnzmXLli1FznuhlStX0rdvXzp27Mjzzz+Ph4cHs2bN4vrrr+fnn38mJiYGgL///puYmBhOnTrFmDFjaNGiBYcOHWLRokVkZGRw3XXXuXyGjuyO4h7g6quvdi7XqlXL+Rn89ddfTJkyhX79+nHw4EFnvy/O74bznThxgs2bNzNq1ChCQ0PZvXs3H374Id999x1xcXF5ivUXX3yRiIgI5+PTp09z//33u7TZtm0b1157LUFBQYwdOxZvb2+mTp1Kt27d+Omnn7jyyiuxWCzMnDmTdu3aOX8+AJ5//nm2bdvGqlWrLtp/RaScMERETNC8eXMDcN6GDh1qWK1WlzYZGRl5XvePf/zD8Pf3NzIzM53runbtanTt2tWl3X/+8x8DMI4cOeJcBxjPP/+88/HYsWON2rVrGx07dnR5/cyZMw3A+O9//5tn/zabzTAMw9i7d68BGLNmzXI+N2jQIKNNmzZG/fr1jWHDhjnXz5o1ywCMwYMHG23btnWuT09PN4KCgoy7777bAIz4+HjDMAwjOzvbqF27ttGmTRvjzJkzzvbffPONARjPPfecc911111nBAYGGvv3788354UaNmzoku18Xbt2NVq3bp3vc4UZNmyYUbVqVcMwDOOOO+4wevToYRiGYeTm5hqhoaHG+PHjnZ/Z66+/7nzd1q1bjSpVqhiA0aFDB+ORRx4xlixZYqSnp+eb7/w+c/7tH//4x0XzvfXWWwZgfPLJJ8512dnZRufOnY2AgAAjNTU1z2uef/55ozj/TJ7fJ2w2mzFkyBDD39/fWLdunUu7JUuWGIDx8ssvu6y/4447DIvFYuzevdtl/fTp0w3A5f/xhX3e0cf27t1rGIZhZGZmGg0aNDD69u2bp59GREQYQ4cOddnHjz/+aADGjz/+aBiGvf80bdrU6NOnj0tfysjIMCIiIoxevXo51w0dOtTw8PBw9t/z5dcP8/t5dRg2bJjRsGFDl3XTpk0zACMuLs4lx4Xy+91QFFu3bjV8fX2N++67z7nO8Xle+J6OHj2a5/fIgAEDDB8fH2PPnj3OdX///bcRGBhoXHfddS6vnzp1qrMf/vbbb4anp6fx6KOPFiuviJhHQ/VExBSzZs0iNjaWefPmMXLkSObNm+dyFATsRxEc0tLSOHbsGNdeey0ZGRns2LHDpa3VauXYsWMcPXqUtWvXsnjxYtq1a+f8C/2FDh06xDvvvMOzzz5LQECAy3Off/45tWrV4l//+lee11kslny3t379ehYuXMjEiRNdhhue795772XHjh3OIXmff/451apVo0ePHi7tEhISOHLkCA888AB+fn7O9f3796dFixZ8++23ABw9epTVq1dz3333uRypu1jOwjiO6Bw7dozs7OxL2sbdd9/NqlWrSE5OZuXKlSQnJ+c7TA+gdevWJCYmcs8997Bv3z6mTJnCgAEDCAkJyffoXXh4OLGxsXlujz766EUzLV26lNDQUAYPHuxc5+3tzcMPP8zp06f56aefLum9FuTJJ59k3rx5/O9//3MemTk/i6enJw8//LDL+v/7v//DMAyWLVvmst7x/8HX17fI+3/vvfc4fvx4npkJwT4E8q+//rro6xMTE53DK48fP+7sE+np6fTo0YPVq1djs9mw2WwsWbKEm266iU6dOuXZzqX0Q5vN5txfYmIiH3/8MWFhYS5HRovzu+Fi2z927BghISH069ePzz//vNgze+bm5rJ8+XIGDBhAo0aNnOvDwsK4++67+eWXX0hNTXWuHzNmDH369OFf//oX9957L40bN2bChAnF2qeImKdSF06rV6/mpptuok6dOlgsFpYsWVKs12dmZjJ8+HDatm2Ll5cXAwYMyLfde++9R8uWLZ3nHnz88cd52pw6dYoHH3yQsLAwfH19adasGUuXLr2EdyXiHjp37kzPnj25++67mTFjBi+++CKzZs1yOX9i27Zt3HrrrVSrVo2goCCCg4OdEwOkpKS4bG/NmjUEBwdTu3Ztrr76anJycli4cGGBX9yef/556tSpwz/+8Y88z+3Zs4fmzZvj5VX00cz//ve/ufbaa7nxxhsLbBMcHEz//v2ZOXMmADNnzmTYsGF5Cq39+/cD0Lx58zzbaNGihfP5P//8E+Cyz0s6344dOwgODnY5X2r+/PnF2ka/fv0IDAzks88+Y968eURHR7sMF7xQs2bNmDt3LseOHWPz5s1MmDABLy8vxowZww8//ODStmrVqvTs2TPPrbDpyPfv30/Tpk3zfNaOL+OOz7QkTJ06lcmTJwNw8uTJfLPUqVOHwMDAImVxnC92YYFfkJSUFCZMmMDjjz9OSEhInuevvvpqfvrpJxYsWMCRI0c4duxYnp+nXbt2ATBs2DBnf3DcZsyYQVZWFikpKRw9epTU1NQS7YMHDx507isyMpI9e/bw+eefu7z/4vxuuNCBAwfyvKfFixeTkpKS7zDQizl69CgZGRn5/qy2bNkSm83GwYMHXdZ/9NFHZGRksGvXLmbPnu1SBIpI+Vapz3FKT0+nffv23Hfffdx2223Ffn1ubi5VqlTh4YcfzjNTlMMHH3zAuHHjmD59OtHR0cTFxTF69GiuuOIKbrrpJsD+18RevXpRu3ZtFi1aRN26ddm/f79zLLdIZXDHHXfw9NNPs27dOrp06cKpU6fo2rUrQUFBvPjii85r/GzYsIGnnnoqz1+G27Vr5/yyevToUd5++226devGhg0bCA0NdWn7+++/M3v2bD755JMCJ6sojuXLl/PDDz+wdu3aQtved999DB06lH/961+sXr2aGTNm5Dnx3Uzh4eHOIz3Hjx/n7bff5t5776VRo0ZcddVVRdqGr68vt912G3PmzOHPP//McyJ9QTw9PWnbti1t27alc+fOdO/enXnz5tGzZ89LfTum+O2333jllVeIj4/nscce44YbbijwyGdRJCcnExAQUORzYCZNmoSHhwdPPvkkx48fz/P8f/7zH3799VeXo28Xcvx8vf7663To0CHfNgEBAZw4caJImYojJCSETz75BLAXQTNnzuSGG27gl19+oW3btsX+3XCh0NBQYmNjXdbNnDmzzC6FsGrVKrKysgDYsmULnTt3LpP9isjlq9SFU9++fenbt2+Bz2dlZfH000/z6aefcurUKdq0acOkSZOcMxlVrVqVDz74AIBff/3VZRYph7lz5/KPf/yDO++8E4BGjRoRHx/PpEmTnIXTzJkzOXHiBGvWrHF+idN0zFLZOC6a6unpCdi/XBw/fpwvvviC6667ztlu7969+b7+iiuucPmC3a1bN+rUqcOsWbMYN26cS9tx48bRoUMH58/lhRo3bsy6deuwWq2FFlaGYfDvf/+bW2+9tUiFRd++ffHz8+Ouu+7immuuoXHjxnkKJ8cMczt37swzYcLOnTudzzuGBl04e+DlcBzRcbj22mupW7cuy5cvL3LhBPbhejNnzsTDw4O77rqr2Dkcw77On4nucjRs2JDNmzdjs9lcjjo5hnXlN6vfpbrvvvv4z3/+w99//02rVq147LHHXCZHcEyIkZaW5nLUqaAs27dvL/IEHn///TdTpkxh4sSJBAYG5ls41apVi7Vr17J9+3aSk5MB2LRpE0888YSzjWMSjKCgoIsWrsHBwQQFBZVoH/Tz83PZ580330yNGjV49913mTp1arF/NxS2fYC3336boKCgYhe4wcHB+Pv7s3PnzjzP7dixAw8PD+rXr+9cl5SUxL/+9S969+6Nj48PTzzxBH369CnR/icipadSD9UrzEMPPcTatWtZsGABmzdvZuDAgdxwww3OIQxFkZWV5XKOAtjHZsfFxWG1WgH46quv6Ny5Mw8++CAhISG0adOGCRMmFDqtqog7KmgI6vTp07FYLM5CwVFAGedNz5ydnc37779fpP04CjHHX3Yd1q5dy5dffsmrr75a4DC+22+/nWPHjvHuu+/mec64YLpox++H/Gbny4+XlxdDhw5l8+bNzlnnLtSpUydq167Nhx9+6JJ/2bJl/P777/Tv3x+wf2m77rrrmDlzZp7ZAy/Meakcf713/P8oqu7du/PSSy/x7rvv5jnid76ff/7Z+bvwfI5+kt8QqEvRr18/kpOT+eyzz5zrcnJyeOeddwgICKBr164lsh+wF5sAderUYdKkSXzyyScsX77cJUtubm6e/vXmm29isVhc/qB38OBBfv311yLNOAgwfvx4QkJC+Oc//3nRdh4eHrRp08Y51LFjx44uz3fs2JHGjRvzxhtvcPr06Tyvd0yr7+HhwYABA/j666/zTKcPJdMPs7OzycnJcf4sXM7vhvyORm3cuJFly5YxYMCAAs9PLIinpye9e/fmyy+/dJkK/vDhw8yfP59rrrmGoKAg5/rRo0djs9n46KOPmDZtGl5eXowcObLEfl5FpHRV6iNOF3PgwAFmzZrFgQMHqFOnDgBPPPEE3333HbNmzSryyZx9+vRhxowZDBgwgKioKNavX8+MGTOcJ7KHhYXx559/snLlSoYMGcLSpUvZvXs3DzzwAFarNd8Te0Xc2d13302LFi249dZbCQkJ4ejRoyxbtowff/yRp59+mrZt2wL28zCuuOIKhg0bxsMPP4zFYmHu3LkFfsE4fPiwc3jPsWPHmDp1Kl5eXnnOOVq+fDm9evW66F/Rhw4dyscff8zjjz9OXFwc1157Lenp6fzwww888MAD3HLLLS7bGz16dLG+4L/00ks8+eSTXHHFFfk+7+3tzaRJkxgxYgRdu3Zl8ODBzunIw8PDXaYzf/vtt7nmmmuIiopizJgxREREsG/fPr799lsSExOLnMnh9OnTfPfdd4B96ua3334bb29vZ7FWVB4eHjzzzDOFtps0aRLr16/ntttuo127dgBs2LCBjz/+mBo1auSZ9CElJcX5//lCF7sw7pgxY5g6dSrDhw9n/fr1hIeHs2jRIn799VfeeuutPOcblZQxY8Ywf/58/vnPf7J161b8/f256aab6N69O08//TT79u2jffv2LF++nC+//JJHH33UebTngw8+YOLEifj7++eZSKIgy5cvZ968efj4+FxWbg8PD2bMmEHfvn1p3bo1I0aMoG7duhw6dIgff/yRoKAgvv76awAmTJjA8uXL6dq1K2PGjKFly5YkJSWxcOFCfvnll2IPO09PT3cZqjd37lwyMzO59dZbgeL/bjjfgQMH6N+/PwMHDqRu3bps3bqV6dOnU6tWrUuepOHll18mNjaWa665hgceeAAvLy+mTp1KVlYWr732mrPdrFmz+Pbbb5k9ezb16tUD4J133uGee+7hgw8+4IEHHrik/YtIGTJrOr/yBjAWL17sfOyY9rdq1aouNy8vL2PQoEF5Xj9s2DDjlltuybM+IyPDGDFihOHl5WV4enoaderUMcaOHWsARnJysmEYhtG0aVOjfv36Rk5OjvN1kydPNkJDQ0v8fYqY7YMPPjD69etn1KlTx/Dy8jKqV69u9OnTx1i6dGmetr/++qtx1VVXGVWqVHH+7Hz//fcu0yYbRt5pqqtXr2506dIlzzYBw2KxGOvXr3dZn9/0yBkZGcbTTz9tREREGN7e3kZoaKhxxx13OKccdkw9XaVKFePQoUMur71wyu+CpjYu7PnPPvvMiIyMNHx9fY0aNWoYQ4YMMf766688r9+6datx6623GtWrVzf8/PyM5s2bG88++2y++ypsOvL8Psdly5bl2/58509HXpD8piP/9ddfjQcffNBo06aNUa1aNcPb29to0KCBMXz4cJfpnfPLd+GtMIcPHzZGjBhh1KpVy/Dx8THatm3rMk33hS5nOvLz7dy50/Dz8zMee+wx57q0tDTjscceM+rUqWN4e3sbTZs2NV5//XWX6btjYmKMgQMHGjt27Mizr4KmI+/QoYPLNgrKdKELpyN32Lhxo3HbbbcZNWvWNHx9fY2GDRsagwYNMlasWOHSbv/+/cbQoUON4OBgw9fX12jUqJHx4IMPGllZWYVmP9+wYcNc/p8GBAQYUVFRxty5c13aFfV3w4XS0tKM0aNHGw0bNjR8fHyM4OBg4957780znX9xpiM3DMPYsGGD0adPHyMgIMDw9/c3unfvbqxZs8b5/MGDB41q1aoZN910U55Mt956q1G1alXjzz//LDC3iJQPFsPQ8WGwT5m6ePFi58x4n332GUOGDGHbtm15hqgEBATkGXriuIhiQTPzWa1WDh8+TFhYGNOmTeOpp57i1KlTeHh40LVrV7y9vV1mj1q2bBn9+vUjKyvrsv9yKCIiIiIil0dD9QoQGRlJbm4uR44ccY5Xvxze3t7OQ/MLFizgxhtvdI6l7tKlC/Pnz3c5afmPP/4gLCxMRZOIiIiISDlQqQun06dPs3v3bufjvXv3kpiYSI0aNWjWrBlDhgxh6NChTJ48mcjISI4ePcqKFSto166dc7z/9u3byc7O5sSJE6SlpTnPKXBM3/rHH38QFxfHlVdeycmTJ/nvf//L1q1bmTNnjnO/999/P++++y6PPPII//rXv9i1axcTJkwo8ph2EREREREpXZV6qN6qVavo3r17nvXDhg1j9uzZWK1WXn75ZT7++GMOHTpErVq1uOqqqxg/frzzBPbw8PB8L5zo+Fh///137r77bnbu3Im3tzfdu3dn0qRJeU4kX7t2LY899hiJiYnUrVuXkSNH8tRTTxV7JisRERERESl5lbpwEhERERERKQpdx0lERERERKQQKpxEREREREQKUekmh7DZbPz9998EBgZisVjMjiMiIiIiIiYxDIO0tDTq1KnjnN26IJWucPr777+pX7++2TFERERERKScOHjwoPPSQQWpdIVTYGAgYP9wgoKCTE4jlYnVamX58uX07t0bb29vs+OIFIv6r7gz9V9xZ+q/pSs1NZX69es7a4SLqXSFk2N4XlBQkAonKVNWqxV/f3+CgoL0i0/cjvqvuDP1X3Fn6r9loyin8GhyCBERERERkUKocBIRERERESmECicREREREZFCVLpznERERESk9OXm5mK1Ws2O4fasViteXl5kZmaSm5trdhy35OPjU+hU40WhwklERERESoxhGCQnJ3Pq1Cmzo1QIhmEQGhrKwYMHdQ3SS+Th4UFERAQ+Pj6XtR0VTiIiIiJSYhxFU+3atfH399eX/ctks9k4ffo0AQEBJXLUpLKx2Wz8/fffJCUl0aBBg8vqjyqcRERERKRE5ObmOoummjVrmh2nQrDZbGRnZ+Pn56fC6RIFBwfz999/k5OTc1lTuuvTFxEREZES4Tinyd/f3+QkIuc4huhd7jliKpxEREREpERpeJ6UJyXVH1U4iYiIiIiIFEKFk4iIiIiIG1q1ahUWi8WtZzB84YUX6NChg9kxikSFk4iIiIiUK7k2g7V7jvNl4iHW7jlOrs0o1f0NHz4ci8XCq6++6rJ+yZIlbj/sMDw8HIvFkud24XstCxaLhSVLlrise+KJJ1ixYkWZZ7kU5aZwevXVV7FYLDz66KMXbbdw4UJatGiBn58fbdu2ZenSpWUTUERERERK3Xdbk7hm0koGT/+NRxYkMnj6b1wzaSXfbU0q1f36+fkxadIkTp48WaLbzc7OLtHtXYoXX3yRpKQkl9u//vUvs2MBEBAQ4DYzMJaLwik+Pp6pU6fSrl27i7Zbs2YNgwcPZuTIkWzcuJEBAwYwYMAAtm7dWkZJRcTJlgt7f4Yti+z3Nl3NXERELs93W5O4/5MNJKVkuqxPTsnk/k82lGrx1LNnT0JDQ5k4ceJF233++ee0bt0aX19fwsPDmTx5ssvz4eHhvPTSSwwdOpSgoCDGjBnD7NmzqV69Ot988w3NmzfH39+fO+64g4yMDObMmUN4eDhXXHEFDz/8sMvMb3PnziUmJob69etTp04d7r77bo4cOVLs9xYYGEhoaKjLrWrVqs7nly5dSrNmzahSpQrdu3dn9uzZLkMA8xtO99ZbbxEeHu58HB8fT69evahVqxbVqlWja9eubNiwweVzAbj11luxWCzOxxdu22az8eKLL1KvXj18fX3p0KED3333nfP5ffv2YbFY+OKLL+jevTv+/v60b9+etWvXFvtzKS7TC6fTp08zZMgQpk+fzhVXXHHRtlOmTOGGG27gySefpGXLlrz00ktERUXx7rvvllFaEQFg+1fwVhuYcyN8PtJ+/1Yb+3oREZHzGIZBRnZOobe0TCvPf7WN/AblOda98NV20jKtRdqeYRRveJ+npycTJkzgnXfe4a+//sq3zfr16xk0aBB33XUXW7Zs4YUXXuDZZ59l9uzZLu3eeOMN2rdvz8aNG3n22WcByMjI4O2332bBggV89913rFq1iltvvZWlS5eydOlS5s6dy9SpU1m0aJFzO1arlfHjx/Pzzz/zxRdfsG/fPoYPH16s91WYgwcPctttt3HTTTeRmJjIqFGj+Pe//13s7aSlpTFs2DB++eUXfvvtN5o2bUq/fv1IS0sD7IUVwKxZs0hKSnI+vtCUKVOYPHkyb7zxBps3b6ZPnz7cfPPN7Nq1y6Xd008/zRNPPEFiYiLNmjVj8ODB5OTkFDt3cZh+AdwHH3yQ/v3707NnT15++eWLtl27di2PP/64y7o+ffrkGSt5vqysLLKyspyPU1NTAXtHdFxrQKQsOPqbu/c7y45v8Px8BGBw/qhvIzUJ/jeU3NtnYbS40ax4UkoqSv+Vykn9t+xYrVYMw8Bms2Gz2QDIyM6hzQuxl71tA0hOzaTtC8uL1H7rC73w9ynaV13DMDAMg1tuuYUOHTrw3HPPMWPGDOd7cNxPnjyZ66+/nqeffhqAJk2asG3bNl5//XWGDh3q3F737t157LHHnI9/+uknrFYr7733Ho0bNwbg9ttv55NPPiEpKYmAgABatGhBt27dWLlyJQMHDgTs514ZhkFaWhqBgYG89dZbXHnllaSmphIQEOCSz7Gcn6eeeopnnnnGZd23337Ltddey/vvv0/jxo15/fXXAWjatCmbN2/mtddec27XUYSev48L13Xr1s1l+x9++CE1atTgxx9/5MYbb3QOxwsKCqJ27drO1164nTfeeIOxY8cyaNAgACZOnMiPP/7Im2++ybvvvuts9/jjj9O3b18Ann/+edq2bcsff/xBixYt8rx/x36sViuenp4uzxXn94KphdOCBQvYsGFDgRXnhZKTkwkJCXFZFxISQnJycoGvmThxIuPHj8+zfvny5bo4m5giNvby//EwjWGj97bH8bygaAKwYGAA2V89TuwewGL6AW0pBW7df6XSU/8tfV5eXoSGhnL69GnnuT1nss0Zyp2WmkaOj2fhDbF/ec7JySE1NZVnnnmGW265hX/84x+cOXMGOPeH923bttGvXz/nY4DIyEimTJnCyZMn8fT0xGaz0aZNG5c2mZmZ+Pv7Exwc7FxfvXp1GjRogM1mc66rUaMGf//9t/NxYmIir776Klu3biUlJcVZNGzfvp0WLVqQkZFhf69paXh45P/vrs1m41//+hd33323y/qwsDBSU1PZsmULkZGRLnnbt2/vst2srCxyc3PzvKfzsx85coRXXnmFX375haNHj2Kz2cjIyOCPP/5wed2ZM2dcHp+/7dTUVP7++286dOjg0qZTp05s3bqV1NRUTp8+DUDjxo2dbQICAgDYu3cvderUyfMZZGdnc+bMGVavXp3nqJTjMywK0wqngwcP8sgjjxAbG4ufn1+p7WfcuHEuR6lSU1OpX78+vXv3JigoqNT2K3Ihq9VKbGwsvXr1wtvb2+w4l8Sy/xe8Ek8U/Dzgbz1B/zbVMRpeU3bBpNRVhP4rlZf6b9nJzMzk4MGDBAQEOL/fBRoGW1/oVehr4/ae4L456wttN3NYR2IiahTaroq3Z5FnxPP29sbLy4ugoCD69u1L7969mTBhAsOGDQNwfmf09PTE19fX5TtklSpVnG08PT3x8PCgZs2aLm38/Pzw9vbOs+7Cbfn4+ODh4UFQUBDp6enccccd9O7dm2nTptGwYUMOHjxI37598fHxISgoyHkQIDAwsMDvtR4eHtStW7fAKb+9vLzyZHO8J8d2q1Sp4szl4HivjnV33nknJ06cYMqUKTRs2BBfX1+6dOmCp6dnnm2f/9jX1zdPG39//zyfi+P/j6NIql69urONo6C8cNsOmZmZVKlSheuuuy5P3XF+gVYY0wqn9evXc+TIEaKiopzrcnNzWb16Ne+++y5ZWVl5DqWFhoZy+PBhl3WHDx8mNDS0wP34+vri6+ubZ723t7d+eYop3LrvnTlepGZeZ46Du75HuSi37r9S6an/lr7c3FwsFgseHh4uR0ACPAs/8tO1eQhh1fxITsnM9zwnCxBazY+uzUPw9CjZKcIdU3Q7Mk+aNIkOHTo4h3051rds2ZI1a9a4vLe1a9fSrFkzl751/rbOf/356xxF3YXrHK/9448/OH78OBMnTqRatWoEBQWxceNG52vO/4wv/Lzze38FPd+qVSu++uorl+fj4uJctlu7dm2Sk5Od+QA2bdrkkn/NmjW8//773Hijfbj+wYMHOXbsmMu+vb29MQyjwM+hevXq1KlTh7Vr19K9e3dnmzVr1hATE1Pgey7sc/Dw8MBiseT7O6A4vxNMG0vTo0cPtmzZQmJiovPWqVMnhgwZQmJiYp6iCaBz58555nmPjY2lc+fOZRVbpHILCCm8TXHaiYiInOXpYeH5m1oB5DMc3O75m1qVeNGUn7Zt2zJkyBDefvttl/X/93//x4oVK3jppZf4448/mDNnDu+++y5PPPFEiWdo0KABPj4+vPvuu+zbt4+vvvqKl1566ZK2lZaWRnJyssvNcaTln//8J7t27eLJJ59k586dzJ8/P89kF926dePo0aO89tpr7Nmzh/fee49ly5a5tGnatClz587l999/Z926dQwZMsR55MohPDycFStWkJycXOC0708++SSTJk3is88+Y+fOnfz73/8mMTGRRx555JLee0kyrXAKDAykTZs2LreqVatSs2ZN2rRpA8DQoUMZN26c8zWPPPII3333HZMnT2bHjh288MILJCQk8NBDD5n1NkQql4ZXQ1DescPnWCCorr2diIhIMd3QJowP7okitJrrcKrQan58cE8UN7QJK7MsL774Yp4JF6Kiovjf//7HggULaNOmDc899xwvvvhiic90BxAcHMzs2bNZtGgRV111Fa+99hpvvPHGJW3rueeeIywszOU2duxYwF6gff755yxZsoT27dvz4YcfMmHCBJfXt2zZkvfff5/33nuP9u3bExcXl6dY/Oijjzh58iRRUVHce++9PPzww85JIBwmT55MbGws9evXJzIyMt+sDz/8MI8//jj/93//R9u2bfnuu+/46quvaNq06SW995JkMYo7V2Mp6tatGx06dOCtt95yPg4PD3epehcuXMgzzzzDvn37aNq0Ka+99hr9+vUr8j5SU1OpVq0aKSkpOsdJypTVamXp0qX069fPvYeKbF0Mi4YX8KQFBn0MrW4uy0RSBipM/5VKSf237GRmZrJ3714iIiIu6xz2XJtB3N4THEnLpHagHzERNcrkSFN55JiAISgo6KLD8UrSqlWr6N69OydPnqR69eplss/SdLF+WZzawPTpyM+3atWqiz4GGDhwoHOKRhExgeNEW4sHGOf9Jc7bH26dqqJJREQum6eHhc6Na5odQ8SF5gsWkeKJn2G/7/IoDPsGuj5lf2yzQcS1psUSERERKU0qnESk6A5vh30/g8UTokfaC6Vu4yCkDeRmwsZ5ZicUERGRy9StWzcMw6gQw/RKkgonESk6x9GmFv2gWj37ssUC0aPOPX+RK5eLiIiIuCsVTiJSNJkpsGmBfTl6tOtz7QaBbzU4uRf2rMj7WhERERE3p8JJRIpm0wKwpkOt5hBxnetzPlUhcoh9OW562WcTERERKWUqnESkcIZxriCKGX1uZr3zOYbr7VoOJ/aWXTYRERGRMqDCSUQK9+cqOL4LfAKg3Z35t6nZGBpfDxiQ8FFZphMREREpdSqcRKRwjkkh2g8Gv4tcHC5mjP1+4ydgPVP6uURERETKiAonEbm4Uwdh51L7smM4XkGa9oZqDeDMSdj6eelnExERESkjKpxE5OISZoJhs08IUbvFxdt6nL2+E0DcNPu5USIiIsVly4W9P8OWRfZ7W26Z7DY5OZlHHnmEJk2a4OfnR0hICF26dOGDDz4gIyMDgPDwcCwWCxaLhSpVqhAeHs6gQYNYuXJlmWQU86hwEpGC5WTBho/tyxdOQV6QyHvB0xeSNsFfCaWXTUREKqbtX8FbbWDOjfD5SPv9W23s60vRn3/+SWRkJMuXL2fChAls3LiRtWvXMnbsWL755ht++OEHZ9sXX3yRpKQkdu7cyccff0z16tXp2bMnr7zySqlmFHN5mR1ARMqxbUsg4xgE1YXm/Yr2mqo1oc3tsGk+xE+H+tGlGlFERCqQ7V/B/4YCF4xYSE2yrx/0MbS6uVR2/cADD+Dl5UVCQgJVq1Z1rm/UqBG33HILxnmjKAIDAwkNDQWgQYMGXHfddYSFhfHcc89xxx130Lx581LJKObSEScRKVjcNPt9pxHgWYy/s8ScPTq1bTGcPlryuURExH0YBmSnF37LTIVlY8lTNNk3Yr/77il7u6JsrxjDxY8fP87y5ct58MEHXYqm81nyuxTHeR555BEMw+DLL78s8n7FveiIk4jk79AGOJQAHt4QNax4r60bBXU7wqH1sGEOXPdE6WQUEZHyz5oBE+qUwIYMSP0bXq1ftOb/+dt+gfYi2L17N4Zh5DlSVKtWLTIzMwF48MEHmTRpUoHbqFGjBrVr12bfvn1FyyduR0ecRCR/jinIWw+AgNrFf73jnKiEWZCbU2KxREREykpcXByJiYm0bt2arKysQtsbhlHokSlxXzriJCJ5ZZw4N52449pMxdX6Vlj+NKT+BX8sg5Y3lVw+ERFxH97+9qM/hdm/BubdUXi7IYug4dVF228RNWnSBIvFws6dO13WN2rUCIAqVaoUuo3jx49z9OhRIiIiirxfcS864iQieW2cCzmZENoO6l3i5A7efhA11L4cN73ksomIiHuxWOxD5gq7Nb4eguoABR2xsdgnK2p8fdG2V4wjPzVr1qRXr168++67pKenX9LbnDJlCh4eHgwYMOCSXi/lnwonEXFlyz03TC9mdLH+4cmj031g8YC9P8HRnYW3FxGRysvDE25wnEN04b89Zx/f8Kq9XSl4//33ycnJoVOnTnz22Wf8/vvv7Ny5k08++YQdO3bg6Xluv2lpaSQnJ3Pw4EFWr17NmDFjePnll3nllVdo0qRJqeQT86lwEhFXu2Lh1AHwqw5tijBk4mKqN4Bmfe3LjmJMRESkIK1utk85HhTmuj6oTqlORQ7QuHFjNm7cSM+ePRk3bhzt27enU6dOvPPOOzzxxBO89NJLzrbPPfccYWFhNGnShHvvvZeUlBRWrFjBU089VWr5xHw6x0lEXMWfHVYXeQ/4FH18eIFiRsHObyHxU+jxHPgGXv42RUSk4mp1M7Tobz/n6fRhCAixn9NUSkeazhcWFsY777zDO++8U2AbzZpXealwEpFzju+B3T8AFogeWTLbjOgGNZvA8d2wacG5azyJiIgUxMMTIq41O4WICw3VE5Fz4j+y3zftBTUalcw2PTzOTU0eP6NYFyQUERERKS9UOImIXXY6JH5iX44u4aNCHQaDd1U4ugP2/VKy2xYREREpAyqcRMRuy0LITIErwqFJz5Ldtl81aH+nfTluWsluW0RERKQMqHASEfvwubizs95Fj7IPrytpjqNYO76FlEMlv30RERGRUqTCSUTg4Do4vAW8/KDDkNLZR0graNgFjFxYP7t09iEiIiJSSlQ4ici54XNtB4J/jdLbj2NGvfWzISe79PYjIiIiUsJUOIlUdmnJsP1L+3JpTxXe4kYIDIP0I/D7V6W7LxEREZESpMJJpLJbPwdsOVAvBsLal+6+PL2h43D7siaJEBERETeiwkmkMsu1wvpZ9uWYMWWzz47DwcPLfl5V0uay2aeIiIjIZVLhJFKZ7fgW0pKgajC0urls9hkYCi3P7it+etnsU0RE3EquLZf45HiW/rmU+OR4cm25pb7Po0ePcv/999OgQQN8fX0JDQ2lT58+/Prrr842Gzdu5M477yQsLAxfX18aNmzIjTfeyNdff41x9gLv+/btw2KxOG+BgYG0bt2aBx98kF27dpX6+5DS42V2ABExUdzZwiVqGHj5lt1+Y0bDti9g80Lo9SJUuaLs9i0iIuXaD/t/4NW4Vzmccdi5LsQ/hH/H/JueDUv4OoPnuf3228nOzmbOnDk0atSIw4cPs2LFCo4fPw7Al19+yaBBg+jZsydz5syhSZMmZGVlsWbNGp555hmuvfZaqlevfu59/PADrVu3JiMjgy1btjBlyhTat2/P119/TY8ePUrtfUjpUeEkUlkd3g77fwGLJ3QaUbb7btAZQtrA4a2wcR5c/VDZ7l9ERMqlH/b/wOOrHsfAcFl/JOMIj696nP92+2+pFE+nTp3i559/ZtWqVXTt2hWAhg0bEhMTA0B6ejojR46kf//+fPHFFy6vbdmyJSNHjnQecXKoWbMmoaGhADRq1IibbrqJHj16MHLkSPbs2YOnp2eJvw8pXRqqJ1JZxZ+94G2LflCtXtnu22KxX2jXkcNmK9v9i4hImTEMgwxrRqG3tKw0JsZNzFM0ARhn/3s17lXSstKKtL0LC5mLCQgIICAggCVLlpCVlZXn+eXLl3P8+HHGjh1b4DYsFstF9+Hh4cEjjzzC/v37Wb9+fZGzSfmhI04ilVFmCmxaYF8uq0khLtRuEMQ+Dyf3wp4V0LSXOTlERKRUnck5w5XzryyRbR3OOMzVC64uUtt1d6/D39u/SG29vLyYPXs2o0eP5sMPPyQqKoquXbty11130a5dO/744w8Amjdv7nxNfHw83bt3dz5esGABN95440X306JFC8B+HpTjaJa4Dx1xEqmMNi0AazoEt4Dwa83J4FMVIofYl+M0SYSIiJjr9ttv5++//+arr77ihhtuYNWqVURFRTF79ux827dr147ExEQSExNJT08nJyen0H04joIVdnRKyicdcRKpbAzjXKESPco+bM4s0aPgt/dh13I4sRdqRJiXRURESkUVryqsu3tdoe3WH17PAyseKLTd+z3ep2NIxyLtt7j8/Pzo1asXvXr14tlnn2XUqFE8//zzvPnmmwDs3LmTq666CgBfX1+aNGlSrO3//vvvAERE6N87d6QjTiKVzZ+r4Pgu8AmE9neZm6VmY2jcAzAg4SNzs4iISKmwWCz4e/sXeru6ztWE+IdgIf8/6FmwEOofytV1ri7S9kriqE6rVq1IT0+nd+/e1KhRg0mTJl3ytmw2G2+//TYRERFERkZedjYpeyqcRCobx6QQ7e8C30Bzs4B9anKAjZ+A9Yy5WURExDSeHp78O+bfAHmKJ8fjp2KewtOj5GejO378ONdffz2ffPIJmzdvZu/evSxcuJDXXnuNW265hYCAAGbMmMG3335L//79+f777/nzzz/ZvHkzr732mj3/BbPkHT9+nOTkZP7880+++uorevbsSVxcHB999JFm1HNTGqonUpmcOgg7l9qXHbPama1pb6jWAFIOwNbPIfIesxOJiIhJejbsyX+7/Tff6zg9FfNUqV3HKSAggCuvvJI333yTPXv2YLVaqV+/PqNHj+Y///kPALfeeitr1qxh0qRJDB06lBMnTlCtWjU6deqU78QQPXvas/r7+9OwYUO6d+/OtGnTij28T8oPFU4ilUnCTDBsEHEd1G5hdho7D0+IHgk/PA9x06DDEHPPuxIREVP1bNiT7vW7s+HIBo5mHCXYP5io2lGlcqTJwdfXl4kTJzJx4sSLtuvUqRMLFy68aJvw8PBiTYUu7kOFk0hlYc2EDXPsy9Gjzc1yoch74ccJkLQJ/kqA+tFmJxIRERN5engSHap/C6R8MfUcpw8++IB27doRFBREUFAQnTt3ZtmyZQW2nz17NhaLxeXm5+dXholF3Nj2JZBxHILqQvN+ZqdxVbUmtLndvhw3zdwsIiIiIvkwtXCqV68er776KuvXrychIYHrr7+eW265hW3bthX4mqCgIJKSkpy3/fv3l2FiETfmmIK80wjwLIcHmx2TRGxfAqePmhpFRERE5EKmfnu66aabXB6/8sorfPDBB/z222+0bt0639dYLBZCQ0PLIp5IxXFoAxxKAA9viBpmdpr81Y2Cuh3h0Hr7kMLrnjA7kYiIiIhTufmzc25uLgsXLiQ9PZ3OnTsX2O706dM0bNgQm81GVFQUEyZMKLDIAsjKyiIrK8v5ODU1FQCr1YrVai25NyBSCEd/M6Pfea6bhgdga3kzub5XQDnt+5ao+/A6tB4j/iNyrnwQPMrNr6hKz8z+K3K51H/LjtVqxTAMbDYbNpvN7DgVgmOiCcfnKsVns9kwDAOr1ZpnKvji/F6wGCZP+7FlyxY6d+5MZmYmAQEBzJ8/n3798j//Yu3atezatYt27dqRkpLCG2+8werVq9m2bRv16tXL9zUvvPAC48ePz7N+/vz5+Pv7l+h7ESmPvHPS6LP1UTwNK6ubPcvJqk3NjlQgD1s2vbc9hm9OGnERD5NUvZPZkUREpBi8vLwIDQ2lfv36+Pj4mB1HBIDs7GwOHjxIcnIyOTk5Ls9lZGRw9913k5KSQlBQ0EW3Y3rhlJ2dzYEDB0hJSWHRokXMmDGDn376iVatWhX6WqvVSsuWLRk8eDAvvfRSvm3yO+JUv359jh07VuiHI1KSrFYrsbGx9OrVC29v7zLbr8fad/BcOR4jpC05I1eW+6m+PX58Cc81U7CFX0fukC/MjiNnmdV/RUqC+m/ZyczM5ODBg4SHh2sCrxJiGAZpaWkEBgZiKef/hpdXmZmZ7Nu3j/r16+fpl6mpqdSqVatIhZPp42B8fHycFwLr2LEj8fHxTJkyhalTpxb6Wm9vbyIjI9m9e3eBbXx9ffH19c33tfrlKWYo075ny4UNswCwXPkPvN3hr38xo2DtO3jsW43HqT8huLnZieQ8+t0p7kz9t/Tl5uZisVjw8PDAw8PUOcgqDMfwPMfnKsXn4eGBxWLJ93dAcX4nlLtP32azuRwhupjc3Fy2bNlCWFhYKacScVO7YuHUAfCrfm667/KuegNo1te+HD/D3CwiIiIiZ5laOI0bN47Vq1ezb98+tmzZwrhx41i1ahVDhgwBYOjQoYwbN87Z/sUXX2T58uX8+eefbNiwgXvuuYf9+/czatQos96CSPkWf3YK8sh7wMeNzumLOfsznfgpZKWZm0VERCq14cOHM2DAgAKff+GFF+jQoUOZ5RHzmFo4HTlyhKFDh9K8eXN69OhBfHw833//Pb169QLgwIEDJCUlOdufPHmS0aNH07JlS/r160dqaipr1qwp0vlQIpXO8T2w+wfAAtEjzU5TPBHdoGZTyE6DTQvMTiMiImXMyM0lfV0cKd98S/q6OIzcXLMjua1u3brx6KOPFvi8xWJx3oKCgoiOjubLL78su4BuxNRznD766KOLPr9q1SqXx2+++SZvvvlmKSYSqUDiz/58Ne0FNRqZm6W4PDwgehR895R9uF70qHI/qYWIiJSM1OXLOTxhIjnJyc51XqGhhPxnHEG9e5uYzDzZ2dmluv1Zs2Zxww03kJqayvvvv88dd9zBhg0baNu2banu192Uu3OcRKQEZKfDxk/sy9Gjzc1yqToMBu+qcHQH7PvF7DQiIlIGUpcv59Ajj7oUTQA5hw9z6JFHSV2+vFT2u2jRItq2bUuVKlWoWbMmPXv2JD09Pd+28fHxBAcHM2nSpAK3N2PGDFq2bImfnx8tWrTg/fffd3n+qaeeolmzZvj7+9OoUSOeffZZl+sJOYb/zZgxg8aNGxMaGgrYjw7NmDGDW2+9FX9/f5o2bcpXX3112e+/evXqhIaG0qxZM1566SVycnL48ccfL3u7FY3ps+qJSCnYshCyUuCKcGjS0+w0l8avGrS/ExJmQtw0iLjW7EQiInIJDMPAOHOm8Ha5uRx++RXI70o5hgEWOPzKBKp27ozlgouY5sdSpUqRpu9OSkpi8ODBvPbaa9x6662kpaXx888/k98Ve1auXMltt93Ga6+9xpgxY/Ld3rx583juued49913iYyMZOPGjYwePZqqVasybNgwAAIDA5k9ezZ16tRhy5YtjB49msDAQMaOHevczu7du/n8889ZtGgRZ877/MaPH89rr73G66+/zjvvvMOQIUPYv38/NWrUKPS9FiYnJ8c5IkzX4cpLhZNIRWMYEHd2NrroUfZhb+4qerS9cNrxLaQcgmp1zU4kIiLFZJw5w86ojiWwIfuRpz+iY4rUvPmG9Vj8C58YKSkpiZycHG677TYaNmwIkO8QtcWLFzN06FBmzJjBnXfeWeD2nn/+eSZPnsxtt90GQEREBNu3b2fq1KnOwumZZ55xtg8PD+eJJ55gwYIFLoVTdnY2H3/8MTVr1iQ1NdW5fvjw4QwePBiACRMm8PbbbxMXF8cNN9xQ6HstyODBg/H09OTMmTPYbDbCw8MZNGjQJW+vonLjb1Qikq8Dv8HhLeDlBx2GmJ3m8oS0goZdwMiF9bPMTiMiIhVQ+/bt6dGjB23btmXgwIFMnz6dkydPurRZt24dAwcOZO7cuRctmtLT09mzZw8jR44kICDAeXv55ZfZs2ePs91nn31Gly5dCA0NJSAggGeeeYYDBw64bKthw4YEBwfn2Ue7du2cy1WrViUoKIgjR45c6tsH7PMIJCYmsmzZMlq1asWMGTNK5AhWRaMjTiIVjWMK8rYDwb8C/NKLGQ37f4X1c+C6seCloQMiIu7EUqUKzTesL7RdRkICB8f8o9B29adNxb9TpyLttyg8PT2JjY1lzZo1LF++nHfeeYenn36adevWERERAUDjxo2pWbMmM2fOpH///gVeNPX06dMATJ8+nSuvvDLPfgDWrl3LkCFDGD9+PH369KFatWosWLCAyZMnu7SvWrVqvvu4cN8Wi8V5kdxLFRoaSpMmTWjSpAmzZs2iX79+bN++ndq1a1/WdisaHXESqUjSkmH72SlEY9x0UogLtbgRAsMg/Qj8fvknwIqISNmyWCx4+PsXeqvapQteoaEFz6JqseAVGkrVLl2KtL2inN90fsYuXbowfvx4Nm7ciI+PD4sXL3Y+X6tWLVauXMnu3bsZNGiQy0QO5wsJCaFOnTr8+eefzkLEcXMUYWvWrKFhw4Y8/fTTdOrUiaZNm7J///6if6ClLCYmho4dO/LKK6+YHaXcUeEkUpGsnwO2HKh/JYS1NztNyfD0ho4j7Mtx08zNIiIipcbi6UnIf8adfXBB0XP2cch/xhVpYojiWLduHRMmTCAhIYEDBw7wxRdfcPToUVq2bOnSrnbt2qxcuZIdO3YwePBgcnJy8t3e+PHjmThxIm+//TZ//PEHW7ZsYdasWfz3v/8FoGnTphw4cIAFCxawZ88e3n77bZcirTQcPXqUxMREl9vhw4cLbP/oo48ydepUDh06VKq53I0KJ5GKItd67jwgd52CvCAdh4GHFxxcB0mbzU4jIiKlJKh3b+pOeQuvkBCX9V4hIdSd8lapXMcpKCiI1atX069fP5o1a8YzzzzD5MmT6du3b562oaGhrFy5ki1btjBkyBBy87kw76hRo5gxYwazZs2ibdu2dO3aldmzZzuPON1888089thjPPTQQ3To0IE1a9bw7LPPlvj7Ot/8+fOJjIx0uU2fPr3A9jfccAMRERE66nQBi5HfXIsVWGpqKtWqVSMlJYWgoCCz40glYrVaWbp0Kf369StwbPRl2bYEFg6DqsHw2Dbw8i35fZhp4QjY9gVEDYWb3zE7TaVT6v1XpBSp/5adzMxM9u7dS0REBH5+fpe8HSM3l4yE9eQcPYpXcDD+nTqW+JEmd2Gz2UhNTSUoKAgPd54p10QX65fFqQ306YtUFHFn/3LUcXjFK5oAYs5eL2PzQjhz8uJtRUTErVk8Pal6ZQzVbuxP1StjKm3RJOWLCieRiuDwdtj/C1g8z50PVNE0uApC2kDOGdg4z+w0IiIiUsmocBKpCOLPXvC2Rb+Ke5FYi8V+QV+wv9/LnHpVREREpDhUOIm4u8wU2LTAvuwYzlZRtRsEvtXg5F7Ys8LsNCIiIlKJqHAScXebFoA1HYJbQPi1ZqcpXT5VIXKIfTmu4NmAREREREqaCicRd2YY5wqI6FEFXzSwInEM19u1HE7sNTeLiIiIVBoqnETc2Z+r4Pgu8AmE9neZnaZs1GwMjXsABiR8ZHYaERERqSRUOIm4M8ekEO3vAt9Ac7OUpZizF/jdMBeyM8zNIiIiIpWCCicRd3XqIOxcal92DF+rLJr2hmoNIPMUbP3c7DQiIiJSCahwEnFXCTPBsEHEdVC7hdlpypaHJ0SPtC/HT7ef6yUiIlIKhg8fzoABAwp8/oUXXqBDhw5llkfMo8JJxB1ZM2HDHPty9Ghzs5gl8l7w9IWkTfBXgtlpRESkBNlsBod2nuSP+GQO7TyJzaY/kF2qvXv3cvfdd1OnTh38/PyoV68et9xyCzt27HBp9+OPP3LjjTcSHByMn58fjRs35s4772T16tXONqtWrcJisWCxWPDw8KBatWpERkYyduxYkpKSyvqtlTkvswOIyCXYvgQyjkNQXWjez+w05qhaE9reAYnzIG4a1I82O5GIiJSAPRuP8PNnu0g/leVcV7W6L9fe2ZTGkbVNTGae7OzsS3qd1WqlV69eNG/enC+++IKwsDD++usvli1bxqlTp5zt3n//fR566CHuvfdePvvsMxo3bkxKSgo//vgjjz32GOvXr3fZ7s6dOwkKCiI1NZUNGzbw2muv8dFHH7Fq1Sratm17OW+1XNMRJxF35JiCvNMI8KzEf/9wnNu1fQmcPmpqFBERuXx7Nh7hu6lbXYomgPRTWXw3dSt7Nh4plf0uWrSItm3bUqVKFWrWrEnPnj1JT0/Pt218fDzBwcFMmjSpwO3NmDGDli1b4ufnR4sWLXj//fddnn/qqado1qwZ/v7+NGrUiGeffRar1ep83jH8b8aMGTRu3JjQ0FAALBYLM2bM4NZbb8Xf35+mTZvy1VdfFZhj27Zt7Nmzh/fff5+rrrqKhg0b0qVLF15++WWuuuoqAA4cOMCjjz7Ko48+ypw5c7j++utp2LAh7dq145FHHiEhIe+ojtq1axMaGkqzZs246667+PXXXwkODub+++8v+EOuAFQ4ibibQxvgUAJ4eEPUMLPTmKtuFNTtCLnZ54YuiohIuWIYBtas3EJvWWdy+PmzPy66rZ8/20XWmZwibc8o4vmvSUlJDB48mPvuu4/ff/+dVatWcdttt+X7+pUrV9KrVy9eeeUVnnrqqXy3N2/ePJ577jleeeUVfv/9dyZMmMCzzz7LnDnn/p0KDAxk9uzZbN++nSlTpjB9+nTefPNNl+3s3r2bzz//nEWLFrkMlxs/fjyDBg1i8+bN9OvXjyFDhnDixIl8swQHB+Ph4cGiRYvIzc3Nt83nn3+O1Wpl7Nix+T5vKcI1IqtUqcI///lPfv31V44cKZ3itjyoxH+qFnFTjinIW98KAZVzyIKLmDGw+B/2yTK6PFq5j8CJiJRDOdk2pj3yU4lsK/1UFjMeW114Q2DMlK54+3oW2i4pKYmcnBxuu+02GjZsCJDvcLPFixczdOhQZsyYwZ133lng9p5//nkmT57MbbfdBkBERATbt29n6tSpDBtm/4PnM88842wfHh7OE088wYIFC1yKl+zsbD7++GNq1qxJamqqc/3w4cMZPHgwABMmTODtt98mLi6OG264IU+WunXr8vbbbzN27FjGjx9Pp06d6N69O0OGDKFRo0YA/PHHHwQFBTmPaoG9mHJkBVi7dm2hQ/BatLBPVLVv3z5q166Y3090xEnEnWScgC2L7MsxlXRSiAu1GgD+NSH1EPyxzOw0IiLiZtq3b0+PHj1o27YtAwcOZPr06Zw8edKlzbp16xg4cCBz5869aNGUnp7Onj17GDlyJAEBAc7byy+/zJ49e5ztPvvsM7p06UJoaCgBAQE888wzHDhwwGVbDRs2JDg4OM8+2rVr51yuWrUqQUFBFz3K8+CDD5KcnMy8efPo3LkzCxcupHXr1sTGxjrbXHhUqU+fPiQmJvLtt9+Snp5e4NGq8zmO0BXlCJW70p9mRdzJho8hNwtC20E9TYYAgLcfRA2FX960n/vV8iazE4mIyHm8fDwYM6Vroe3+3nWKb97dVGi7Gx9qT52m1Yu036Lw9PQkNjaWNWvWsHz5ct555x2efvpp1q1bR0REBACNGzemZs2azJw5k/79++Pt7Z3vtk6fPg3A9OnTufLKK/PsB+xHb4YMGcL48ePp06cP1apVY8GCBUyePNmlfdWqVfPdx4X7tlgs2Gy2i77HwMBAbrrpJm666SZefvll+vTpw8svv0yvXr1o2rQpKSkpJCcnO486BQQE0KRJE7y8il4q/P7774D9CFpFpSNOIu7ClgsJH9mXY8ZABf6LTrF1ug8sHrD3Jzi60+w0IiJyHovFgrevZ6G3+q1qULW670W3FXCFL/Vb1SjS9opz5MNisdClSxfGjx/Pxo0b8fHxYfHixc7na9WqxcqVK9m9ezeDBg1ymcjhfCEhIdSpU4c///yTJk2auNwcRdiaNWto2LAhTz/9NJ06daJp06bs37+/yFkvl8VioUWLFs7JL+644w68vb0vOtlFYc6cOcO0adO47rrr8j1KVlHoiJOIu9gVC6cOgF91aHO72WnKl+oNoFlf2Pmt/Rywfq+bnUhERIrJw8PCtXc25bupWwtsc82gpnh4lOwfDtetW8eKFSvo3bs3tWvXZt26dRw9epSWLVu6tKtduzYrV66ke/fuDB48mAULFuR7RGb8+PE8/PDDVKtWjRtuuIGsrCwSEhI4efIkjz/+OE2bNuXAgQMsWLCA6Ohovv32W5cirSQlJiby/PPPc++999KqVSt8fHz46aefmDlzpnNyiwYNGjB58mQeeeQRTpw4wfDhw4mIiODEiRN88sknwLmjZQ5HjhwhMzOTtLQ01q9fz2uvvcaxY8f44osvSuV9lBc64iTiLuKm2e8j7wEff3OzlEcxZ6cmT/wUstLMzSIiIpekcWRtbvhHmzxHngKu8OWGf7Qples4BQUFsXr1avr160ezZs145plnmDx5Mn379s3TNjQ0lJUrV7JlyxaGDBmS77k/o0aNYsaMGcyaNYu2bdvStWtXZs+e7TzidPPNN/PYY4/x0EMP0aFDB9asWcOzzz5b4u8LoF69eoSHhzN+/HiuvPJKoqKimDJlCuPHj+fpp592tvvXv/7F8uXLOXr0KHfccQdNmzalX79+7N27l++++y7PxBDNmzenTp06dOzYkVdffZWePXuydetWWrVqVSrvo7ywGEWdq7GCSE1NpVq1aqSkpBAUFGR2HKlErFYrS5cupV+/fgWOjS7Q8T3wThRggYc3QI1GpZLRrdls8F4MHN8F/d7Q5Bkl7LL6r4jJ1H/LTmZmJnv37iUiIgI/P79L3o7NZpC06xTpqVlUDfIlrGn1Ej/S5C5sNhupqakEBQXh4aFjHpfiYv2yOLWBPn0RdxB/9tympr1UNBXEw+PcBXHjZ0Dl+puQiEiF4uFhoW7zK2gWHUrd5ldU2qJJyhcVTiLlXXY6bLSPMSZaR1EuqsNg8K4KR3fAvp/NTiMiIiIViAonkfJuy0LISoErwqFJT7PTlG9+1aD92etrxE03N4uIiIhUKCqcRMozw4C4Gfbl6FH24WhycY6jcju+hZRD5mYRERGRCkPfwkTKswO/weEt4FUFOgwxO417CGkFDa8BIxfWzzI7jYhIpVTJ5h6Tcq6k+qMKJ5HyLP7scLO2d4B/DXOzuBPH1OTr50BOtrlZREQqEceshRkZGSYnETknO9v+XeDC61EVly6AK1JepSXD9i/ty5pau3ha3AiBYZCWBL9/ZS88RUSk1Hl6elK9enWOHDkCgL+/PxaLZsS7HDabjezsbDIzMzUd+SWw2WwcPXoUf3//fC9YXBwqnETKq/VzwJYD9a+EsPZmp3Evnt7QcQSsmmC/cLAKJxGRMhMaGgrgLJ7k8hiGwZkzZ6hSpYqK0Evk4eFBgwYNLvvzU+EkUh7lWs+dn6MpyC9Nx2Gw+jU4uA6SNkNYO7MTiYhUChaLhbCwMGrXro3VajU7jtuzWq2sXr2a6667ThdwvkQ+Pj4lcrROhZNIebTjG/sws6rB0Opms9O4p8BQaHkzbPvCfq7Yze+YnUhEpFLx9PS87HNKxP455uTk4Ofnp8LJZBooKVIeOaYg7zgcvHxNjeLWYsbY7zcvhDMnzc0iIiIibs3UwumDDz6gXbt2BAUFERQUROfOnVm2bNlFX7Nw4UJatGiBn58fbdu2ZenSpWWUVqSMHN4O+38Bi6f9PB25dA2ugpA2kHMGNs4zO42IiIi4MVMLp3r16vHqq6+yfv16EhISuP7667nlllvYtm1bvu3XrFnD4MGDGTlyJBs3bmTAgAEMGDCArVu3lnFykVLkmIK8RT+oVtfcLO7OYrFfOBggfgbYbObmEREREbdlauF000030a9fP5o2bUqzZs145ZVXCAgI4Lfffsu3/ZQpU7jhhht48sknadmyJS+99BJRUVG8++67ZZxcpJRkpsCmz+zLjmFmcnnaDQLfanByL+xZYXYaERERcVPl5hyn3NxcFixYQHp6Op07d863zdq1a+nZs6fLuj59+rB27dqyiChS+jYtAGs6BLeA8GvNTlMx+FSFyCH25bjp5mYRERERt2X6rHpbtmyhc+fOZGZmEhAQwOLFi2nVqlW+bZOTkwkJCXFZFxISQnJycoHbz8rKIisry/k4NTUVsE/tqCkypSw5+luB/c4w8IqbhgXIjboPW05O2YWr6CKH4f3b+xi7lpNzZBdcEW52IrdTaP8VKcfUf8Wdqf+WruJ8rqYXTs2bNycxMZGUlBQWLVrEsGHD+Omnnwosnopr4sSJjB8/Ps/65cuX4+/vXyL7ECmO2NjYfNcHp27l6uO7sXr4sTypGjma+KREXRXYlpC0Lexb9Czb6w42O47bKqj/irgD9V9xZ+q/pSMjI6PIbU0vnHx8fGjSpAkAHTt2JD4+nilTpjB16tQ8bUNDQzl8+LDLusOHDzuvUJ2fcePG8fjjjzsfp6amUr9+fXr37k1QUFAJvQuRwlmtVmJjY+nVq1e+12HwXLgAAI/IIfS+4fayjlfhWf7wgIX30CR1LeHDp4K3/nBSHIX1X5HyTP1X3Jn6b+lyjEYrCtMLpwvZbDaXoXXn69y5MytWrODRRx91rouNjS3wnCgAX19ffH3zXgfH29tbnU9MkW/fO3UQdn0HgOdV/8BTfbPktewH1RtgOXUA7x1fQdS9ZidyS/rdKe5M/Vfcmfpv6SjOZ2rq5BDjxo1j9erV7Nu3jy1btjBu3DhWrVrFkCH2E7mHDh3KuHHjnO0feeQRvvvuOyZPnsyOHTt44YUXSEhI4KGHHjLrLYiUjISZYNgg4joIbm52morJwxM6jbQvx08HwzA3j4iIiLgVUwunI0eOMHToUJo3b06PHj2Ij4/n+++/p1evXgAcOHCApKQkZ/urr76a+fPnM23aNNq3b8+iRYtYsmQJbdq0MestiFw+ayZsmGNfjh5tbpaKLvJe8PSFpE3wV4LZaURERMSNmDpU76OPPrro86tWrcqzbuDAgQwcOLCUEomYYPsSyDgOQXWheT+z01RsVWtC2zsgcR7ETYP60WYnEhERETdRbq7jJFJpOa4t1GkEeJa70w4rnuhR9vvtS+D0UVOjiIiIiPtQ4SRipkMb4FACeHhD1DCz01QOdaOgbkfIzT43RFJERESkECqcRMwUP8N+3/pWCKhtbpbKJGaM/T5hJuTqQsMiIiJSOBVOImbJOAFbFtmXYzQpRJlqNQD8a0LqIfhjmdlpRERExA2ocBIxy4aPITcLQttBPU1SUKa8/c4NjYybZm4WERERcQsqnETMYMuFhLOzSsaMAYvF3DyVUaf7wOIBe1fD0Z1mpxEREZFyToWTiBl2xcKpA+BXHdrcbnaayql6fWjW177sONdMREREpAAqnETM4BgeFnUv+Pibm6Uyc5xblvgpZKWZm0VERETKNRVOImXtxB7YswKwQKeRZqep3Bp1g5pNITsNNi0wO42IiIiUYyqcRMqYx/pZ9oWmvaBGhLlhKjuL5dwFceNngGGYm0dERETKLRVOImXIMzcLj03z7Q8c1xISc3UYDN5V4egO2Pez2WlERESknFLhJFKG6p1cgyUrFa6IgMY9zI4jAH7VoP2d9uW46eZmERERkXJLhZNIWTEMIo7+YF+OHgke+vErN6LPThKx41tIOWRuFhERESmX9M1NpIxY/lpHtcyDGF5VoMMQs+PI+UJaQcNrwMgFxzloIiIiIudR4SRSRjzOXvDWaH0b+NcwOY3kEXN2koj1cyAn29wsIiIiUu6ocBIpC2nJWHZ8DUCupiAvn1rcCIFhkH4Efv/K7DQiIiJSzqhwEikL6+dgseVwvGpTCG1ndhrJj6c3dBxhX3ZcoFhERETkLBVOIqUt1+o8b2ZvLc2kV651HAYeXnBwHSRtMjuNiIiIlCMqnERK245vIC0Jo2pt/q4eY3YauZjAUGh1i31ZU5OLiIjIeVQ4iZS2uBkA2Drci+HhZXIYKZRjavIti+DMSXOziIiISLmhwkmkNB3eDvt/AYsntqhhZqeRomhwFYS0gZwzsHGe2WlERESknFDhJFKa4s8O92rRH4LqmJtFisZigZizR53iZ4DNZm4eERERKRdUOImUlswU2PSZfdnxRVzcQ9uB4FsNTu6FPSvMTiMiIiLlgAonkdKyaQFY0yG4BYRfa3YaKQ6fqhA5xL6sSSJEREQEFU4ipcMwzn3hjh5lH/4l7iV6lP1+13I4sdfcLCIiImI6FU4ipeHPVXB8F/gEQvu7zE4jl6JmY2jcAzAg4SOz04iIiIjJVDiJlAbH0ab2d4FvoLlZ5NI5zk3bMBeyM8zNIiIiIqZS4SRS0k4dhD+W2Zc1KYR7a9obqjeAzFOw9XOz04iIiIiJVDiJlLSEmWDYIOI6CG5udhq5HB6e0GmkfTl+uv3cNREREamUVDiJlCRrJmyYY1+O1tGmCiFqKHj5QdIm+CvB7DQiIiJiEhVOIiVp+xLIOA5BdaF5P7PTSEnwrwFtbrcvx00zN4uIiIiYRoWTSElyTArRaQR4epmbRUqOY2ry7Uvg9BFTo4iIiIg5VDiJlJRDG+BQAnj6QNRws9NISaobBXU7QW72uaGYIiIiUqmocBIpKfEz7PetBkBAsKlRpBQ4ZkhMmAW5OeZmERERkTKnwkmkJGScgC2L7MuagrxiajUA/GtC6qFz082LiIhIpaHCSaQkbPgYcrMgrD3UizY7jZQGbz+IGmZf1iQRIiIilY4KJ5HLZcuFhI/sy9GjwWIxN4+Unk73gcUD9q6GozvNTiMiIiJlSIWTyOXaFQunDoBf9XPTVkvFVL0+NOtrX3ac0yYiIiKVggonkcvlGLYVdS/4+JubRUqf4xy2xE8hK83cLCIiIlJmVDiJXI7je2DPCsACnUaanUbKQqNuULMpZKfBpgVmpxEREZEyosJJ5HI4hms17QU1IszNImXDYjl3Qdz4GWAY5uYRERGRMqHCSeRSZafDxnn25Zgx5maRstVhMHhXhaM7YN/PZqcRERGRMmBq4TRx4kSio6MJDAykdu3aDBgwgJ07Lz5T1ezZs7FYLC43Pz+/Mkoscp4tCyErBa6IgMY9zE4jZcmvGrS/074cN93cLCIiIlImTC2cfvrpJx588EF+++03YmNjsVqt9O7dm/T09Iu+LigoiKSkJOdt//79ZZRY5CzDOPeFOXoUeOjgbaUTfXaSiB3fQsohc7OIiIhIqfMyc+ffffedy+PZs2dTu3Zt1q9fz3XXXVfg6ywWC6GhoaUdT6RgB36Dw1vBqwpEDjE7jZghpBU0vAb2/wLrZ8H1z5idSEREREqRqYXThVJSUgCoUaPGRdudPn2ahg0bYrPZiIqKYsKECbRu3TrftllZWWRlZTkfp6amAmC1WrFarSWUXCobz3VT8QBsrW8j1ysAitCXHP1N/a7isHQcgdf+XzDWzyan86Pg5Wt2pFKj/ivuTP1X3Jn6b+kqzudqMYzyMSWUzWbj5ptv5tSpU/zyyy8Ftlu7di27du2iXbt2pKSk8MYbb7B69Wq2bdtGvXr18rR/4YUXGD9+fJ718+fPx99f19yR4vO1nqL31sfwIJdVzV8kxT/c7EhiEouRQ69t/0cV60kSGv6TQzWuNjuSiIiIFENGRgZ33303KSkpBAUFXbRtuSmc7r//fpYtW8Yvv/ySbwFUEKvVSsuWLRk8eDAvvfRSnufzO+JUv359jh07VuiHI5Ifj59fx3P1JGz1YsgdtrTIr7NarcTGxtKrVy+8vb1LMaGUpUvtD+5G/VfcmfqvuDP139KVmppKrVq1ilQ4lYuheg899BDffPMNq1evLlbRBODt7U1kZCS7d+/O93lfX198ffMOn/H29lbnk+LLtcLGjwHwiBmDxyX0IfW9Cib6PvhlMh5/xeFxbDuEtTc7UalS/xV3pv4r7kz9t3QU5zM1dSowwzB46KGHWLx4MStXriQiovgXEM3NzWXLli2EhYWVQkKRC+z4BtKSoGptaHWL2WmkPAgMPdcXNDW5iIhIhWVq4fTggw/yySefMH/+fAIDA0lOTiY5OZkzZ8442wwdOpRx48Y5H7/44ossX76cP//8kw0bNnDPPfewf/9+Ro0aZcZbkMombob9vuMw8PIxN4uUH46pybcsgjMnzc0iIiIipcLUwumDDz4gJSWFbt26ERYW5rx99tlnzjYHDhwgKSnJ+fjkyZOMHj2ali1b0q9fP1JTU1mzZg2tWrUy4y1IZXJ4m33qaYsndBxhdhopTxpcBSFtIOcMbJxndhoREREpBaae41SUeSlWrVrl8vjNN9/kzTffLKVEIhcRf/ZoU4v+UK2uuVmkfLFYIGY0fP2IvZ9c9YAuiiwiIlLB6F92kaLITIFNZ4+Exow2N4uUT20Hgm81OLkX9qwwO42IiIiUMBVOIkWR+ClY0yG4BYRfa3YaKY98qkLkPfZlTRIhIiJS4ahwEimMYZwbphc9yj4sSyQ/0SPt97uWw4m95mYRERGREqXCSaQwf66C47vAJxDa32V2GinPajaGxj0AAxI+MjuNiIiIlCAVTiKFcQy76jAYfAPNzSLlX8wY+/2GuZCdYW4WERERKTEqnEQu5tRB+GOZfTla1wqTImjaC6o3gMxTsPVzs9OIiIhICVHhJHIxCTPBsEHEdRDc3Ow04g48PKHT2XOd4qbZz5ETERERt6fCSaQg1kzYMMe+7Bh+JVIUUUPByw+SN8Nf8WanERERkRKgwkmkINuXQMZxCKoHzfqanUbciX8NaHO7fVlTk4uIiFQIKpxECuL4wttpOHh6mRpF3JDjnLjtS+D0EVOjiIiIyOVT4SSSn0Mb4FACePpA1HCz04g7qhsFdTtBbva5IZ8iIiLitlQ4ieTHccHbVgMgINjUKOLGYkbb7xNmQW6OuVlERETksqhwErlQ+nHYssi+7PjiK3IpWg0A/5qQeujctPYiIiLillQ4iVxo41zIzYKw9lAv2uw04s68/SBqmH05bpq5WUREROSyqHASOZ8tFxI+si9HjwaLxdw84v463QcWD9i7Go7uNDuNiIiIXCIVTiLn27UcTh0Av+rnppMWuRzV60PzfvZlx7lzIiIi4nZUOImczzEFedS94ONvbhapOBxTkyd+Cllp5mYRERGRS6LCScTh+B7YswKwQKeRZqeRiqRRN6jZFLLTYNMCs9OIiIjIJVDhJOLgGEbVtDfUiDA3i1QsFsu5GRrjZ4BhmJtHREREik2FkwhAdjpsnGdf1hTkUhra3wXeVeHoDtj3s9lpREREpJhUOIkAbFkIWSlwRQQ07mF2GqmI/KpB+zvty45z6URERMRtqHASMYxzX2SjR4GHfiyklESfPZq541tIOWRuFhERESkWfUMUOfAbHN4KXlUgcojZaaQiC2kFDa8BIxfWzzI7jYiIiBSDCieR+LNHm9reAVWuMDeLVHwxZ6cmXz8bcrJMjSIiIiJFp8JJKre0ZNj+pX1Zk0JIWWhxIwSGQfpR2P6V2WlERESkiFQ4SeW2fg7YcqD+lRDW3uw0Uhl4ekPHEfbleE0SISIi4i5UOEnllWuFhJn25WgdbZIy1HE4eHjDwXWQtMnsNCIiIlIEKpyk8trxDZxOhqq1odUtZqeRyiQwBFrdbF/W1OQiIiJuQYWTVF5xM+z3HYeBl4+5WaTycRzl3LIIzpw0N4uIiIgUSoWTVE6Ht8H+X8Diee58E5Gy1OAqCGkLOWdg4zyz04iIiEghVDhJ5RR/9mhTi/5Qra65WaRysljOTU0ePwNsNnPziIiIyEWpcJLKJzMFNn1mX9YU5GKmtgPBtxqc3At7VpidRkRERC5ChZNUPomfgjUdgltA+LVmp5HKzKcqRN5jX46bZm4WERERuSgVTlK5GMa5YXrRo+zDpUTMFD3Sfr8rFk7sNTeLiIiIFEiFk1Quf66C47vAJxDa32V2GhGo2Rga9wAMSPjI7DQiIiJSABVOUrk4rpnTYTD4BpqbRcQhZoz9fsNcyM4wN4uIiIjkq1iF02uvvcaZM2ecj3/99VeysrKcj9PS0njggQdKLp1ISTp1AP5YZl+OHmVuFpHzNe0F1RtA5inY+rnZaURERCQfxSqcxo0bR1pamvNx3759OXTokPNxRkYGU6dOLbl0IiUpYSYYNoi4DoKbm51G5BwPT+h09lynuGn2c/FERESkXClW4WRc8I/5hY9Fyi1rJmz42L7sGBYlUp5EDQUvP0jeDH/Fm51GRERELqBznKRy2L4EMo5DUD1o1tfsNCJ5+deANrfblx3n4omIiEi5ocJJKgfHNXI6DQdPL1OjiBTIcUHm7Uvg9BFTo4iIiIirYn+DnDFjBgEBAQDk5OQwe/ZsatWqBeBy/pNIuXFoAxxaD54+EDXc7DQiBasTCXU7waEE2DAHrnvS7EQiIiJyVrEKpwYNGjB9+rkhJKGhocydOzdPm6KaOHEiX3zxBTt27KBKlSpcffXVTJo0iebNL37i/sKFC3n22WfZt28fTZs2ZdKkSfTr1684b0UqE8cFb1sNgIBgU6OIFCpmNCxOgIRZ0OUxHSEVEREpJ4r1L/K+fftKdOc//fQTDz74INHR0eTk5PCf//yH3r17s337dqpWrZrva9asWcPgwYOZOHEiN954I/Pnz2fAgAFs2LCBNm3alGg+qQDSj8OWRfZlTQoh7qDVAPj+aUg9ZJ8+v+VNZicSERERTD7H6bvvvmP48OG0bt2a9u3bM3v2bA4cOMD69esLfM2UKVO44YYbePLJJ2nZsiUvvfQSUVFRvPvuu2WYXNzGxrmQmwVh7aFeJ7PTiBTO288+wx6cOzdPRERETFesI05r167l+PHj3Hjjjc51H3/8Mc8//zzp6ekMGDCAd955B19f30sKk5KSAkCNGjUumuHxxx93WdenTx+WLFmSb/usrCyXi/SmpqYCYLVasVqtl5RT3IQtF6/4j7AAOVH3YeTkmBrH0d/U76RQHYbi9etbWPauxpq0DWo1MzuR+q+4NfVfcWfqv6WrOJ9rsQqnF198kW7dujkLpy1btjBy5EiGDx9Oy5Ytef3116lTpw4vvPBCsQID2Gw2Hn30Ubp06XLRIXfJycmEhIS4rAsJCSE5OTnf9hMnTmT8+PF51i9fvhx/f/9i5xT3EZKykatSDpDtWZXv/6qK7e+lZkcCIDY21uwI4gZigiIJS1nPwc+fY0v9oWbHcVL/FXem/ivuTP23dGRkZBS5bbEKp8TERF566SXn4wULFnDllVc6J4yoX78+zz///CUVTg8++CBbt27ll19+KfZrL2bcuHEuR6hSU1OpX78+vXv3JigoqET3JeWL56ez7ffRw7mhxwBTs4D9LxqxsbH06tULb29vs+NIOWfZWxXm305E6m/U7zEdfANNzaP+K+5M/Vfcmfpv6XKMRiuKYhVOJ0+edDna89NPP9G377mLiUZHR3Pw4MHibBKAhx56iG+++YbVq1dTr169i7YNDQ3l8OHDLusOHz5MaGhovu19fX3zHTro7e2tzleRHd8Df64ELHjGjMazHP2/Vt+TImnaA2o2xXJ8F97bPz93jSeTqf+KO1P/FXem/ls6ivOZFmtyiJCQEPbu3QtAdnY2GzZs4KqrrnI+n5aWVqydG4bBQw89xOLFi1m5ciURERGFvqZz586sWLHCZV1sbCydO3cu8n6lEnBMQd60N9QovF+JlDsWy7liKW46GIa5eURERCq5YhVO/fr149///jc///wz48aNw9/fn2uvvdb5/ObNm2ncuHGRt/fggw/yySefMH/+fAIDA0lOTiY5OZkzZ8442wwdOpRx48Y5Hz/yyCN89913TJ48mR07dvDCCy+QkJDAQw89VJy3IhVZdjpsnGdfLid/pRe5JO3vAu+qcGwn7PvZ7DQiIiKVWrEKp5deegkvLy+6du3K9OnTmTZtGj4+Ps7nZ86cSe/evYu8vQ8++ICUlBS6detGWFiY8/bZZ5852xw4cICkpCTn46uvvpr58+czbdo02rdvz6JFi1iyZImu4STnbP4fZKXAFRHQuIfZaUQunV81e/EE9qNOIiIiYppineNUq1YtVq9eTUpKCgEBAXh6ero8v3DhQgIDi34Cs1GEoSerVq3Ks27gwIEMHDiwyPuRSsQwzg3Tix4FHqZeqkzk8sWMhoSPYMe3kHIIqtU1O5GIiEilVKzC6b777itSu5kzZ15SGJHLduA3OLwVvKpA5BCz04hcvtotoeE1sP8XWD8Lrn/G7EQiIiKVUrEKp9mzZ9OwYUMiIyOLdLRIpMzFTbPftxsIVa4wN4tISYkZfbZwmg3XPQlel3aRcREREbl0xSqc7r//fj799FP27t3LiBEjuOeee6hRo0ZpZRMpnrRk+P0r+3K0JoWQCqRFfwgMg7Qk2P6V/Q8DIiIiUqaKdQLIe++9R1JSEmPHjuXrr7+mfv36DBo0iO+//15HoMR86+eALQfqXwlh7cxOI1JyPL2h4wj7crwmiRARETFDsc+c9/X1ZfDgwcTGxrJ9+3Zat27NAw88QHh4OKdPny6NjCKFy7VCwtlz62LGmJtFpDR0HA4e3nBwHSRtMjuNiIhIpXNZU455eHhgsVgwDIPc3NySyiRSfDu+gdPJULU2tLzZ7DQiJS8wBFqd7duamlxERKTMFbtwysrK4tNPP6VXr140a9aMLVu28O6773LgwAECAgJKI6NI4eLOTkHecRh4+Vy8rYi7cpy7t2URZJwwN4uIiEglU6zJIR544AEWLFhA/fr1ue+++/j000+pVatWaWUTKZrD2+wzjlk8z50HIlIRNbgKQtrC4S2QOA+u/pfZiURERCqNYhVOH374IQ0aNKBRo0b89NNP/PTTT/m2++KLL0oknEiROC5426K/Lg4qFZvFAjGj4OtHIP4juOpBXeRZRESkjBSrcBo6dCgWi6W0sogUX2YKbPrMvhyjKcilEmg7EJY/Byf3wp4V0LSX2YlEREQqhWJfAFekXEn8FKzpENwCwq81O41I6fOpCpH3wG/v2S/4rMJJRESkTGiMh7gvm+3cNW2iR9mHMYlUBtEj7fe7YuHEXnOziIiIVBIqnMR97V0Fx3eDTyC0v8vsNCJlp2ZjaNITMCDhI7PTiIiIVAoqnMR9OaYg7zAYfAPNzSJS1hxTk2+YC9kZ5mYRERGpBFQ4iXs6dQD+WGZfjh5lbhYRMzTtBdUbQOYp2Pq52WlEREQqPBVO4p4SZoJhg4iuENzc7DQiZc/D89wfDeKmgWGYm0dERKSCU+Ek7seaCRs+ti9rCnKpzCLvBS8/SN4Mf8WbnUZERKRCU+Ek7mf7Esg4DkH1oFlfs9OImMe/BrS53b4cN93cLCIiIhWcCidxP3HT7PedRoBnsS5FJlLxOI66bl8Cp4+YGkVERKQiU+Ek7uXQBji0Hjx9IGqY2WlEzFcnEup2gtxs2DDH7DQiIiIVlgoncS/xZ6cgbzUAAoJNjSJSbjiOOiXMgtwcc7OIiIhUUCqcxH2kH4cti+zLMWPMzSJSnrQaAP61IPUQ7FxqdhoREZEKSYWTuI+NcyE3C8LaQ71OZqcRKT+8/SBqqH05XpNEiIiIlAYVTuIebLkQ/5F9OXo0WCzm5hEpbzrdBxYP2Lsaju40O42IiEiFo8JJ3MOu5ZByAKpcAW3vMDuNSPlTvT4072df1tTkIiIiJU6Fk7gHxxfByHvAu4q5WUTKq+hR9vtNCyArzdwsIiIiFYwKJyn/ju2GPSsAC3QaaXYakfKrUTeo2RSy0+zFk4iIiJQYFU5S/iWcPbepaW+oEWFuFpHyzGI5NzV53HQwDHPziIiIVCAqnKR8y06HjfPsy44vhCJSsPZ3gXdVOLYT9v1sdhoREZEKQ4WTlG+b/wdZKXBFBDTuYXYakfLPr5q9eAJNEiEiIlKCVDhJ+WUYED/Dvhw9CjzUXUWKxHF0dse3kHLI3CwiIiIVhL6JSvl14Dc4vBW8qkDkELPTiLiP2i2h4TVg5ML6WWanERERqRBUOEn5FTfNft9uoP36TSJSdI6jTutnQ06WqVFEREQqAhVOUj6lJcPvX9mXozUphEixtegPgWGQfhS2f2V2GhEREbenwknKp/VzwJYD9a+EsHZmpxFxP57e0HGEfTlek0SIiIhcLhVOUv7kWiFhpn05Zoy5WUTcWcfh4OENB9dB0iaz04iIiLg1FU5S/uz4Bk4nQ9Xa0PJms9OIuK/AEGh19mdIU5OLiIhcFhVOUv44vuB1HAZePuZmEXF3jqO2WxZBxglzs4iIiLgxFU5SvhzeBvt/BYvnufMzROTS1b8SQtpCzhlInGd2GhEREbelwknKF8cFb1v0h2p1zc0iUhFYLBAzyr4c/xHYbObmERERcVMqnKT8yEyBTZ/ZlzUphEjJaTsQ/KrByb2wZ4XZaURERNySCicpPxI/BWs6BLeE8GvMTiNScfhUhQ732JcdF5YWERGRYjG1cFq9ejU33XQTderUwWKxsGTJkou2X7VqFRaLJc8tOTm5bAJL6bHZzl1rJnqkfXiRiJSc6JH2+12xcGKvuVlERETckKmFU3p6Ou3bt+e9994r1ut27txJUlKS81a7du1SSihlZu8qOL4bfAKh/V1mpxGpeGo2hiY9AQMSPjI7jYiIiNvxMnPnffv2pW/fvsV+Xe3atalevXrJBxLzxJ2dFKLDYPANNDeLSEUVPRp2/wAb5kK3/4CPv9mJRERE3IaphdOl6tChA1lZWbRp04YXXniBLl26FNg2KyuLrKws5+PU1FQArFYrVqu11LNKEaQcxOuPZVgAa+RwqKD/Xxz9Tf1OTBPeDa9qDbCkHCBn0/8wOgwp8kvVf8Wdqf+KO1P/LV3F+VzdqnAKCwvjww8/pFOnTmRlZTFjxgy6devGunXriIqKyvc1EydOZPz48XnWL1++HH9//bW1PGj59/9oZtg4GtCKNXG7gd1mRypVsbGxZkeQSqxJ1c60TjnA6ZX/5adD1Yt9PqH6r7gz9V9xZ+q/pSMjI6PIbS2GYRilmKXILBYLixcvZsCAAcV6XdeuXWnQoAFz587N9/n8jjjVr1+fY8eOERQUdDmRpSTkZOL1TnssGcfJuX0ORov+ZicqNVarldjYWHr16oW3t7fZcaSyyjiB1zvtsORkkjNsGUa96CK9TP1X3Jn6r7gz9d/SlZqaSq1atUhJSSm0NnCrI075iYmJ4ZdffinweV9fX3x9ffOs9/b2VucrD7YtgozjEFQPr1Y3gqfbd8lCqe+JqaqFQJs7IPETvDbMgoiri/Vy9V9xZ+q/4s7Uf0tHcT5Tt7+OU2JiImFhYWbHkEvlmIK804hKUTSJlAsxo+z32xbD6SPmZhEREXETpn5TPX36NLt3nzufZe/evSQmJlKjRg0aNGjAuHHjOHToEB9//DEAb731FhEREbRu3ZrMzExmzJjBypUrWb58uVlvQS7HofX2m6cPRA0zO41I5VEnEup2gkMJsGEOXPek2YlERETKPVOPOCUkJBAZGUlkZCQAjz/+OJGRkTz33HMAJCUlceDAAWf77Oxs/u///o+2bdvStWtXNm3axA8//ECPHj1MyS+XyTEFeasBEBBsahSRSidmjP0+YRbk5pibRURExA2YesSpW7duXGxuitmzZ7s8Hjt2LGPHji3lVFIm0o/D1s/ty44vcCJSdloPgO//A6mHYOdSaHWz2YlERETKNbc/x0nc1Ma5kJsFYe2hXiez04hUPl6+EDXUvuw411BEREQKpMJJyp4tF+I/si/HjCn2dWREpIR0ug8sHrB3NRzdaXYaERGRck2Fk5S9Xcsh5QBUuQLa3G52GpHKq3p9aN7Pvhyno04iIiIXo8JJyp7jC1rkPeBdxdwsIpVd9NmpyTctgKw0c7OIiIiUYyqcpGwd2w17VgAW6DTS7DQi0qgb1GwK2Wn24klERETypcJJylbC2XObmvaGGhHmZhER+zmGMaPty3HT4SIznYqIiFRmKpyk7GSnw8Z59mXHFzURMV/7u8C7KhzbCft+NjuNiIhIuaTCScrO5v9BVgpcEQGNddFikXLDr5q9eAKIm2ZuFhERkXJKhZOUDcOA+Bn25ehR4KGuJ1KuOI4C71gKKYfMzSIiIlIO6durlI0Da+HwVvCqApFDzE4jIheq3RLCrwUjF9bPMjuNiIhIuaPCScqGYwrydgPt128SkfLHMTX5+tmQk2VqFBERkfJGhZOUvrRk+P0r+3K0JoUQKbda9IfAMEg/Ctu/MjuNiIhIuaLCSUrf+tlgy4H6V0FYO7PTiEhBPL2h03325fjp5mYREREpZ1Q4SenKtULC2fMlNAW5SPkXNQw8vOHgOkjaZHYaERGRckOFk5SuHd/A6WSoWhta3mx2GhEpTGAItDr7sxqno04iIiIOKpykdDm+eHUcDl4+pkYRkSKKGWO/37IIMk6Ym0VERKScUOEkpefwNtj/K1g8odMIs9OISFHVvxJC2kLOGUicZ3YaERGRckGFk5QexwVvW/SHoDrmZhGRorNYIObs1OTxH4HNZm4eERGRckCFk5SOzBTY9Jl92THsR0TcR9uB4FcNTu6FPSvMTiMiImI6FU5SOhI/BWs6BLeE8GvMTiMixeVTFTrcY1+Om2ZuFhERkXJAhZOUPJvt3DVgokfah/2IiPuJHmm/3xVrP/IkIiJSialwkpK3dxUc3w0+gdD+LrPTiMilqtkYmvQEDDzWzzI7jYiIiKlUOEnJizs7KUSHweAbaG4WEbk80fYLV3tsmo+nLcvkMCIiIuZR4SQl69QB+GOZfTl6lLlZROTyNe0F1RtiyTxF3ZO/mZ1GRETENCqcpGQlzATDBhFdIbi52WlE5HJ5eDrPdYo4+gMYhsmBREREzKHCSUqONRM2fGxfjhltbhYRKTmR92J4+VH9zH4shxLMTiMiImIKFU5ScrYthozjEFQPmvU1O42IlBT/GhitbgPAY/1HJocRERExhwonKTmOKcg7jQBPL3OziEiJyu10HwCW7V/C6SMmpxERESl7KpykZBxab795+kDUMLPTiEhJC+vACf/GWGxW2DDH7DQiIiJlToWTlAzHFOStb4WAYHOziEip2Bvc076QMAtyc8wNIyIiUsZUOMnlSz8OWz+3L0drUgiRiurv6jEY/rUg9RDsXGp2HBERkTKlwkku38a5kJsFYe2hXiez04hIKbF5eGPrcI/9geOcRhERkUpChZNcHlsuxJ+dZStmDFgs5uYRkVJlixoOFg/YuxqO7jQ7joiISJlR4SSXZ9dySDkAVa6ANrebnUZESlu1etC8n305TkedRESk8lDhJJcnbpr9PvIe8K5ibhYRKRuOC1xv+hQyU83NIiIiUkZUOMmlO7Yb9qwELNBppNlpRKSsRHSFWs0g+zRs/szsNCIiImVChZNcuoSz5zY17Q01IszNIiJlx2KB6FH25bjpYBjm5hERESkDKpzk0mSnw8Z59uWYMeZmEZGy134w+ATAsZ2w72ez04iIiJQ6FU5yaTb/D7JSoEYjaHy92WlEpKz5BUG7O+3LjnMdRUREKjAVTlJ8hgHxM+zLnUaCh7qRSKXkmCRix1JIOWRuFhERkVKmb7xSfAfWwuGt4FUFIoeYnUZEzFK7JYRfC0YurJ9ldhoREZFSZWrhtHr1am666Sbq1KmDxWJhyZIlhb5m1apVREVF4evrS5MmTZg9e3ap55QLOK7d0m6g/fpNIlJ5OSaJWD8bcrJMjSIiIlKaTC2c0tPTad++Pe+9916R2u/du5f+/fvTvXt3EhMTefTRRxk1ahTff/99KScVp7Rk+P0r+3L0aHOziIj5WvSHwDBIPwrbvzI7jYiISKnxMnPnffv2pW/fvkVu/+GHHxIREcHkyZMBaNmyJb/88gtvvvkmffr0Ka2Ycr71s8GWA/WvgrB2ZqcREbN5ekOn++DHVyB+uv1ItIiISAXkVuc4rV27lp49e7qs69OnD2vXrjUpUSWTa4WEs+cxxOhok4icFTUMPLzh4DpI2mR2GhERkVJh6hGn4kpOTiYkJMRlXUhICKmpqZw5c4YqVarkeU1WVhZZWefG3aempgJgtVqxWq2lG7iCsfz+JV6nkzGq1ianaV/Q51csjv6mfifu6KL9168Gni1vwmPbF9h+m0rujVPKOJ3Ixen3r7gz9d/SVZzP1a0Kp0sxceJExo8fn2f98uXL8ff3NyGR++qy6zVqAX8EdmbH9z+YHcdtxcbGmh1B5JIV1H9rZLfiWr7AtnkhsbYuWL0CyjiZSOH0+1fcmfpv6cjIyChyW7cqnEJDQzl8+LDLusOHDxMUFJTv0SaAcePG8fjjjzsfp6amUr9+fXr37k1QUFCp5q1QjmzHe+NODIsnjQa+TKOgMLMTuR2r1UpsbCy9evXC29vb7DgixVJo/zX6YsxYgteRrfSpfRTbVYPKPqRIAfT7V9yZ+m/pcoxGKwq3Kpw6d+7M0qVLXdbFxsbSuXPnAl/j6+uLr69vnvXe3t7qfMWxwX5uk6VFf7xrNjA5jHtT3xN3dtH+e+UY+PphPDfMwrPLw7o4tpQ7+v0r7kz9t3QU5zM19V+106dPk5iYSGJiImCfbjwxMZEDBw4A9qNFQ4cOdbb/5z//yZ9//snYsWPZsWMH77//Pv/73/947LHHzIhfeWSmwOb/2ZdjxpibRUTKr7YDwa8anNwHuzWcV0REKhZTC6eEhAQiIyOJjIwE4PHHHycyMpLnnnsOgKSkJGcRBRAREcG3335LbGws7du3Z/LkycyYMUNTkZe2xE/Bmg7BLSH8GrPTiEh55eMPHe6xL8dPNzeLiIhICTN1qF63bt0wDKPA52fPnp3vazZu3FiKqcSFzXbuC1DMKLBYzM0jIuVb9Ej47T3YFQsn/oQajcxOJCIiUiI0AF0ubu8qOL4bfAKh3Z1mpxGR8q5mY2jSEzAg/iOz04iIiJQYFU5ycXEz7PcdBoNvoLlZRMQ9RJ+9QPbGTyC76NO8ioiIlGcqnKRgpw7AH8vsy44vQiIihWnaC6o3hMxTsPVzs9OIiIiUCBVOUrCEmWDYIKIrBDczO42IuAsPT/u5TgBx0+Ai57KKiIi4CxVOkj9rJmz42L4co6NNIlJMkfeClx8kb4a/4s1OIyIictlUOEn+ti2GjOMQVA+a9TU7jYi4G/8a0OYO+3KcpiYXERH3p8JJ8ueYgrzTCPA0ddZ6EXFXMaPs99sWw+kj5mYRERG5TCqcJK9D6+03Tx+IGmZ2GhFxV3UioV402KywYY7ZaURERC6LCifJyzEFeetbISDY3Cwi4t4cM3ImzILcHHOziIiIXAYVTuIq/fi56YM1BbmIXK7WA8C/FqQegp1LzU4jIiJyyVQ4iauNH0NuFoS1h3qdzE4jIu7Oyxc6nh3yG69JIkRExH2pcJJzbLkQP9O+HDMGLBZz84hIxdBxBFg8YO9qOLLD7DQiIiKXRIWTnLNrOaQcgCpXQJvbzU4jIhVF9frQvJ99OX6GuVlEREQukQonOSdumv0+8l7wrmJuFhGpWBwX0t70KWSmmptFRETkEqhwErtju2HPSsAC0SPNTiMiFU1EV6jVDLJPw+bPzE4jIiJSbCqcxC7hI/t9095wRbipUUSkArJYIPrsBXHjpoNhmJtHRESkmFQ4CWSnw8Z59uWYMeZmEZGKq/1g8AmAYzth389mpxERESkWFU4Cm/8HWSlQoxE0vt7sNCJSUfkFQbs77cuOcypFRETchAqnys4wzs1y1WkkeKhLiEgpckwSsWMppBwyN4uIiEgx6FtyZXdgLRzeCl5VIHKI2WlEpKKr3RLCrwUjF9bPMjuNiIhIkalwquziptvv2w20X79JRKS0OSaJWD8bcrJMjSIiIlJUKpwqs7Rk+P0r+3L0aHOziEjl0aI/BNaB9KOw/Suz04iIiBSJCqfKbP1ssOVA/asgrJ3ZaUSksvD0hk4j7Mvx083NIiIiUkQqnCqrXCsknD2/IEZHm0SkjEUNAw9vOLgOkjaZnUZERKRQKpwqq9+/htPJULU2tLzZ7DQiUtkEhkCrW+zLcTrqJCIi5Z8Kp8rKMQV5x+Hg5WNqFBGppBxHu7cshIwT5mYREREphAqnyujwNtj/K1g8z51nICJS1upfCSFtIScTEueZnUZEROSiVDhVRo5hMS1vhKA65mYRkcrLYjl31Cl+Bths5uYRERG5CBVOlU1mCmz+n31ZU5CLiNnaDgS/anByH+z+wew0IiIiBVLhVNkkfgrWdAhuCeHXmJ1GRCo7H3/ocI99WVOTi4hIOabCqTKx2c59MYkZZR8mIyJituiR9vtdsXDiT3OziIiIFECFU2WydxUc3w0+gdDuTrPTiIjY1WwMTXoCBsR/ZHYaERGRfKlwqkzizk5B3mEw+Aaam0VE5HwxY+z3Gz+B7Axzs4iIiORDhVNlceoA/LHMvqxJIUSkvGnSE6o3hMxTsPVzs9OIiIjkocKpskiYCYYNIrpCcDOz04iIuPLwPHeuU9w0MAxz84iIiFxAhVNlYM2E9XPsyzE62iQi5VTkveDlB8mb4a94s9OIiIi4UOFUGWxbDGdOQFA9aNbX7DQiIvnzrwFt7rAvx2lqchERKV9UOFUGjinIO40ATy9zs4iIXEzMKPv9tsVw+oi5WURERM6jwqmiO7TefvP0gahhZqcREbm4OpFQLxpsVtgwx+w0IiIiTiqcKjrHFOStb4WAYHOziIgUhWPmz4RZkJtjbhYREZGzVDhVZOnHz03rqynIRcRdtB4A/rUg9RDsXGp2GhEREUCFU8W28WPIzYKwDlCvk9lpRESKxssXOp4dWhyvSSJERKR8KBeF03vvvUd4eDh+fn5ceeWVxMXFFdh29uzZWCwWl5ufn18ZpnUTtlyIn2lfjhkNFou5eUREiqPjCLB4wN7VcGSH2WlERETML5w+++wzHn/8cZ5//nk2bNhA+/bt6dOnD0eOFDybUlBQEElJSc7b/v37yzCxm9i1HFIOQJUroM3tZqcRESme6vWheT/7cvwMc7OIiIhQDgqn//73v4wePZoRI0bQqlUrPvzwQ/z9/Zk5c2aBr7FYLISGhjpvISEhZZjYTcRNs99H3gveVczNIiJyKRwX7N70KWSmmptFREQqPVMv6pOdnc369esZN26cc52Hhwc9e/Zk7dq1Bb7u9OnTNGzYEJvNRlRUFBMmTKB169b5ts3KyiIrK8v5ODXV/o+v1WrFarWW0DspZ47vxnvPSgws5EQOg4r6Pt2Mo79V2H4nFZop/bfe1XjVbIrl+C5yN87H1mlk2e1bKhT9/hV3pv5buorzuZpaOB07dozc3Nw8R4xCQkLYsSP/Me3Nmzdn5syZtGvXjpSUFN544w2uvvpqtm3bRr169fK0nzhxIuPHj8+zfvny5fj7+5fMGyln2vw1j8bA4aB2rFuzDdhmdiQ5T2xsrNkRRC5ZWfffiCpX0Y5dZPw0hZWHQ3W+plwW/f4Vd6b+WzoyMjKK3NZiGIZRilku6u+//6Zu3bqsWbOGzp07O9ePHTuWn376iXXr1hW6DavVSsuWLRk8eDAvvfRSnufzO+JUv359jh07RlBQUMm8kfIkOx2vt9tiyUol567PMBr3MDuRnGW1WomNjaVXr154e3ubHUekWEzrv1lpeL3dBkt2OjlDFmOEX1t2+5YKQ79/xZ2p/5au1NRUatWqRUpKSqG1galHnGrVqoWnpyeHDx92WX/48GFCQ0OLtA1vb28iIyPZvXt3vs/7+vri6+ub7+sqZOfbtBiyUqFGI7ya9QYP009jkwtU2L4nlUKZ91/vGtDuLkj4CK8NM6Hp9WW3b6lw9PtX3Jn6b+kozmdq6rdqHx8fOnbsyIoVK5zrbDYbK1ascDkCdTG5ubls2bKFsLCw0orpPgwD4s5e8yR6lIomEakYHJNE7FgKKX+Zm0VERCot079ZP/7440yfPp05c+bw+++/c//995Oens6IESMAGDp0qMvkES+++CLLly/nzz//ZMOGDdxzzz3s37+fUaNGmfUWyo8Da+HINvCqAh3uNjuNiEjJqN0Swq8FIxcSZpmdRkREKilTh+oB3HnnnRw9epTnnnuO5ORkOnTowHfffeecMOLAgQN4nHfk5OTJk4wePZrk5GSuuOIKOnbsyJo1a2jVqpVZb6H8cBxtajfQfv0mEZGKInoU7PsZNsyBrmPBK+8QbBERkdJkeuEE8NBDD/HQQw/l+9yqVatcHr/55pu8+eabZZDKzaQlw+9f2ZejR5ubRUSkpLXoD4F1IO1v2P6V/Q9EIiIiZej/27v34KjLe4/jn839IuEWSEKTAMqlXBMNRKMwWuHIxWphwKrlYEDFIwLHmoOjoBAo7TCdUrWDFAYUi4LCCR0i1YhCjtaW4kkACaiJiOVaSEIKISHXTbLnjzWhOYlsEpZ99rd5v2Yy+e0vz/72E/gC+fI8v2eNL9WDmxz4g9RQJ8XdJsWMNJ0GANzLP1Aa5VzC3fQG3wAAeBCNky+ot19Z95/MbBMAH3VLquQXKJ3Jkc7lmU4DAOhkaJx8Qf6fpMuFUnhvacj9ptMAwPXRJUoa+hPnceM9nQAAeAiNky/Ifc35OWmWFBBkNAoAXFeNs+pHMqTKC2azAAA6FRonqyv6Ujq5V7L5X1n/DwC+Ku5WKXqEVFctHdpiOg0AoBOhcbK6xuUqQ34sRfQxmwUArjeb7crOobmvSQ0NZvMAADoNGicrqyqVDm9zHrMFOYDOYsQDUkhX6eIJ6dge02kAAJ0EjZOV5b0j2SulXkOkfmNMpwEAzwgKk26e6TzOZZMIAIBn0DhZVUPDlU0hkh93Ll8BgM5i1KPOz9/sli783WwWAECnQONkVcc/kf55TArqIo180HQaAPCsnjdJA8ZLcki5r5tOAwDoBGicrKpxU4jEn0nBXcxmAQATkp9wfv58s1RbaTYLAMDn0ThZUekp6egu5/Hox81mAQBTBoyXuvWVqkulL7abTgMA8HE0Tla0f6PkaJD63yn1GmQ6DQCY4ecvjX7MeZyzQXI4zOYBAPg0GiersVdLBzY5jxuXqQBAZ3XzTCkgRCo8LJ3JNZ0GAODDaJys5ssdUtUFKSJWGjTRdBoAMCushzR8uvM4Z73ZLAAAn0bjZDWN71kyarbkH2A2CwB4g+Tv3gD8y0zpcrHRKAAA30XjZCX/OOD88A+Sbkk1nQYAvEOfRCl2tNRglw5uMp0GAOCjaJysJOe7N7wdNlW6oZfZLADgTUZ/N+u0/w2pvs5sFgCAT6JxsoqKf0pf/NF53PgDAgDAadgUKSxSKvuH9HWW6TQAAB9E42QVn78p1ddIMYlS7CjTaQDAuwQES0nfLWFuvBcUAAA3onGygoZ6KXej8zh5jmSzmc0DAN4oabZk85OOfyoVF5hOAwDwMTROVnD0Q+nSKSm0uzR8muk0AOCdusVJgyc7j3NfM5sFAOBzaJysoHHZyc0zpcBQs1kAwJs1bk2e945UXWY2CwDAp9A4ebuSY9K3/yPJJo1+zHQaAPBu/e+UIgdJtZelw9tMpwEA+BAaJ2/XuNxk0ASpez+jUQDA69lsV3YezdkgORxm8wAAfAaNkzerrZAOve08ZgtyAGibhIekoBukkq+dG0UAAOAGNE7e7PB/SzWXpB43SjfdbToNAFhDSIQ08kHnMVuTAwDchMbJWzkczmUmkjT6ccmP3yoAaLPGTSIKsqRLZ8xmAQD4BH4a91an9knFX0oBoVLiz0ynAQBr6T1E6jdWctRL+98wnQYA4ANonLxV42zTyAec798EAGifxlmng5ukuhqzWQAAlkfj5I3KC6X8nc5jNoUAgI4ZfK/UpY9UcV76aqfpNAAAi6Nx8kYH/iA11Elxt0kxI02nAQBr8g+QRs12HuesN5sFAGB5NE7ept5+ZT1+MrNNAHBNbkmV/AKlMznSuTzTaQAAFkbj5G3y/yRdLpTCe0tD7jedBgCsrUuUNPQnzuMctiYHAHQcjZO3yX3N+TlplhQQZDQKAPiExtn7IxlS5QWzWQAAlkXj5E2KvpRO7pVs/lfW5QMArk3crVL0CKmuWjq0xXQaAIBF0Th5k8ZlJEN+LEX0MZsFAHyFzXZlh9Lc16SGBrN5AACWROPkLapKpcPbnMdsQQ4A7jXiASmkq3TxhHRsj+k0AAALonHyFnnvSPZKqdcQqd8Y02kAwLcEhUk3z3Qe57JJBACg/WicvEFDw5VNIZIfdy4rAQC416hHnZ+/2S1d+LvZLAAAy6Fx8gbHP5H+eUwK6iKNfNB0GgDwTT1vkgb8mySHlPu66TQAAIsJMB2gM6utqlbOus0KPLpPPR2J6js5SYHBXUzHAtqkqqpaO17fqoriSwrv3VVTH3tIoaEhpmMBV5c8Rzq2WzW5m/Xe52GqvFCl0MgbdO+8BQoNDzedDmiTqooKvb9mtapKLlO/sBwr169XNE5r1qzRb37zGxUWFiohIUGrV69WcnLy947PyMjQkiVLdOLECQ0cOFC//vWvNXnyZA8mvnbZ6S/r+Kl41QTfKOlGSVLwjovqf+BljVv+jNlwgAsbV7yquuMxsgfFS5Jqi6W35r2vgP7n9OiS+YbTAVcxYLx2np+gwqoHZQ/qLkkqK5PeWrBL4TF5mvGrXxgOCFzdlheWquJcguxBzp+TqF9YidXr1/hSvW3btiktLU3p6ek6ePCgEhISNGHCBBUXF7c6/m9/+5sefvhhPfbYY/r88881ZcoUTZkyRV988YWHk3dcdvrLKigcqZqgbs3O1wR1U0HhSGWnv2wmGNAGG1e8qqozQ2QP7NbsvD2wm6rODNHGFa+aCQa0wZYly3W67j9ard/Skju15YWlZoIBbbDlhaUqLbmT+oUl+UL9Gp9xeumllzRnzhzNnu18w9d169bp/fff18aNG/X888+3GP+73/1OEydO1LPPPitJWrFihXbv3q1XX31V69at82j2jqitqtbxU/FSkFpuAmGzSQ6Hjp+KV/GpIgWGBBnJiOujrq5ONZdrVXq+VAEBxv/odUhNda3sx/tIgfre+q07HqMTx08oJIRle77EbrfrcnmFCs8VKjAw0HScDqmurFDFuYSr1m/FuQSd+PZbhYRZY9kI2ob6hZV1nvodqaqKCq9etmdzOBwOUy9eW1ursLAwbd++XVOmTGk6n5qaqtLSUr377rstnhMfH6+0tDT9/Oc/bzqXnp6uzMxM5eXltRhfU1OjmpqapsdlZWWKi4tTSUmJIiIi3Pr9tMX/rn5DeUcHePx1AQAAAG8W1T9HP0n7L4++ZllZmSIjI3Xp0iWXvYHR//YuKSlRfX29oqKimp2PiopSQUFBq88pLCxsdXxhYWGr41euXKnly5e3OP/RRx8pLCysg8k7ru7bM5JonAAAAIB/deHMeWVlZXn0NSsrK9s81prrhdph0aJFSktLa3rcOON0zz33mJlx+rZIhUddjxsS/7VGzJp+/QPBY+rs9frLX/+isWPGKiDQ33ScDtm1OVNlf7/J5biw/kc1/uF7PZAInmK312nfvn1KSUlRYKA1/+n4yxt/0MVzd7gcF9l7j8ZMu8cDieApdXUNOvT5ISXenKiAAOO3d3fIX//4kUqKx7scR/36ns5Uvz1ie3l8w7eysrI2jzX6r19kZKT8/f1VVFTU7HxRUZGio6NbfU50dHS7xgcHBys4OLjF+cDAQCPrRJPnPqKCp953bgzR2hvdOhwKrr2oMc/MVhBbO/sUu92u4BuCFNmnp2XXKE9fMFNvzXvfeWPn99RvoL1UD/3nLLYm9zF2u11HvghXXHysZet36sI0vbVgl4v6vaj7n3vBq9fYo/3sdrsOnSlXn5FjLVu/9w8YTf12Up2pfu+dv8Dj32N7Xs9o2xoUFKSkpCRlZ2c3nWtoaFB2drZSUlJafU5KSkqz8ZK0e/fu7x3vbYJCQ9Q//pTzwf+/vey7x/3jT9M0wSuFhoYooP8554Pvqd+A/udomuCVQsPDFR7z3b2w31O/4TGH+aETXon6hZX5Sv0an+9LS0vThg0btGnTJuXn52vu3LmqqKho2mXvkUce0aJFi5rGP/3009q1a5d++9vfqqCgQMuWLdP+/fs1f7513jtm3PJn9MPowwquLW12Prj2on4YfZj3cYJXe3TJfIXG5ivQXtrsfKC9VKGx+byPE7zajF/9Qt0i/9xK/V5Ut8g/W+J9RNB5Ub+wMl+oX+ML1R988EGdP39eS5cuVWFhoRITE7Vr166mDSBOnTolP78r/d3tt9+ut99+Wy+++KIWL16sgQMHKjMzU8OHDzf1LXTIuOXPqLaqWjnrNquyuFRhvbsp+cl/Z6YJlvDokvmqqqrWjte3qqL4ksJ7d9XUxx5ipgmWMONXv/ied67nvlJ4P+oXVmb1+jW6HbkJZWVl6tq1a5u2HATcyW63KysrS5MnT7bsGmV0XtQvrIz6hZVRv9dXe3oD40v1AAAAAMDb0TgBAAAAgAs0TgAAAADgAo0TAAAAALhA4wQAAAAALtA4AQAAAIALNE4AAAAA4AKNEwAAAAC4QOMEAAAAAC7QOAEAAACACzROAAAAAOACjRMAAAAAuEDjBAAAAAAuBJgO4GkOh0OSVFZWZjgJOhu73a7KykqVlZUpMDDQdBygXahfWBn1Cyujfq+vxp6gsUe4mk7XOJWXl0uS4uLiDCcBAAAA4A3Ky8vVtWvXq46xOdrSXvmQhoYGnT17Vl26dJHNZjMdR5I0evRo5ebm+tRru+O6Hb1Ge57n7rFXG1NWVqa4uDidPn1aERERbXpNK6B+3XsN6tezqF/3XqO9z2vreOq3ddSve69B/XqWt9Svw+FQeXm5+vTpIz+/q9/F1OlmnPz8/BQbG2s6RjP+/v7G/iBcr9d2x3U7eo32PM/dY9syJiIiwqf+4qN+3XsN6tezqF/3XqO9z2vreOq3ddSve69B/XqWN9Wvq5mmRmwO4QXmzZvnc6/tjut29BrteZ67x5r8vTSF+nXvNahfz6J+3XuN9j6vreOp39ZRv+69BvXrWVas3063VA8wpaysTF27dtWlS5d86n+M0DlQv7Ay6hdWRv16D2acAA8JDg5Wenq6goODTUcB2o36hZVRv7Ay6td7MOMEAAAAAC4w4wQAAAAALtA4AQAAAIALNE4AAAAA4AKNEwAAAAC4QOMEAAAAAC7QOAFeYOrUqerevbumT59uOgrQLqdPn9Zdd92loUOHauTIkcrIyDAdCWiz0tJSjRo1SomJiRo+fLg2bNhgOhLQbpWVlerbt68WLlxoOorPYztywAt88sknKi8v16ZNm7R9+3bTcYA2O3funIqKipSYmKjCwkIlJSXp6NGjCg8PNx0NcKm+vl41NTUKCwtTRUWFhg8frv3796tnz56mowFt9sILL+jYsWOKi4vTqlWrTMfxacw4AV7grrvuUpcuXUzHANotJiZGiYmJkqTo6GhFRkbqwoULZkMBbeTv76+wsDBJUk1NjRwOh/j/ZFjJN998o4KCAk2aNMl0lE6Bxgm4Rp9++qnuu+8+9enTRzabTZmZmS3GrFmzRv369VNISIhuvfVW5eTkeD4o0Ap31u+BAwdUX1+vuLi465wacHJH/ZaWliohIUGxsbF69tlnFRkZ6aH06OzcUb8LFy7UypUrPZQYNE7ANaqoqFBCQoLWrFnT6te3bdumtLQ0paen6+DBg0pISNCECRNUXFzs4aRAS+6q3wsXLuiRRx7R+vXrPREbkOSe+u3WrZvy8vJ0/Phxvf322yoqKvJUfHRy11q/7777rgYNGqRBgwZ5Mnbn5gDgNpIcO3bsaHYuOTnZMW/evKbH9fX1jj59+jhWrlzZbNzHH3/smDZtmidiAq3qaP1WV1c7xo4d63jzzTc9FRVo4Vr+/m00d+5cR0ZGxvWMCbSqI/X7/PPPO2JjYx19+/Z19OzZ0xEREeFYvny5J2N3Osw4AddRbW2tDhw4oPHjxzed8/Pz0/jx47Vv3z6DyQDX2lK/DodDs2bN0t13362ZM2eaigq00Jb6LSoqUnl5uSTp0qVL+vTTTzV48GAjeYF/1Zb6XblypU6fPq0TJ05o1apVmjNnjpYuXWoqcqdA4wRcRyUlJaqvr1dUVFSz81FRUSosLGx6PH78eD3wwAPKyspSbGwsTRW8Qlvqd+/evdq2bZsyMzOVmJioxMREHTlyxERcoJm21O/Jkyc1duxYJSQkaOzYsVqwYIFGjBhhIi7QTFt/foBnBZgOAEDas2eP6QhAh4wZM0YNDQ2mYwAdkpycrEOHDpmOAVyzWbNmmY7QKTDjBFxHkZGR8vf3b3GzcVFRkaKjow2lAtqG+oWVUb+wMurXO9E4AddRUFCQkpKSlJ2d3XSuoaFB2dnZSklJMZgMcI36hZVRv7Ay6tc7sVQPuEaXL1/WsWPHmh4fP35chw4dUo8ePRQfH6+0tDSlpqZq1KhRSk5O1iuvvKKKigrNnj3bYGrAifqFlVG/sDLq14JMb+sHWN3HH3/skNTiIzU1tWnM6tWrHfHx8Y6goCBHcnKy47PPPjMXGPgX1C+sjPqFlVG/1mNzOBwOz7ZqAAAAAGAt3OMEAAAAAC7QOAEAAACACzROAAAAAOACjRMAAAAAuEDjBAAAAAAu0DgBAAAAgAs0TgAAAADgAo0TAAAAALhA4wQAwFXYbDZlZmaajgEAMIzGCQDgtWbNmiWbzdbiY+LEiaajAQA6mQDTAQAAuJqJEyfqjTfeaHYuODjYUBoAQGfFjBMAwKsFBwcrOjq62Uf37t0lOZfRrV27VpMmTVJoaKhuvPFGbd++vdnzjxw5orvvvluhoaHq2bOnnnjiCV2+fLnZmI0bN2rYsGEKDg5WTEyM5s+f3+zrJSUlmjp1qsLCwjRw4EDt3Lmz6WsXL17UjBkz1KtXL4WGhmrgwIEtGj0AgPXROAEALG3JkiWaNm2a8vLyNGPGDD300EPKz8+XJFVUVGjChAnq3r27cnNzlZGRoT179jRrjNauXat58+bpiSee0JEjR7Rz504NGDCg2WssX75cP/3pT3X48GFNnjxZM2bM0IULF5pe/6uvvtIHH3yg/Px8rV27VpGRkZ77BQAAeITN4XA4TIcAAKA1s2bN0ubNmxUSEtLs/OLFi7V48WLZbDY9+eSTWrt2bdPXbrvtNt1yyy36/e9/rw0bNui5557T6dOnFR4eLknKysrSfffdp7NnzyoqKko/+MEPNHv2bP3yl79sNYPNZtOLL76oFStWSHI2YzfccIM++OADTZw4Uffff78iIyO1cePG6/SrAADwBtzjBADwaj/60Y+aNUaS1KNHj6bjlJSUZl9LSUnRoUOHJEn5+flKSEhoapok6Y477lBDQ4O+/vpr2Ww2nT17VuPGjbtqhpEjRzYdh4eHKyIiQsXFxZKkuXPnatq0aTp48KDuueceTZkyRbfffnuHvlcAgPeicQIAeLXw8PAWS+fcJTQ0tE3jAgMDmz222WxqaGiQJE2aNEknT55UVlaWdu/erXHjxmnevHlatWqV2/MCAMzhHicAgKV99tlnLR4PGTJEkjRkyBDl5eWpoqKi6et79+6Vn5+fBg8erC5duqhfv37Kzs6+pgy9evVSamqqNm/erFdeeUXr16+/pusBALwPM04AAK9WU1OjwsLCZucCAgKaNmDIyMjQqFGjNGbMGG3ZskU5OTl6/fXXJUkzZsxQenq6UlNTtWzZMp0/f14LFizQzJkzFRUVJUlatmyZnnzySfXu3VuTJk1SeXm59u7dqwULFrQp39KlS5WUlKRhw4appqZG7733XlPjBgDwHTROAACvtmvXLsXExDQ7N3jwYBUUFEhy7ni3detWPfXUU4qJidE777yjoUOHSpLCwsL04Ycf6umnn9bo0aMVFhamadOm6aWXXmq6Vmpqqqqrq/Xyyy9r4cKFioyM1PTp09ucLygoSIsWLdKJEycUGhqqsWPHauvWrW74zgEA3oRd9QAAlmWz2bRjxw5NmTLFdBQAgI/jHicAAAAAcIHGCQAAAABc4B4nAIBlsdocAOApzDgBAAAAgAs0TgAAAADgAo0TAAAAALhA4wQAAAAALtA4AQAAAIALNE4AAAAA4AKNEwAAAAC4QOMEAAAAAC7QOAEAAACAC/8HmcloMTmOZawAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in subtract\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIoCAYAAABeertyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAZ8NJREFUeJzt3XlcVdX+//H3YQYRnBBEEZyHUjHNIXMqZxvU/GpqOZWN3kwqh+uAQ2mOqZlZmkNZaZZZV81UlLQkzelqmppTmoniCIIy7t8f/jjXI+CGI3BAX8/Hg0edddbe+3MWhy1v9trrWAzDMAQAAAAAyJKTowsAAAAAgIKO4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAACg0DEMQxcvXtSff/7p6FIA3CMITgAAoFCIi4vTyJEjVa1aNbm5ualkyZKqWrWqDh065OjSANwDCE4ACpy5c+eqbdu28vf3l6urqwICAtS8eXN9+umnSktLc3R5sFPfvn1lsVjk4+Oja9euZXj+zz//lMVikcVi0dSpU22eO3HihPr166dKlSrJw8NDAQEBatasmcLDw236tWjRwrqPW7+qV6+ep68PeevChQtq3LixZs2apa5du+q7777T+vXrFRkZqZCQEEeXB+Ae4OLoAgDgVosXL1aZMmU0atQo+fj46PLly/r111/Vt29f/fDDD/ryyy8dXSLs5OLiooSEBP3nP/9Rt27dbJ77/PPP5eHhoevXr9u0HzlyRA8++KA8PT3Vv39/hYSE6MyZM9q1a5cmTZqksWPH2vQvV66cJk6cmOHYvr6+uf+CkG/eeustnTlzRlFRUbrvvvscXQ6AexDBCUCBs3nzZrm6utq0vfbaaypZsqRmz56tiRMn8hfmQsrd3V1NmjTRl19+mSE4ffHFF+rYsaO++eYbm/b33ntPV69e1Z49exQcHGzz3Llz5zIcw9fXV88880zuFw+HOXfunBYvXqy5c+cSmgA4DFP1ABQ4t4amdOlhycnpf6eu7777Th07dlRgYKDc3d1VqVIljR8/XqmpqTbb3jqFq1SpUurYsaN+//13m34Wi0VjxoyxaZsyZYosFotatGhh0379+nWNGTNGVatWlYeHh8qUKaMuXbro6NGjkm5ML7NYLFq0aJHNdq+++qosFov69u1rbVu0aJEsFovc3NwUExNj0z8qKspa944dO2yeW758uerVqydPT0+VKlVKzzzzjE6fPp1h7A4ePKhu3brJz89Pnp6eqlatmkaMGCFJGjNmTJbT29K/IiMjreN4//33Z9h/TvTs2VM//PCDLl++bG377bff9Oeff6pnz54Z+h89elTlypXLEJokqXTp0ndUy63OnTun5557Tv7+/vLw8FCdOnW0ePFi6/Pp39Pbfd38fb1V+vZTp07Ve++9p+DgYHl6eqp58+YZ3ot79+5V3759VbFiRev0xP79++vChQs2/T788EPVqVNHvr6+KlKkiOrUqaNPPvnEpk/6NMnQ0NAMNU2cOFEWi0Xe3t4ZnluyZIn1/VWiRAk9/fTTOnXqlE2f9PfEzp079dBDD8nT01MVKlTQ3LlzbfolJSVp9OjRqlevnrXWpk2batOmTVmOV7rffvtNaWlpSkpKUv369eXh4aGSJUuqR48eOnnyZI7HLSfv+czOCek/rydOnLC2ZfdcBKDw4ooTgALr8uXLSklJUVxcnHbu3KmpU6fq6aefVvny5a19Fi1aJG9vb4WFhcnb21sbN27U6NGjFRsbqylTptjsr3r16hoxYoQMw9DRo0c1ffp0dejQIcMvXrfWkNm0r9TUVD322GOKiIjQ008/rUGDBikuLk7r16/X77//rkqVKmW6vyNHjmjevHlZHs/Z2VlLlizR4MGDrW0LFy7MdArbokWL1K9fPz344IOaOHGizp49q5kzZ+qXX37R7t27VaxYMUk3fpFs2rSpXF1d9cILLygkJERHjx7Vf/7zH73zzjvq0qWLKleubN3v4MGDVaNGDb3wwgvWtho1amRZc0516dJFL730klasWKH+/ftLunG1qXr16nrggQcy9A8ODtaGDRu0ceNGPfLII6b7T01N1fnz5zO0e3p6qkiRIllud+3aNbVo0UJHjhzRwIEDVaFCBS1fvlx9+/bV5cuXNWjQIPn5+emzzz6zbrNixQp9++23Nm1Zfe9v9umnnyouLk6vvvqqrl+/rpkzZ+qRRx7Rvn375O/vL0lav369jh07pn79+ikgIED79+/Xxx9/rP379+vXX3+VxWKRdGPBhDZt2qhSpUoyDENfffWVnn/+eRUrVkxPPfWU9ZguLi7av3+/du/erbp161rbFy1aJA8Pjww1vvPOOxo1apS6deum559/XjExMXr//ffVrFkzm/eXJF26dEkdOnRQt27d1KNHD3311Vd6+eWX5ebmZv0ex8bGav78+erRo4cGDBiguLg4ffLJJ2rbtq22b9+eaahLlx56Bg4cqHr16undd99VTEyMZs2apZ9//lm7d+9WqVKlsj1uefGez8m5CEAhZQBAAVWtWjVDkvWrd+/eRnJysk2fhISEDNu9+OKLhpeXl3H9+nVrW/PmzY3mzZvb9Pv3v/9tSDLOnTtnbZNkhIeHWx8PGTLEKF26tFGvXj2b7RcsWGBIMqZPn57h+GlpaYZhGMbx48cNScbChQutz3Xr1s24//77jaCgIKNPnz7W9oULFxqSjB49ehi1atWytsfHxxs+Pj5Gz549DUnGb7/9ZhiGYSQlJRmlS5c27r//fuPatWvW/qtWrTIkGaNHj7a2NWvWzChatKjx119/ZVrnrYKDg21qu1nz5s2N++67L9PnzPTp08coUqSIYRiG0bVrV+PRRx81DMMwUlNTjYCAAGPs2LHWMZsyZYp1u99//93w9PQ0JBmhoaHGoEGDjJUrVxrx8fGZ1nfze+bmrxdffPG29c2YMcOQZCxZssTalpSUZDRu3Njw9vY2YmNjM2wTHh5u5OSf0vTX5+npafz999/W9m3bthmSjMGDB1vbMntvf/nll4YkY/PmzVkeIyUlxfDx8TEGDhxobUsf+8cff9ymfcuWLYanp6fRqVMn6/fGMAzjxIkThrOzs/HOO+/Y7Hvfvn2Gi4uLTXv6mE+bNs3alpiYaISGhhqlS5c2kpKSrHUlJiba7O/SpUuGv7+/0b9//yxfj2H87+ejZs2aNuOyadMmQ5LxxhtvWNvsGbfbvectFovNz9PN9Rw/fvy2x83sXASg8GKqHoACa+HChVq/fr0+//xzPffcc/r8889t/iIs3biKkC4uLk7nz59X06ZNlZCQoIMHD9r0TU5O1vnz5xUTE6OoqCh9++23ql27tvUv1bc6ffq03n//fY0aNSrDNKZvvvlGpUqV0r/+9a8M26VfCbjVzp07tXz5ck2cONFmuuHNnn32WR08eNA6Je+bb76Rr6+vHn30UZt+O3bs0Llz5/TKK6/YXC3o2LGjqlevrtWrV0uSYmJitHnzZvXv39/mSt3t6jSTfkXn/PnzSkpKsmsfPXv2VGRkpKKjo7Vx40ZFR0dnOk1Pku677z7t2bNHzzzzjE6cOKGZM2eqU6dO8vf3z/TqXUhIiNavX5/h6/XXX79tTWvWrFFAQIB69OhhbXN1ddVrr72mq1ev6qeffrLrtWamU6dOKlu2rPVxgwYN1LBhQ61Zs8badvN7+/r16zp//rwaNWokSdq1a5fN/tK/J3/99Zfee+89xcbGqmnTphmO279/f33xxRdKTEyUdONnrEuXLhkWzlixYoXS0tLUrVs36/f6/PnzCggIUJUqVTJMr3NxcdGLL75ofezm5qYXX3xR586d086dOyXduJrq5uYmSUpLS9PFixeVkpKi+vXrZ3g9WXn11VdtxqVFixaqV6+e9f2e03HLjtKlS+vvv/827ZeTcxGAwumeDk6bN2/W448/rsDAQFksFq1cuTJH21+/fl19+/ZVrVq15OLiok6dOmXaLzIyUg888IDc3d1VuXLlDPc7AMhc48aN1apVK/Xs2VPz58/XuHHjtHDhQv3yyy/WPvv371fnzp3l6+srHx8f+fn5WRcGuHLlis3+tm7dKj8/P5UuXVoPPfSQUlJStHz58iwDRHh4uAIDA21+IUx39OhRVatWTS4u2Z/xPGzYMDVt2lSPPfZYln38/PzUsWNHLViwQJK0YMEC9enTJ0PQ+uuvvyRJ1apVy7CP6tWrW58/duyYJN3xfUk3O3jwoPz8/Gzul/riiy9ytI8OHTqoaNGiWrZsmT7//HM9+OCDNlOnblW1alV99tlnOn/+vPbu3asJEybIxcVFL7zwgjZs2GDTt0iRImrVqlWGL7PlyP/66y9VqVIlw1inT9lKH9PcUKVKlQxtVatWtbln5uLFixo0aJD8/f3l6ekpPz8/VahQQVLG9/aff/4pPz8/hYSEaMSIEZozZ06GxTekG8HaxcVF3333neLj4/XVV1+pX79+Gfr9+eefMgxDVapUsX6v07/++OOPDItyBAYGZpgGWbVqVUmyeU2LFy9W7dq1rfco+fn5afXq1Rlez63Sf0Yz+x7WqFHD7nHLjoceekhff/21fvzxR8XExOj8+fO6evVqhn45ORcBKJzu6Xuc4uPjVadOHfXv319dunTJ8fapqany9PTUa6+9lmEVqHTHjx9Xx44d9dJLL+nzzz9XRESEnn/+eZUpU0Zt27a905cA3FO6du2qESNGaNu2bWrSpIkuX76s5s2by8fHR+PGjbN+xs+uXbs0dOjQDJ/5VLt2bU2bNk2SrPdHtGjRQrt27VJAQIBN3z/++EOLFi3SkiVLslysIifWrVunDRs2KCoqyrRv//791bt3b/3rX//S5s2bNX/+fG3ZsuWOa8gtISEh1is9Fy5c0KxZs/Tss8+qYsWK1r/sm3F3d1eXLl20ePFiHTt2LMPN91lxdnZWrVq1VKtWLTVu3FgtW7bU559/rlatWtn7cgqsbt26aevWrXrrrbcUGhoqb29vpaWlqV27dhne2+XLl9f69esVFxenVatWafDgwQoKCsoQ0l1dXfXMM89o4cKFSkhIUMmSJfXII4/Y3KMl3bgiZLFY9MMPP8jZ2TlDbZktJGFmyZIl6tu3rzp16qS33npLpUuXlrOzsyZOnGhdUCUrN1/NMZOTccuOyZMnq0OHDmrXrl2WfXJ6LgJQON3Twal9+/Zq3759ls8nJiZqxIgR+vLLL3X58mXdf//9mjRpknVlrSJFiujDDz+UJP3yyy82K0Slmzt3ripUqGD9Za1GjRr6+eef9d577xGcgBxK/9DU9F/kIiMjdeHCBa1YsULNmjWz9jt+/Him2xcvXtzmF+wWLVooMDBQCxcu1PDhw236Dh8+XKGhoerevXum+6pUqZK2bdum5ORk02BlGIaGDRumzp07ZytYtG/fXh4eHnr66af18MMPq1KlShmCU/oKc4cOHcqwYMKhQ4esz1esWFGSMqzYdifSr+ika9q0qcqWLat169ZlOzhJN6brLViwQE5OTnr66adzXEf9+vUlSWfOnMnxtpkJDg7W3r17lZaWZnPVKX2aVWar+tnrzz//zNB2+PBh68qRly5dUkREhMaOHavRo0ffdjtJ8vLysn5POnfurBMnTmj8+PGZXt3s37+/6tSpo1OnTqlPnz6ZXnFNX2iiQoUK1itHt/PPP/8oPj7e5qrT4cOHJf1vNcyvv/5aFStW1IoVK2yOeeuHGGcm/YpRZu/3gwcP2j1u2VG5cmXt379f+/bt08WLFyXd+EPIzQs+5PRcBKBwuqen6pkZOHCgoqKitHTpUu3du1f/93//p3bt2uXoBBwVFZXhL6Ft27bN1l+dgXvVzfd53GzevHmyWCzWX5zSA5RhGNY+SUlJmjNnTraOkx7E0u/3SBcVFaXvvvtO7777bpbT+J566imdP39es2fPzvDczfVIsp5DMludLzMuLi7q3bu39u7da12R7Fb169dX6dKlNXfuXJv6f/jhB/3xxx/q2LGjpBtT/5o1a6YFCxZkWD3w1jrtlf7X9MyuTNxOy5YtNX78eM2ePTvDFb+bbdmyRcnJyRna098nmU1XtEeHDh0UHR2tZcuWWdtSUlL0/vvvy9vbW82bN8+V40jSypUrbZaN3759u7Zt22b9Y15m721JmjFjhum+U1NTdenSpQzv63T33Xef6tWrpwMHDmS5dHqXLl3k7OyssWPHZqjBMIwMS6KnpKToo48+sj5OSkrSRx99JD8/P9WrVy/L17Rt27Zs/XtYt25dBQQEZHi/b9myRTt27LAGxDsZt9txdXXVAw88YJ32WbNmTZvn7/RcBKBwuKevON3OyZMntXDhQp08eVKBgYGSpDfffFNr167VwoULNWHChGztJzo62rq0bDp/f3/Fxsbq2rVrOZp+ANwrevbsqerVq6tz587y9/dXTEyMfvjhB23atEkjRoxQrVq1JN2496B48eLq06ePXnvtNVksFn322WdZBoKzZ89qyZIlkqTz58/ro48+kouLS4a/yq9bt06tW7e+7fSv3r1769NPP1VYWJi2b9+upk2bKj4+Xhs2bNArr7yiJ5980mZ/AwYMyNEv+OPHj9dbb72l4sWLZ/q8q6urJk2apH79+ql58+bq0aOHdTnykJAQm+XMZ82apYcfflgPPPCAXnjhBVWoUEEnTpzQ6tWrtWfPnmzXlO7q1atau3atpBv3k8yaNUuurq7WsJZdTk5OGjlypGm/SZMmaefOnerSpYtq164t6cZN/p9++qlKlCiRYdGHK1euWL/Pt7rdB+O+8MIL+uijj9S3b1/t3LlTISEh+vrrr/XLL79oxowZKlq0aPZfnInKlSvr4Ycf1ssvv6zExETNmDFDJUuW1JAhQyRJPj4+atasmSZPnqzk5GTrFb3MrmA0a9ZMLVq0UPny5XX16lV9/fXX2r17t6ZOnZrl8Tdu3KjExESVKFEi0+crVaqkt99+W8OHD9eJEyfUqVMnFS1aVMePH9e3336rF154QW+++aa1f2BgoCZNmqQTJ06oatWqWrZsmfbs2aOPP/7YekX2scce04oVK9S5c2d17NhRx48f19y5c1WzZs1M7xm6mYuLiyZPnqzevXuradOm6tWrl3W6bbly5TR06NAcj1tuyum5CEAh5Yil/AoiSca3335rfZy+pG+RIkVsvlxcXIxu3bpl2L5Pnz7Gk08+maG9SpUqxoQJE2zaVq9ebUjKdOlSAIbx4YcfGh06dDACAwMNFxcXo1ixYkbbtm2NNWvWZOj7yy+/GI0aNTI8PT2NwMBAY8iQIcaPP/5oSDI2bdpk7XfrMtXFihUzmjRpkmGfkgyLxWLs3LnTpj2z5cwTEhKMESNGGBUqVDBcXV2NgIAAo2vXrsbRo0cNw7Bdevr06dM22966/HH68sbpy43fKqvnly1bZtStW9dwd3c3SpQoYfTq1ctmmet0v//+u9G5c2ejWLFihoeHh1GtWjVj1KhRmR7LbDnyzMbxhx9+yLT/zW5ejjwrmS1H/ssvvxivvvqqcf/99xu+vr6Gq6urUb58eaNv377Wsc6qvlu/zJw9e9bo16+fUapUKcPNzc2oVauWzXLyt7J3OfIpU6YY06ZNM4KCggx3d3ejadOmxn//+1+bvn///bf1e+br62v83//9n/HPP/9kWDL/5ZdfNipUqGB9DzRq1MhYvHixzb7Mxj6r57/55hvj4Ycftv4bWL16dePVV181Dh06ZO2TvkT9jh07jMaNGxseHh5GcHCwMXv2bJt9paWlGRMmTDCCg4MNd3d3o27dusaqVauMPn36GMHBwdkav6+++srm/d6jR48My+xnd9xudrv3fGYyW448u+ciAIWXxTD4c4h0Y8Web7/91roy3rJly9SrVy/t378/w/QTb2/vDNNK0j8g8daV+Zo1a6YHHnjAZprAwoUL9frrr7PKDgDcY06cOKEKFSpoypQpNldsCrMWLVro/PnzuXofHQAUREzVy0LdunWVmpqqc+fOZfpZGNnVuHHjDPdrrF+/Xo0bN77TEgEAAADkk3s6OF29elVHjhyxPj5+/Lj27NmjEiVKqGrVqurVq5d69+6tadOmqW7duoqJiVFERIRq165tnct/4MABJSUl6eLFi4qLi7PeLxAaGipJeumllzR79mwNGTJE/fv318aNG/XVV1/ZfFgfAAAAgILtng5OO3bsUMuWLa2Pw8LCJEl9+vTRokWLtHDhQr399tt64403dPr0aZUqVUqNGjWyuZG8Q4cONh+KWLduXUn/W1mnQoUKWr16tQYPHqyZM2eqXLlymj9/PkuRAwAAAIUI9zgBAAAAgAk+xwkAAAAATBCcAAAAAMDEPXePU1pamv755x8VLVpUFovF0eUAAAAAcBDDMBQXF6fAwEA5Od3+mtI9F5z++ecfBQUFOboMAAAAAAXEqVOnVK5cudv2ueeCU9GiRSXdGBwfHx8HVyMlJydr3bp1atOmjVxdXR1dDgAH4nwAIB3nAyB/xMbGKigoyJoRbueeC07p0/N8fHwKTHDy8vKSj48PJ0bgHsf5AEA6zgdA/srOLTwsDgEAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJlwcXcC9LDkpUZE/fqVTh/9UpFOsWrTtJlc3d0eXVeAlJyVq8/qvdeH8JZUsVVzNWndl3LKBcbMP42Y/xs4+jJt9GDf7MG72Y+zsU5jHzWIYhuGog2/evFlTpkzRzp07debMGX377bfq1KnTbbeJjIxUWFiY9u/fr6CgII0cOVJ9+/bN9jFjY2Pl6+urK1euyMfH585ewB349ou5OvKrn7ySilvbEtwuqXKjGHXu+ZLD6iroGDf7MG72ye9xS05O1po1a9ShQwe5urrm+v7zE+85+zBu9rkbxy0/zgd347jlF8bOPgVx3HKSDRw6VS8+Pl516tTRBx98kK3+x48fV8eOHdWyZUvt2bNHr7/+up5//nn9+OOPeVxp7vr2i7k6vbmKPJOK2bR7JhXT6c1V9O0Xcx1TWAHHuNmHcbMP42Y/xs4+jJt9GDf7MG72Y+zsczeMm0On6rVv317t27fPdv+5c+eqQoUKmjZtmiSpRo0a+vnnn/Xee++pbdu2eVVmrkpOStSRX/3kKckii81zFllkyNCRKD+db31Kri6F47JlfkhOSdTRqNLyEOOWE4ybfRw1bikpyUpOvKrYy+fk4lI4rzjxnrMP42afu3nc8vJ8cDePW15j7OyTrXH71U/JXRML9LQ9h07Vu5nFYjGdqtesWTM98MADmjFjhrVt4cKFev3113XlypVMt0lMTFRiYqL1cWxsrIKCgnT+/HmHTNWLXLtUh1eXzffjAgAAAAVZ1Y6n1aLd0/l6zNjYWJUqVSpbU/UK1eIQ0dHR8vf3t2nz9/dXbGysrl27Jk9PzwzbTJw4UWPHjs3Qvm7dOnl5eeVZrVk5dfhPWURwAgAAAG526OCfSkhbk6/HTEhIyHbfQhWc7DF8+HCFhYVZH6dfcWrTpo1jrjg5xerwUfN+wY8eU+MW2Z/GeLeLivxBf0VUNO3HuNli3OzjqHFLSU7Rlp9/VtOHH5aLa+E8PfOesw/jZp+7edzy8nxwN49bXmPs7JPdcatWvYpatOuQDxX9T2xsbLb7Fqp/mQMCAnT27FmbtrNnz8rHxyfTq02S5O7uLnf3jHMlXV1dHbJqVYu23bRn/Sp5JhXLMMdTkgwZuuZ2WW2ffLZAz/HMb22ffFYztjBuOcW42cdR45acnCxXd2+V8AsstKvq8Z6zD+Nmn7t53PLyfHA3j1teY+zsk91xa9G2W77/+5eT4xWqD8Bt3LixIiIibNrWr1+vxo0bO6iinHN1c1flRjGSbrxJbpb+uHKjGH7YbsG42Ydxsw/jZj/Gzj6Mm30YN/swbvZj7Oxzt4ybQ4PT1atXtWfPHu3Zs0fSjeXG9+zZo5MnT0q6Mc2ud+/e1v4vvfSSjh07piFDhujgwYOaM2eOvvrqKw0ePNgR5dutc8+XVLbZn7rmdtmm/ZrbZZVt9ifr/2eBcbMP42Yfxs1+jJ19GDf7MG72Ydzsx9jZ524YN4euqhcZGamWLVtmaO/Tp48WLVqkvn376sSJE4qMjLTZZvDgwTpw4IDKlSunUaNGFcoPwJVuLE0e+eNXOnTwzxtzOtt2K/BJuyAozJ847UiMm33yc9zupg/AlXjP2Ytxs8/dNm75dT6428YtPzF29ilo45aTbFBgliPPLwUpOEl33y9KAOzH+QBAOs4HQP7ISTYoVPc4AQAAAIAjEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMODw4ffDBBwoJCZGHh4caNmyo7du337b/jBkzVK1aNXl6eiooKEiDBw/W9evX86laAAAAAPcihwanZcuWKSwsTOHh4dq1a5fq1Kmjtm3b6ty5c5n2/+KLLzRs2DCFh4frjz/+0CeffKJly5bp3//+dz5XDgAAAOBe4tDgNH36dA0YMED9+vVTzZo1NXfuXHl5eWnBggWZ9t+6dauaNGminj17KiQkRG3atFGPHj1Mr1IBAAAAwJ1wcdSBk5KStHPnTg0fPtza5uTkpFatWikqKirTbR566CEtWbJE27dvV4MGDXTs2DGtWbNGzz77bJbHSUxMVGJiovVxbGysJCk5OVnJycm59Grsl15DQagFgGNxPgCQjvMBkD9y8jPmsOB0/vx5paamyt/f36bd399fBw8ezHSbnj176vz583r44YdlGIZSUlL00ksv3Xaq3sSJEzV27NgM7evWrZOXl9edvYhctH79ekeXAKCA4HwAIB3nAyBvJSQkZLuvw4KTPSIjIzVhwgTNmTNHDRs21JEjRzRo0CCNHz9eo0aNynSb4cOHKywszPo4NjZWQUFBatOmjXx8fPKr9CwlJydr/fr1at26tVxdXR1dDgAH4nwAIB3nAyB/pM9Gyw6HBadSpUrJ2dlZZ8+etWk/e/asAgICMt1m1KhRevbZZ/X8889LkmrVqqX4+Hi98MILGjFihJycMt6y5e7uLnd39wztrq6uBepEVNDqAeA4nA8ApON8AOStnPx8OWxxCDc3N9WrV08RERHWtrS0NEVERKhx48aZbpOQkJAhHDk7O0uSDMPIu2IBAAAA3NMcOlUvLCxMffr0Uf369dWgQQPNmDFD8fHx6tevnySpd+/eKlu2rCZOnChJevzxxzV9+nTVrVvXOlVv1KhRevzxx60BCgAAAABym0ODU/fu3RUTE6PRo0crOjpaoaGhWrt2rXXBiJMnT9pcYRo5cqQsFotGjhyp06dPy8/PT48//rjeeecdR70EAAAAAPcAhy8OMXDgQA0cODDT5yIjI20eu7i4KDw8XOHh4flQGQAAAADc4NAPwAUAAACAwoDgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmXBxdAAAAAO4uhmEoJSVFqampji4FkKurq5ydne94PwQnAAAA5JqkpCSdOXNGCQkJji4FkCRZLBaVK1dO3t7ed7QfghMAAAByRVpamo4fPy5nZ2cFBgbKzc1NFovF0WXhHmYYhmJiYvT333+rSpUqd3TlieAEAACAXJGUlKS0tDQFBQXJy8vL0eUAkiQ/Pz+dOHFCycnJdxScWBwCAAAAucrJiV8xUXDk1lVP3tUAAAAAYILgBAAAAAAmCE4AAAAoUFLTDEUdvaDv9pxW1NELSk0zHF1SgRQZGSmLxaLLly87uhS7jRkzRqGhoY4uI1sITgAAACgw1v5+Rg9P2qge837VoKV71GPer3p40kat/f1Mnh2zb9++slgsevfdd23aV65cWehXBQwJCZHFYsnwdetrzQ8Wi0UrV660aXvzzTcVERGR77XYw+HB6YMPPlBISIg8PDzUsGFDbd++/bb9L1++rFdffVVlypSRu7u7qlatqjVr1uRTtQAAAMgra38/o5eX7NKZK9dt2qOvXNfLS3blaXjy8PDQpEmTdOnSpVzdb1JSUq7uzx7jxo3TmTNnbL7+9a9/ObosSZK3t7dKlizp6DKyxaHBadmyZQoLC1N4eLh27dqlOnXqqG3btjp37lym/ZOSktS6dWudOHFCX3/9tQ4dOqR58+apbNmy+Vw5AAAAssMwDCUkpZh+xV1PVvj3+5XZpLz0tjHfH1Dc9eRs7c8wcja9r1WrVgoICNDEiRNv2++bb77RfffdJ3d3d4WEhGjatGk2z4eEhGj8+PHq3bu3fHx89MILL2jRokUqVqyYVq1apWrVqsnLy0tdu3ZVQkKCFi9erJCQEBUvXlyvvfaaUlNTrfv67LPPVL9+fRUtWlQBAQHq2bNnlr8n30769jd/FSlSxPr8mjVrVLVqVXl6eqply5ZatGiRzRTAzKbTzZgxQyEhIdbHv/32m1q3bq1SpUrJ19dXzZs3165du2zGRZI6d+4si8VifXzrvtPS0jRu3DiVK1dO7u7uCg0N1dq1a63PnzhxQhaLRStWrFDLli3l5eWlOnXqKCoqKsfjklMO/Ryn6dOna8CAAerXr58kae7cuVq9erUWLFigYcOGZei/YMECXbx4UVu3bpWrq6sk2XzDAAAAULBcS05VzdE/3vF+DEnRsddVa8y6bPU/MK6tvNyy/6uus7OzJkyYoJ49e+q1115TuXLlMvTZuXOnunXrpjFjxqh79+7aunWrXnnlFZUsWVJ9+/a19ps6dapGjx6t8PBwSdKWLVuUkJCgWbNmaenSpYqLi1OXLl3UuXNnFStWTGvWrNGxY8f01FNPqUmTJurevbskKTk5WePHj1e1atV07tw5hYWFqW/fvrk62+rUqVPq0qWLXn31Vb3wwgvasWOH3njjjRzvJy4uTn369NH7778vwzA0bdo0dejQQX/++aeKFi2q3377TaVLl9bChQvVrl27LD9PaebMmZo2bZo++ugj1a1bVwsWLNATTzyh/fv3q0qVKtZ+I0aM0NSpU1WlShWNGDFCPXr00JEjR+TiknfxxmHBKSkpSTt37tTw4cOtbU5OTmrVqlWWifH7779X48aN9eqrr+q7776Tn5+fevbsqaFDh2Y5+ImJiUpMTLQ+jo2NlXTjjZicnJyLr8g+6TUUhFoAOBbnAwDpCuv5IDk5WYZhKC0tTWlpaZJk/W9+u7kGM4ZhyDAMPfnkkwoNDdXo0aM1f/78DK9h2rRpeuSRRzRixAhJUuXKlbV//35NmTJFvXv3tu6vZcuWGjx4sPXxTz/9pOTkZH3wwQeqVKmSJOmpp57SkiVLdObMGXl7e6t69epq0aKFNm7cqP/7v/+TJJswFhISohkzZqhhw4aKjY2Vt7e3TX23e61Dhw7VyJEjbdpWr16tpk2bas6cOapUqZKmTJkiSapSpYr27t2ryZMnW/ebfvXu5mPc2taiRQub/c+dO1clSpTQpk2b9Nhjj1mn4/n4+Kh06dLWbW/dz9SpUzVkyBB169ZNkjRx4kRt2rRJ7733nmbPnm3tFxYWpvbt20uSwsPDVatWLR0+fFjVq1fP8PrTj5PZB+Dm5GfMYcHp/PnzSk1Nlb+/v027v7+/Dh48mOk2x44d08aNG9WrVy+tWbNGR44c0SuvvKLk5GRror/VxIkTNXbs2Azt69atK1CfaL1+/XpHlwCggOB8ACBdYTsfuLi4KCAgQFevXrXe22MYhqLCGpluu+vUFb26/A/Tfh/8Xw09EORr2i/5Wrxir2dvYYfk5GSlpKQoNjZWI0eO1JNPPqkXX3xR165dk/S/P7zv379fHTp0sD6WpLp162rmzJm6dOmSnJ2dlZaWpvvvv9+mz/Xr1+Xl5SU/Pz9re7FixVS+fHmlpaVZ20qUKKF//vnH+njPnj1699139fvvv+vKlSvW0HDgwAFVr15dCQkJkm5c7cnqQ4fT0tL0r3/9Sz179rRpL1OmjGJjY7Vv3z7VrVvXpt46derY7DcxMVGpqakZXtPNtZ87d07vvPOOfv75Z8XExCgtLU0JCQk6fPiwzXbXrl2zeXzzvmNjY/XPP/8oNDTUpk/9+vX1+++/KzY2VlevXpUkVapUydrH29tbknT8+HEFBgZmGIOkpCRdu3ZNmzdvVkpKis1z6WOYHQ6dqpdTaWlpKl26tD7++GM5OzurXr16On36tKZMmZJlcBo+fLjCwsKsj2NjYxUUFKQ2bdrIx8cnv0rPUnJystavX6/WrVtbpx8CuDdxPgCQrrCeD65fv65Tp07J29tbHh4e1nbzmCO1KVFMAT8e19nY65ne52SRFODroTZ1guXslLsr3bm6usrFxUU+Pj5q37692rRpowkTJqhPnz6SZP2d0dnZWe7u7ja/Q3p6elr7ODs7y8nJSSVLlrTp4+HhIVdX1wxtt+7Lzc1NTk5O8vHxUXx8vLp27ao2bdro888/l5+fn06ePKn27dvLzc1NPj4+1osARYsWzfL3WicnJ5UtWzbLJb9dXFwy1Jb+mtL36+npaa0rXfprTW/r3r27Ll68qJkzZyo4OFju7u5q0qSJnJ2dM+z75sfu7u4Z+nh5eWUYl/TvT3pIKlasmLVPeqC8dd/prl+/Lk9PTzVr1szmfSnJJqCZcVhwKlWqlJydnXX27Fmb9rNnzyogICDTbcqUKSNXV1ebS2w1atRQdHS0kpKS5ObmlmEbd3d3ubu7Z2h3dXUtUCeiglYPAMfhfAAgXWE7H6SmpspiscjJySnLKyBZcXKSxjxRUy8v2SWLZBOe0mNS+OM15eqS+e0ZdyJ9ie70midNmqTQ0FDrtK/09ho1amjr1q02ry0qKkpVq1a1+T7dvK+bt7+5LX2Z81vb0rc9fPiwLly4oEmTJikoKEiSrIstpI/vzfu93XjfWs/Natasqe+//97m+fRVrtP3W7p0aUVHR1vrk6T//ve/NvVv3bpVc+bM0WOPPSbpxr1T58+ftzm2q6urDMPIchyKFSumwMBARUVFqWXLltY+W7duVYMGDbJ8zWbj4OTkJIvFkunPU05+vhy2qp6bm5vq1atns257WlqaIiIi1Lhx40y3adKkiY4cOWIzv/Lw4cMqU6ZMpqEJAAAAhUe7+8vow2ceUICv7VWBAF8PffjMA2p3f5l8qaNWrVrq1auXZs2aZdP+xhtvKCIiQuPHj9fhw4e1ePFizZ49W2+++Wau11C+fHm5ubnp/fff17Fjx/T9999r/Pjxdu0rLi5O0dHRNl/pV1peeukl/fnnn3rrrbd06NAhffHFF1q0aJHN9i1atFBMTIwmT56so0eP6oMPPtAPP/xg06dKlSr67LPP9Mcff2jbtm3q1auX9cpVupCQEEVERCg6OjrLZd/feustTZo0ScuWLdOhQ4c0bNgw7dmzR4MGDbLrtecmhy5HHhYWpnnz5mnx4sX6448/9PLLLys+Pt66yl7v3r1tFo94+eWXdfHiRQ0aNEiHDx/W6tWrNWHCBL366quOegkAAADIRe3uL6Ofhz6iLwc00synQ/XlgEb6eegj+Raa0o0bNy7DggsPPPCAvvrqKy1dulT333+/Ro8erXHjxtks4pBb/Pz8tGjRIi1fvlw1a9bUu+++q6lTp9q1r9GjR6tMmTI2X0OGDJF0I6B98803WrlyperUqaO5c+dqwoQJNtvXqFFDc+bM0QcffKA6depo+/btGcLiJ598okuXLumBBx7Qs88+q9dee826CES6adOmaf369QoKClLdunUzrfW1115TWFiY3njjDdWqVUtr167V999/b7OinqNYjJwucp/LZs+erSlTpig6OlqhoaGaNWuWGjZsKOlGug0JCbFJvVFRURo8eLD27NmjsmXL6rnnnrvtqnq3io2Nla+vr65cuVJg7nFas2aNOnToUKguxQPIfZwPAKQrrOeD69ev6/jx46pQoUKGe0lQeERGRqply5a6dOmSihUr5uhy7tjt3pc5yQYOXxxi4MCBGjhwYKbPRUZGZmhr3Lixfv311zyuCgAAAAD+x6FT9QAAAACgMHD4FScAAAAABUeLFi3k4Lt5CiSuOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJhgOXIAAAAULGmp0l9bpatnJW9/KfghycnZ0VXhHscVJwAAABQcB76XZtwvLX5M+ua5G/+dcf+N9jwWHR2tQYMGqXLlyvLw8JC/v7+aNGmiDz/8UAkJCZKkkJAQWSwWWSwWeXp6KiQkRN26ddPGjRvzvD44FsEJAAAABcOB76Wvekux/9i2x5650Z6H4enYsWOqW7eu1q1bpwkTJmj37t2KiorSkCFDtGrVKm3YsMHad9y4cTpz5owOHTqkTz/9VMWKFVOrVq30zjvv5Fl9cDym6gEAACDvGIaUnGDeLy1V+mGIJCOznUiySGuHShVbZG/anquXZLFku8xXXnlFLi4u2rFjh4oUKWJtr1ixop588kkZxv/qKlq0qAICAiRJ5cuXV7NmzVSmTBmNHj1aXbt2VbVq1bJ9XBQeBCcAAADkneQEaUJgLuzIuHEl6t2g7HX/9z+SWxHzfpIuXLhgvdJ0c2i6mcUkhA0aNEjjx4/Xd999pyFDhmSvRhQqTNUDAADAPe3IkSMyDCPDlaJSpUrJ29tb3t7eGjp06G33UaJECZUuXVonTpzIw0rhSFxxAgAAQN5x9bpx9cfMX1ulz7ua9+v19Y1V9rJz3Du0fft2paWlqVevXkpMTDTtbxiG6ZUpFF4EJwAAAOQdiyV7U+YqPSL5BN5YCCLT+5wsN56v9EiuL01euXJlWSwWHTp0yKa9YsWKkiRPT0/TfVy4cEExMTGqUKFCrtaGgoOpegAAAHA8J2ep3aT//+DWqzb//3G7d/Pk85xKliyp1q1ba/bs2YqPj7drHzNnzpSTk5M6deqUu8WhwCA4AQAAoGCo+YTU7VPJp4xtu0/gjfaaT+TZoefMmaOUlBTVr19fy5Yt0x9//KFDhw5pyZIlOnjwoJyd/xfY4uLiFB0drVOnTmnz5s164YUX9Pbbb+udd95R5cqV86xGOBZT9QAAAFBw1HxCqt7xxj1PV89K3v437mnKgytNN6tUqZJ2796tCRMmaPjw4fr777/l7u6umjVr6s0339Qrr7xi7Tt69GiNHj1abm5uCggIUKNGjRQREaGWLVvmaY1wLIITAAAAChYnZ6lC03w/bJkyZfT+++/r/fffz7IPq+bdu5iqBwAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYCJHwWny5Mm6du2a9fEvv/xi8ynKcXFxNiuOAAAAAMDdIEfBafjw4YqLi7M+bt++vU6fPm19nJCQoI8++ij3qgMAAACAAiBHwckwjNs+BgAAAIC7Efc4AQAAAIAJghMAAAAAmHDJ6Qbz58+Xt7e3JCklJUWLFi1SqVKlJMnm/icAAADAHqlpqdp1bpdiEmLk5+WnB0o/IGcnZ0eXhXtcjoJT+fLlNW/ePOvjgIAAffbZZxn6AAAAAPbY8NcGvbv9XZ1NOGtt8/fy17AGw9QquFWeHTcmJkajR4/W6tWrdfbsWRUvXlx16tTR6NGj1aRJE0nS7t279e6772rz5s26ePGiAgICVKtWLb344ot67LHHZLFYdOLECVWoUMG6X29vb5UvX14tWrTQ66+/ripVquTZa0DeylFwOnHiRB6VAQAAgHvdhr82KCwyTIZsFyA7l3BOYZFhmt5iep6Fp6eeekpJSUlavHixKlasqLNnzyoiIkIXLlyQJH333Xfq1q2bWrVqpcWLF6ty5cpKTEzU1q1bNXLkSDVt2lTFihX732vZsEH33XefEhIStG/fPs2cOVN16tTRf/7zHz366KN58hqQt3I8VQ8AAADILsMwdC3lmmm/1LRUTdw+MUNokmRte3f7u2oY0DBb0/Y8XTxlsViyVePly5e1ZcsWRUZGqnnz5pKk4OBgNWjQQJIUHx+v5557Th07dtSKFStstq1Ro4aee+65DKtNlyxZUgEBAZKkihUr6vHHH9ejjz6q5557TkePHpWzM1MPC5scBaeoqChduHBBjz32mLXt008/VXh4uOLj49WpUye9//77cnd3z/VCAQAAUPhcS7mmhl80zJV9nU04q4eWPpStvtt6bpOXq1e2+np7e8vb21srV65Uo0aNMvwuu27dOl24cEFDhgzJch9mIc3JyUmDBg1S586dtXPnTmsoQ+GRo1X1xo0bp/3791sf79u3T88995xatWqlYcOG6T//+Y8mTpyY60UCAAAAecXFxUWLFi3S4sWLVaxYMTVp0kT//ve/tXfvXknS4cOHJUnVqlWzbvPbb79ZA5e3t7dWrVplepzq1atL4vaXwipHV5z27Nmj8ePHWx8vXbpUDRs2tC4YERQUpPDwcI0ZMyZXiwQAAEDh5OniqW09t5n223l2p16JeMW035xH56ief71sHTcnnnrqKXXs2FFbtmzRr7/+qh9++EGTJ0/W/PnzM+1fu3Zt7dmzR5JUpUoVpaSkmB4jfTpfdqcQomDJUXC6dOmS/P39rY9/+ukntW/f3vr4wQcf1KlTp3KvOgAAABRqFoslW1PmHgp8SP5e/jqXcC7T+5wsssjfy18PBT6UZ0uTe3h4qHXr1mrdurVGjRql559/XuHh4XrvvfckSYcOHVKjRo0kSe7u7qpcuXKO9v/HH39Iks2qeyg8cjRVz9/fX8ePH5ckJSUladeuXdY3j3Tjc5xcXV1zt0IAAADc9ZydnDWswTBJN0LSzdIfD20wNF8/z6lmzZqKj49XmzZtVKJECU2aNMnufaWlpWnWrFmqUKGC6tatm4tVIr/kKDh16NBBw4YN05YtWzR8+HB5eXmpadOm1uf37t2rSpUq5XqRAAAAuPu1Cm6l6S2mq7RXaZt2fy//PF2K/MKFC3rkkUe0ZMkS7d27V8ePH9fy5cs1efJkPfnkk/L29tb8+fO1evVqdezYUT/++KOOHTumvXv3avLkyZKUYZW8CxcuKDo6WseOHdP333+vVq1aafv27frkk09YUa+QytFUvfHjx6tLly5q3ry5vL29tWjRIrm5uVmfX7Bggdq0aZPrRQIAAODe0Cq4lVoGtdSuc7sUkxAjPy8/PVD6gTy90uTt7a2GDRvqvffe09GjR5WcnKygoCANGDBA//73vyVJnTt31tatWzVp0iT17t1bFy9elK+vr+rXr6+lS5farDotSa1a3Qh5Xl5eCg4OVsuWLfXxxx/neHofCo4cBadSpUpp8+bNunLliry9vTOk5eXLl6to0aK5WiAAAADuLc5Oznow4MF8O567u7smTpxoujp0/fr1tXz58tv2CQkJyfCZTrg75Cg49e/fP1v9FixYYFcxAAAAAFAQ5Sg4LVq0SMHBwapbty5JGgAAAMA9I0fB6eWXX9aXX36p48ePq1+/fnrmmWdUokSJvKoNAAAAAAqEHK2q98EHH+jMmTMaMmSI/vOf/ygoKEjdunXTjz/+yBUoAAAAAHetHAUn6cbNcz169ND69et14MAB3XfffXrllVcUEhKiq1ev5kWNAAAAAOBQOQ5ONhs7OcliscgwDKWmpuZWTQAAAABQoOQ4OCUmJurLL79U69atVbVqVe3bt0+zZ8/WyZMn5e3tnRc1AgAAAIBD5WhxiFdeeUVLly5VUFCQ+vfvry+//FKlSpXKq9oAAAAAoEDIUXCaO3euypcvr4oVK+qnn37STz/9lGm/FStW5EpxAAAAAFAQ5Cg49e7dWxaLJa9qAQAAAGSkpiphx06lxMTIxc9PXvXryeLs7JBa+vbtq8uXL2vlypWZPj9mzBitXLlSe/bsyde6kP9y/AG4AAAAQF6JXbdOZydMVEp0tLXNJSBA/v8eLp82bRxYWeHUokULhYaGasaMGZk+f/NFkaJFi6patWoaOXKknnzyyXyqsPC4o1X1AAAAgNwSu26dTg963SY0SVLK2bM6Peh1xa5b56DKHCspKSlP979w4UKdOXNGO3bsUJMmTdS1a1ft27cvT49ZGBGcAAAAkGcMw1BaQoLpV2pcnM6+/Y5kGJntRJKhs+9MUGpcXLb2Z2S2nyx8/fXXqlWrljw9PVWyZEm1atVK8fHxmfb97bff5Ofnp0mTJmW5v/nz56tGjRry8PBQ9erVNWfOHJvnhw4dqqpVq8rLy0sVK1bUqFGjlJycbH1+zJgxCg0N1fz581WhQgV5eHhIunF1aP78+ercubO8vLxUpUoVff/999l+nVkpVqyYAgICVLVqVY0fP14pKSnatGnTHe/3bpOjqXoAAABAThjXrunQA/VyYUc3rjwdfrBBtrpX27VTFi8v035nzpxRjx49NHnyZHXu3FlxcXHasmVLpsFr48aN6tKliyZPnqwXXngh0/19/vnnGj16tGbPnq26detq9+7dGjBggIoUKaI+ffpIujElbtGiRQoMDNS+ffs0YMAAFS1aVEOGDLHu58iRI/rmm2+0YsUKOd90f9fYsWM1efJkTZkyRe+//7569eqlv/76SyVKlMjWuNxOSkqKPvnkE0mSm5vbHe/vbkNwAgAAwD3rzJkzSklJUZcuXRQcHCxJqlWrVoZ+3377rXr37q358+ere/fuWe4vPDxc06ZNU5cuXSRJFSpU0IEDB/TRRx9Zg9PIkSOt/UNCQvTmm29q6dKlNsEpKSlJn376qfz8/Gz237dvX/Xo0UOSNGHCBM2aNUvbt29Xu3bt7BwBqUePHnJ2dta1a9eUlpamkJAQdevWze793a0ITgAAAMgzFk9PVdu107Rfwo4dOvXCi6b9gj7+SF7162fruNlRp04dPfroo6pVq5batm2rNm3aqGvXripevLi1z7Zt27Rq1Sp9/fXX6tSpU5b7io+P19GjR/Xcc89pwIAB1vaUlBT5+vpaHy9btkyzZs3S0aNHdfXqVaWkpMjHx8dmX8HBwRlCkyTVrl3b+v9FihSRj4+Pzp07l63XmpX33ntPrVq10rFjxzR48GDNmjUrV65g3W0ITgAAAMgzFoslW1PmijRpIpeAAKWcPZv5fU4Wi1z8/VWkSZNcXZrc2dlZ69ev19atW7Vu3Tq9//77GjFihLZt26YKFSpIkipVqqSSJUtqwYIF6tixo1xdXTPd19WrVyVJ8+bNU8OGDTMcR5KioqLUq1cvjR07Vm3btpWvr6+WLl2qadOm2fQvUqRIpse49dgWi0VpaWk5f+E3CQgIUOXKlVW5cmUtXLhQHTp00IEDB1S6dOk72u/dhsUhAAAA4HAWZ2f5/3v4/39wy+eG/v/H/v8enief52SxWNSkSRONHTtWu3fvlpubm7799lvr86VKldLGjRt15MgRdevWzWYhh5v5+/srMDBQx44dswaR9K/0ELZ161YFBwdrxIgRql+/vqpUqaK//vor11+TvRo0aKB69erpnXfecXQpBQ5XnAAAAFAg+LRpI82ckfFznPz98+xznLZt26aIiAi1adNGpUuX1rZt2xQTE6MaNWrY9CtdurQ2btyoli1bqkePHlq6dKlcXDL+Kj127Fi99tpr8vX1Vbt27ZSYmKgdO3bo0qVLCgsLU5UqVXTy5EktXbpUDz74oFavXm0T0vJCTExMhg/oLVOmjPz9/TPt//rrr6tz584aMmSIypYtm6e1FSZccQIAAECB4dOmjSpHbFD5xYsVOHWqyi9erMoRG/Lsw299fHy0efNmdejQQVWrVtXIkSM1bdo0tW/fPkPfgIAAbdy4Ufv27VOvXr2Umpqaoc/zzz+v+fPna+HChapVq5aaN2+uRYsWWa84PfHEExo8eLAGDhyo0NBQbd26VaNGjcqT15buiy++UN26dW2+5s2bl2X/du3aqUKFClx1uoXFyMki93eB2NhY+fr66sqVKxluwnOE5ORkrVmzRh06dMhyviyAewPnAwDpCuv54Pr16zp+/LjNZw8Bjna792VOsgFXnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAIAs9O3bV506dcry+TFjxig0NDTf6oHjEJwAAABQoKSlGTp96JIO/xat04cuKS3NcHRJhdbx48fVs2dPBQYGysPDQ+XKldOTTz6pgwcP2vTbtGmTHnvsMfn5+cnDw0OVKlVS9+7dtXnzZmufyMhIWSwWWSwWOTk5ydfXV3Xr1tWQIUN05syZ/H5p+c7F0QUAAAAA6Y7uPqcty/5U/OVEa1uRYu5q2r2KKtUt7cDKHCcpKUlubm453i45OVmtW7dWtWrVtGLFCpUpU0Z///23fvjhB12+fNnab86cORo4cKCeffZZLVu2TJUqVdKVK1e0adMmDR48WDt37rTZ76FDh+Tj46PY2Fjt2rVLkydP1ieffKLIyEjVqlXrTl9ugVUgrjh98MEHCgkJkYeHhxo2bKjt27dna7ulS5fKYrHc9vIpAAAACoeju89p7Ue/24QmSYq/nKi1H/2uo7vP5clxv/76a9WqVUuenp4qWbKkWrVqpfj4+Ez7/vbbb/Lz89OkSZOy3N/8+fNVo0YNeXh4qHr16pozZ47N80OHDlXVqlXl5eWlihUratSoUUpOTrY+nz79b/78+apQoYI8PDwkSRaLRfPnz1fnzp3l5eWlKlWq6Pvvv8+yjv379+vo0aOaM2eOGjVqpODgYDVp0kRvv/22GjVqJEk6efKkXn/9db3++utavHixHnnkEQUHB6t27doaNGiQduzYkWG/pUuXVkBAgKpWraqnn35av/zyi/z8/PTyyy9nPch3AYcHp2XLliksLEzh4eHatWuX6tSpo7Zt2+rcudv/YJw4cUJvvvmmmjZtmk+VAgAAIKcMw1ByYqrpV+K1FG1Zdvi2+9qy7E8lXkvJ1v4MI3vT+86cOaMePXqof//++uOPPxQZGakuXbpkuv3GjRvVunVrvfPOOxo6dGim+/v88881evRovfPOO/rjjz80YcIEjRo1SosXL7b2KVq0qBYtWqQDBw5o5syZmjdvnt577z2b/Rw5ckTffPONVqxYoT179ljbx44dq27dumnv3r3q0KGDevXqpYsXL2Zai5+fn5ycnPT1118rNTU10z7ffPONkpOTNWTIkEyft1gsmbbfzNPTUy+99JJ++eUX09/hCzOHT9WbPn26BgwYoH79+kmS5s6dq9WrV2vBggUaNmxYptukpqaqV69eGjt2rLZs2WJzqREAAAAFR0pSmj4e9FOu7Cv+cqLmD95s3lHSCzOby9Xd2bTfmTNnlJKSoi5duig4OFiSMp1u9u2336p3796aP3++unfvnuX+wsPDNW3aNHXp0kWSVKFCBR04cEAfffSR+vTpI0kaOXKktX9ISIjefPNNLV261Ca8JCUl6dNPP5Wfn5/N/vv27asePXpIkiZMmKBZs2Zp+/btateuXYZaypYtq1mzZmnIkCEaO3as6tevr5YtW6pXr16qWLGiJOnw4cPy8fFRQECAdbtvvvnGWqskRUVFmU7Bq169uqQbFzdKl747p1Q6NDglJSVp586dGj58uLXNyclJrVq1UlRUVJbbjRs3TqVLl9Zzzz2nLVu23PYYiYmJSkz83+Xe2NhYSTfmfN58SdRR0msoCLUAcCzOBwDSFdbzQXJysgzDUFpamtLS0iTJ+t/8dqMG86sltWrV0qOPPqpatWqpTZs2at26tbp27arixYtLunHFbNu2bVq1apW++uorderUyeY1pV+ZSktLU3x8vI4eParnnntOAwYMsPZJSUmRr6+vdbtly5Zp9uzZOnr0qK5evaqUlBT5+PhYnzcMQ8HBwSpZsmSG8bv//vutbZ6envLx8VF0dHSW4/zyyy/rmWeeUWRkpLZt26bly5drwoQJWrlypVq3bi3DMGSxWGy2b926tXbt2qXTp0/rkUceUXJycobv6a3HS7+ilf79L0jS0tJuXPlMTpazs22YzsnPmEOD0/nz55Wamip/f3+bdn9//wwrfaT7+eef9cknn9hcsrydiRMnauzYsRna161bJy8vrxzXnFfWr1/v6BIAFBCcDwCkK2znAxcXFwUEBOjq1atKSkqSdOMX6e7j6phue+74VW1aeNS0X8t+lVS6grdpv4TrV2VJNA9OkrR8+XJt27ZNmzZt0qxZszRy5Eht2LBBwcHBSk5OVnBwsIoVK6Z58+apadOmcnV1tW6bmJio1NRUxcbGWqepzZgxQ/Xr17c5hrOzs2JjY7V9+3Y9++yzGjZsmN5++235+PhoxYoVmj17tvUP/ImJifLw8LA+vllKSkqG9oSEhEz73qx58+Zq3ry53nrrLT311FMaN26cGjZsqKCgIF25ckV//vmnze/kpUuX1vXr1yVJ8fHxio2NVUJCgiQpLi5OTk62d/yk/25esmRJ01ryW1JSkq5du6bNmzcrJSXF5rn015QdDp+qlxNxcXF69tlnNW/ePJUqVSpb2wwfPlxhYWHWx7GxsQoKClKbNm3k4+OTV6VmW3JystavX6/WrVvb/BACuPdwPgCQrrCeD65fv65Tp07J29vbuqBBdhUrUUzbv/07w8IQN/Mu7q6q9crJySl7gSgn2rRpozZt2ujtt99WhQoVtGHDBg0ePFiurq7y9/fXypUr9cgjj2jAgAFatmyZ9fvi7u4uZ2dn+fj4yMfHR4GBgYqOjs7ys5327t2r4OBgjRs3zto2Z84cWSwW6++mN+/zVulXmdJZLBZ5eHjk6Pfa++67T1FRUfLx8bHe/vLhhx9q+vTpNv28vW8E1CJFisjHx8d60aFo0aI2x7t27Zo+++wzNWvWzDoFsCC5fv26PD091axZswzvy5yEPIcGp1KlSsnZ2Vlnz561aT979qzNPMt0R48e1YkTJ/T4449b29IvBbq4uOjQoUOqVKmSzTbu7u5yd3fPsC9XV9cCdSIqaPUAcBzOBwDSFbbzQWpqqvUzfm69ImHGyUlq2r2K1n70e5Z9Hu5WRS4u5vct5cS2bdsUERGhNm3aqHTp0tq2bZtiYmJUs2ZNOTk5WT+3KCAgQBs3brTeI7R06VK5uLhYF09If71jx47Va6+9pmLFiqldu3ZKTEzUjh07dOnSJYWFhalq1ao6efKkvvrqKz344INavXq1Vq5cabOPW/d5s8zGNqvx3rNnj8LDw/Xss8+qZs2acnNz008//aSFCxdq6NChcnJyUkhIiKZNm6ZBgwbp0qVL6tu3rypUqKCLFy9qyZIlkm68D28+xvnz55WUlKS4uDjt3LlTkydP1vnz57VixYocf9/zQ/r3MbOfp5z8fDk0OLm5ualevXqKiIiwLimelpamiIgIDRw4MEP/6tWra9++fTZtI0eOVFxcnGbOnKmgoKD8KBsAAAB5oFLd0mr34v0ZPsfJu7i7Hu6WN5/j5OPjo82bN2vGjBmKjY1VcHCwpk2bpvbt22fomx6eWrRooV69eumLL77I0Of555+Xl5eXpkyZorfeektFihRRrVq19Prrr0uSnnjiCQ0ePFgDBw5UYmKiOnbsqFGjRmnMmDG5/trKlSunkJAQjR07VidOnJDFYrE+Hjx4sLXfv/71L9WoUUPTp09X165dFRsbq5IlS6px48Zau3ZthoUhqlWrJovFIm9vb1WsWFFt2rRRWFhYphc+7iYWI7trNeaRZcuWqU+fPvroo4/UoEEDzZgxQ1999ZUOHjwof39/9e7dW2XLltXEiRMz3b5v3766fPmyNambiY2Nla+vr65cuVJgpuqtWbNGHTp0KFR/UQKQ+zgfAEhXWM8H169f1/Hjx20+e8geaWmGzvx5WfGxiSri464yVYrlyfQ83Btu977MSTZw+D1O3bt3V0xMjEaPHm2dD7p27VrrzWknT54skJf8AAAAkDecnCwqW624o8sAbDg8OEnSwIEDM52aJ0mRkZG33XbRokW5XxAAAAAA3IRLOQAAAABgguAEAAAAACYITgAAAABgguAEAACAXOXgRZsBG7n1fiQ4AQAAIFekL52ekJDg4EqA/0lKSpIkOTvf2YcnF4hV9QAAAFD4OTs7q1ixYjp37pwkycvLSxYLn78Ex0lLS1NMTIy8vLzk4nJn0YfgBAAAgFwTEBAgSdbwBDiak5OTypcvf8chnuAEAACAXGOxWFSmTBmVLl1aycnJji4HkJubm5yc7vwOJYITAAAAcp2zs/Md31MCFCQsDgEAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCiQASnDz74QCEhIfLw8FDDhg21ffv2LPvOmzdPTZs2VfHixVW8eHG1atXqtv0BAAAA4E45PDgtW7ZMYWFhCg8P165du1SnTh21bdtW586dy7R/ZGSkevTooU2bNikqKkpBQUFq06aNTp8+nc+VAwAAALhXODw4TZ8+XQMGDFC/fv1Us2ZNzZ07V15eXlqwYEGm/T///HO98sorCg0NVfXq1TV//nylpaUpIiIinysHAAAAcK9wceTBk5KStHPnTg0fPtza5uTkpFatWikqKipb+0hISFBycrJKlCiR6fOJiYlKTEy0Po6NjZUkJScnKzk5+Q6qzx3pNRSEWgA4FucDAOk4HwD5Iyc/Yw4NTufPn1dqaqr8/f1t2v39/XXw4MFs7WPo0KEKDAxUq1atMn1+4sSJGjt2bIb2devWycvLK+dF55H169c7ugQABQTnAwDpOB8AeSshISHbfR0anO7Uu+++q6VLlyoyMlIeHh6Z9hk+fLjCwsKsj2NjY633Rfn4+ORXqVlKTk7W+vXr1bp1a7m6ujq6HAAOxPkAQDrOB0D+SJ+Nlh0ODU6lSpWSs7Ozzp49a9N+9uxZBQQE3HbbqVOn6t1339WGDRtUu3btLPu5u7vL3d09Q7urq2uBOhEVtHoAOA7nAwDpOB8AeSsnP18OXRzCzc1N9erVs1nYIX2hh8aNG2e53eTJkzV+/HitXbtW9evXz49SAQAAANzDHD5VLywsTH369FH9+vXVoEEDzZgxQ/Hx8erXr58kqXfv3ipbtqwmTpwoSZo0aZJGjx6tL774QiEhIYqOjpYkeXt7y9vb22GvAwAAAMDdy+HBqXv37oqJidHo0aMVHR2t0NBQrV271rpgxMmTJ+Xk9L8LYx9++KGSkpLUtWtXm/2Eh4drzJgx+Vk6AAAAgHuEw4OTJA0cOFADBw7M9LnIyEibxydOnMj7ggAAAADgJg7/AFwAAAAAKOgITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgwsXRBdzLUpJTtPP7CJ2P+q92prir/hOt5OLKt8RMSnKKdq/apLh/olU0MEB1H2vJuGUD42Yfxs1+jJ19GDf7MG72Ydzsx9jZpzCPm8UwDMPRRXzwwQeaMmWKoqOjVadOHb3//vtq0KBBlv2XL1+uUaNG6cSJE6pSpYomTZqkDh06ZOtYsbGx8vX11ZUrV+Tj45NbLyHHfpq/TC5zZqhEwmVr20WvYkp55XU1f767w+oq6Bg3+zBu9snvcUtOTtaaNWvUoUMHubq65vr+8xPvOfswbva5G8ctP84Hd+O45RfGzj4Fcdxykg0cPlVv2bJlCgsLU3h4uHbt2qU6deqobdu2OnfuXKb9t27dqh49eui5557T7t271alTJ3Xq1Em///57Plduv5/mL5Pf1DEqftObRpKKJVyW39Qx+mn+MscUVsAxbvZh3OzDuNmPsbMP42Yfxs0+jJv9GDv73A3j5vArTg0bNtSDDz6o2bNnS5LS0tIUFBSkf/3rXxo2bFiG/t27d1d8fLxWrVplbWvUqJFCQ0M1d+5c0+M5+opTSnKKtjVsquIJl2XJ5Pk0SZe9ium+davl4lI4Llvmh5SUFO1v3UHFr11h3HKAcbOPo8YtOSVFGzZEqFWrR+VaSL8fvOfsw7jZ524et7w8H9zN45bXGDv7ZGfcLnkVU6NtW/J92l5OsoFDg1NSUpK8vLz09ddfq1OnTtb2Pn366PLly/ruu+8ybFO+fHmFhYXp9ddft7aFh4dr5cqV+u9//5uhf2JiohITE62PY2NjFRQUpPPnzzskOO38PkK+Iwbn+3EBAACAguzKO++p3hOP5usxY2NjVapUqWwFJ4dG4fPnzys1NVX+/v427f7+/jp48GCm20RHR2faPzo6OtP+EydO1NixYzO0r1u3Tl5eXnZWbr/zUf/VQ/l+VAAAAKBg2x+1XWddEs075qKEhIRs973rryEOHz5cYWFh1sfpV5zatGnjmCtOKe7Sqi9N+50fMUH3t2+eDxUVDr//8JNKvfNv036Mmy3GzT6OGreU5BRF/vSTWjRvXmhWGLoV7zn7MG72uZvHLS/PB3fzuOU1xs4+2R23+xo3UL0O+X/FKbvu+ql6tyoo9zgVS7ic6cocjpzjWZAxbvZh3OzjqHG7G1bV4z1nH8bNPnfzuOXl+eBuHre8xtjZpyCPW6FZVc/NzU316tVTRESEtS0tLU0RERFq3Lhxpts0btzYpr8krV+/Psv+BY2Lq4tSXnldFt14k9wsTZJFUuorr/PDdgvGzT6Mm30YN/sxdvZh3OzDuNmHcbMfY2efu2XcHF5dWFiY+vTpo/r166tBgwaaMWOG4uPj1a9fP0lS7969VbZsWU2cOFGSNGjQIDVv3lzTpk1Tx44dtXTpUu3YsUMff/yxI19GjjR/vrt+kjKsY3/Jq5hSWf8/S4ybfRg3+zBu9mPs7MO42Ydxsw/jZj/Gzj53w7g5fDlySZo9e7b1A3BDQ0M1a9YsNWzYUJLUokULhYSEaNGiRdb+y5cv18iRI60fgDt58uRC9wG40o3Llju+36D9Udt1X+MGqv9EqwKftAuCwvyJ047EuNknP8ftbpiqdzPec/Zh3Oxzt41bfp0P7rZxy0+MnX0K2rgVmuXIHaEgBSfp7vtFCYD9OB8ASMf5AMgfheYeJwAAAAAoDAhOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJlwcXUB+MwxDkhQbG+vgSm5ITk5WQkKCYmNj5erq6uhyADgQ5wMA6TgfAPkjPROkZ4TbueeCU1xcnCQpKCjIwZUAAAAAKAji4uLk6+t72z4WIzvx6i6Slpamf/75R0WLFpXFYrlt3wcffFC//fZbjo+Rk+1iY2MVFBSkU6dOycfHJ8fHupfZ+/1xNEfXnR/Hz4tj5MY+72QfnA8KNkf/XNnL0XVzPuB8cDdy9M+VvRxd9716PjAMQ3FxcQoMDJST0+3vYrrnrjg5OTmpXLly2err7Oxs18nKnu18fHw4MeaQvd8fR3N03flx/Lw4Rm7s8072wfmgYHP0z5W9HF035wPOB3cjR/9c2cvRdd/L5wOzK03pWBziNl599dV83Q45U1jH2dF158fx8+IYubHPO9kH54OCrbCOs6Pr5nyQv9s6+vt9ryis4+zouu/l80F23XNT9Qqa2NhY+fr66sqVK4XyryMAcg/nAwDpOB8ABQ9XnBzM3d1d4eHhcnd3d3QpAByM8wGAdJwPgIKHK04AAAAAYIIrTgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguBUgK1atUrVqlVTlSpVNH/+fEeXA8CBOnfurOLFi6tr166OLgWAA506dUotWrRQzZo1Vbt2bS1fvtzRJQH3DJYjL6BSUlJUs2ZNbdq0Sb6+vqpXr562bt2qkiVLOro0AA4QGRmpuLg4LV68WF9//bWjywHgIGfOnNHZs2cVGhqq6Oho1atXT4cPH1aRIkUcXRpw1+OKUwG1fft23XfffSpbtqy8vb3Vvn17rVu3ztFlAXCQFi1aqGjRoo4uA4CDlSlTRqGhoZKkgIAAlSpVShcvXnRsUcA9guCURzZv3qzHH39cgYGBslgsWrlyZYY+H3zwgUJCQuTh4aGGDRtq+/bt1uf++ecflS1b1vq4bNmyOn36dH6UDiCX3en5AMDdIzfPBzt37lRqaqqCgoLyuGoAEsEpz8THx6tOnTr64IMPMn1+2bJlCgsLU3h4uHbt2qU6deqobdu2OnfuXD5XCiCvcT4AkC63zgcXL15U79699fHHH+dH2QBEcMoz7du319tvv63OnTtn+vz06dM1YMAA9evXTzVr1tTcuXPl5eWlBQsWSJICAwNtrjCdPn1agYGB+VI7gNx1p+cDAHeP3DgfJCYmqlOnTho2bJgeeuih/CoduOcRnBwgKSlJO3fuVKtWraxtTk5OatWqlaKioiRJDRo00O+//67Tp0/r6tWr+uGHH9S2bVtHlQwgj2TnfADg3pCd84FhGOrbt68eeeQRPfvss44qFbgnEZwc4Pz580pNTZW/v79Nu7+/v6KjoyVJLi4umjZtmlq2bKnQ0FC98cYbrKgH3IWycz6QpFatWun//u//tGbNGpUrV45QBdyFsnM++OWXX7Rs2TKtXLlSoaGhCg0N1b59+xxRLnDPcXF0AcjaE088oSeeeMLRZQAoADZs2ODoEgAUAA8//LDS0tIcXQZwT+KKkwOUKlVKzs7OOnv2rE372bNnFRAQ4KCqADgC5wMA6TgfAAUbwckB3NzcVK9ePUVERFjb0tLSFBERocaNGzuwMgD5jfMBgHScD4CCjal6eeTq1as6cuSI9fHx48e1Z88elShRQuXLl1dYWJj69Omj+vXrq0GDBpoxY4bi4+PVr18/B1YNIC9wPgCQjvMBUIgZyBObNm0yJGX46tOnj7XP+++/b5QvX95wc3MzGjRoYPz666+OKxhAnuF8ACAd5wOg8LIYhmE4IK8BAAAAQKHBPU4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AgHvGokWLVKxYsVzb34kTJ2SxWLRnz55c2ycAoGAiOAEA8lXfvn1lsVisXyVLllS7du20d+/eHO1nzJgxCg0NzZsisykoKEhnzpzR/fff79A6AAB5j+AEAMh37dq105kzZ3TmzBlFRETIxcVFjz32mKPLyjFnZ2cFBATIxcXF0aUAAPIYwQkAkO/c3d0VEBCggIAAhYaGatiwYTp16pRiYmKsfYYOHaqqVavKy8tLFStW1KhRo5ScnCzpxpS7sWPH6r///a/1ytWiRYskSZcvX9aLL74of39/eXh46P7779eqVatsjv/jjz+qRo0a8vb2toa4rFy6dEm9evWSn5+fPD09VaVKFS1cuFBSxql6t15NS/+KjIyUJCUmJurNN99U2bJlVaRIETVs2ND6HACgYONPZAAAh7p69aqWLFmiypUrq2TJktb2okWLatGiRQoMDNS+ffs0YMAAFS1aVEOGDFH37t31+++/a+3atdqwYYMkydfXV2lpaWrfvr3i4uK0ZMkSVapUSQcOHJCzs7N1vwkJCZo6dao+++wzOTk56ZlnntGbb76pzz//PNP6Ro0apQMHDuiHH35QqVKldOTIEV27di3TvjNnztS7775rffzuu+/qyy+/VPXq1SVJAwcO1IEDB7R06VIFBgbq22+/Vbt27bRv3z5VqVLljscSAJB3CE4AgHy3atUqeXt7S5Li4+NVpkwZrVq1Sk5O/5sIMXLkSOv/h4SE6M0339TSpUs1ZMgQeXp6ytvbWy4uLgoICLD2W7dunbZv364//vhDVatWlSRVrFjR5tjJycmaO3euKlWqJOlGmBk3blyWtZ48eVJ169ZV/fr1rbVkxdfXV76+vpKkFStW6KOPPtKGDRsUEBCgkydPauHChTp58qQCAwMlSW+++abWrl2rhQsXasKECabjBgBwHIITACDftWzZUh9++KGkG1Ph5syZo/bt22v79u0KDg6WJC1btkyzZs3S0aNHdfXqVaWkpMjHx+e2+92zZ4/KlStnDU2Z8fLysoYmSSpTpozOnTuXZf+XX35ZTz31lHbt2qU2bdqoU6dOeuihh25bx+7du/Xss89q9uzZatKkiSRp3759Sk1NzVBbYmKizZU2AEDBRHACAOS7IkWKqHLlytbH8+fPl6+vr+bNm6e3335bUVFR6tWrl8aOHau2bdvK19dXS5cu1bRp0267X09PT9Nju7q62jy2WCwyDCPL/u3bt9dff/2lNWvWaP369Xr00Uf16quvaurUqZn2j46O1hNPPKHnn39ezz33nLX96tWrcnZ21s6dO22mDkqyXn0DABRcBCcAgMNZLBY5OTlZ7x3aunWrgoODNWLECGufv/76y2YbNzc3paam2rTVrl1bf//9tw4fPnzbq0455efnpz59+qhPnz5q2rSp3nrrrUyD0/Xr1/Xkk0+qevXqmj59us1zdevWVWpqqs6dO6emTZvmWm0AgPxBcAIA5LvExERFR0dLujFVb/bs2bp69aoef/xxSVKVKlV08uRJLV26VA8++KBWr16tb7/91mYfISEhOn78uHV6XtGiRdW8eXM1a9ZMTz31lKZPn67KlSvr4MGDslgsateunV21jh49WvXq1dN9992nxMRErVq1SjVq1Mi074svvqhTp04pIiLCZoXAEiVKqGrVqurVq5d69+6tadOmqW7duoqJiVFERIRq166tjh072lUfACB/sBw5ACDfrV27VmXKlFGZMmXUsGFD/fbbb1q+fLlatGghSXriiSc0ePBgDRw4UKGhodq6datGjRpls4+nnnpK7dq1U8uWLeXn56cvv/xSkvTNN9/owQcfVI8ePVSzZk0NGTIkw5WpnHBzc9Pw4cNVu3ZtNWvWTM7Ozlq6dGmmfX/66SedOXNGNWvWtL6+MmXKaOvWrZKkhQsXqnfv3nrjjTdUrVo1derUSb/99pvKly9vd30AgPxhMW43sRsAAAAAwBUnAAAAADBDcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAE/8P1+ntkW24qc4AAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# Загрузка данных\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# Реализации методов\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# Константы\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# Функция оценки с возвратом NaN при расходимости\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)) or np.any(np.isinf(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# Диапазоны гиперпараметров (learning rate до 0.1, как раньше, но можно и до 0.5)\n",
+        "lr_range = np.logspace(-8, -1, 8)   # 1e-8 ... 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# График 1: learning rate\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n",
+        "    plt.semilogx(lr_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Learning rate')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от learning rate')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# График 2: эпохи\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n",
+        "    plt.semilogx(epochs_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от количества эпох')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# График 3: размер батча\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n",
+        "    plt.semilogx(batch_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Batch size')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от размера батча')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xXa2bT4F0Jpp",
+        "outputId": "bc285e4b-8310-4ee9-b83e-080063b6c4e0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "CFB9NiwQzDS8",
+        "outputId": "388897a8-4a7a-44a2-e235-cb589deb8c29"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-e59627e2-0e62-474b-b4ba-6ece67f2f7a3\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-e59627e2-0e62-474b-b4ba-6ece67f2f7a3\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (ожидается файл data.csv) ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()  # выберите файл data.csv на компьютере\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Построение графиков и таблиц ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate', 'lr_plot')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох', 'epochs_plot')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ektA0t4m0026",
+        "outputId": "2a161cb0-4e2b-41c9-e12b-0298888b0051"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-ab4c66b8-e97a-4ee3-99c8-cde5edd9f429\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-ab4c66b8-e97a-4ee3-99c8-cde5edd9f429\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (1).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329          NaN\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329          NaN\n",
+            "1.000000e-06          8.313329       NaN  8.346550    8.313329          NaN\n",
+            "1.000000e-05          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-04          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-03          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-02          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-01          8.313329       NaN       NaN    8.313329          NaN\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIoCAYAAABeertyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAhHNJREFUeJzs3XlcVPX+x/H3sIOAKwIqCu47bpC2qeVulrZYZrnkcisry1/ZtU3N0qy8ZdnikkuL6dXSNjVJMyvNndxNze2q4JICgsDAnN8fE5MjIKDAYeD1vI95zJkz33POe6avXD58z/kei2EYhgAAAAAAuXIzOwAAAAAAlHQUTgAAAACQBwonAAAAAMgDhRMAAAAA5IHCCQAAAADyQOEEAAAAAHmgcAIAAACAPFA4AQAAAEAeKJwAAAAAIA8UTgAAAACQBwonAMXqww8/VNeuXRUcHCxPT0+FhISoffv2+vjjj2Wz2cyOh6s0aNAgWSwWBQYG6uLFi9ne379/vywWiywWi958802n9w4fPqzBgwerTp068vHxUUhIiG6++WaNHTvWqV2HDh0c+7j80bBhwyL9fAAAeJgdAEDZMm/ePIWGhurFF19UYGCgzp8/r99++02DBg3S8uXL9fnnn5sdEVfJw8NDKSkp+uabb9S3b1+n9z777DP5+PgoNTXVaf2BAwcUFRUlX19fPfTQQwoPD9fJkye1detWTZ48WePHj3dqX6NGDU2aNCnbscuXL1/4HwgAgEtQOAEoVmvXrpWnp6fTuieeeEKVK1fWtGnTNGnSJIWHh5sTDtfE29tbN9xwgz7//PNshdP8+fPVs2dPffHFF07r33rrLV24cEGxsbGqVauW03unTp3Kdozy5cvrgQceKPzwAADkgVP1ABSry4umLFnFkpvbPz+WvvrqK/Xs2VPVqlWTt7e36tSpowkTJigzM9Np28tP4apSpYp69uypnTt3OrWzWCwaN26c07o33nhDFotFHTp0cFqfmpqqcePGqX79+vLx8VFoaKjuvPNOHTx4UJL99DKLxaK5c+c6bTdixAhZLBYNGjTIsW7u3LmyWCzy8vLS6dOnndqvX7/ekXvz5s1O7y1atEitW7eWr6+vqlSpogceeEDHjx/P9t3t3btXffv2VVBQkHx9fdWgQQM9//zzkqRx48blenpb1mPNmjWO77Fp06bZ9l8Q999/v5YvX67z58871m3atEn79+/X/fffn639wYMHVaNGjWxFkyRVrVr1mrJc7tSpUxoyZIiCg4Pl4+OjyMhIzZs3z/F+1n/TKz0u/e96uZz6RFJSklq3bq2IiAidPHnSsT45OVn/93//p7CwMHl7e6tBgwZ68803ZRhGtv1m9Z/LH5f22aw2hw8fdqyz2Wxq3rx5tkzh4eHZPseaNWuc+kKWDRs2qFu3bipfvrz8/PzUvn17/frrr9kyHj9+XEOGDHH8W42IiNAjjzyi9PT0XPNf+sjKl3XKZ9ajYsWK6tChg37++Wen4+X3Z8PlkpKSNHToUNWqVUve3t6qUaOGHn74YcXHx+frO896XP5zZNu2berevbsCAwPl7++vW2+9Vb/99pvjfcMw1LFjRwUFBTn9QSA9PV3NmjVTnTp1lJycfMXsAMzHiBMAU5w/f14ZGRlKSkrSli1b9Oabb+q+++5TzZo1HW3mzp0rf39/jRo1Sv7+/lq9erVeeuklJSYm6o033nDaX8OGDfX888/LMAwdPHhQ//nPf9SjRw8dPXr0ihlyOu0rMzNTt912m1atWqX77rtPI0eOVFJSkmJiYrRz507VqVMnx/0dOHBAM2fOzPV47u7u+vTTT/XUU0851s2ZMyfHU9jmzp2rwYMHKyoqSpMmTVJ8fLymTp2qX3/9Vdu2bVOFChUkSdu3b9dNN90kT09PDR8+XOHh4Tp48KC++eYbvfrqq7rzzjtVt25dx36feuopNWrUSMOHD3esa9SoUa6ZC+rOO+/Uww8/rC+//FIPPfSQJPtoU8OGDdWqVats7WvVqqUffvhBq1ev1i233JLn/jMzM3XmzJls6319fVWuXLlct7t48aI6dOigAwcO6LHHHlNERIQWLVqkQYMG6fz58xo5cqSCgoL0ySefOLb58ssvtWTJEqd1uf23z4nVatVdd92lo0eP6tdff1VoaKgk+y/Rt99+u3788UcNGTJELVq00Pfff69nnnlGx48f11tvvZXj/t566y1VqVJFkvTqq6/mefxPPvlEO3bsyHfey61evVrdu3dX69atNXbsWLm5uWnOnDm65ZZb9PPPPys6OlqSdOLECUVHR+v8+fMaPny4GjZsqOPHj2vx4sVKSUnRzTff7PQdZmXPKu4l6frrr3csV6lSxfEd/O9//9PUqVPVo0cPHTt2zNHvC/Kz4VJ//fWXtm/frqFDhyokJEQHDhzQhx9+qBUrVmjjxo3ZivWXX35ZERERjtcXLlzQI4884tRm165duummmxQYGKjRo0fL09NT06dPV4cOHfTTTz/puuuuk8Vi0ezZs9W8eXPHvw9JGjt2rHbt2qU1a9Zcsf8CKCEMADBBgwYNDEmOx4ABAwyr1erUJiUlJdt2//rXvww/Pz8jNTXVsa59+/ZG+/btndo999xzhiTj1KlTjnWSjLFjxzpejx492qhatarRunVrp+1nz55tSDL+85//ZDu+zWYzDMMwDh06ZEgy5syZ43ivb9++RtOmTY2wsDBj4MCBjvVz5swxJBn9+vUzmjVr5lifnJxsBAYGGvfff78hydi0aZNhGIaRnp5uVK1a1WjatKlx8eJFR/tvv/3WkGS89NJLjnU333yzERAQYBw5ciTHnJerVauWU7ZLtW/f3mjSpEmO7+Vl4MCBRrly5QzDMIy7777buPXWWw3DMIzMzEwjJCTEGD9+vOM7e+ONNxzb7dy50/D19TUkGS1atDBGjhxpLF261EhOTs4x36V95tLHv/71ryvme/vttw1JxqeffupYl56ebrRr187w9/c3EhMTs20zduxYoyD/N3lpn7DZbEb//v0NPz8/Y8OGDU7tli5dakgyXnnlFaf1d999t2GxWIwDBw44rZ85c6Yhyem/8eV9PquPHTp0yDAMw0hNTTVq1qxpdO/ePVs/jYiIMAYMGOB0jB9//NGQZPz444+GYdj7T7169YyuXbs69aWUlBQjIiLC6Ny5s2PdgAEDDDc3N0f/vVRO/TCnf69ZBg4caNSqVctp3YwZMwxJxsaNG51yXC6nnw35sXPnTsPb29t46KGHHOuyvs/LP9Pp06ez/Rzp3bu34eXlZRw8eNCx7sSJE0ZAQIBx8803O20/ffp0Rz/87bffDHd3d+PJJ58sUF4A5uFUPQCmmDNnjmJiYvTZZ59pyJAh+uyzz5xGQST7KEKWpKQknTlzRjfddJNSUlK0d+9ep7ZWq1VnzpzR6dOntX79ei1ZskTNmzd3/IX+csePH9e7776rF198Uf7+/k7vffHFF6pSpYoef/zxbNtZLJYc97dlyxYtWrRIkyZNcjrd8FIPPvig9u7d6zgl74svvlD58uV16623OrXbvHmzTp06pUcffVQ+Pj6O9T179lTDhg313XffSZJOnz6ttWvX6qGHHnIaqbtSzrxkjeicOXNG6enpV7WP+++/X2vWrFFcXJxWr16tuLi4HE/Tk6QmTZooNjZWDzzwgA4fPqypU6eqd+/eCg4OznH0Ljw8XDExMdkeTz755BUzLVu2TCEhIerXr59jnaenp5544glduHBBP/3001V91tw888wz+uyzz/Tf//7XMTJzaRZ3d3c98cQTTuv/7//+T4ZhaPny5U7rs/47eHt75/v47733ns6ePZttZkLJfgrk//73vytuHxsb6zi98uzZs44+kZycrFtvvVVr166VzWaTzWbT0qVL1atXL7Vp0ybbfq6mH9psNsfxYmNj9fHHHys0NNRpZLQgPxuutP8zZ84oODhYPXr00BdffFHgmT0zMzO1cuVK9e7dW7Vr13asDw0N1f33369ffvlFiYmJjvXDhw9X165d9fjjj+vBBx9UnTp1NHHixAIdE4B5ynThtHbtWvXq1UvVqlWTxWLR0qVLC7R9amqqBg0apGbNmsnDw0O9e/fOsd17772nRo0aOa49+Pjjj7O1OX/+vEaMGKHQ0FB5e3urfv36WrZs2VV8KsA1tGvXTp06ddL999+vWbNm6eWXX9acOXOcrp/YtWuX+vTpo/LlyyswMFBBQUGOiQESEhKc9rdu3ToFBQWpatWquv7665WRkaFFixbl+ovb2LFjVa1aNf3rX//K9t7BgwfVoEEDeXjk/2zmf//737rpppt022235domKChIPXv21OzZsyVJs2fP1sCBA7MVWkeOHJEkNWjQINs+GjZs6Hj/zz//lKRrvi7pUnv37lVQUJDT9VLz588v0D569OihgIAALVy4UJ999pmioqKcThe8XP369fXJJ5/ozJkz2r59uyZOnCgPDw8NHz5cP/zwg1PbcuXKqVOnTtkeeU1HfuTIEdWrVy/bd531y3jWd1oYpk+frilTpkiSzp07l2OWatWqKSAgIF9Zsq4Xu7zAz01CQoImTpyoUaNGKTg4ONv7119/vX766SctWLBAp06d0pkzZ7L9e9q/f78kaeDAgY7+kPWYNWuW0tLSlJCQoNOnTysxMbFQ++CxY8ccx2rZsqUOHjyoL774wunzF+Rnw+WOHj2a7TMtWbJECQkJOZ4GeiWnT59WSkpKjv9WGzVqJJvNpmPHjjmt/+ijj5SSkqL9+/dr7ty5TkUggJKtTF/jlJycrMjISD300EO68847C7x9ZmamfH199cQTT2SbKSrLBx98oDFjxmjmzJmKiorSxo0bNWzYMFWsWFG9evWSZP9rYufOnVW1alUtXrxY1atX15EjRxzncgNlwd13363nn39eGzZs0A033KDz58+rffv2CgwM1Msvv+y4x8/WrVv17LPPZvvLcPPmzR2/rJ4+fVrvvPOOOnTooK1btyokJMSp7Z49ezR37lx9+umnuU5WURArV67UDz/8oPXr1+fZ9qGHHtKAAQP0+OOPa+3atZo1a1a2C9/NFB4e7hjpOXv2rN555x09+OCDql27ttq2bZuvfXh7e+vOO+/UvHnz9Oeff2a7kD437u7uatasmZo1a6Z27dqpY8eO+uyzz9SpU6er/Tim+O233/Tqq69q06ZNeuqpp9StW7dcRz7zIy4uTv7+/vm+Bmby5Mlyc3PTM888o7Nnz2Z7/7nnntOvv/7qNPp2uax/X2+88YZatGiRYxt/f3/99ddf+cpUEMHBwfr0008l2Yug2bNnq1u3bvrll1/UrFmzAv9suFxISIhiYmKc1s2ePbvYboWwZs0apaWlSZJ27Nihdu3aFctxAVy7Ml04de/eXd27d8/1/bS0ND3//PP6/PPPdf78eTVt2lSTJ092zGRUrlw5ffDBB5KkX3/91WkWqSyffPKJ/vWvf+nee++VJNWuXVubNm3S5MmTHYXT7Nmz9ddff2ndunWOX+KYjhllTdZNU93d3SXZf7k4e/asvvzyS918882OdocOHcpx+4oVKzr9gt2hQwdVq1ZNc+bM0ZgxY5zajhkzRi1atHD8u7xcnTp1tGHDBlmt1jwLK8Mw9O9//1t9+vTJV2HRvXt3+fj46L777tONN96oOnXqZCucsmaY27dvX7YJE/bt2+d4P+vUoMtnD7wWWSM6WW666SZVr15dK1euzHfhJNlP15s9e7bc3Nx03333FThH1mlfl85Edy1q1aql7du3y2azOY06ZZ3WldOsflfroYce0nPPPacTJ06ocePGeuqpp5wmR8iaECMpKclp1Cm3LLt37873BB4nTpzQ1KlTNWnSJAUEBORYOFWpUkXr16/X7t27FRcXJ0n6/fff9fTTTzvaZE2CERgYeMXCNSgoSIGBgYXaB318fJyOefvtt6tSpUqaNm2apk+fXuCfDXntX5LeeecdBQYGFrjADQoKkp+fn/bt25ftvb1798rNzU1hYWGOdSdPntTjjz+uLl26yMvLS08//bS6du1aqP0PQNEp06fq5eWxxx7T+vXrtWDBAm3fvl333HOPunXr5jiFIT/S0tKcrlGQ7Odmb9y4UVarVZL09ddfq127dhoxYoSCg4PVtGlTTZw4Mc9pVQFXlNspqDNnzpTFYnEUClkFlHHJ9Mzp6el6//3383WcrEIs6y+7WdavX6+vvvpKr732Wq6n8d111106c+aMpk2blu0947LporN+PuQ0O19OPDw8NGDAAG3fvt0x69zl2rRpo6pVq+rDDz90yr98+XLt2bNHPXv2lGT/pe3mm2/W7Nmzs80eeHnOq5X11/us/x751bFjR02YMEHTpk3LNuJ3qZ9//tnxs/BSWf0kp1OgrkaPHj0UFxenhQsXOtZlZGTo3Xfflb+/v9q3b18ox5HsxaYkVatWTZMnT9ann36qlStXOmXJzMzM1r/eeustWSwWpz/oHTt2TL/++mu+ZhyUpPHjxys4OFgPP/zwFdu5ubmpadOmjlMdW7du7fR+69atVadOHb355pu6cOFCtu2zptV3c3NT79699c0332SbTl8qnH6Ynp6ujIwMx7+Fa/nZkNNo1LZt27R8+XL17t071+sTc+Pu7q4uXbroq6++cpoKPj4+XvPnz9eNN96owMBAx/phw4bJZrPpo48+0owZM+Th4aEhQ4YU2r9XAEWrTI84XcnRo0c1Z84cHT16VNWqVZMkPf3001qxYoXmzJmT74s5u3btqlmzZql3795q1aqVtmzZolmzZjkuZA8NDdWff/6p1atXq3///lq2bJkOHDigRx99VFarNccLewFXdv/996thw4bq06ePgoODdfr0aS1fvlw//vijnn/+eTVr1kyS/TqMihUrauDAgXriiSdksVj0ySef5PoLRnx8vOP0njNnzmj69Ony8PDIds3RypUr1blz5yv+FX3AgAH6+OOPNWrUKG3cuFE33XSTkpOT9cMPP+jRRx/VHXfc4bS/YcOGFegX/AkTJuiZZ55RxYoVc3zf09NTkydP1uDBg9W+fXv169fPMR15eHi403Tm77zzjm688Ua1atVKw4cPV0REhA4fPqzvvvtOsbGx+c6U5cKFC1qxYoUk+9TN77zzjjw9PR3FWn65ubnphRdeyLPd5MmTtWXLFt15551q3ry5JGnr1q36+OOPValSpWyTPiQkJDj+O1/uSjfGHT58uKZPn65BgwZpy5YtCg8P1+LFi/Xrr7/q7bffzna9UWEZPny45s+fr4cfflg7d+6Un5+fevXqpY4dO+r555/X4cOHFRkZqZUrV+qrr77Sk08+6Rjt+eCDDzRp0iT5+fllm0giNytXrtRnn30mLy+va8rt5uamWbNmqXv37mrSpIkGDx6s6tWr6/jx4/rxxx8VGBiob775RpI0ceJErVy5Uu3bt9fw4cPVqFEjnTx5UosWLdIvv/xS4NPOk5OTnU7V++STT5Samqo+ffpIKvjPhksdPXpUPXv21D333KPq1atr586dmjlzpqpUqXLVkzS88soriomJ0Y033qhHH31UHh4emj59utLS0vT666872s2ZM0ffffed5s6dqxo1akiS3n33XT3wwAP64IMP9Oijj17V8QEUI7Om8ytpJBlLlixxvM6a9rdcuXJODw8PD6Nv377Zth84cKBxxx13ZFufkpJiDB482PDw8DDc3d2NatWqGaNHjzYkGXFxcYZhGEa9evWMsLAwIyMjw7HdlClTjJCQkEL/nIDZPvjgA6NHjx5GtWrVDA8PD6NChQpG165djWXLlmVr++uvvxpt27Y1fH19Hf92vv/+e6dpkw0j+zTVFSpUMG644YZs+5RkWCwWY8uWLU7rc5oeOSUlxXj++eeNiIgIw9PT0wgJCTHuvvtux5TDWVNP+/r6GsePH3fa9vIpv3Ob2jiv9xcuXGi0bNnS8Pb2NipVqmT079/f+N///pdt+507dxp9+vQxKlSoYPj4+BgNGjQwXnzxxRyPldd05Dl9j8uXL8+x/aUunY48NzlNR/7rr78aI0aMMJo2bWqUL1/e8PT0NGrWrGkMGjTIaXrnnPJd/shLfHy8MXjwYKNKlSqGl5eX0axZM6dpui93LdORX2rfvn2Gj4+P8dRTTznWJSUlGU899ZRRrVo1w9PT06hXr57xxhtvOE3fHR0dbdxzzz3G3r17sx0rt+nIW7Ro4bSP3DJd7vLpyLNs27bNuPPOO43KlSsb3t7eRq1atYy+ffsaq1atcmp35MgRY8CAAUZQUJDh7e1t1K5d2xgxYoSRlpaWZ/ZLDRw40Om/qb+/v9GqVSvjk08+cWqX358Nl0tKSjKGDRtm1KpVy/Dy8jKCgoKMBx98MNt0/gWZjtwwDGPr1q1G165dDX9/f8PPz8/o2LGjsW7dOsf7x44dM8qXL2/06tUrW6Y+ffoY5cqVM/78889ccwMoGSyGwfiwZJ8ydcmSJY6Z8RYuXKj+/ftr165d2U5R8ff3z3bqSdZNFHObmc9qtSo+Pl6hoaGaMWOGnn32WZ0/f15ubm5q3769PD09nWaPWr58uXr06KG0tLRr/sshAAAAgGvDqXq5aNmypTIzM3Xq1CnH+erXwtPT0zE0v2DBAt12222Oc6lvuOEGzZ8/3+mi5T/++EOhoaEUTQAAAEAJUKYLpwsXLujAgQOO14cOHVJsbKwqVaqk+vXrq3///howYICmTJmili1b6vTp01q1apWaN2/uON9/9+7dSk9P119//aWkpCTHNQVZ07f+8ccf2rhxo6677jqdO3dO//nPf7Rz507NmzfPcdxHHnlE06ZN08iRI/X4449r//79mjhxYr7PaQcAAABQtMr0qXpr1qxRx44ds60fOHCg5s6dK6vVqldeeUUff/yxjh8/ripVqqht27YaP3684wL28PDwHG+cmPW17tmzR/fff7/27dsnT09PdezYUZMnT852Ifn69ev11FNPKTY2VtWrV9eQIUP07LPPFngmKwAAAACFr0wXTgAAAACQH9zHCQAAAADyQOEEAAAAAHkoc5ND2Gw2nThxQgEBAbJYLGbHAQAAAGASwzCUlJSkatWqOWa3zk2ZK5xOnDihsLAws2MAAAAAKCGOHTvmuHVQbspc4RQQECDJ/uUEBgaanAZlidVq1cqVK9WlSxd5enqaHQcoEPovXBn9F66M/lu0EhMTFRYW5qgRrqTMFU5Zp+cFBgZSOKFYWa1W+fn5KTAwkB98cDn0X7gy+i9cGf23eOTnEh4mhwAAAACAPFA4AQAAAEAeKJwAAAAAIA9l7honAAAAFL3MzExZrVazY7g8q9UqDw8PpaamKjMz0+w4LsnLyyvPqcbzg8IJAAAAhcYwDMXFxen8+fNmRykVDMNQSEiIjh07xj1Ir5Kbm5siIiLk5eV1TfuhcAIAAEChySqaqlatKj8/P37Zv0Y2m00XLlyQv79/oYyalDU2m00nTpzQyZMnVbNmzWvqjxROAAAAKBSZmZmOoqly5cpmxykVbDab0tPT5ePjQ+F0lYKCgnTixAllZGRc05TufPsAAAAoFFnXNPn5+ZmcBPhH1il613qNGIUTAAAAChWn56EkKaz+SOEEAAAAAHmgcAIAAABc0Jo1a2SxWFx6BsNx48apRYsWZsfIFwonAAAAlCiZNkPrD57VV7HHtf7gWWXajCI93qBBg2SxWPTaa685rV+6dKnLn3YYHh4ui8WS7XH5Zy0OFotFS5cudVr39NNPa9WqVcWe5WqUmMLptddek8Vi0ZNPPnnFdosWLVLDhg3l4+OjZs2aadmyZcUTEAAAAEVuxc6TunHyavWb+ZtGLohVv5m/6cbJq7Vi58kiPa6Pj48mT56sc+fOFep+09PTC3V/V+Pll1/WyZMnnR6PP/642bEkSf7+/i4zA2OJKJw2bdqk6dOnq3nz5ldst27dOvXr109DhgzRtm3b1Lt3b/Xu3Vs7d+4spqQAHGyZ0qGfpR2L7c827mYOALg2K3ae1COfbtXJhFSn9XEJqXrk061FWjx16tRJISEhmjRp0hXbffHFF2rSpIm8vb0VHh6uKVOmOL0fHh6uCRMmaMCAAQoMDNTw4cM1d+5cVahQQd9++60aNGggPz8/3X333UpJSdG8efMUHh6uihUr6oknnnCa+e2TTz5RdHS0wsLCVK1aNd1///06depUgT9bQECAQkJCnB7lypVzvL9s2TLVr19fvr6+6tixo+bOnet0CmBOp9O9/fbbCg8Pd7zetGmTOnfurCpVqqh8+fJq3769tm7d6vS9SFKfPn1ksVgcry/ft81m08svv6waNWrI29tbLVq00IoVKxzvHz58WBaLRV9++aU6duwoPz8/RUZGav369QX+XgrK9MLpwoUL6t+/v2bOnKmKFStese3UqVPVrVs3PfPMM2rUqJEmTJigVq1aadq0acWUFoAkaffX0ttNpXm3SV8MsT+/3dS+HgCASxiGoZT0jDwfSalWjf16l3I6KS9r3bivdysp1Zqv/RlGwU7vc3d318SJE/Xuu+/qf//7X45ttmzZor59++q+++7Tjh07NG7cOL344ouaO3euU7s333xTkZGR2rZtm1588UVJUkpKit555x0tWLBAK1as0Jo1a9SnTx8tW7ZMy5Yt0yeffKLp06dr8eLFjv1YrVaNHz9eP//8s7788ksdPnxYgwYNKtDnysuxY8d05513qlevXoqNjdXQoUP173//u8D7SUpK0sCBA/XLL7/ot99+U7169dSjRw8lJSVJshdWkjRnzhydPHnS8fpyU6dO1ZQpU/Tmm29q+/bt6tq1q26//Xbt37/fqd3zzz+vp59+WrGxsapfv7769eunjIyMAucuCNNvgDtixAj17NlTnTp10iuvvHLFtuvXr9eoUaOc1nXt2jXbuZKXSktLU1pamuN1YmKiJHtHzLrXAFAcsvqbq/c7y95v5f7FYEmGLj3r20g8Kf13gDLvmiOj4W1mxUMRKS39F2UT/bf4WK1WGYYhm80mm80mSUpJz1DTcTHXvG9DUlxiqpqNW5mv9jvHdZafV/5+1TUMQ4Zh6I477lCLFi300ksvadasWY7PkPU8ZcoU3XLLLXr++eclSXXr1tWuXbv0xhtvaMCAAY79dezYUU899ZTj9U8//SSr1ar33ntPderUkSTddddd+vTTT3Xy5En5+/urYcOG6tChg1avXq177rlHkv3aK8MwlJSUpICAAL399tu67rrrlJiYKH9/f6d8Wcs5efbZZ/XCCy84rfvuu+9000036f3331edOnX0xhtvSJLq1aun7du36/XXX3fsN6sIvfQYl6/r0KGD0/4//PBDVapUST/++KNuu+02x+l4gYGBqlq1qmPby/fz5ptvavTo0erbt68kadKkSfrxxx/11ltvadq0aY52o0aNUvfu3SVJY8eOVbNmzfTHH3+oYcOG2T5/1nGsVqvc3d2d3ivIzwVTC6cFCxZo69atuVacl4uLi1NwcLDTuuDgYMXFxeW6zaRJkzR+/Phs61euXMnN2WCKmJhr/z8P0xg2ddk1Su6XFU2SZJEhQ1L616MUc1CSxfQBbRQBl+6/KPPov0XPw8NDISEhunDhguPanovp5pzKnZSYpAwv97wbyv7Lc0ZGhhITE/XCCy/ojjvu0L/+9S9dvHhR0j9/eN+1a5d69OjheC1JLVu21NSpU3Xu3Dm5u7vLZrOpadOmTm1SU1Pl5+enoKAgx/oKFSqoZs2astlsjnWVKlXSiRMnHK9jY2P12muvaefOnUpISHAUDbt371bDhg2VkpJi/6xJSXJzy/n/d202mx5//HHdf//9TutDQ0OVmJioHTt2qGXLlk55IyMjnfablpamzMzMbJ/p0uynTp3Sq6++ql9++UWnT5+WzWZTSkqK/vjjD6ftLl686PT60n0nJibqxIkTatGihVObNm3aaOfOnUpMTNSFCxckSXXq1HG08ff3lyQdOnRI1apVy/YdpKen6+LFi1q7dm22Uams7zA/TCucjh07ppEjRyomJkY+Pj5FdpwxY8Y4jVIlJiYqLCxMXbp0UWBgYJEdF7ic1WpVTEyMOnfuLE9PT7PjXBXLkV/kEftX7u9L8rP+pZ5NK8iodWPxBUORKw39F2UX/bf4pKam6tixY/L393f8fhdgGNo5rnOe22489Jcemrclz3azB7ZWdESlPNv5errne0Y8T09PeXh4KDAwUN27d1eXLl00ceJEDRw4UJIcvzO6u7vL29vb6XdIX19fRxt3d3e5ubmpcuXKTm18fHzk6emZbd3l+/Ly8pKbm5sCAwOVnJysu+++W126dNGMGTNUq1YtHTt2TN27d5eXl5cCAwMdgwABAQG5/l7r5uam6tWr5zrlt4eHR7ZsWZ8pa7++vr6OXFmyPmvWunvvvVd//fWXpk6dqlq1asnb21s33HCD3N3ds+370tfe3t7Z2vj5+WX7XrL++2QVSRUqVHC0ySooL993ltTUVPn6+urmm2/OVndcWqDlxbTCacuWLTp16pRatWrlWJeZmam1a9dq2rRpSktLyzaUFhISovj4eKd18fHxCgkJyfU43t7e8vb2zrbe09OTH54whUv3vYtn89XM4+JZyVU/I67Ipfsvyjz6b9HLzMyUxWKRm5ub0wiIv3veIz/tGwQrtLyP4hJSc7zOySIppLyP2jcIlrtb4U4RnjVFd1bmyZMnq0WLFo7TvrLWN2rUSOvWrXP6bOvXr1f9+vWd+tal+7p0+0vXZRV1l6/L2vaPP/7Q2bNnNWnSJJUvX16BgYHatm2bY5tLv+PLv++cPl9u7zdu3Fhff/210/sbN2502m/VqlUVFxfnyCdJv//+u1P+devW6f3339dtt9lP1z927JjOnDnjdGxPT08ZhpHr91ChQgVVq1ZN69evV8eOHR1t1q1bp+jo6Fw/c17fg5ubmywWS44/AwryM8G0c2luvfVW7dixQ7GxsY5HmzZt1L9/f8XGxmYrmiSpXbt22eZ5j4mJUbt27YorNlC2+Qfn3aYg7QAA+Ju7m0VjezWWpBxOB7cb26txoRdNOWnWrJn69++vd955x2n9//3f/2nVqlWaMGGC/vjjD82bN0/Tpk3T008/XegZatasKS8vL02bNk2HDx/W119/rQkTJlzVvpKSkhQXF+f0yBppefjhh7V//34988wz2rdvn+bPn59tsosOHTro9OnTev3113Xw4EG99957Wr58uVObevXq6ZNPPtGePXu0YcMG9e/f3zFylSU8PFyrVq1SXFxcrtO+P/PMM5o8ebIWLlyoffv26d///rdiY2M1cuTIq/rshcm0wikgIEBNmzZ1epQrV06VK1dW06ZNJUkDBgzQmDFjHNuMHDlSK1as0JQpU7R3716NGzdOmzdv1mOPPWbWxwDKllrXS4HZzx3+h0UKrG5vBwBAAXVrGqoPHmilkPLOp1OFlPfRBw+0UremocWW5eWXX8424UKrVq303//+VwsWLFDTpk310ksv6eWXXy70me4kKSgoSHPnztXixYvVtm1bvf7663rzzTeval8vvfSSQkNDnR6jR4+WZC/QvvjiCy1dulSRkZH68MMPNXHiRKftGzVqpPfff1/vvfeeIiMjtXHjxmzF4kcffaRz586pVatWevDBB/XEE084JoHIMmXKFMXExCgsLEwtW7bMMesTTzyhUaNG6f/+7//UrFkzrVixQl9//bXq1at3VZ+9MFmMgs7VWIQ6dOigFi1a6O2333a8Dg8Pd6p6Fy1apBdeeEGHDx9WvXr19Prrr6tHjx75PkZiYqLKly+vhIQErnFCsbJarVq2bJl69Ojh2qeK7FwiLR6Uwxt//wWw78dS49uLMxGKQanpvyiT6L/FJzU1VYcOHVJERMQ1XcOeaTO08dBfOpWUqqoBPoqOqFQsI00lUdYEDIGBgVc8Ha8wrVmzRh07dtS5c+dUoUKFYjlmUbpSvyxIbWD6dOSXWrNmzRVfS9I999zjmKIRgAmyLrS1uEnGJX+JC6wmdXuNogkAcM3c3SxqV6ey2TEAJyWqcALgAjbOtD/f8KRU5xbpQrz9mqZa10tu+Zv2FQAAwNVQOAHIv/jd0pFfJIu7FDVEKl/D7EQAAKCQdejQQSXoap4SgztUAsi/TbPszw17UDQBAIAyhcIJQP6kJki/L7AvRw0zNwsAAEAxo3ACkD+/L5CsyVKVBlLEzWanAQAAKFYUTgDyZhj/TAoRPeyfmfUAAADKCAonAHn7c410dr/k5S81v9fsNAAAAMWOwglA3rImhYjsJ/lw42gAAFD2UDgBuLLzx6R9y+zLUUPNzQIAAGASCicAV7Z5tmTYpPCbpKoNzU4DACgLbJnSoZ+lHYvtz7bMYjlsXFycRo4cqbp168rHx0fBwcG64YYb9MEHHyglJUWSFB4eLovFIovFIl9fX4WHh6tv375avXp1sWSEebgBLoDcWVOlrfPsy9HDzc0CACgbdn8trXhWSjzxz7rAalK3yVLj24vssH/++aduuOEGVahQQRMnTlSzZs3k7e2tHTt2aMaMGapevbpuv91+/JdfflnDhg1Tenq6Dh8+rE8//VSdOnXShAkT9PzzzxdZRpiLwglA7nYvlVLOSoHVpQY9zE4DACjtdn8t/XeAJMN5feJJ+/q+HxdZ8fToo4/Kw8NDmzdvVrly5Rzra9eurTvuuEOG8U+mgIAAhYSESJJq1qypm2++WaGhoXrppZd09913q0GDBkWSEebiVD0AucuagrzNYMmdv7MAAK6CYUjpyXk/UhOl5aOVrWiy78T+tOJZe7v87M/IaT85O3v2rFauXKkRI0Y4FU2XsuRxK46RI0fKMAx99dVX+T4uXAu/CQHI2fGt0vHNkpun1Gqg2WkAAK7KmiJNrFYIOzLsp++9Fpa/5s+dkLxyLoIud+DAARmGkW2kqEqVKkpNTZUkjRgxQpMnT851H5UqVVLVqlV1+PDh/OWDy2HECUDOsqYgb9Jb8q9qahQAAMywceNGxcbGqkmTJkpLS8uzvWEYeY5MwXUx4gQgu5S/pJ1f2JeZFAIAcC08/eyjP3k5sk767O682/VfLNW6Pn/Hzae6devKYrFo3759Tutr164tSfL19c1zH2fPntXp06cVERGR7+PCtTDiBCC7bZ9IGalSSHOpRpTZaQAArsxisZ8yl9ejzi322fOU24iNxT5ZUZ1b8re/Aoz8VK5cWZ07d9a0adOUnJx8VR9z6tSpcnNzU+/eva9qe5R8FE4AnNky/zlNL3pYgf6PBwCAq+bmbp9yXFL24unv191es7crAu+//74yMjLUpk0bLVy4UHv27NG+ffv06aefau/evXJ3/+e4SUlJiouL07Fjx7R27VoNHz5cr7zyil599VXVrVu3SPLBfJyqB8DZ/hjp/FHJp4LUNB+nTAAAUFga326fcjzH+zi9VqT3capTp462bdumiRMnasyYMfrf//4nb29vNW7cWE8//bQeffRRR9uXXnpJL730kry8vBQSEqK2bdtq1apV6tixY5Hlg/konAA42/T3FOQtH5C88n9+OAAAhaLx7VLDnvZrni7ES/7B9muaimik6VKhoaF699139e677+bahlnzyi4KJwD/OHtQOvCDJIsUNcTsNACAssrNXYq4yewUgBOucQLwj00f2Z/rdZYq1TY3CwAAQAlC4QTALj1Z2vapfTlqmLlZAAAAShgKJwB2OxZJaQlSxXCpbiez0wAAAJQoFE4AJMOQNv49BXnUUMmNHw0AAACX4rcjANLR36T4HZKHj9Siv9lpAAAAShwKJwD/TEHe7B7Jr5K5WQAAAEogCiegrEuKk3Z/ZV+OZlIIAACAnFA4AWXdlnmSLUOqES2FRpqdBgAAoESicALKskyrtGWOfTl6uLlZAAAASjAKJ6As2/udlHRSKhckNb7d7DQAAEiSMm2Z2hS3Scv+XKZNcZuUacss8mOePn1ajzzyiGrWrClvb2+FhISoa9eu+vXXXx1ttm3bpnvvvVehoaHy9vZWrVq1dNttt+mbb76RYRiSpMOHD8tisTgeAQEBatKkiUaMGKH9+/cX+edA0fEwOwAAE238e1KIVgMlD29zswAAIOmHIz/otY2vKT4l3rEu2C9Y/47+tzrVKrr7DN51111KT0/XvHnzVLt2bcXHx2vVqlU6e/asJOmrr75S37591alTJ82bN09169ZVWlqa1q1bpxdeeEE33XSTKlSo8M/n+OEHNWnSRCkpKdqxY4emTp2qyMhIffPNN7r11luL7HOg6FA4AWVV/G7pyC+SxV1qM9jsNAAA6IcjP2jUmlEyZDitP5VySqPWjNJ/OvynSIqn8+fP6+eff9aaNWvUvn17SVKtWrUUHR0tSUpOTtaQIUPUs2dPffnll07bNmrUSEOGDHGMOGWpXLmyQkJCJEm1a9dWr169dOutt2rIkCE6ePCg3N3dC/1zoGhxqh5QVm36+4a3DXtI5WuYmwUAUGoZhqEUa0qej6S0JE3aOClb0SRJxt//e23ja0pKS8rX/i4vZK7E399f/v7+Wrp0qdLS0rK9v3LlSp09e1ajR4/OdR8Wi+WKx3Bzc9PIkSN15MgRbdmyJd/ZUHIw4gSURakJ0u8L7MtRTEEOACg6FzMu6rr51xXKvuJT4nX9guvz1XbD/Rvk5+mXr7YeHh6aO3euhg0bpg8//FCtWrVS+/btdd9996l58+b6448/JEkNGjRwbLNp0yZ17NjR8XrBggW67bbbrnichg0bSrJfB5U1mgXXwYgTUBb9vkCyJktVGkgRN5udBgAA09111106ceKEvv76a3Xr1k1r1qxRq1atNHfu3BzbN2/eXLGxsYqNjVVycrIyMjLyPEbWKFheo1MomRhxAsoaw/hnUojoYRI/vAEARcjXw1cb7t+QZ7st8Vv06KpH82z3/q3vq3Vw63wdt6B8fHzUuXNnde7cWS+++KKGDh2qsWPH6q233pIk7du3T23btpUkeXt7q27dugXa/549eyRJERERBc4G8zHiBJQ1f66Rzu6XvAKkyPvMTgMAKOUsFov8PP3yfFxf7XoF+wXLopz/oGeRRSF+Ibq+2vX52l9hjOo0btxYycnJ6tKliypVqqTJkydf9b5sNpveeecdRUREqGXLltecDcWPwgkoa7ImhYi8T/IOMDcLAAB/c3dz17+j/y1J2YqnrNfPRj8rd7fCn43u7NmzuuWWW/Tpp59q+/btOnTokBYtWqTXX39dd9xxh/z9/TVr1ix999136tmzp77//nv9+eef2r59u15//XV7/stmyTt79qzi4uL0559/6uuvv1anTp20ceNGffTRR8yo56I4VQ8oS84fk/Ytsy9HDTU3CwAAl+lUq5P+0+E/Od7H6dnoZ4vsPk7+/v667rrr9NZbb+ngwYOyWq0KCwvTsGHD9Nxzz0mS+vTpo3Xr1mny5MkaMGCA/vrrL5UvX15t2rTJcWKITp3sWf38/FSrVi117NhRM2bMKPDpfSg5KJyAsmTzbMmw2SeEqNrQ7DQAAGTTqVYndQzrqK2ntup0ymkF+QWpVdVWRTLSlMXb21uTJk3SpEmTrtiuTZs2WrRo0RXbhIeHF2gqdLgOCiegrLCmSlvn2ZeZghwAUIK5u7krKiTK7BiAE1Ovcfrggw/UvHlzBQYGKjAwUO3atdPy5ctzbT937lxZLBanh4+PTzEmBlzY7qVSylkpsLrUoIfZaQAAAFyKqSNONWrU0GuvvaZ69erJMAzNmzdPd9xxh7Zt26YmTZrkuE1gYKD27dvneM08+EA+ZU1B3maw5M5gMwAAQEGY+ttTr169nF6/+uqr+uCDD/Tbb7/lWjhZLBaFhIQURzyg9Di+VTq+WXLzlFoNNDsNAACAyykxf3bOzMzUokWLlJycrHbt2uXa7sKFC6pVq5ZsNptatWqliRMn5lpkSVJaWprS0tIcrxMTEyVJVqtVVqu18D4AkIes/mZGv3PfMENukmyNblemd0WJvo8CMrP/AteK/lt8rFarDMOQzWaTzWYzO06pkDXRRNb3ioKz2WwyDENWqzXbVPAF+blgMUye9mPHjh1q166dUlNT5e/vr/nz56tHj5yvv1i/fr3279+v5s2bKyEhQW+++abWrl2rXbt2qUaNGjluM27cOI0fPz7b+vnz58vPz69QPwtQEnlmJKnrziflbli1tv6LOleuntmRAACllIeHh0JCQhQWFiYvLy+z4wCSpPT0dB07dkxxcXHKyMhwei8lJUX333+/EhISFBgYeMX9mF44paen6+jRo0pISNDixYs1a9Ys/fTTT2rcuHGe21qtVjVq1Ej9+vXThAkTcmyT04hTWFiYzpw5k+eXAxQmq9WqmJgYde7cWZ6ensV2XLf178h99csygpspY8hqiesCcRXM6r9AYaD/Fp/U1FQdO3ZM4eHhTOBVSAzDUFJSkgICAri2/yqlpqbq8OHDCgsLy9YvExMTVaVKlXwVTqafqufl5eW4EVjr1q21adMmTZ06VdOnT89zW09PT7Vs2VIHDhzItY23t7e8vb1z3JYfnjBDsfY9W6a0da4kyXLdcHny1z9cI352wpXRf4teZmamLBaL3Nzc5OZm6uTNpUbW6XlZ3ysKzs3NTRaLJcefAQX5mVDivn2bzeY0QnQlmZmZ2rFjh0JDQ4s4FeCi9sdI549KPhWkpnebnQYAAMBlmVo4jRkzRmvXrtXhw4e1Y8cOjRkzRmvWrFH//v0lSQMGDNCYMWMc7V9++WWtXLlSf/75p7Zu3aoHHnhAR44c0dChQ836CEDJtnGG/bnlA5IX1/QBAFBQgwYNUu/evXN9f9y4cWrRokWx5YF5TD1V79SpUxowYIBOnjyp8uXLq3nz5vr+++/VuXNnSdLRo0edhiTPnTunYcOGKS4uThUrVlTr1q21bt26fF0PBZQ5Zw9KB1dJskhRQ8xOAwBAvhmZmUrZvEUZp0/LIyhIfm1ay3LZbGjInw4dOqhFixZ6++23c3z/0uumAgIC1KBBA73wwgu64447iimh6zC1cProo4+u+P6aNWucXr/11lt66623ijARUIps+vvfV73OUqXa5mYBACCfEleuVPzEScqIi3Os8wgJUfBzYxTYpYuJycyTnp5epPufM2eOunXrpsTERL3//vu6++67tXXrVjVr1qxIj+tqStw1TgAKQXqytO1T+3LUMHOzAACQT4krV+r4yCediiZJyoiP1/GRTypx5coiOe7ixYvVrFkz+fr6qnLlyurUqZOSk5NzbLtp0yYFBQVp8uTJue5v1qxZatSokXx8fNSwYUO9//77Tu8/++yzql+/vvz8/FS7dm29+OKLTvcTyjr9b9asWapTp45CQkIk2UeHZs2apT59+sjPz0/16tXT119/fc2fv0KFCgoJCVH9+vU1YcIEZWRk6Mcff7zm/ZY2ps+qB6AI7FgkpSVIFcOlup3MTgMAKMMMw5Bx8WLe7TIzFf/Kq1JOd8oxDMkixb86UeXatcvXaXsWX998Td998uRJ9evXT6+//rr69OmjpKQk/fzzz8rpjj2rV6/WnXfeqddff13Dhw/PcX+fffaZXnrpJU2bNk0tW7bUtm3bNGzYMJUrV04DBw6UZD8lbu7cuapWrZp27NihYcOGKSAgQKNHj3bs58CBA/riiy+0ePFiXbzk+xs/frxef/11vfHGG3r33XfVv39/HTlyRJUqVcrzs+YlIyPDcUYY9+HKjsIJKG0MQ9o4y74cNVRi6lIAgImMixe1r1XrQtiRfeTpj6jofDVvsHWLLH55T4x08uRJZWRk6M4771StWrUkKcdT1JYsWaIBAwZo1qxZuvfee3Pd39ixYzVlyhTdeeedkqSIiAjt3r1b06dPdxROL7zwgqN9eHi4nn76aS1YsMCpcEpPT9fHH3+sypUrKzEx0bF+0KBB6tevnyRp4sSJeuedd7Rx40Z169Ytz8+am379+snd3V0XL16UzWZTeHi4+vbte9X7K60onIDS5uhvUvwOycNHatHf7DQAAJRokZGRuvXWW9WsWTN17dpVXbp00d13362KFSs62mzYsEHffvutFi9efMUZ9pKTk3Xw4EENGTJEw4b9c6p8RkaGypcv73i9cOFCvfPOOzp48KAuXLigjIyMbDdfrVWrloKCghz3ccrSvHlzx3K5cuUUGBioU6dOXe3Hl2SfR6BTp076888/9dRTT+mdd94plBGs0obCCShtNs20Pze7R/Ljhx4AwFwWX1812Lolz3Ypmzfr2PB/5dkubMZ0+bVpk6/j5oe7u7tiYmK0bt06rVy5Uu+++66ef/55bdiwQREREZKkOnXqqHLlypo9e7Z69uyZ601TL1y4IEmaOXOmrrvuumzHkaT169erf//+Gj9+vLp27ary5ctrwYIFmjJlilP7cuXK5XiMy49tsViyFVcFFRISorp166pu3bqaM2eOevTood27d6tq1arXtN/ShnN4gNIkKU7a/ZV9OZpJIQAA5rNYLHLz88vzUe6GG+QREiLldl2SxSKPkBCVu+GGfO0vP9c3XZrxhhtu0Pjx47Vt2zZ5eXlpyZIljverVKmi1atX68CBA+rbt6/TRA6XCg4OVrVq1fTnn386CpGsR1YRtm7dOtWqVUvPP/+82rRpo3r16unIkSP5/0KLWHR0tFq3bq1XX33V7CglDiNOQGmyZZ5ky5BqREuhkWanAQAg3yzu7gp+boyOj3zSXjxdOjnD30VQ8HNjCv1+Ths2bNCqVavUpUsXVa1aVRs2bNDp06fVqFEjp3ZVq1bV6tWr1bFjR/Xr108LFiyQh0f2X6XHjx+vJ554QuXLl1e3bt2UlpamzZs369y5cxo1apTq1auno0ePasGCBYqKitJ3333nVKQVhdOnTys2NtZpXWhoqIKDg3Ns/+STT6pPnz4aPXq0qlevXqTZXAkjTkBpkWmVtsyxL0fnPNMPAAAlWWCXLqo+9W15XPYLvUdwsKpPfbtI7uMUGBiotWvXqkePHqpfv75eeOEFTZkyRd27d8/WNiQkRKtXr9aOHTvUv39/ZWZmZmszdOhQzZo1S3PmzFGzZs3Uvn17zZ071zHidPvtt+upp57SY489phYtWmjdunV68cUXC/1zXWr+/Plq2bKl02PmzJm5tu/WrZsiIiIYdbqMxchprsVSLDExUeXLl1dCQkK2i/CAomS1WrVs2TL16NEj13Ojr8muJdKiQVK5IOmpXZKHd+EfA2VWkfdfoAjRf4tPamqqDh06pIiICPn4+Fz1fozMTKVs3qKM06flERQkvzatC32kyVXYbDYlJiYqMDBQbsyUe1Wu1C8LUhtwqh5QWmRNQd56EEUTAMClWdzdVe66/E07DhQXylagNIjfLR35RbK4S60Hm50GAACg1KFwAkqDrCnIG/aQynMRJwAAQGGjcAJcXWqC9PtC+zKTQgAAABQJCifA1f2+QLImS0ENpfCbzE4DAABQKlE4Aa7MMKSNf5+mFzU095sGAgAA4JpQOAGu7M810tn9kleAFHmf2WkAAABKLQonwJVt+nsK8sj7JO8Ac7MAAACUYhROgKs6f0zat8y+HDXU3CwAAAClHIUT4Ko2z5YMmxRxs1S1odlpAAAolQYNGqTevXvn+v64cePUokWLYssD81A4Aa7ImiptnWdfjhpmbhYAAAqZzWbo+L5z+mNTnI7vOyebzTA7kss6dOiQ7r//flWrVk0+Pj6qUaOG7rjjDu3du9ep3Y8//qjbbrtNQUFB8vHxUZ06dXTvvfdq7dq1jjZr1qyRxWKRxWKRm5ubypcvr5YtW2r06NE6efJkcX+0YudhdgAAV2H3UinlrBRYXWrQw+w0AAAUmoPbTunnhfuVfD7Nsa5cBW/ddG891WlZ1cRk5klPT7+q7axWqzp37qwGDRroyy+/VGhoqP73v/9p+fLlOn/+vKPd+++/r8cee0wPPvigFi5cqDp16ighIUE//vijnnrqKW3ZssVpv/v27VNgYKASExO1detWvf766/roo4+0Zs0aNWvW7Fo+aonGiBPgirKmIG8zWHLn7x8AgNLh4LZTWjF9p1PRJEnJ59O0YvpOHdx2qkiOu3jxYjVr1ky+vr6qXLmyOnXqpOTk5Bzbbtq0SUFBQZo8eXKu+5s1a5YaNWokHx8fNWzYUO+//77T+88++6zq168vPz8/1a5dWy+++KKsVqvj/azT/2bNmqU6deooJCREkmSxWDRr1iz16dNHfn5+qlevnr7++utcc+zatUsHDx7U+++/r7Zt26pWrVq64YYb9Morr6ht27aSpKNHj+rJJ5/Uk08+qXnz5umWW25RrVq11Lx5c40cOVKbN2/Ott+qVasqJCRE9evX13333adff/1VQUFBeuSRR3L/kksBCifA1RzfKh3fLLl5Sq0Gmp0GAIArMgxD1rTMPB9pFzP088I/rrivnxfuV9rFjHztzzDyd3rfyZMn1a9fPz300EPas2eP1qxZozvvvDPH7VevXq3OnTvr1Vdf1bPPPpvj/j777DO99NJLevXVV7Vnzx5NnDhRL774oubNm+doExAQoLlz52r37t2aOnWqZs6cqbfeestpPwcOHNAXX3yhxYsXO50uN378ePXt21fbt29Xjx491L9/f/311185ZgkKCpKbm5sWL16szMzMHNt88cUXslqtGj16dI7vW/Jxj0hfX189/PDD+vXXX3XqVNEUtyUBf6oGXE3WFORN+kj+ZfOUBQCA68hIt2nGyJ8KZV/J59M066m1eTeUNHxqe3l6u+fZ7uTJk8rIyNCdd96pWrVqSVKOp5stWbJEAwYM0KxZs3Tvvffmur+xY8dqypQpuvPOOyVJERER2r17t6ZPn66BA+1/8HzhhRcc7cPDw/X0009rwYIFTsVLenq6Pv74Y1WuXFmJiYmO9YMGDVK/fv0kSRMnTtQ777yjjRs3qlu3btmyVK9eXe+8845Gjx6t8ePHq02bNurYsaP69++v2rVrS5L++OMPBQYGOka1JHsxlZVVktavX5/nKXgNG9onqjp8+LCqVi2dv58w4gS4kpS/pB2L7cvRTAoBAMC1ioyM1K233qpmzZrpnnvu0cyZM3Xu3DmnNhs2bNA999yjTz755IpFU3Jysg4ePKghQ4bI39/f8XjllVd08OBBR7uFCxfqhhtuUEhIiPz9/fXCCy/o6NGjTvuqVauWgoKCsh2jefPmjuVy5copMDDwiqM8I0aMUFxcnD777DO1a9dOixYtUpMmTRQTE+Noc/moUteuXRUbG6vvvvtOycnJuY5WXSprhC4/I1SuihEnwJVs/VjKTJNCmks1osxOAwBAnjy83DR8avs8253Yf17fTvs9z3a3PRapavUq5Ou4+eHu7q6YmBitW7dOK1eu1Lvvvqvnn39eGzZsUEREhCSpTp06qly5smbPnq2ePXvK09Mzx31duHBBkjRz5kxdd9112Y4j2Udv+vfvr/Hjx6tr164qX768FixYoClTpji1L1euXI7HuPzYFotFNpvtip8xICBAvXr1Uq9evfTKK6+oa9eueuWVV9S5c2fVq1dPCQkJiouLc4w6+fv7q27duvLwyH+psGfPHkn2EbTSihEnwFXYMqXNH9mXo4dLpfgvOgCA0sNiscjT2z3PR1jjSipXwfuK+/Kv6K2wxpXytb+CjHxYLBbdcMMNGj9+vLZt2yYvLy8tWbLE8X6VKlW0evVqHThwQH379nWayOFSwcHBqlatmv7880/VrVvX6ZFVhK1bt061atXS888/rzZt2qhevXo6cuRIvrNeK4vFooYNGzomv7j77rvl6el5xcku8nLx4kXNmDFDN998c46jZKUFI06Aq9gfI50/KvlUkJreZXYaAAAKlZubRTfdW08rpu/Mtc2NfevJza1w/3C4YcMGrVq1Sl26dFHVqlW1YcMGnT59Wo0aNXJqV7VqVa1evVodO3ZUv379tGDBghxHZMaPH68nnnhC5cuXV7du3ZSWlqbNmzfr3LlzGjVqlOrVq6ejR49qwYIFioqK0nfffedUpBWm2NhYjR07Vg8++KAaN24sLy8v/fTTT5o9e7ZjcouaNWtqypQpGjlypP766y8NGjRIERER+uuvv/Tpp59K+me0LMupU6eUmpqqpKQkbdmyRa+//rrOnDmjL7/8skg+R0lB4QS4io0z7M8tH5C8/MzNAgBAEajTsqq6/atptvs4+Vf01o19i+Y+ToGBgVq7dq3efvttJSYmqlatWpoyZYq6d++erW1ISIhWr16tDh06qH///po/f362NkOHDpWfn5/eeOMNPfPMMypXrpyaNWumJ598UpJ0++2366mnntJjjz2mtLQ09ezZUy+++KLGjRtX6J+tRo0aCg8P1/jx43X48GFZLBbH66eeesrR7vHHH1ejRo30n//8R3fffbcSExNVuXJltWvXTitWrMg2MUSDBg1ksVjk7++v2rVrq0uXLho1apTTBBOlkcXI71yNpURiYqLKly+vhIQEBQYGmh0HZYjVatWyZcvUo0ePXM+NztXZg9K7rSRZpCe2SpVqF0lGIDfX1H8Bk9F/i09qaqoOHTqkiIgI+fj4XPV+bDZDJ/efV3JimsoFeiu0XoVCH2lyFTabTYmJiQoMDJSbG1fZXI0r9cuC1AaMOAGuYNPf1zbV60zRBAAo9dzcLKreoKLZMQAnlK1ASZeeLG2zn2OsKKYgBwAAMAOFE1DS7VgkpSVIFcOlup3MTgMAAFAmUTgBJZlhSBtn2pejhkqc2wwAAGAKfgsDSrKjv0nxOyUPX6lFf7PTAACQL2Vs7jGUcIXVHymcgJJs09+jTc3ulvwqmZsFAIA8ZM1amJKSYnIS4B/p6emSst+PqqCYVQ8oqZLipN1f2ZejmRQCAFDyubu7q0KFCjp16pQkyc/PTxZL2ZxGvLDYbDalp6crNTWV6civgs1m0+nTp+Xn55fjDYsLgsIJKKm2zJNsGVLYdVJopNlpAADIl6yboGYVT7g2hmHo4sWL8vX1pQi9Sm5ubqpZs+Y1f38UTkBJlGmVtsyxLzMFOQDAhVgsFoWGhqpq1aqyWq1mx3F5VqtVa9eu1c0338wNnK+Sl5dXoYzWUTgBJdHeb6Wkk1K5IKnx7WanAQCgwNzd3a/5mhLYv8eMjAz5+PhQOJmMEyWBkmjjLPtz60GSh7epUQAAAGBy4fTBBx+oefPmCgwMVGBgoNq1a6fly5dfcZtFixapYcOG8vHxUbNmzbRs2bJiSgsUk/jd0pFfJIu71Hqw2WkAAAAgkwunGjVq6LXXXtOWLVu0efNm3XLLLbrjjju0a9euHNuvW7dO/fr105AhQ7Rt2zb17t1bvXv31s6dO4s5OVCEsqYgb9hDKl/d3CwAAACQZHLh1KtXL/Xo0UP16tVT/fr19eqrr8rf31+//fZbju2nTp2qbt266ZlnnlGjRo00YcIEtWrVStOmTSvm5EARSU2Qfl9oX44ebm4WAAAAOJSYa5wyMzO1YMECJScnq127djm2Wb9+vTp16uS0rmvXrlq/fn1xRASK3u8LJGuyFNRQCr/J7DQAAAD4m+mz6u3YsUPt2rVTamqq/P39tWTJEjVu3DjHtnFxcQoODnZaFxwcrLi4uFz3n5aWprS0NMfrxMRESfapHZkiE8Upq7/l2u8MQx4bZ8giKbPVYNkyMoovHJCHPPsvUILRf+HK6L9FqyDfq+mFU4MGDRQbG6uEhAQtXrxYAwcO1E8//ZRr8VRQkyZN0vjx47OtX7lypfz8/ArlGEBBxMTE5Lg+KHGnrj97QFY3H608WUEZTHyCEii3/gu4AvovXBn9t2ikpKTku63phZOXl5fq1q0rSWrdurU2bdqkqVOnavr06dnahoSEKD4+3mldfHy84w7VORkzZoxGjRrleJ2YmKiwsDB16dJFgYGBhfQpgLxZrVbFxMSoc+fOOd6HwX3R55Ikt5b91aXbXcUdD7iivPovUJLRf+HK6L9FK+tstPwwvXC6nM1mczq17lLt2rXTqlWr9OSTTzrWxcTE5HpNlCR5e3vL2zv7fXA8PT3pfDBFjn3v/DFp//eSJPe2/5I7fRMlFD874crov3Bl9N+iUZDv1NTCacyYMerevbtq1qyppKQkzZ8/X2vWrNH339t/gRwwYICqV6+uSZMmSZJGjhyp9u3ba8qUKerZs6cWLFigzZs3a8aMGWZ+DODabZ4tGTYp4mYpqIHZaQAAAHAZUwunU6dOacCAATp58qTKly+v5s2b6/vvv1fnzp0lSUePHpWb2z8T/11//fWaP3++XnjhBT333HOqV6+eli5dqqZNm5r1EYBrZ02Vts6zL0cNMzcLAAAAcmRq4fTRRx9d8f01a9ZkW3fPPffonnvuKaJEgAl2L5VSzkqB1aUGPcxOAwAAgByUmPs4AWXWxpn25zaDJfcSd9khAAAAROEEmOv4Vun4ZsnNU2o10Ow0AAAAyAWFE2CmTbPsz036SP5Vzc0CAACAXFE4AWZJ+Uvasdi+HM2kEAAAACUZhRNglq0fS5lpUkhzqUaU2WkAAABwBRROgBlsmdLmv2eVjB4uWSzm5gEAAMAVUTgBZtgfI50/KvlUkJreZXYaAAAA5IHCCTDDxhn255YPSF5+5mYBAABAniicgOL210Hp4CpJFilqiNlpAAAAkA8UTkAxc9sy275Qr7NUqba5YQAAAJAvHmYHAMoS98w0ue3+3P4ieri5YQAAAJBvjDgBxajGuXWypCVKFSOkOreaHQcAAAD5ROEEFBfDUMTpH+zLUUMkN/75AQAAuAp+cwOKieV/G1Q+9ZgMD1+pRX+z4wAAAKAAKJyAYuL29w1vjSZ3Sn6VTE4DAACAgqBwAopDUpwse7+RJGW2YQpyAAAAV0PhBBSHLfNksWXobLl6Ukhzs9MAAACggCicgKKWaZW2zJEkHarCTHoAAACuiMIJKGp7v5WSTsooF6STFaLMTgMAAICrQOEEFLWNsyRJthYDZHPzNDkMAAAArgaFE1CU4ndLR36RLO6ytRpodhoAAABcJQonoChtmml/bthTCqxmbhYAAABcNQonoKikJki/L7QvRw8zNwsAAACuCYUTUFRiP5esyVJQQyn8JrPTAAAA4BpQOAFFwTCkTfZJIRQ1VLJYzM0DAACAa0LhBBSFP9dIZ/dLXgFS5H1mpwEAAMA1onACisLGvyeFiLxP8g4wNwsAAACuGYUTUNjOH5P+WG5fZlIIAACAUoHCCShsm2dLhk2KuFkKamB2GgAAABQCCiegMFlTpa3z7MtRjDYBAACUFhROQGHavVRKOSsFVpca9DA7DQAAAAoJhRNQmLImhWgzWHL3MDcLAAAACg2FE1BYjm+Vjm+W3DylVgPNTgMAAIBCROEEFJasG9426SP5VzU3CwAAAAoVhRNQGJLPSjsW25eZghwAAKDUoXACCsO2T6TMNCk0UqoRZXYaAAAAFDIKJ+Ba2TKlzR/Zl6OGSRaLuXkAAABQ6CicgGu1f6V0/qjkU0FqepfZaQAAAFAEKJyAa5U1BXmrByUvP3OzAAAAoEhQOAHX4uxB6eAqSRapzRCz0wAAAKCIUDgB1yJrCvJ6naVKEeZmAQAAQJGhcAKuVnqytO0z+3L0cHOzAAAAoEiZWjhNmjRJUVFRCggIUNWqVdW7d2/t27fvitvMnTtXFovF6eHj41NMiYFL7FgkpSVIFSOkOreanQYAAABFyNTC6aefftKIESP022+/KSYmRlarVV26dFFycvIVtwsMDNTJkycdjyNHjhRTYuBvhvHPpBBRQyQ3Bm8BAABKMw8zD75ixQqn13PnzlXVqlW1ZcsW3XzzzbluZ7FYFBISUtTxgNwd/U2K3yl5+Eot+pudBgAAAEXM1MLpcgkJCZKkSpUqXbHdhQsXVKtWLdlsNrVq1UoTJ05UkyZNcmyblpamtLQ0x+vExERJktVqldVqLaTkKGvcN0yXmyRbkzuV6Rkg5aMvZfU3+h1cEf0Xroz+C1dG/y1aBfleLYZhGEWYJd9sNptuv/12nT9/Xr/88kuu7davX6/9+/erefPmSkhI0Jtvvqm1a9dq165dqlGjRrb248aN0/jx47Otnz9/vvz8uOcOCs7bel5ddj4lN2VqTYOXleAXbnYkAAAAXIWUlBTdf//9SkhIUGBg4BXblpjC6ZFHHtHy5cv1yy+/5FgA5cZqtapRo0bq16+fJkyYkO39nEacwsLCdObMmTy/HCAnbj+/Ife1k2WrEa3MgcvyvZ3ValVMTIw6d+4sT0/PIkwIFD76L1wZ/ReujP5btBITE1WlSpV8FU4l4lS9xx57TN9++63Wrl1boKJJkjw9PdWyZUsdOHAgx/e9vb3l7e2d43Z0PhRYplXaOk+S5BY9XG5X0Yfoe3Bl9F+4MvovXBn9t2gU5Ds1dSowwzD02GOPacmSJVq9erUiIgp+A9HMzEzt2LFDoaGhRZAQuMzeb6ULcVK5qlLjO8xOAwAAgGJi6ojTiBEjNH/+fH311VcKCAhQXFycJKl8+fLy9fWVJA0YMEDVq1fXpEmTJEkvv/yy2rZtq7p16+r8+fN64403dOTIEQ0dOtS0z4EyZOMs+3PrgZKHl7lZAAAAUGxMLZw++OADSVKHDh2c1s+ZM0eDBg2SJB09elRul9wj59y5cxo2bJji4uJUsWJFtW7dWuvWrVPjxo2LKzbKqvhd0pFfJIu71Hqw2WkAAABQjEwtnPIzL8WaNWucXr/11lt66623iigRcAWb/h5tathTKl/d3CwAAAAoVqZe4wS4jNQE6feF9uXoYeZmAQAAQLGjcALyI/ZzyZosBTWUwm8yOw0AAACKGYUTkBfD+Oc0vaihksVibh4AAAAUOwonIC9/rpHO7pe8AqTI+8xOAwAAABNQOAF52TjT/hx5n+QdYG4WAAAAmILCCbiS88ekP5bbl5kUAgAAoMyicAKuZPNsybBJETdLQQ3MTgMAAACTUDgBubGmSlvn2Zejh5ubBQAAAKaicAJys3uplHJWCqwh1e9udhoAAACYiMIJyM3GGfbnNoMkdw9TowAAAMBcFE5ATo5vlY5vkdy9pFaDzE4DAAAAk1E4ATnJuuFt496Sf5CpUQAAAGA+CifgcslnpR2L7ctMQQ4AAABROAHZbftEykyTQiOlGlFmpwEAAEAJQOEEXMqWKW3+yL4cNUyyWMzNAwAAgBKBwgm41P6V0vmjkk8FqeldZqcBAABACUHhBFxq40z7c6sHJS8/c7MAAACgxKBwArKcPSgdXCXJIrUZYnYaAAAAlCAUTkCWrCnI63WRKkWYmwUAAAAlCoUTIEnpydK2z+zLTEEOAACAy1A4AZK0/b9SWoJUMUKqc6vZaQAAAFDCUDgBhvHPaXpRQyU3/lkAAADAGb8hAkd/k+J3Sh6+Usv+ZqcBAABACUThBGycYX9udrfkW9HcLAAAACiRKJxQtiXFSXu+ti8zKQQAAAByQeGEsm3LPMmWIYVdJ4VGmp0GAAAAJRSFE8quTKu0ebZ9OYrRJgAAAOSOwgll195vpQtxUrmqUuM7zE4DAACAEozCCWXXxr+nIG89UPLwMjcLAAAASjQKJ5RN8bukI79IFnep9WCz0wAAAKCEo3BC2ZR1w9uGPaXy1c3NAgAAgBKPwgllT2qC9PtC+zJTkAMAACAfKJxQ9sR+LlmTpaCGUvhNZqcBAACAC6BwQtlis0mbZtqXo4ZKFou5eQAAAOASKJxQthxaI509IHkFSJH3mZ0GAAAALoLCCWVL1hTkLfpJ3gHmZgEAAIDLKFDh9Prrr+vixYuO17/++qvS0tIcr5OSkvToo48WXjqgMJ0/Kv2x3L4cNdTcLAAAAHApBSqcxowZo6SkJMfr7t276/jx447XKSkpmj59euGlAwrT5tmSYZMibpaCGpidBgAAAC6kQIWTYRhXfA2UWNZUaevH9uXo4eZmAQAAgMvhGieUDbuXSilnpcAaUv3uZqcBAACAi6FwQtmwcYb9uc0gyd3D1CgAAABwPQX+DXLWrFny9/eXJGVkZGju3LmqUqWKJDld/wSUGMe3Sse3SO5eUqtBZqcBAACACypQ4VSzZk3NnDnT8TokJESffPJJtjb5NWnSJH355Zfau3evfH19df3112vy5Mlq0ODKF+4vWrRIL774og4fPqx69epp8uTJ6tGjR0E+CsqSTX9PQd64t+QfZGoUAAAAuKYCFU6HDx8u1IP/9NNPGjFihKKiopSRkaHnnntOXbp00e7du1WuXLkct1m3bp369eunSZMm6bbbbtP8+fPVu3dvbd26VU2bNi3UfCgFks9KOxbbl5kUAgAAAFfJ1Is9VqxY4fR67ty5qlq1qrZs2aKbb745x22mTp2qbt266ZlnnpEkTZgwQTExMZo2bZo+/PDDIs8MF7PtEykzTQqNlGq0MTsNAAAAXFSBCqf169fr7Nmzuu222xzrPv74Y40dO1bJycnq3bu33n33XXl7e19VmISEBElSpUqVrphh1KhRTuu6du2qpUuX5tg+LS3N6Sa9iYmJkiSr1Sqr1XpVOeEibJny2PSRLJIyWj0kIyPD1DhZ/Y1+B1dE/4Uro//CldF/i1ZBvtcCFU4vv/yyOnTo4CicduzYoSFDhmjQoEFq1KiR3njjDVWrVk3jxo0rUGBJstlsevLJJ3XDDTdc8ZS7uLg4BQcHO60LDg5WXFxcju0nTZqk8ePHZ1u/cuVK+fn5FTgnXEdwwja1TTiqdPdy+v5/5WQ7sczsSJKkmJgYsyMAV43+C1dG/4Uro/8WjZSUlHy3LVDhFBsbqwkTJjheL1iwQNddd51jwoiwsDCNHTv2qgqnESNGaOfOnfrll18KvO2VjBkzxmmEKjExUWFhYerSpYsCAwML9VgoWdw/n2t/jhqkbrf2NjWLZP+LRkxMjDp37ixPT0+z4wAFQv+FK6P/wpXRf4tW1tlo+VGgwuncuXNOoz0//fSTunf/52aiUVFROnbsWEF2KUl67LHH9O2332rt2rWqUaPGFduGhIQoPj7eaV18fLxCQkJybO/t7Z3jqYOenp50vtLszAHpz9WSLHKPHib3EvTfmr4HV0b/hSuj/8KV0X+LRkG+0wLdADc4OFiHDh2SJKWnp2vr1q1q27at4/2kpKQCHdwwDD322GNasmSJVq9erYiIiDy3adeunVatWuW0LiYmRu3atcv3cVEGbP7I/lyvi1Qp734FAAAAXEmBCqcePXro3//+t37++WeNGTNGfn5+uummmxzvb9++XXXq1Mn3/kaMGKFPP/1U8+fPV0BAgOLi4hQXF6eLFy862gwYMEBjxoxxvB45cqRWrFihKVOmaO/evRo3bpw2b96sxx57rCAfBaVZerK07TP7cvQwc7MAAACgVChQ4TRhwgR5eHioffv2mjlzpmbMmCEvLy/H+7Nnz1aXLl3yvb8PPvhACQkJ6tChg0JDQx2PhQsXOtocPXpUJ0+edLy+/vrrNX/+fM2YMUORkZFavHixli5dyj2c8I/t/5XSEqSKEVKdW81OAwAAgFKgQNc4ValSRWvXrlVCQoL8/f3l7u7u9P6iRYsUEBCQ7/0ZhpFnmzVr1mRbd8899+iee+7J93FQhhiGtGmWfTlqqORWoL8NAAAAADkqUOH00EMP5avd7NmzryoMcM2O/ibF75Q8fKWW/c1OAwAAgFKiQIXT3LlzVatWLbVs2TJfo0VAsds4w/7c7G7Jt6K5WQAAAFBqFKhweuSRR/T555/r0KFDGjx4sB544AFVqlSpqLIBBZMUJ+352r7MpBAAAAAoRAW6AOS9997TyZMnNXr0aH3zzTcKCwtT37599f333zMCBfNtmSfZMqSw66TQSLPTAAAAoBQp8JXz3t7e6tevn2JiYrR79241adJEjz76qMLDw3XhwoWiyAjkLdMqbf772rro4eZmAQAAQKlzTVOOubm5yWKxyDAMZWZmFlYmoOD2fitdiJPKVZUa3W52GgAAAJQyBS6c0tLS9Pnnn6tz586qX7++duzYoWnTpuno0aPy9/cvioxA3jb+PQV564GSh9eV2wIAAAAFVKDJIR599FEtWLBAYWFheuihh/T555+rSpUqRZUNyJ/4XdKRXySLu9R6sNlpAAAAUAoVqHD68MMPVbNmTdWuXVs//fSTfvrppxzbffnll4USDsiXrBveNuwpla9ubhYAAACUSgUqnAYMGCCLxVJUWYCCS02Qfl9oX2YKcgAAABSRAt8AFyhRYj+XrMlSUEMp/Caz0wAAAKCUuqZZ9QBT2WzSppn25aihEqOhAAAAKCIUTnBdh9ZIZw9IXgFS5H1mpwEAAEApRuEE15U1BXmLfpJ3gLlZAAAAUKpROME1nT8q/bHcvhw11NwsAAAAKPUonOCaNs+WDJsU0V4KamB2GgAAAJRyFE5wPdZUaevH9mWmIAcAAEAxoHCC69m9VEo5KwXWkOp3NzsNAAAAygAKJ7iejTPsz20GS+4FuhUZAAAAcFUonOBajm+xP9y9pFYDzU4DAACAMoLCCa4lawryxr0l/yBTowAAAKDsoHCC60g+K+38wr4cPdzcLAAAAChTKJzgOrZ9ImWmSaGRUo02ZqcBAABAGULhBNdgy5Q2fWRfjhomWSzm5gEAAECZQuEE17B/pZRwVPKtKDW72+w0AAAAKGMonOAaNs60P7d8QPL0NTcLAAAAyhwKJ5R8Zw5IB1dJskhthpidBgAAAGUQhRNKvs1/X9tUr4tUKcLcLAAAACiTKJxQsqUnS9s+sy9HDzM3CwAAAMosCieUbNv/K6UlSBUjpDq3mp0GAAAAZRSFE0ouw5A2zbIvRw2V3OiuAAAAMAe/iaLkOvqbFL9T8vCVWvY3Ow0AAADKMAonlFwbZ9ifm99jv38TAAAAYBIKJ5RMSXHSnq/ty1FMCgEAAABzUTihZNoyV7JlSGHXSaHNzU4DAACAMo7CCSVPplXaPMe+HD3c3CwAAACAKJxQEu39VroQJ5WrKjW63ew0AAAAAIUTSqCNM+3PrQdKHl7mZgEAAABE4YSSJn6XdORXyeIutR5sdhoAAABAEoUTSpqsG9427CmVr25uFgAAAOBvFE4oOVITpN8X2peZFAIAAAAlCIUTSo7YzyVrshTUSAq/0ew0AAAAgIOphdPatWvVq1cvVatWTRaLRUuXLr1i+zVr1shisWR7xMXFFU9gFB2bTdr096QQUUMki8XcPAAAAMAlTC2ckpOTFRkZqffee69A2+3bt08nT550PKpWrVpECVFsDq2Rzh6QvAKkyPvMTgMAAAA48TDz4N27d1f37t0LvF3VqlVVoUKFwg8E82z8e1KIFv0k7wBzswAAAACXMbVwulotWrRQWlqamjZtqnHjxumGG27ItW1aWprS0tIcrxMTEyVJVqtVVqu1yLMiHxKOyeOP5bJIsrYcJJXS/y5Z/Y1+B1dE/4Uro//CldF/i1ZBvleXKpxCQ0P14Ycfqk2bNkpLS9OsWbPUoUMHbdiwQa1atcpxm0mTJmn8+PHZ1q9cuVJ+fn5FHRn50OjEf1XfsOm0f2Ot23hA0gGzIxWpmJgYsyMAV43+C1dG/4Uro/8WjZSUlHy3tRiGYRRhlnyzWCxasmSJevfuXaDt2rdvr5o1a+qTTz7J8f2cRpzCwsJ05swZBQYGXktkFIaMVHm8GylLylll3DVPRsOeZicqMlarVTExMercubM8PT3NjgMUCP0Xroz+C1dG/y1aiYmJqlKlihISEvKsDVxqxCkn0dHR+uWXX3J939vbW97e3tnWe3p60vlKgl2LpZSzUmANeTS+TXJ3+S6ZJ/oeXBn9F66M/gtXRv8tGgX5Tl3+Pk6xsbEKDQ01OwauVtYU5G0Gl4miCQAAAK7J1N9UL1y4oAMH/rme5dChQ4qNjVWlSpVUs2ZNjRkzRsePH9fHH38sSXr77bcVERGhJk2aKDU1VbNmzdLq1au1cuVKsz4CrsXxLfaHu5fUaqDZaQAAAIBcmVo4bd68WR07dnS8HjVqlCRp4MCBmjt3rk6ePKmjR4863k9PT9f//d//6fjx4/Lz81Pz5s31ww8/OO0DLiRrCvLGvSX/IFOjAAAAAFdiauHUoUMHXWluirlz5zq9Hj16tEaPHl3EqVAsks9KO7+wL0cPNzcLAAAAkAeXv8YJLmrbJ1JmmhQaKdVoY3YaAAAA4IoonFD8bJnSpo/sy9HDJYvF3DwAAABAHiicUPz2r5QSjkq+FaWmd5mdBgAAAMgThROK38a/pyBv+YDk6WtuFgAAACAfKJxQvM4ckA6ukmSR2gwxOw0AAACQLxROKF6b/762qV4XqVKEuVkAAACAfKJwQvFJT5a2fWZfjh5mbhYAAACgACicUHy2/1dKS5AqRkh1bjU7DQAAAJBvFE4oHoYhbZplX44aKrnR9QAAAOA6+O0VxePoeil+p+ThK7Xsb3YaAAAAoEAonFA8sqYgb36P/f5NAAAAgAuhcELRS4qT9nxtX45iUggAAAC4HgonFL0tcyVbhhR2nRTa3Ow0AAAAQIFROKFoZVqlzXPsy9HDzc0CAAAAXCUKJxStvd9KF+KkclWlRrebnQYAAAC4KhROKFpZk0K0HiR5eJkaBQAAALhaFE4oOvG7pCO/ShZ3qc1gs9MAAAAAV43CCUUn64a3DXtKgdXMzQIAAABcAwonFI3UBOn3hfZlJoUAAACAi6NwQtGI/VyyJktBjaTwG81OAwAAAFwTCicUPptN2vT3pBBRQySLxdw8AAAAwDWicELhO7RGOntA8gqQIu8zOw0AAABwzSicUPg2/j0pRIt+kneAuVkAAACAQkDhhMJ1/qj0x3L7ctRQc7MAAAAAhYTCCYVr82zJsEkR7aWgBmanAQAAAAoFhRMKjzVV2vqxfTl6mLlZAAAAgEJE4YTCs2uJlHJWCqwh1e9udhoAAACg0FA4ofBkTUHeZrDk7mFuFgAAAKAQUTihcBzfYn+4e0mtBpqdBgAAAChUFE4oHFlTkDfpI/kHmZsFAAAAKGQUTrh2yWelnV/Yl6OYFAIAAAClD4UTrt22T6TMNCk0UqrRxuw0AAAAQKGjcMK1sWVKmz6yL0cPlywWc/MAAAAARYDCCddm/0op4ajkW1FqepfZaQAAAIAiQeGEa7Nxhv255QOSp6+5WQAAAIAiQuGEq3fmgHRwtSSL1GaI2WkAAACAIkPhhKu3+e9rm+p1kSpFmJsFAAAAKEIUTrg66cnSts/sy9FMQQ4AAIDSjcIJV2f7f6W0BKlihFTnVrPTAAAAAEWKwgkFZxjSpln25aihkhvdCAAAAKUbv/Gi4I6ul+J3Sh6+Usv+ZqcBAAAAipyphdPatWvVq1cvVatWTRaLRUuXLs1zmzVr1qhVq1by9vZW3bp1NXfu3CLPictsnGl/bn6P/f5NAAAAQClnauGUnJysyMhIvffee/lqf+jQIfXs2VMdO3ZUbGysnnzySQ0dOlTff/99ESeFQ1KctOdr+3IUk0IAAACgbPAw8+Ddu3dX9+7d893+ww8/VEREhKZMmSJJatSokX755Re99dZb6tq1a1HFxKW2zJVsGVJYWym0udlpAAAAgGLhUtc4rV+/Xp06dXJa17VrV61fv96kRGVMplXaPMe+zBTkAAAAKENMHXEqqLi4OAUHBzutCw4OVmJioi5evChfX99s26SlpSktLc3xOjExUZJktVpltVqLNnApY9m9VB4X4mSUq6qMet0lvr8Cyepv9Du4IvovXBn9F66M/lu0CvK9ulThdDUmTZqk8ePHZ1u/cuVK+fn5mZDIdd2w/w1VkfRHQDvt/f4Hs+O4rJiYGLMjAFeN/gtXRv+FK6P/Fo2UlJR8t3WpwikkJETx8fFO6+Lj4xUYGJjjaJMkjRkzRqNGjXK8TkxMVFhYmLp06aLAwMAizVuqnNotz237ZFjcVfueV1Q7MNTsRC7HarUqJiZGnTt3lqenp9lxgAKh/8KV0X/hyui/RSvrbLT8cKnCqV27dlq2bJnTupiYGLVr1y7Xbby9veXt7Z1tvaenJ52vILbar22yNOwpz8o1TQ7j2uh7cGX0X7gy+i9cGf23aBTkOzV1cogLFy4oNjZWsbGxkuzTjcfGxuro0aOS7KNFAwYMcLR/+OGH9eeff2r06NHau3ev3n//ff33v//VU089ZUb8siM1Qdr+X/ty9HBzswAAAAAmMLVw2rx5s1q2bKmWLVtKkkaNGqWWLVvqpZdekiSdPHnSUURJUkREhL777jvFxMQoMjJSU6ZM0axZs5iKvKjFfi5Zk6WgRlL4jWanAQAAAIqdqafqdejQQYZh5Pr+3Llzc9xm27ZtRZgKTmw2adNM+3L0UMliMTcPAAAAYAKXuo8TTHBojXT2gOQVIDW/1+w0AAAAgCkonHBlG2fZn1v0k7wDzM0CAAAAmITCCbk7f1T6Y7l9OWqYuVkAAAAAE1E4IXebZ0uGTYpoLwXVNzsNAAAAYBoKJ+TMmipt/di+HM1oEwAAAMo2CifkbNcSKeWsFFhDqt/d7DQAAACAqSickLOsKcjbDJbcTZ21HgAAADAdhROyO77F/nD3kloNNDsNAAAAYDoKJ2SXNQV5kz6Sf5C5WQAAAIASgMIJzpLPSju/sC8zBTkAAAAgicIJl9v2sZSZJoVGSjXamJ0GAAAAKBEonPAPW6a0abZ9OXq4ZLGYmwcAAAAoISic8I/9K6WEo5JvRanpXWanAQAAAEoMCif8Y+MM+3PLByVPX3OzAAAAACUIhRPszhyQDq6WZJGihpidBgAAAChRKJxgt/kj+3O9LlLFcFOjAAAAACUNhROk9GRp22f25ejh5mYBAAAASiAKJ0jb/yulJUiVakt1bjE7DQAAAFDiUDiVdYYhbZplX24zRHKjSwAAAACX47fksu7oeil+p+ThK7Xsb3YaAAAAoESicCrrNs60Pze/x37/JgAAAADZUDiVZUlx0p6v7ctRw8zNAgAAAJRgFE5l2Za5ki1DCmsrhTY3Ow0AAABQYlE4lVWZVmnzHPtyNKNNAAAAwJVQOJVVe76RLsRJ5apKjW43Ow0AAABQolE4lVVZU5C3HiR5eJkaBQAAACjpKJzKovhd0pFfJYu71Gaw2WkAAACAEo/CqSzKmoK80W1SYDVzswAAAAAugMKprElNkLb/177MFOQAAABAvlA4lTWxn0vWZCmokRR+o9lpAAAAAJdA4VSW2GzSpr9P04seKlks5uYBAAAAXASFU1lyaI109oDkFSA1v9fsNAAAAIDLoHAqSzb+PQV5i36Sd4C5WQAAAAAXQuFUVpw/Kv2x3L7MpBAAAABAgVA4lRWbZ0uGTYpoLwXVNzsNAAAA4FIonMoCa6q0ZZ59OZrRJgAAAKCgKJzKgl1LpIt/SYE1pPrdzU4DAAAAuBwKp7IgawryNoMldw9zswAAAAAuiMKptDu+xf5w95JaDTQ7DQAAAOCSKJxKu6wpyJv0kfyDzM0CAAAAuCgKp9Is+ay08wv7MlOQAwAAAFeNwqk02/axlJkmhbaQarQxOw0AAADgskpE4fTee+8pPDxcPj4+uu6667Rx48Zc286dO1cWi8Xp4ePjU4xpXYQtU9o0274cPUyyWMzNAwAAALgw0wunhQsXatSoURo7dqy2bt2qyMhIde3aVadOncp1m8DAQJ08edLxOHLkSDEmdhH7V0oJRyXfilLTu8xOAwAAALg00wun//znPxo2bJgGDx6sxo0b68MPP5Sfn59mz56d6zYWi0UhISGOR3BwcDEmdhEbZ9ifWz4oefqamwUAAABwcabe1Cc9PV1btmzRmDFjHOvc3NzUqVMnrV+/PtftLly4oFq1aslms6lVq1aaOHGimjRpkmPbtLQ0paWlOV4nJiZKkqxWq6xWayF9khLm7AF5HlwtQxZltBwoldbP6WKy+lup7Xco1ei/cGX0X7gy+m/RKsj3amrhdObMGWVmZmYbMQoODtbevXtz3KZBgwaaPXu2mjdvroSEBL355pu6/vrrtWvXLtWoUSNb+0mTJmn8+PHZ1q9cuVJ+fn6F80FKmKb/+1R1JMUHNteGdbsk7TI7Ei4RExNjdgTgqtF/4crov3Bl9N+ikZKSku+2FsMwjCLMckUnTpxQ9erVtW7dOrVr186xfvTo0frpp5+0YcOGPPdhtVrVqFEj9evXTxMmTMj2fk4jTmFhYTpz5owCAwML54OUJOnJ8ninmSxpicq4b6GMOreanQh/s1qtiomJUefOneXp6Wl2HKBA6L9wZfRfuDL6b9FKTExUlSpVlJCQkGdtYOqIU5UqVeTu7q74+Hin9fHx8QoJCcnXPjw9PdWyZUsdOHAgx/e9vb3l7e2d43alsvP9vkRKS5Qq1ZZH/S6Sm+mXseEypbbvoUyg/8KV0X/hyui/RaMg36mpv1V7eXmpdevWWrVqlWOdzWbTqlWrnEagriQzM1M7duxQaGhoUcV0HYYhbZxpX44aStEEAAAAFBJTR5wkadSoURo4cKDatGmj6Ohovf3220pOTtbgwYMlSQMGDFD16tU1adIkSdLLL7+stm3bqm7dujp//rzeeOMNHTlyREOHDjXzY5QMR9dLp3ZJHr5Si/vNTgMAAACUGqYXTvfee69Onz6tl156SXFxcWrRooVWrFjhmDDi6NGjcrtk5OTcuXMaNmyY4uLiVLFiRbVu3Vrr1q1T48aNzfoIJUfWaFPze+z3bwIAAABQKEwvnCTpscce02OPPZbje2vWrHF6/dZbb+mtt94qhlQuJilO2vO1fTlqmLlZAAAAgFKGi2BKiy1zJVuGFNZWCm1udhoAAACgVKFwKg0yrdLmOfblaEabAAAAgMJG4VQa7PlGuhAnlasqNbrd7DQAAABAqUPhVBpsmmV/bj1I8vAyNQoAAABQGlE4ubr4XdKRXyWLu9RmsNlpAAAAgFKJwsnVZU1B3ug2KbCauVkAAACAUorCyZVdPC9tX2hfZgpyAAAAoMhQOLmy3z+XrClSUCMp/Eaz0wAAAAClFoWTq7LZ/pkUInqoZLGYmwcAAAAoxSicXNWhNdLZA5JXgNT8XrPTAAAAAKUahZOrypoUosX9kneAuVkAAACAUo7CyRWdPyr9scK+HDXU3CwAAABAGUDh5Io2z5YMmxTRXgqqb3YaAAAAoNSjcHI11lRpyzz7cvRwc7MAAAAAZQSFk6vZtUS6+JcUWEOq383sNAAAAECZQOHkajb9PSlEm8GSu4e5WQAAAIAygsLJlRzfYn+4e0mtBpqdBgAAACgzKJxcyca/b3jbpI/kH2RuFgAAAKAMoXByFclnpZ1f2JejhpmbBQAAAChjKJxcxbaPpcw0KbSFVKON2WkAAACAMoXCyRXYMqVNs+3L0cMki8XcPAAAAEAZQ+HkCv74Xko4KvlWlJreZXYaAAAAoMyhcHIFWVOQt3xQ8vQ1NwsAAABQBlE4lXRnDkgHV0uySFFDzE4DAAAAlEkUTiXdpr+nIK/fVaoYbmoUAAAAoKyicCrJ0pOl2Pn2ZaYgBwAAAExD4VSSbf+vlJYgVaot1bnF7DQAAABAmUXhVFIZhrTx70khooZKbvynAgAAAMzCb+Ml1dH10qldkoev1OJ+s9MAAAAAZRqFU0mVNdrU/B77/ZsAAAAAmIbCqSRKipP2fG1fZlIIAAAAwHQUTiXRlrmSLUMKayuFNjc7DQAAAFDmUTiVNJlWafMc+3I0o00AAABASUDhVNLs+Ua6ECeVqyo1ut3sNAAAAABE4VTybJplf249SPLwMjUKAAAAADsKp5Ikfpd05FfJ4i61GWx2GgAAAAB/o3AqSbKmIG90mxRYzdwsAAAAABwonEqKi+el7Qvty0xBDgAAAJQoFE4lxe+fS9YUKaiRFH6j2WkAAAAAXILCqSSw2f6ZFCJ6qGSxmJsHAAAAgBMKp5Lg0Brp7AHJK0Bqfq/ZaQAAAABcxsPsAGVZRlqa9n36oSpu/VjlbV7y6dFX7t4BZscC8iXDmqFt3/6opBNxCqgWopa3dZSHJz9S4Brov3Bl9F+4MlfuvyUi5Xvvvac33nhDcXFxioyM1Lvvvqvo6Ohc2y9atEgvvviiDh8+rHr16mny5Mnq0aNHMSa+dr+//py8P/9SbhctSpCUoCpy++07pe7xUeToiWbHA67op1kL5fH+26qUcl7+f6/bMKGCMh59Uu2HMmqKko3+C1dG/4Urc/X+a/qpegsXLtSoUaM0duxYbd26VZGRkeratatOnTqVY/t169apX79+GjJkiLZt26bevXurd+/e2rlzZzEnv3q/v/6cvGZ/KeOi83rbRYu8Zn+p319/zpxgQD78NGuhgt4cp4op553WV0g5r6A3x+mnWQvNCQbkA/0Xroz+C1dWGvqvxTAMw8wA1113naKiojRt2jRJks1mU1hYmB5//HH9+9//ztb+3nvvVXJysr799lvHurZt26pFixb68MMP8zxeYmKiypcvr4SEBAUGBhbeB8mnjLQ0HWgb+XfRlNMkEIYsvlLI97/Iw8urmNOhKFkzMvTDD6vUqdOt8vQoEYO9BZaRkaFdnXuo4sWEHHuvTdJ5vwpqsvI7ebjoZ0TO6L9wZfRfuLKy0n/P+VVQ2w0/F/tpewWpDUwtnNLT0+Xn56fFixerd+/ejvUDBw7U+fPn9dVXX2XbpmbNmho1apSefPJJx7qxY8dq6dKl+v3337O1T0tLU1pamuN1YmKiwsLCdObMGVMKp33zpsn9zRnFflwAAACgJEt49S21vv3WYj1mYmKiqlSpkq/CydSy9cyZM8rMzFRwcLDT+uDgYO3duzfHbeLi4nJsHxcXl2P7SZMmafz48dnWr1y5Un5+fleZ/Or5/r5VYcV+VAAAAKBk27V+o+I90vJuWIhSUlLy3dY1x/sKYMyYMRo1apTjddaIU5cuXcwZcTr9pxSzOc92qSMGKbzfsGJIhOKSYc3Qmp9+Uof27V1m9pjL7Vz+k6q8mvc1eGeen6im3dsXQyIUF/ovXBn9F66sLPXfJu2i1bpH8Y845Zep336VKlXk7u6u+Ph4p/Xx8fEKCQnJcZuQkJACtff29pa3t3e29Z6envL09LzK5Fev4YOP6sB70698jZOf1Gz4k/LIITdcl9VqlZevlypUqWhK3ysM7e7rpQ1vva4KKedznFkm6xzldvf1ctkf7sgZ/ReujP4LV1aW+m/b2zsVe/8tyHdq6qx6Xl5eat26tVatWuVYZ7PZtGrVKrVr1y7Hbdq1a+fUXpJiYmJybV/SeHh7K63fnX+/uvzyMvvrtPvupGhCieTh6aGMR5+URfYfcpeyyf6ngMxHn+T/tFEi0X/hyui/cGWlpf+anm7UqFEaOHCg2rRpo+joaL399ttKTk7W4MGDJUkDBgxQ9erVNWnSJEnSyJEj1b59e02ZMkU9e/bUggULtHnzZs2Y4ToTLkSOnqjfJXl/7jwlucXPXjRxHyeUZO2H3qufJMd9GLKc86ugTBe5DwPKLvovXBn9F66sNPRf0wune++9V6dPn9ZLL72kuLg4tWjRQitWrHBMAHH06FG5uf0zMHb99ddr/vz5euGFF/Tcc8+pXr16Wrp0qZo2bWrWR7gqkaMnKmPkWO379EOlnzgir2q11OCBhxlpgktoP/ReZQy8y+nO321d6M7fKNvov3Bl9F+4Mlfvv6bfx6m4mX0fJ5RdVqtVy5YtU48ePVz2HGWUXfRfuDL6L1wZ/bdoFaQ2MPUaJwAAAABwBRROAAAAAJAHCicAAAAAyAOFEwAAAADkgcIJAAAAAPJA4QQAAAAAeaBwAgAAAIA8UDgBAAAAQB4onAAAAAAgDxROAAAAAJAHCicAAAAAyAOFEwAAAADkgcIJAAAAAPLgYXaA4mYYhiQpMTHR5CQoa6xWq1JSUpSYmChPT0+z4wAFQv+FK6P/wpXRf4tWVk2QVSNcSZkrnJKSkiRJYWFhJicBAAAAUBIkJSWpfPnyV2xjMfJTXpUiNptNJ06cUEBAgCwWi9lxJElRUVHatGlTqTp2Yez3avdRkO0Ku+2V2iQmJiosLEzHjh1TYGBgvo7pCui/hbsP+m/xov8W7j4Kul1+29N/c0b/Ldx90H+LV0npv4ZhKCkpSdWqVZOb25WvYipzI05ubm6qUaOG2TGcuLu7m/YPoaiOXRj7vdp9FGS7wm6bnzaBgYGl6gcf/bdw90H/LV7038LdR0G3y297+m/O6L+Fuw/6b/EqSf03r5GmLEwOUQKMGDGi1B27MPZ7tfsoyHaF3dbM/5Zmof8W7j7ov8WL/lu4+yjodvltT//NGf23cPdB/y1erth/y9ypeoBZEhMTVb58eSUkJJSqvxihbKD/wpXRf+HK6L8lByNOQDHx9vbW2LFj5e3tbXYUoMDov3Bl9F+4MvpvycGIEwAAAADkgREnAAAAAMgDhRMAAAAA5IHCCQAAAADyQOEEAAAAAHmgcAIAAACAPFA4ASVAnz59VLFiRd19991mRwEK5NixY+rQoYMaN26s5s2ba9GiRWZHAvLt/PnzatOmjVq0aKGmTZtq5syZZkcCCiwlJUW1atXS008/bXaUUo/pyIESYM2aNUpKStK8efO0ePFis+MA+Xby5EnFx8erRYsWiouLU+vWrfXHH3+oXLlyZkcD8pSZmam0tDT5+fkpOTlZTZs21ebNm1W5cmWzowH59vzzz+vAgQMKCwvTm2++aXacUo0RJ6AE6NChgwICAsyOARRYaGioWrRoIUkKCQlRlSpV9Ndff5kbCsgnd3d3+fn5SZLS0tJkGIb4ezJcyf79+7V37151797d7ChlAoUTcI3Wrl2rXr16qVq1arJYLFq6dGm2Nu+9957Cw8Pl4+Oj6667Ths3biz+oEAOCrP/btmyRZmZmQoLCyvi1IBdYfTf8+fPKzIyUjVq1NAzzzyjKlWqFFN6lHWF0X+ffvppTZo0qZgSg8IJuEbJycmKjIzUe++9l+P7Cxcu1KhRozR27Fht3bpVkZGR6tq1q06dOlXMSYHsCqv//vXXXxowYIBmzJhRHLEBSYXTfytUqKDff/9dhw4d0vz58xUfH19c8VHGXWv//eqrr1S/fn3Vr1+/OGOXbQaAQiPJWLJkidO66OhoY8SIEY7XmZmZRrVq1YxJkyY5tfvxxx+Nu+66qzhiAjm62v6bmppq3HTTTcbHH39cXFGBbK7l52+WRx55xFi0aFFRxgRydDX999///rdRo0YNo1atWkblypWNwMBAY/z48cUZu8xhxAkoQunp6dqyZYs6derkWOfm5qZOnTpp/fr1JiYD8paf/msYhgYNGqRbbrlFDz74oFlRgWzy03/j4+OVlJQkSUpISNDatWvVoEEDU/ICl8pP/500aZKOHTumw4cP680339SwYcP00ksvmRW5TKBwAorQmTNnlJmZqeDgYKf1wcHBiouLc7zu1KmT7rnnHi1btkw1atSgqEKJkJ/+++uvv2rhwoVaunSpWrRooRYtWmjHjh1mxAWc5Kf/HjlyRDfddJMiIyN100036fHHH1ezZs3MiAs4ye/vDyheHmYHACD98MMPZkcArsqNN94om81mdgzgqkRHRys2NtbsGMA1GzRokNkRygRGnIAiVKVKFbm7u2e72Dg+Pl4hISEmpQLyh/4LV0b/hSuj/5ZMFE5AEfLy8lLr1q21atUqxzqbzaZVq1apXbt2JiYD8kb/hSuj/8KV0X9LJk7VA67RhQsXdODAAcfrQ4cOKTY2VpUqVVLNmjU1atQoDRw4UG3atFF0dLTefvttJScna/DgwSamBuzov3Bl9F+4MvqvCzJ7Wj/A1f3444+GpGyPgQMHOtq8++67Rs2aNQ0vLy8jOjra+O2338wLDFyC/gtXRv+FK6P/uh6LYRhG8ZZqAAAAAOBauMYJAAAAAPJA4QQAAAAAeaBwAgAAAIA8UDgBAAAAQB4onAAAAAAgDxROAAAAAJAHCicAAAAAyAOFEwAAAADkgcIJAIArsFgsWrp0qdkxAAAmo3ACAJRYgwYNksViyfbo1q2b2dEAAGWMh9kBAAC4km7dumnOnDlO67y9vU1KAwAoqxhxAgCUaN7e3goJCXF6VKxYUZL9NLoPPvhA3bt3l6+vr2rXrq3Fixc7bb9jxw7dcsst8vX1VeXKlTV8+HBduHDBqc3s2bPVpEkTeXt7KzQ0VI899pjT+2fOnFGfPn3k5+enevXq6euvv3a8d+7cOfXv319BQUHy9fVVvXr1shV6AADXR+EEAHBpL774ou666y79/vvv6t+/v+677z7t2bNHkpScnKyuXbuqYsWK2rRpkxYtWqQffvjBqTD64IMPNGLECA0fPlw7duzQ119/rbp16zodY/z48erbt6+2b9+uHj16qH///vrrr78cx9+9e7eWL1+uPXv26IMPPlCVKlWK7wsAABQLi2EYhtkhAADIyaBBg/Tpp5/Kx8fHaf1zzz2n5557ThaLRQ8//LA++OADx3tt27ZVq1at9P7772vmzJl69tlndezYMZUrV06StGzZMvXq1UsnTpxQcHCwqlevrsGDB+uVV17JMYPFYtELL7ygCRMmSLIXY/7+/lq+fLm6deum22+/XVWqVNHs2bOL6FsAAJQEXOMEACjROnbs6FQYSVKlSpUcy+3atXN6r127doqNjZUk7dmzR5GRkY6iSZL+v537dWkuiuM4/r6iwjY0+YM125iCRS3+SANhbbA1kVv9wbBYLLo/QNQs2BQHBovIRIwDMQxs2rSIaBTBlWF4YCDCc+VRHqe8X+mecy6H74kfzo+JiQkajQbX19cEQcDd3R2ZTOavNQwPDze/E4kE3d3dPDw8ADA/P08+n6dWqzE9PU0ul2N8fPyf1ipJal0GJ0lSS0skEu+Ozn2VWCz2of86OjretIMgoNFoAJDNZrm9veX4+JjT01MymQyLi4usr69/eb2SpO/jHSdJ0o92fn7+rp1OpwFIp9NcXl7y/PzcHK9Wq7S1tZFKpejq6mJgYICzs7NP1dDb20sYhuzu7rK1tcX29van5pMktR53nCRJLa1er3N/f/+mr729vfkAw8HBAaOjo0xOTrK3t8fFxQU7OzsAzMzMsLa2RhiGlEolHh8fKRaLzM7O0t/fD0CpVGJubo6+vj6y2SxPT09Uq1WKxeKH6ltdXWVkZIShoSHq9TpHR0fN4CZJ+j0MTpKkllapVEgmk2/6UqkUV1dXwJ8X78rlMgsLCySTSfb39xkcHAQgHo9zcnLC0tISY2NjxONx8vk8GxsbzbnCMOTl5YXNzU2Wl5fp6emhUCh8uL7Ozk5WVla4ubkhFosxNTVFuVz+gpVLklqJr+pJkn6sIAg4PDwkl8t9dymSpF/OO06SJEmSFMHgJEmSJEkRvOMkSfqxPG0uSfpf3HGSJEmSpAgGJ0mSJEmKYHCSJEmSpAgGJ0mSJEmKYHCSJEmSpAgGJ0mSJEmKYHCSJEmSpAgGJ0mSJEmKYHCSJEmSpAivmovfuk82eUYAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох:\n",
+            "        Normal Equation             GD  SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                              \n",
+            "2              8.313329   6.835259e+22  inf    8.313329          NaN\n",
+            "20             8.313329  3.980530e+196  NaN    8.313329          NaN\n",
+            "200            8.313329            NaN  NaN    8.313329          NaN\n",
+            "2000           8.313329            NaN  NaN    8.313329          NaN\n",
+            "20000          8.313329            NaN  NaN    8.313329          NaN\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча:\n",
+            "            Normal Equation  GD  SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                   \n",
+            "1                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "2                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "4                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "8                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "16                 8.313329 NaN  NaN    8.313329          NaN\n",
+            "32                 8.313329 NaN  NaN    8.313329          NaN\n",
+            "64                 8.313329 NaN  NaN    8.313329          NaN\n",
+            "128                8.313329 NaN  NaN    8.313329          NaN\n",
+            "256                8.313329 NaN  NaN    8.313329          NaN\n",
+            "512                8.313329 NaN  NaN    8.313329          NaN\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (ожидается файл data.csv) ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "# Начальные значения по умолчанию (будут переопределены после первого графика)\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    # Заменяем NaN на прочерк для читаемости\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate', 'lr_plot')\n",
+        "\n",
+        "# Определяем лучший learning rate для GD (первый не-NaN или минимальный MSE среди не-NaN)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    # Берем первый (самый маленький) работающий learning rate\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} для следующих графиков (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8  # запасной вариант\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох (с новым DEFAULT_LR) ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча (с новым DEFAULT_LR) ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "McU9ae5C1kXf",
+        "outputId": "f650fb8b-388e-4d44-a73c-7ea6aa8def06"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-6e86feb7-d490-426d-ab6a-594e618da620\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-6e86feb7-d490-426d-ab6a-594e618da620\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (2).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329            -\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329            -\n",
+            "1.000000e-06          8.313329         -  8.346550    8.313329            -\n",
+            "1.000000e-05          8.313329         -         -    8.313329            -\n",
+            "1.000000e-04          8.313329         -         -    8.313329            -\n",
+            "1.000000e-03          8.313329         -         -    8.313329            -\n",
+            "1.000000e-02          8.313329         -         -    8.313329            -\n",
+            "1.000000e-01          8.313329         -         -    8.313329            -\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 для следующих графиков (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох:\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  2560.000199  2583.409888    8.313329            -\n",
+            "20             8.313329   217.565243   237.582879    8.313329            -\n",
+            "200            8.313329     8.325835     8.325835    8.313329            -\n",
+            "2000           8.313329     8.325615     8.325615    8.313329            -\n",
+            "20000          8.313329     8.323616     8.323616    8.313329            -\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча:\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "2                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "4                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "8                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "16                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "32                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "64                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "128                8.313329  8.325737  8.325737    8.313329            -\n",
+            "256                8.313329  8.325737  8.325737    8.313329            -\n",
+            "512                8.313329  8.325737  8.325737    8.313329            -\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (ожидается файл data.csv) ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X_raw = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X_raw.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Масштабирование признаков ----------\n",
+        "scaler = StandardScaler()\n",
+        "X = scaler.fit_transform(X_raw)   # теперь X имеет среднее 0 и дисперсию 1\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы (после масштабирования) ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)   # X уже масштабированы\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1 (после масштабирования 0.1 может быть великоват, но посмотрим)\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01      # будет переопределён после первого графика\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (после масштабирования)', 'lr_plot')\n",
+        "\n",
+        "# Определяем лучший learning rate для GD (первый не-NaN или минимальный MSE среди не-NaN)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]   # наименьший работающий\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} для следующих графиков (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (после масштабирования)', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (после масштабирования)', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "_-mhD1it2XH3",
+        "outputId": "de63a58b-2b82-4d28-b7f4-ead5222b99c1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-07c43c14-b8e0-498b-a6e8-e2ce6b9a55a1\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-07c43c14-b8e0-498b-a6e8-e2ce6b9a55a1\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (3).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate (после масштабирования):\n",
+            "               Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                                    \n",
+            "1.000000e-08          8.313329  3245.435934  3245.437183    8.313329            -\n",
+            "1.000000e-07          8.313329  2267.501029  2267.588249    8.313329            -\n",
+            "1.000000e-06          8.313329    70.020689    70.259705    8.313329            -\n",
+            "1.000000e-05          8.313329     8.313329     8.313329    8.313329            -\n",
+            "1.000000e-04          8.313329     8.313329     8.313332    8.313329            -\n",
+            "1.000000e-03          8.313329    69.773860     8.320042    8.313329            -\n",
+            "1.000000e-02          8.313329            -     8.470672    8.313329            -\n",
+            "1.000000e-01          8.313329            -            -    8.313329            -\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 для следующих графиков (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох (после масштабирования):\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  3377.142741  3377.142744    8.313329            -\n",
+            "20             8.313329  3374.720457  3374.720483    8.313329            -\n",
+            "200            8.313329  3350.593198  3350.593456    8.313329            -\n",
+            "2000           8.313329  3118.629709  3118.632111    8.313329            -\n",
+            "20000          8.313329  1523.346396  1523.358092    8.313329            -\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча (после масштабирования):\n",
+            "            Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                                    \n",
+            "1                  8.313329  3245.435934  3245.437223    8.313329            -\n",
+            "2                  8.313329  3245.435934  3245.437222    8.313329            -\n",
+            "4                  8.313329  3245.435934  3245.437219    8.313329            -\n",
+            "8                  8.313329  3245.435934  3245.437214    8.313329            -\n",
+            "16                 8.313329  3245.435934  3245.437204    8.313329            -\n",
+            "32                 8.313329  3245.435934  3245.437183    8.313329            -\n",
+            "64                 8.313329  3245.435934  3245.437143    8.313329            -\n",
+            "128                8.313329  3245.435934  3245.437062    8.313329            -\n",
+            "256                8.313329  3245.435934  3245.436901    8.313329            -\n",
+            "512                8.313329  3245.435934  3245.436579    8.313329            -\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X_raw = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X_raw.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Масштабирование признаков ----------\n",
+        "scaler = StandardScaler()\n",
+        "X = scaler.fit_transform(X_raw)   # теперь X имеет среднее 0 и дисперсию 1\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            # Упростим: без batch_size, используем значения по умолчанию\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=1e-6,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception as e:\n",
+        "        # print(f\"Error in {method} with lr={lr}, epochs={epochs}, batch={batch_size}: {e}\")\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01      # будет переопределён после первого графика\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (после масштабирования)', 'lr_plot')\n",
+        "\n",
+        "# Выбираем learning rate с наименьшим MSE для GD (среди не-NaN)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_mask = ~np.isnan(gd_mses)\n",
+        "if np.any(valid_mask):\n",
+        "    valid_lr = lr_range[valid_mask]\n",
+        "    valid_mse = gd_mses[valid_mask]\n",
+        "    best_idx = np.argmin(valid_mse)\n",
+        "    best_lr = valid_lr[best_idx]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (MSE = {valid_mse[best_idx]:.6f}) для следующих графиков.\")\n",
+        "else:\n",
+        "    best_lr = 1e-5   # запасной вариант\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-5.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (после масштабирования)', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (после масштабирования)', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "3q2uAZw825E8",
+        "outputId": "893b186c-f997-4ec8-c76c-cb6259b02bed"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c473765b-e8c6-497d-9e10-b587d587ee25\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c473765b-e8c6-497d-9e10-b587d587ee25\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (4).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate (после масштабирования):\n",
+            "               Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                                    \n",
+            "1.000000e-08          8.313329  3245.435934  3245.437183    8.313329  3310.765416\n",
+            "1.000000e-07          8.313329  2267.501029  2267.588249    8.313329  2767.249684\n",
+            "1.000000e-06          8.313329    70.020689    70.259705    8.313329   465.183031\n",
+            "1.000000e-05          8.313329     8.313329     8.313329    8.313329     8.313411\n",
+            "1.000000e-04          8.313329     8.313329     8.313332    8.313329     8.313361\n",
+            "1.000000e-03          8.313329    69.773860     8.320042    8.313329     8.314865\n",
+            "1.000000e-02          8.313329            -     8.470672    8.313329     8.348210\n",
+            "1.000000e-01          8.313329            -            -    8.313329     8.732265\n",
+            "\n",
+            "Выбран learning rate = 1.00e-04 (MSE = 8.313329) для следующих графиков.\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох (после масштабирования):\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  1389.676642  1519.252432    8.313329  2267.536287\n",
+            "20             8.313329     8.765660     9.423590    8.313329    70.248428\n",
+            "200            8.313329     8.313329     8.313338    8.313329     8.313361\n",
+            "2000           8.313329     8.313329     8.313334    8.313329     8.313361\n",
+            "20000          8.313329     8.313329     8.313333    8.313329     8.313361\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча (после масштабирования):\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "2                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "4                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "8                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "16                 8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "32                 8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "64                 8.313329  8.313329  8.313333    8.313329     8.313361\n",
+            "128                8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "256                8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "512                8.313329  8.313329  8.313332    8.313329     8.313361\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values   # без масштабирования\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,  # возвращаем batch_size\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)   # без масштабирования\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01      # будет переопределён\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (без масштабирования)', 'lr_plot')\n",
+        "\n",
+        "# Выбираем наименьший работающий learning rate для GD (как в первом варианте)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (без масштабирования)', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (без масштабирования)', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "-8DUiLJA3nQS",
+        "outputId": "cc034d99-d565-42cc-992c-11925356a955"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-cbb98c2d-a6ff-45b0-b0b8-2b943ba7a582\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-cbb98c2d-a6ff-45b0-b0b8-2b943ba7a582\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (5).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate (без масштабирования):\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329            -\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329            -\n",
+            "1.000000e-06          8.313329         -  8.346550    8.313329            -\n",
+            "1.000000e-05          8.313329         -         -    8.313329            -\n",
+            "1.000000e-04          8.313329         -         -    8.313329            -\n",
+            "1.000000e-03          8.313329         -         -    8.313329            -\n",
+            "1.000000e-02          8.313329         -         -    8.313329            -\n",
+            "1.000000e-01          8.313329         -         -    8.313329            -\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох (без масштабирования):\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  2560.000199  2583.409888    8.313329            -\n",
+            "20             8.313329   217.565243   237.582879    8.313329            -\n",
+            "200            8.313329     8.325835     8.325835    8.313329            -\n",
+            "2000           8.313329     8.325615     8.325615    8.313329            -\n",
+            "20000          8.313329     8.323616     8.323616    8.313329            -\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча (без масштабирования):\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "2                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "4                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "8                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "16                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "32                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "64                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "128                8.313329  8.325737  8.325737    8.313329            -\n",
+            "256                8.313329  8.325737  8.325737    8.313329            -\n",
+            "512                8.313329  8.325737  8.325737    8.313329            -\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (файл data.csv должен лежать в папке с ноутбуком) ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            # batch_size не передаём, используем значение по умолчанию\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Определяем best_lr по первому графику ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate')\n",
+        "\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "JTCgt5Iu4AnF",
+        "outputId": "a535d63e-a80c-42f6-917b-5c0070ac391e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIoCAYAAACFwRFQAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAn9dJREFUeJzs3Xl8U1XaB/DfTZom3dK9TVugLV3YN0ERGQRkKYKM4CiKOiw6OC6MIK/ioLIzICguKIqCCC4IKoiOIlKBigMIAtaVpS2b0HSlbbqmWe77R0kgdIem9yb5fT+ffF5zc3Lvc3PSefNwznmOIIqiCCIiIiIiIromCqkDICIiIiIicgdMroiIiIiIiFoAkysiIiIiIqIWwOSKiIiIiIioBTC5IiIiIiIiagFMroiIiIiIiFoAkysiIiIiIqIWwOSKiIiIiIioBTC5IiIiIiIiagFMroiIiFyUIAiYN2+e1GE0Ki4uDpMmTZI6DCIip2NyRURubdWqVUhJSUFkZCRUKhV0Oh0GDhyI9957D1arVerw6CpNmjQJgiBAq9WisrKy1usZGRkQBAGCIODFF190eO306dOYPHkyEhISoNFooNPpcPPNN2Pu3LkO7QYNGmQ/x5WPjh07OvX+SD4qKiowb948pKWlSR0KEbkAL6kDICJypvXr1yMqKgqzZ8+GVqtFcXExfvjhB0yaNAlff/01PvroI6lDpKvk5eWFiooK/Pe//8W4ceMcXvvwww+h0WhQVVXlcDwzMxPXX389fHx88MADDyAuLg56vR5HjhzB0qVLMX/+fIf2bdq0wZIlS2pdOzAwsOVvyI0dP34cCoVr/ntuRUWF/XsxaNAgaYMhItljckVEbm3Pnj1QqVQOxx5//HGEhobi9ddfx5IlSxAXFydNcHRN1Go1+vfvj48++qhWcrVhwwaMGjUKmzdvdjj+8ssvo6ysDOnp6YiNjXV4LS8vr9Y1AgMDcf/997d88C7MbDbDarXC29u7ye9Rq9VOjKh5riZ+IqKmcs1/RiIiaqIrEysbW0J1+b+mf/755xg1ahSio6OhVquRkJCAhQsXwmKxOLz3yuliYWFhGDVqFH777TeHdnWth3nhhRcgCEKtfwGvqqrCvHnzkJycDI1Gg6ioKNxxxx3IysoCUDOVTRAErFu3zuF9jz32GARBcFjPsm7dOgiCAG9vb+Tn5zu0379/vz3uQ4cOObz2ySefoHfv3vDx8UFYWBjuv/9+nD9/vtZnd+zYMYwbNw7h4eHw8fFBhw4d8OyzzwIA5s2bV+9UOtvDNr1q0KBB6Nq1a63zN8e9996Lr7/+GsXFxfZjP/74IzIyMnDvvffWap+VlYU2bdrUSqwAICIi4ppiuVJeXh4efPBBREZGQqPRoEePHli/fr39dVufNvS4mnVK58+fxwMPPIDIyEio1Wp06dIFa9eudWhTXV2NOXPmoHfv3ggMDISfnx8GDBiA3bt3O7Szxfjiiy/ilVdeQUJCAtRqNf744w97X2dmZmLSpEkICgpCYGAgJk+ejIqKCofzXLnmyvYd3bt3L2bMmIHw8HD4+flh7Nixtb6zVqsV8+bNQ3R0NHx9fTF48GD88ccfTVrH1VD8TfkMTp8+jfDwcADA/Pnz7f1y+d/1sWPHcOeddyIkJAQajQZ9+vTBF1980Vg3EZGb4sgVEXmE4uJimM1mlJaW4vDhw3jxxRdxzz33oF27dvY269atg7+/P2bMmAF/f3/s2rULc+bMgcFgwAsvvOBwvo4dO+LZZ5+FKIrIysrCSy+9hJEjR+Ls2bMNxlDXFDOLxYLbbrsNO3fuxD333INp06ahtLQUqamp+O2335CQkFDn+TIzM7F69ep6r6dUKvHBBx/giSeesB97991365wut27dOkyePBnXX389lixZgtzcXLz66qvYu3cvfvrpJwQFBQEAfvnlFwwYMAAqlQoPPfQQ4uLikJWVhf/+97/4z3/+gzvuuAOJiYn28z7xxBPo1KkTHnroIfuxTp061Rtzc91xxx14+OGHsWXLFjzwwAMAakatOnbsiOuuu65W+9jYWHz77bfYtWsXbrnllkbPb7FYUFBQUOu4j48P/Pz86n1fZWUlBg0ahMzMTEydOhXx8fH45JNPMGnSJBQXF2PatGkIDw/H+++/b3/Pli1b8Nlnnzkcq6/v65Obm4sbb7wRgiBg6tSpCA8Px9dff40HH3wQBoMB06dPBwAYDAasWbMG48ePx5QpU1BaWop33nkHKSkpOHjwIHr27Olw3nfffRdVVVV46KGHoFarERISYn9t3LhxiI+Px5IlS3DkyBGsWbMGERERWLp0aaPx/utf/0JwcDDmzp2L06dP45VXXsHUqVOxadMme5tZs2Zh2bJlGD16NFJSUvDzzz8jJSWl1ne4IXXF35TPIDw8HG+++SYeeeQRjB07FnfccQcAoHv37gCA33//Hf3790dMTAz+/e9/w8/PDx9//DHGjBmDzZs3Y+zYsU2OkYjchEhE5AE6dOggArA/JkyYIJpMJoc2FRUVtd73z3/+U/T19RWrqqrsxwYOHCgOHDjQod0zzzwjAhDz8vLsxwCIc+fOtT+fOXOmGBERIfbu3dvh/WvXrhUBiC+99FKt61utVlEURfHUqVMiAPHdd9+1vzZu3Dixa9euYtu2bcWJEyfaj7/77rsiAHH8+PFit27d7MfLy8tFrVYr3nvvvSIA8ccffxRFURSrq6vFiIgIsWvXrmJlZaW9/ZdffikCEOfMmWM/dvPNN4sBAQHimTNn6ozzSrGxsQ6xXW7gwIFily5d6nytMRMnThT9/PxEURTFO++8UxwyZIgoiqJosVhEnU4nzp8/3/6ZvfDCC/b3/fbbb6KPj48IQOzZs6c4bdo0cevWrWJ5eXmd8V3+nbn88c9//rPB+F555RURgPjBBx/Yj1VXV4v9+vUT/f39RYPBUOs9c+fOFZv7/5av/I49+OCDYlRUlFhQUODQ7p577hEDAwPt33Gz2SwajUaHNkVFRWJkZKT4wAMP2I/ZPkOtVuvw3b483svbi6Iojh07VgwNDXU4duX3wPYdHTp0qMN354knnhCVSqVYXFwsiqIo5uTkiF5eXuKYMWMczjdv3jwRQL3frabE39TPID8/v9bnbDNkyBCxW7duDv/7YLVaxZtuuklMSkpqMDYick+cFkhEHuHdd99FamoqPvzwQzz44IP48MMPHUZTgJrRCJvS0lIUFBRgwIABqKiowLFjxxzamkwmFBQUID8/H/v378dnn32G7t27IywsrM7rnz9/Hq+99hpmz54Nf39/h9c2b96MsLAw/Otf/6r1PkEQ6jzf4cOH8cknn2DJkiX1Fgr4+9//jmPHjtmn/23evBmBgYEYMmSIQ7tDhw4hLy8Pjz76KDQajf34qFGj0LFjR3z11VcAgPz8fOzZswcPPPCAw4hfQ3E2xjYyVFBQgOrq6qs6x7333ou0tDTk5ORg165dyMnJqXNKIAB06dIF6enpuP/++3H69Gm8+uqrGDNmDCIjI+scBYyLi0Nqamqth20EqD7btm2DTqfD+PHj7cdUKhUef/xxlJWV4bvvvruqe22IKIrYvHkzRo8eDVEU7Z9rQUEBUlJSUFJSgiNHjgCoGdW0rTmyWq24cOECzGYz+vTpY29zub/97W/26XFXevjhhx2eDxgwAIWFhTAYDI3G/NBDDzl8dwYMGACLxYIzZ84AAHbu3Amz2YxHH33U4X11/a00pK74m/sZXOnChQvYtWsXxo0bZ//fi4KCAhQWFiIlJQUZGRl1TqslIvfG5KoRe/bswejRoxEdHQ1BELB169ZmvT8tLQ233347oqKi4Ofnh549e+LDDz+s1a64uBiPPfYYoqKioFarkZycjG3btrXQXRBRv379MHToUNx7771Ys2YNFixYgHfffRd79+61t/n9998xduxYBAYGQqvVIjw83F7MoKSkxOF8+/btQ3h4OCIiInDTTTfBbDbjk08+qTfJmDt3LqKjo/HPf/6z1mtZWVno0KEDvLyaPlP73//+NwYMGIDbbrut3jbh4eEYNWqUfb3N2rVrMXHixFrJmO2HbIcOHWqdo2PHjvbXT548CQDXvE7qcseOHUN4eLjD+q0NGzY06xwjR45EQEAANm3ahA8//BDXX3+9w9TEKyUnJ+P9999HQUEBfvnlFyxevBheXl546KGH8O233zq09fPzw9ChQ2s9GivFfubMGSQlJdX6rG1TIm2faUvKz89HcXEx3n77bftnantMnjwZgGPRjvXr16N79+7QaDQIDQ1FeHg4vvrqq1rfdQCIj4+v97pXJtrBwcEAgKKiokZjbuy9ts/pyv4MCQmxt22K+uJvzmdwpczMTIiiiNmzZ9f6vG1l/esqkkJE7o1rrhpRXl6OHj164IEHHrDPtW6Offv2oXv37nj66acRGRmJL7/8EhMmTEBgYKD9R1F1dTWGDRuGiIgIfPrpp4iJicGZM2fsaxyIqOXdeeedePbZZ3HgwAH0798fxcXFGDhwILRaLRYsWGDfA+nIkSN4+umna+2J1b17dyxfvhxAzY/aFStWYNCgQThy5Ah0Op1D26NHj2LdunX44IMP6i2w0Rw7duzAt99+i/379zfa9oEHHsCECRPwr3/9C3v27MGaNWvw/fffX3MMLSUuLs4+YlRYWIgVK1bg73//O9q3b48bb7yxSedQq9W44447sH79epw8ebLJm+oqlUp069YN3bp1Q79+/TB48GB8+OGHGDp06NXejqRs39H7778fEydOrLONba3QBx98gEmTJmHMmDF46qmnEBERAaVSiSVLltiLqFzu8lHdKymVyjqPi6LYaMzX8t7mqCv+5n4GV7J93k8++SRSUlLqbNNQkk9E7onJVSNuvfVW3HrrrfW+bjQa8eyzz+Kjjz5CcXExunbtiqVLl9orgT3zzDMO7adNm4YdO3Zgy5Yt9uRq7dq1uHDhAvbt22f/4cXS0ETOZdt41vbjLi0tDYWFhdiyZQtuvvlme7tTp07V+f7g4GCHH+GDBg1CdHQ03n33XcyaNcuh7axZs9CzZ0/cfffddZ4rISEBBw4cgMlkajT5EkUR//73vzF27NgmJR+33norNBoN7rnnHvzlL39BQkJCreTKVjnv+PHjtYo8HD9+3P56+/btAaBWVcRrYRsZshkwYABiYmKwY8eOJidXQM3UwLVr10KhUOCee+5pdhx9+vQBAOj1+ma/ty6xsbH45ZdfYLVaHUavbNNL66pWeK3Cw8MREBAAi8XSaIL46aefon379tiyZYvDaOuVGylLzfY5ZWZmOow+FRYWNmlkrCFN/QzqG422/T2oVCqXTciJqOVxWuA1mjp1Kvbv34+NGzfil19+wV133YURI0YgIyOj3veUlJQ4VFr64osv0K9fPzz22GOIjIxE165dsXjx4lrln4mo+eqbXrt69WoIgmBPJmxJ1uX/Yl5dXY033nijSdexJWtGo9Hh+P79+/H555/j+eefr/dH2t/+9jcUFBTg9ddfr/Xalf+Cb/vfmrqqDtbFy8sLEyZMwC+//GKvpnelPn36ICIiAqtWrXKI/+uvv8bRo0cxatQoADU/3m+++WasXbu2VlXElhppsI0G1DeiUZ/Bgwdj4cKFeP3112uNHF7u+++/h8lkqnXc9j2pa2rk1Rg5ciRycnIcqt6ZzWa89tpr8Pf3x8CBA1vkOpdTKpX429/+hs2bN9eZAF9e4ryu7/uBAweaNBramoYMGQIvLy+8+eabDsfr+ltprqZ+Br6+vgDgUO4fqCndP2jQILz11lt1JuVXlpQnIs/AkatrcPbsWbz77rs4e/YsoqOjAdRMD9i+fTveffddLF68uNZ7Pv74Y/z4449466237MdOnjyJXbt24b777sO2bduQmZmJRx99FCaTSXb/ikjkau6991507NgRY8eORWRkJPLz8/H1119j9+7dePbZZ9GtWzcAwE033YTg4GBMnDgRjz/+OARBwPvvv19v0pCbm4sPPvgAAFBQUIC33noLXl5etdZA7dixA8OGDWvwX7YnTJiA9957DzNmzMDBgwcxYMAAlJeX49tvv8Wjjz6K22+/3eF8U6ZMaVYSsHDhQjz11FP1rlFRqVRYunQpJk+ejIEDB2L8+PH2UuxxcXEOpdxXrFiBv/zlL7juuuvw0EMPIT4+HqdPn8ZXX32F9PT0JsdkU1ZWhu3btwOoKRCwYsUKqFQqe0LXVAqFAs8991yj7ZYuXYrDhw/jjjvusE+RO3LkCN577z2EhITUKlRRUlJi7+crNbS58EMPPYS33noLkyZNwuHDhxEXF4dPP/0Ue/fuxSuvvIKAgICm31wzPP/889i9ezf69u2LKVOmoHPnzrhw4QKOHDmCb7/9FhcuXAAA3HbbbdiyZQvGjh2LUaNG4dSpU1i1ahU6d+6MsrIyp8R2NSIjIzFt2jQsX74cf/3rXzFixAj8/PPP+PrrrxEWFnbVhVSApn8GPj4+6Ny5MzZt2oTk5GSEhISga9eu6Nq1K1auXIm//OUv6NatG6ZMmYL27dsjNzcX+/fvx7lz5/Dzzz+3xMdARK5EmiKFrgmA+Nlnn9mf28oU+/n5OTy8vLzEcePG1Xr/rl27RF9fX3H9+vUOx5OSksS2bduKZrPZfmz58uWiTqdz2r0QeYo333xTHDlypBgdHS16eXmJQUFBYkpKirht27Zabffu3SveeOONoo+PjxgdHS3OnDlT/Oabb0QA4u7du+3trizRHRQUJPbv37/WOQGIgiCIhw8fdjheVyn3iooK8dlnnxXj4+NFlUol6nQ68c477xSzsrJEUbxUUtrHx0c8f/68w3vrK3NtK7V+pfpe37Rpk9irVy9RrVaLISEh4n333SeeO3eu1vt/++03cezYsWJQUJCo0WjEDh06iLNnz67zWo2VYq/rc/z666/rbH+5y0ux16euUux79+4VH3vsMbFr165iYGCgqFKpxHbt2omTJk2yf9b1xXflozG5ubni5MmTxbCwMNHb21vs1q2bQyn9K7VEKXbbdR977DGxbdu29u/SkCFDxLffftvexmq1iosXLxZjY2NFtVot9urVS/zyyy/FiRMnirGxsfZ2dX2GV8abn5/vcNz2/Tp16pT9WFO/o7t3767192Y2m8XZs2eLOp1O9PHxEW+55Rbx6NGjYmhoqPjwww83+Pk0FH9TPwNRFMV9+/aJvXv3Fr29vWt95llZWeKECRNEnU4nqlQqMSYmRrztttvETz/9tMHYiMg9CaLYwqtG3ZggCPjss88wZswYAMCmTZtw33334ffff681hcXf399hasp3332HUaNG4aWXXqpV/nngwIFQqVQOVaq+/vprjBw5Ekaj0V4qloiIiGqm6AUHB2PRokV49tlnpQ6HiMiO0wKvQa9evWCxWJCXl4cBAwbU2y4tLQ233XYbli5dWiuxAoD+/ftjw4YNDgufT5w4gaioKCZWRETk0SorK2tV+3vllVcAwF48iohILphcNaKsrAyZmZn256dOnUJ6ejpCQkKQnJyM++67DxMmTMDy5cvRq1cv5OfnY+fOnejevTtGjRqF3bt347bbbsO0adPwt7/9DTk5OQAAb29ve1GLRx55BK+//jqmTZuGf/3rX8jIyMDixYvx+OOPS3LPREREcrFp0yasW7cOI0eOhL+/P/73v//ho48+wvDhw9G/f3+pwyMicsBpgY1IS0vD4MGDax2fOHEi1q1bB5PJhEWLFuG9997D+fPnERYWhhtvvBHz589Ht27dMGnSJKxfv77W+wcOHIi0tDT78/379+OJJ55Aeno6YmJi8OCDD+Lpp59udsUsIiIid3LkyBHMnDkT6enpMBgMiIyMxN/+9jcsWrQI/v7+UodHROSAyRUREREREVEL4D5XRERERERELYDJFRERERERUQtgQYs6WK1WZGdnIyAg4Jo2KCQiIiIiItcmiiJKS0sRHR1tr+xdHyZXdcjOzkbbtm2lDoOIiIiIiGTizz//RJs2bRpsw+SqDgEBAQBqPkCtVitpLCaTCTt27MDw4cOhUqkkjYVqsE/kh30iL+wP+WGfyA/7RF7YH/Ijpz4xGAxo27atPUdoiKTJ1ZIlS7BlyxYcO3YMPj4+uOmmm7B06VJ06NChwfd98sknmD17Nk6fPo2kpCQsXboUI0eOtL8uiiLmzp2L1atXo7i4GP3798ebb76JpKSkJsVlmwqo1WplkVz5+vpCq9VK/sWiGuwT+WGfyAv7Q37YJ/LDPpEX9of8yLFPmrJcSNKCFt999x0ee+wx/PDDD0hNTYXJZMLw4cNRXl5e73v27duH8ePH48EHH8RPP/2EMWPGYMyYMfjtt9/sbZYtW4YVK1Zg1apVOHDgAPz8/JCSkoKqqqrWuC0iIiIiIvJAko5cbd++3eH5unXrEBERgcOHD+Pmm2+u8z2vvvoqRowYgaeeegoAsHDhQqSmpuL111/HqlWrIIoiXnnlFTz33HO4/fbbAQDvvfceIiMjsXXrVtxzzz3OvSkiIiIiIvJIslpzVVJSAgAICQmpt83+/fsxY8YMh2MpKSnYunUrAODUqVPIycnB0KFD7a8HBgaib9++2L9/f53JldFohNFotD83GAwAaoYjTSbTVd9PS7BdX+o46BL2ifywT+SF/SE/7BP5YZ/IC/tDfuTUJ82JQTbJldVqxfTp09G/f3907dq13nY5OTmIjIx0OBYZGYmcnBz767Zj9bW50pIlSzB//vxax3fs2AFfX996YxEEAUqlst7XW4qXlxd2797t9OtQ08mtT8xms9QhyEJqaqrUIdBl2B/ywz6RH/aJvLA/5EcOfVJRUdHktrJJrh577DH89ttv+N///tfq1541a5bDaJitIsjw4cPrLGghiiLy8vLsI1zOJIoiqqqqoNFouOeWTMixTxQKBdq1ayebBZ+tzWQyITU1FcOGDfPYz0BO2B/ywz6RH/aJvLA/5EdOfdKc3/yySK6mTp2KL7/8Env27Gm0drxOp0Nubq7DsdzcXOh0OvvrtmNRUVEObXr27FnnOdVqNdRqda3jKpWqzs7U6/UoLS1FZGQkfH19nfoD22q1oqysDP7+/o1uWkatQ259Ytv0Oj8/H+3atZNNwieF+v5mSRrsD/lhn8gP+0Re2B/yI4c+ac71JU2uRFHEv/71L3z22WdIS0tDfHx8o+/p168fdu7cienTp9uPpaamol+/fgCA+Ph46HQ67Ny5055MGQwGHDhwAI888sg1x2yxWFBcXIyIiAiEhoZe8/kaY7VaUV1dDY1GI4sf8iTPPgkPD0d2djbMZrPk/wNERERE5KkkTa4ee+wxbNiwAZ9//jkCAgLsa6ICAwPh4+MDAJgwYQJiYmKwZMkSAMC0adMwcOBALF++HKNGjcLGjRtx6NAhvP322wBq1kFNnz4dixYtQlJSEuLj4zF79mxER0djzJgx1xyzbUFbQ2uxiFqbt7c3gJrkn8kVERERkTQkTa7efPNNAMCgQYMcjr/77ruYNGkSAODs2bMOowM33XQTNmzYgOeeew7PPPMMkpKSsHXrVociGDNnzkR5eTkeeughFBcX4y9/+Qu2b98OjUbTYrF78tQrkh9+H4mIiIikJ/m0wMakpaXVOnbXXXfhrrvuqvc9giBgwYIFWLBgwbWER0RERERE1GTyWDBCVI+0tDQIgoDi4mKpQ7lq8+bNq7eYChERERG5DyZXErFYRezPKsTn6eexP6sQFmvjo3jXYtKkSRAEAc8//7zD8a1bt7r8lLK4uDgIglDrceW9tgZBEOwbWts8+eST2LlzZ6vHQkREREStSxal2D3N9t/0mP/fP6AvqbIfiwrUYO7ozhjRNaqBd14bjUaDpUuX4p///CeCg4Nb7LzV1dX2ggpSWbBgAaZMmeJwLCAgQKJoHPn7+8Pf31/qMIiIiIjIyThy1cq2/6bHIx8ccUisACCnpAqPfHAE23/TO+3aQ4cOhU6ns1derM/mzZvRpUsXqNVqxMXFYfny5Q6vx8XFYeHChZgwYQK0Wi0eeughrFu3DkFBQfjyyy/RoUMH+Pr64s4770RFRQXWr1+PuLg4BAcH4/HHH4fFYrGf6/3330efPn0QEBAAnU6He++9F3l5ec2+N9v7L3/4+fnZX9+2bRuSk5Ph4+ODwYMHY926dQ7TDeuauvfKK68gLi7O/vzHH3/EsGHDEBERgXbt2mHw4ME4cuSIw+cCAGPHjoUgCPbnV57barViwYIFaNOmDdRqNXr27Int27fbXz99+jQEQcCWLVswePBg+Pr6okePHti/f3+zPxciIiJqmNUqIjujGBXZXsjOKIbVybOJyL0xuWoBoiiiotrc6KO0yoS5X/yOuv5kbcfmffEHSqtMDu+rrLbUeb6mFAS5nFKpxOLFi/Haa6/h3LlzdbY5fPgwxo0bh3vuuQe//vor5s2bh9mzZ2PdunUO7V588UX06NEDP/30E2bPng0AqKiowIoVK7Bx40Zs374daWlpGDt2LLZt24Zt27bh/fffx1tvvYVPP/3Ufh6TyYSFCxfi559/xtatW3H69Gl7pciW8ueff+KOO+7A6NGjkZ6ejn/84x/497//3ezzlJaWYuLEidizZw9SU1ORmJiIkSNHorS0FEBN8gXUVLvU6/X251d69dVXsXz5crz44ov45ZdfkJKSgr/+9a/IyMhwaPfss8/iySefRHp6OpKTkzF+/HiYzeZmx01ERER1y/opD+89sw9frvgVF372wZcrfsV7z+xD1k/N/4deIoDTAltEpcmCznO+uebziAByDFXoNm9Hk9r/sSAFvt7N68KxY8eiZ8+emDt3Lt55551ar7/00ksYMmSIPWFKTk7GH3/8gRdeeMEh6bnlllvwf//3f/bn33//PUwmE958800kJCQAAO688068//77yM3Nhb+/Pzp37ozBgwdj9+7duPvuuwEADzzwgP0c7du3x4oVK3D99dejrKysWVPpnn76aTz33HMOx77++msMGDDAHpNtBK5Dhw749ddfsXTp0iaf33bPQM3Ik8FgwFtvvYWQkBB89913uO222xAeHg4ACAoKgk6nq/c8L774Ip5++mncc889AIClS5di9+7deOWVV7By5Up7uyeffBKjRo0CAMyfPx9dunRBZmYmOnbs2Ky4iYiIqLasn/Kw/a3fah0vLzZi+1u/YcQ/uyKhV4QEkZEr48iVB1q6dCnWr1+Po0eP1nrt6NGj6N+/v8Ox/v37IyMjw2E6X58+fWq919fX155YAUBkZCTi4uIckqTIyEiHaX+HDx/G6NGj0a5dOwQEBGDgwIEAavY3a46nnnoK6enpDg9bjEePHkXfvn0d2vfr169Z5weA3NxcTJkyBR06dEC7du0QFBSEsrKyZsVqMBiQnZ1d52d8ZX90797d/t9RUTVr8a5myiQRERE5slpFfL8po8E2//s4g1MEqdk4ctUCfFRK/LEgpdF2B09dwKR3654qdrl1k6/HDfEhAGpGSUoNpQjQBjhspmy77tW4+eabkZKSglmzZl31FLzL1zPZqFQqh+eCINR5zGq1AgDKy8uRkpKClJQUfPjhhwgPD8fZs2eRkpKC6urqZsUTFhaGxMTEZt7FJQqFotY0S5PJ5PB84sSJKCwsxMsvv4zQ0FCEhoaif//+zY61qS7/7GwVHW2fHREREV09fUYxyouNDbYpKzJCn1GMmA4tVwSM3B+TqxYgCEKTpucNSApHVKAGOSVVda67EgDoAjUYkBQOpeLSj2mztxK+3l61kqtr8fzzz6Nnz57o0KGDw/FOnTph7969Dsf27t2L5ORkKJVXl8zV59ixYygsLMTzzz+Ptm3bAgAOHTrUotcAau7piy++cDj2ww8/ODwPDw9HTk4ORFG0JzLp6ekObfbu3Ys33ngDI0eOhMFgQElJCQoKChzaqFQqhxG+K2m1WkRHR2Pv3r32UTrbuW+44YaruT0iIiJqpnJDw4lVc9sR2XBaYCtSKgTMHd0ZQE0idTnb87mjO9sTK2fq1q0b7rvvPqxYscLh+P/93/9h586dWLhwIU6cOIH169fj9ddfx5NPPtniMbRr1w7e3t547bXXcPLkSXzxxRdYuHDhVZ2rtLQUOTk5Dg+DwQAAePjhh5GRkYGnnnoKx48fx4YNG2oV6Bg0aBDy8/OxbNkyZGVlYeXKlfj6668d2iQlJeH999/H0aNHcejQIfz973+Hj4+PQ5u4uDjs3LkTOTk5KCoqqjPWp556CkuXLsWmTZtw/Phx/Pvf/0Z6ejqmTZt2VfdOREREzeOnVbdoOyIbJletbETXKLx5/3XQBWocjusCNXjz/uucus/VlRYsWFBrmtl1112Hjz/+GBs3bkTXrl0xZ84cLFiwoMUr+AE1o0Xr1q3DJ598gs6dO+P555/Hiy++eFXnmjNnDqKiohweM2fOBFCTxG3evBlbt25Fjx49sGrVKixevNjh/Z06dcIbb7yBlStXokePHjh48GCthPKdd95BUVER+vTpg4cffhhTp05FRITjQtfly5cjNTUVbdu2Ra9eveqM9fHHH8eMGTPwf//3f+jWrRu2b9+OL774AklJSVd170RERNQ8UUlB8AtqOHHyD1YjKimodQIityGIza3n7QEMBgMCAwNRUlICrVbr8FpVVRVOnTqF+Ph4aDSaes7QOItVxMFTF5BXWoWIAA1uiA+pc8TKVplOq9W26LRAT5eWlobBgwejqKgIQUFBzXqvHPukpb6XrspkMmHbtm0YOXJkrXV+1PrYH/LDPpEf9on06qsWaMNqgdKS099IQ7nBlbjmSiJKhYB+CaFSh0FERETkkRJ6RWD4lC7Ysfp3h+MafxUG3deBiRVdFXn8szsRERERUSsLCKmZ7aFSK+AdYgYAdLhRx8SKrhpHrsgjDRo0qFbpdSIiIvIs+owSADVrsEoV51F9wQv6zBKJoyJXxpErIiIiIvJI2ZnFAICoxECoQ2q2Usk/W4rqKrOEUZErY3JFRERERB5HtIrQ25KrBC28fEQEhKghWkXkZHH0iq4OkysiIiIi8jgX9OUwVpjh5a1AWFt/ADUjWABwPqNYwsjIlTG5IiIiIiKPYxu10rUPhEJZ85M4KqkmudIzuaKrxOSKiIiIiDxO9sUEKioxyH7MNnKVe9oAU7VFgqjI1TG5IiIiIiKPIooisi9WBYy+mFABQECoBn5BalgtInJPct0VNR+TKyIiIiLyKKWFVSgvNkKhEBDZ/lJyJQgCopOCAHDdFV0dJldSsVqAU98Dv35a83+trTP0nJOTg2nTpiExMREajQaRkZHo378/3nzzTVRUVAAA4uLiIAgCBEGAj48P4uLiMG7cOOzatatVYiQiIiJyJtuUwPDYAKi8lQ6vxSQHAeC6K7o63ERYCn98AWx/GjBkXzqmjQZGLAU6/9Vplz158iT69++PoKAgLF68GN26dYNarcavv/6Kt99+GzExMfjrX2uuv2DBAkyZMgXV1dU4ffo0PvjgAwwdOhQLFy7Es88+67QYiYiIiJzNtr9V9GXrrWxsI1c5pwywmKxQqjgWQU3H5Kq1/fEF8PEEAKLjcYO+5vi495yWYD366KPw8vLCoUOH4OfnZz/evn173H777RDFSzEFBARAp9MBANq1a4ebb74ZUVFRmDNnDu6880506NDBKTESEREROZvett7qYiJ1uaBIX/hovVFpqEbuaUOdbYjqw1S8JYgiUF3e+KPKAHw9E7USq5qT1Pyf7U/XtLv8faaKus8n1nWeuhUWFmLHjh147LHHHBKrywmC0OA5pk2bBlEU8fnnnzf5ukRERERyUl5iRHFuBSAAuoTAWq8LgmAf0crOKGrl6MjVceSqJZgqgMXRLXAisWaq4PNt7UcUAILqa/5MNuBdd6J0pczMTIiiWGvEKSwsDFVVVQCAxx57DEuXLq33HCEhIYiIiMDp06ebdE0iIiIiubGNWoVG+0Hjp6qzTUxyELKO5NnXZhE1FUeuPNzBgweRnp6OLl26wGg0NtpeFMVGR7iIiIiI5Kqh9VY2tqmA+pMGWCxW5wdFboMjVy1B5VszitSYM/uAD+9svN19nwKxNwEArFYrDKWl0AYEQKG4IhdW+TY5xMTERAiCgOPHjzscb9++PQDAx8en0XMUFhYiPz8f8fHxTb4uERERkZzoLyZXUQ2spQqJ8oPazwvGcjPyz5RC17729EGiunDkqiUIQs30vMYeCbfUVAVEfSM/AqCNqWl3+ftUvnWfrxkjSKGhoRg2bBhef/11lJeXX9Vtvvrqq1AoFBgzZsxVvZ+IiIhISsZKMwrOlQFoeORKUFy+7qrY+YGR22By1ZoUyppy6wBqJ1gXn494vqadE7zxxhswm83o06cPNm3ahKNHj+L48eP44IMPcOzYMSiVl65bWlqKnJwc/Pnnn9izZw8eeughLFq0CP/5z3+QmJjolPiIiIiInEmfWQyIgDbcB35B6gbbxiQHA2ByRc3DaYGtrfNfa8qt17nP1fNO3ecqISEBP/30ExYvXoxZs2bh3LlzUKvV6Ny5M5588kk8+uij9rZz5szBnDlz4O3tDZ1OhxtvvBE7d+7E4MGDnRYfERERkTPZS7AnNj7Nz77uKrMYVqsIhYJrzqlxTK6k0PmvQMdRNWuwynIB/8iaNVZOGrG6XFRUFF577TW89tpr9bZhNUAiIiJyR7b1Vk3Zuyq0jT+8fbxQXWlGwZ+liIjVOjc4cgtMrqSiUALxA6SOgoiIiMgjmKstyD1tAABENbDeykahEBCVGIgzvxYiO6OYyRU1CddcEREREZHbyz1tgNUiwlfrjcDwxqskA5dGuLjuipqKyRURERERub3LpwQ2dc9Oe3KVWQzRKjopMnInTK6IiIiIyO3ZRp+aMiXQJrxdALzUShjLzSjMvrqtbMizMLkiIiIiIrdmtViRc7JmvVV0UtM3BFYqFYhKqGnPqYHUFEyuiIiIiMitFZwrg8logdrXCyHR/s1676V1V0VOiIzcDZMrIiIiInJrtlEnXUJgs/eruryohShy3RU1jMkVEREREbk1W3IV3Yz1VjaRsVooVQpUlppQlFPRsoGR22FyRURERERuSxRF6DNLADRt8+ArKVUK6NrX7HHFdVfUGCZXREREROS2ivQVqCo3wUulQHi7gKs6R3RSMAAmV9Q4JlcSsVgt+DHnR2w7uQ0/5vwIi9Xi9Gvm5+fjkUceQbt27aBWq6HT6ZCSkoK9e/fa2/z000+4++67ERUVBbVajdjYWNx2223473//a59nfPr0aQiCYH8EBASgS5cueOyxx5CRkeH0+yAiIiJqquyL+1tFxmuh9Lq6n772dVcnirjuihokaXK1Z88ejB49GtHR0RAEAVu3bm2w/aRJkxx+1NseXbp0sbeZN29erdc7duzo5Dtpnm/PfIuUzSl44JsH8PT3T+OBbx5AyuYUfHvmW6de929/+xt++uknrF+/HidOnMAXX3yBQYMGobCwEADw+eef48Ybb0RZWRnWr1+Po0ePYvv27Rg7diyee+45lJSUON7Ht99Cr9fj559/xuLFi3H06FH06NEDO3fudOp9EBERETWVbfPgqKuYEmiji9dC4SWgvKQaJfmVLRMYuSUvKS9eXl6OHj164IEHHsAdd9zRaPtXX30Vzz//vP252WxGjx49cNdddzm069KlC7799lKi4uUl6W06+PbMt5iRNgMiHP/VI68iDzPSZuClQS9haOzQFr9ucXExvv/+e6SlpWHgwIEAgNjYWNxwww0AavriwQcfxKhRo7BlyxaH93bq1AkPPvhgrX+pCQ0NhU6nAwC0b98eo0ePxpAhQ/Dggw8iKysLSqWyxe+DiIiIqDmupZiFjZe3EpFxWugzS5CdUYygCN+WCY7cjqRZx6233opbb721ye0DAwMRGHhp47etW7eiqKgIkydPdmjn5eVl/9HfGkRRRKW58X/FsFgtWHJwSa3ECoD92PMHn0dfXV8oFTWJidVqRaW5El4mLygUjgONPl4+EISmlRP19/eHv78/tm7dihtvvBFqtdrh9R07dqCwsBAzZ86s9xyNXUuhUGDatGkYO3YsDh8+bE/ciIiIiKRgKKxEWZERgkJAZLz2ms4VnRhkT646949uoQjJ3chnSOcqvPPOOxg6dChiY2MdjmdkZCA6OhoajQb9+vXDkiVL0K5du3rPYzQaYTQa7c8NhpodvE0mE0wmk0Nbk8kEURRhtVphtVoBABWmCvTb2K9F7im3Ihc3bbypSW3337Mfvqqm/cuJQqHA2rVr8c9//hOrVq3Cddddh5tvvhl33303unfvjuPHjwMAkpKS7Pf1448/YsiQIfZzbNiwAbfddpv99cs/A5vk5GQAwMmTJ9GnT58mxeZqbCN4tu+BHFitVoiiCJPJ5JEjhra/0yv/Xkka7A/5YZ/ID/ukdZw7fgEAENbWH4JSrPfzbkp/RLavKYZx/kQR+60VyOlvpDkxuGxylZ2dja+//hobNmxwON63b1+sW7cOHTp0gF6vx/z58zFgwAD89ttvCAiou0LMkiVLMH/+/FrHd+zYAV9fx+TFNipWVlaG6upqAGjSqJUzlJaWwuxlbnL7YcOG4Y8//sD+/ftx6NAhpKam4oUXXsCKFStQVVVlP6ftx3lcXBz27NkDAOjduzdKS0thMBhQVlYGoGYqoS0RvTwmAKiqqqr1mrux3ascVFdXo7KyEnv27IHZ3PTvhLtJTU2VOgS6DPtDftgn8sM+ca6i39QAvFGpKMS2bdsabd9Qf1jNAAR/lF0w4ovNX8PLh4UtWoMc/kYqKpq+v5nLJlfr169HUFAQxowZ43D88mmG3bt3R9++fREbG4uPP/4YDz74YJ3nmjVrFmbMmGF/bjAY0LZtWwwfPhxareMQclVVFf7880/4+/tDo9EAAALEAOy/Z3+jMR/JPYLHdj/WaLuVg1fiusjr7M9LS0vrTAybMy3QRqvV4vbbb8ftt9+OhQsXYsqUKVi6dCmWL18OoCZpvXyULzw8/NL1fHyg1Wrh7+8PAPDz86v1+fz5558AgM6dO9d6zV2Iomjvk+Z+/s5SVVUFHx8f3HzzzfbvpScxmUxITU3FsGHDoFKppA7H47E/5Id9Ij/sk9bx8ZFDACpx09CeiOseWm+7pvbH1ox05J0uRce21yH5hkgnREw2cvobac6AgUsmV6IoYu3atfj73/8Ob2/vBtsGBQUhOTkZmZmZ9bZRq9W11iABgEqlqtWZFosFgiBAoVA4rIHyV/o3Gnf/Nv0R6RuJvIq8OtddCRAQ6RuJ/m36O6y5MnuZ4avyrbXmqiV06dIFn3/+OUaMGIGQkBC88MIL+Oyzz+psa7tnWxxXfgZWqxWvv/464uPj0bt3b6fEKwe2qYC274EcKBQKCIJQ53fWk3j6/csN+0N+2Cfywz5xngpDNYpza2YXte0Q2qTPubH+iEkKRt7pUuSeLEWX/m1aLFaqnxz+RppzfXn8Mmym7777DpmZmfWORF2urKwMWVlZiIqKaoXIGqZUKPHvG/4NoCaRupzt+dM3PG1PrFpSYWEhbrnlFnzwwQf45ZdfcOrUKXzyySdYtmwZbr/9dvj7+2PNmjX46quvMGrUKHzzzTc4efIkfvnlFyxbtqwm/ivW8hQWFiInJwcnT57EF198gaFDh+LgwYN45513PHLdDxEREcmHPqsYABAS7QeNf8v8OI9ODgIAZJ8obpHzkfuRdOSqrKzMYUTp1KlTSE9PR0hICNq1a4dZs2bh/PnzeO+99xze984776Bv377o2rVrrXM++eSTGD16NGJjY5GdnY25c+dCqVRi/PjxTr+fphgaOxQvDXoJzx98HrkVufbjkb6RePqGp51Shh2oqRbYt29fvPzyy8jKyoLJZELbtm0xZcoUPPPMMwCAsWPHYt++fVi6dCkmTJiACxcuIDAwEH369MHGjRtx2223Od7L0JpYfX19ERsbi8GDB+Ptt99GYmKiU+6BiIiIqKn0GTX7c0ZdQwn2K0UlBkEQgJL8SpQXG+EXVHvmE3k2SZOrQ4cOYfDgwfbntnVPEydOxLp166DX63H27FmH95SUlGDz5s149dVX6zznuXPnMH78eBQWFiI8PBx/+ctf8MMPPzisHZLa0NihGNx2MI7kHUF+RT7CfcNxXcR1ThmxslGr1ViyZAmWLFnSYLs+ffrgk08+abBNXFwcdycnIiIiWcu+uHlwdFJgww2bQe3jhbC2Acg/W4rsjGIkXc91V+RI0uRq0KBBDf5IX7duXa1jgYGBDVbs2LhxY0uE5nRKhRLX666XOgwiIiIit1NdaUbBnzVVfa9l8+C6RCcGIf9sKc4zuaI6uOSaKyIiIiKi+uScLIEoAtowDfyDW7aK7qV1V0Utel5yD0yuiIiIiMitZGcUA2jZ9VY2tpGwopwKVBiqW/z85NqYXBERERGRW7Gvt3JCcqXxVyE0xg8AoL94HSIbJldERERE5DbMJgvyTl9cb5UU5JRr2JK28xdHyIhsmFwRERERkdvIO10Ki9kKnwAVAiN8nHKN6ORgANzvimpjckVEREREbuPyKYGCIDjlGrYRscLsMlSVm5xyDXJNTK6IiIiIyG3Y1kFFOWlKIAD4ar0RFOkLiFx3RY6YXBERERGRW7BaReizSgA4p5jF5Wwl2bnuii7H5IowadIkjBkzpt7X582bh549e7ZaPERERERXo/BcGUxVFnhrlAht4+/Ua8VcHBnjuiu6HJMriYgWC8oPHETJl1+h/MBBiBaL1CG5rEGDBmH69On1vi4Igv2h1Wpx/fXX4/PPP2+9AImIiKhV2Pa30iUEQaFwznorG9u6q4I/S1FdaXbqtch1MLmSgGHHDmQOGYqzEyci+8kncXbiRGQOGQrDjh1ShyaZ6mrnbsL37rvvQq/X49ChQ+jfvz/uvPNO/Prrr069JhEREbUuezGLpECnX8s/WANtmAaiCPtURCImV63MsGMHzk+bDnNOjsNxc24uzk+b7rQE69NPP0W3bt3g4+OD0NBQDB06FOXl5XW2/fHHHxEeHo6lS5fWe741a9agU6dO0Gg06NixI9544w2H159++mkkJyfD19cX7du3x+zZs2EyXaqmY5tquGbNGsTHx0Oj0QCoGWVas2YNxo4dC19fXyQlJeGLL7645vsPCgqCTqdDcnIyFi5cCLPZjN27d1/zeYmIiEgeRFG8VMzCyeutbOwl2TOKWuV6JH9eUgfgDkRRhFhZ2Xg7iwW5i/4DiGJdJwEEIPc/i+HXrx8EpRIAYLVaYa2shNXLC1A45sKCj0+TSozq9XqMHz8ey5Ytw9ixY1FaWorvv/8eYh1x7Nq1C3fccQeWLVuGhx56qM7zffjhh5gzZw5ef/119OrVCz/99BOmTJkCPz8/TJw4EQAQEBCAdevWITo6Gr/++iumTJmCgIAAzJw5036ezMxMbN68GVu2bIHy4v0CwPz587Fs2TK88MILeO2113DffffhzJkzCAkJafReG2M2m/HOO+8AALy9va/5fERERCQPxbkVqCw1QemlQGSstlWuGZMUhGP79PbpiERMrlqAWFmJ49f1boET1Yxgnbj+hlov5dbRvMORwxB8fRs9rV6vh9lsxh133IHY2FgAQLdu3Wq1++yzzzBhwgSsWbMGd999d73nmzt3LpYvX4477rgDABAfH48//vgDb731lj25eu655+zt4+Li8OSTT2Ljxo0OyVV1dTXee+89hIeHO5x/0qRJGD9+PABg8eLFWLFiBQ4ePIgRI0Y0eq/1GT9+PJRKJSorK2G1WhEXF4dx48Zd9fmIiIhIXmwJTmS8FkpV60zOsq27yjtdCpPRApVa2fAbyO0xufIAPXr0wJAhQ9CtWzekpKRg+PDhuPPOOxEcHGxvc+DAAXz55Zf49NNPG6wcWF5ejqysLDz44IOYMmWK/bjZbEZg4KX5zZs2bcKKFSuQlZWFsrIymM1maLWO/4oUGxtbK7ECgO7du9v/28/PD1qtFnl5eVdz63Yvv/wyhg4dipMnT+KJJ57AihUrWmQkjIiIiORBn1mz7ikq0fnrrWwCQjXwD1ajrMiInJMlaNuJvy08HZOrFiD4+KDDkcONtqs4dAh/PvTPRtu1ffst+PbpA6BmWqChtBTagAAo6pgW2BRKpRKpqanYt28fduzYgddeew3PPvssDhw4gPj4eABAQkICQkNDsXbtWowaNQoqlarOc5WVlQEAVq9ejb59+9a6DgDs378f9913H+bPn4+UlBQEBgZi48aNWL58uUN7Pz+/Oq9x5bUFQYDVam3SvdZHp9MhMTERiYmJePfddzFy5Ej88ccfiIiIuKbzEhERkTxcKmYR1GrXFAQB0clBOHEgF9kZxUyuiAUtWoIgCFD4+jb68OvfH146HVDfOilBgJdOB7/+/R3f6+NT5/mast7q8hj79++P+fPn46effoK3tzc+++wz++thYWHYtWsXMjMzMW7cOIfiE5eLjIxEdHQ0Tp48aU9WbA9borZv3z7Exsbi2WefRZ8+fZCUlIQzZ840/QN1shtuuAG9e/fGf/7zH6lDISIiohZQeqEKpYVVEARA1771Rq4AICbJVtSiuFWvS/LEkatWJCiViHxmFs5Pm16TYF1eUOJiohT5zCx7MYuWcuDAAezcuRPDhw9HREQEDhw4gPz8fHTq1MmhXUREBHbt2oXBgwdj/Pjx2LhxI7y8an9F5s+fj8cffxyBgYEYMWIEjEYjDh06hKKiIsyYMQNJSUk4e/YsNm7ciOuvvx5fffWVQyLnDPn5+UhPT3c4FhUVhcjIyDrbT58+HWPHjsXMmTMRExPj1NiIiIjIuWxVAsPaBsBb07o/b20jZbmnDDCbLPBScd2VJ+PIVSvTDh+OmFdfgdcVP/q9IiMR8+or0A4f3vLX1GqxZ88ejBw5EsnJyXjuueewfPly3HrrrbXa6nQ67Nq1C7/++ivuu+8+WOrY3Pgf//gH1qxZg3fffRfdunXDwIEDsW7dOvvI1V//+lc88cQTmDp1Knr27Il9+/Zh9uzZLX5fl9uwYQN69erl8Fi9enW97UeMGIH4+HiOXhEREbkB26hRa04JtAmM8IGv1hsWsxW5pwytfn2SF45cSUA7fDgChgxBxaHDMOfnwys8HL59erf4iJVNp06dsH379npfX7duncPzqKgoHD9+3P583rx5mDdvnkObe++9F/fee2+951y2bBmWLVvmcGz69OkNnhNAneXhi4uL670OAKSlpTX4el3nFAQBR48ebfB9RERE5BqyLxaziG6l/a0uZ1t3lXkoD9kZxYhJDm78TeS2mFxJRFAq4de3dsl1IiIiImq6yrJqFOnLAbRupcDLxSRdSq7Is3FaIBERERG5LFsJ9mCdL3wCvCWJIfpiUYucrBJYzNdW4ZhcG5MrIiIiInJZthLsURKst7IJjvKFxl8Fs8mKvDOlksVB0mNyRUREREQuS28rZiHBeisbQRDsxTSyM4oki4Okx+SKiIiIiFxSdZUZ+X+WAZCmUuDlLiVXxZLGQdJickVERERELin3pAGiVYR/iBoBIRpJY7ElV/rMElgtXHflqZhcEREREZFLsq23knrUCgBCY/yh9vWCyWixj6aR52FyRUREREQuKVsG661sFAoBURfjyD5RLGksJB0mV0RERETkciwmK3JPGwDAntRIzb7u6uKIGnkeJleESZMmYcyYMfW+Pm/ePPTs2bPV4iEiIiJqTN7ZUlhMVmj8VQjW+UodDoDL110Vw2oVpQ2GJMHkSiJWq4jzx4tw4sccnD9exD/Aa3Dq1Cnce++9iI6OhkajQZs2bXD77bfj2LFjDu12796N2267DeHh4dBoNEhISMDdd9+NPXv22NukpaVBEAQIggCFQoHAwED06tULM2fOhF6vb+1bIyIionrYSp5HJwZBEASJo6kR3tYfKo0SxgozCs9z3ZUn8pI6AE+U9VMevt+UgfJio/2YX5AaA+5OQkKvCAkjk051dTW8vZu/q7rJZMKwYcPQoUMHbNmyBVFRUTh37hy+/vprFBcX29u98cYbmDp1Kv7+979j06ZNSEhIQElJCXbv3o0nnngChw8fdjjv8ePHodVqYTAYcOTIESxbtgzvvPMO0tLS0KVLl2u9XSIiIrpG+swSAEBUYqDEkVyiUCoQlRCIs79fQHZGMcLbBkgdErUyjly1sqyf8rD9rd8cEisAKC82YvtbvyHrpzynXPfTTz9Ft27d4OPjg9DQUAwdOhTl5eV1tv3xxx8RHh6OpUuX1nu+NWvWoFOnTtBoNOjYsSPeeOMNh9effvppJCcnw9fXF+3bt8fs2bNhMpnsr9umGq5Zswbx8fHQaGrKpwqCgDVr1mDs2LHw9fVFUlISvvjii3rj+P3335GVlYU33ngDN954I2JjY9G/f38sWrQIN954IwDg7NmzmD59OqZPn47169fjlltuQWxsLLp3745p06bh0KFDtc4bEREBnU6H5ORk3HPPPdi7dy/Cw8PxyCOP1P8hExERUauwWkXos2qSKzlUCrwc97vybEyuWoAoijAZLY0+jJVmfL/pRIPn+n5TBoyVZof3mavrPp8oNm0qoV6vx/jx4/HAAw/g6NGjSEtLwx133FHn+3ft2oVhw4bhP//5D55++uk6z/fhhx9izpw5+M9//oOjR49i8eLFmD17NtavX29vExAQgHXr1uGPP/7Aq6++itWrV+Pll192OE9mZiY2b96MLVu2ID093X58/vz5GDduHH755ReMHDkS9913Hy5cuFBnLOHh4VAoFPj0009hsVjqbLN582aYTCbMnDmzztebMpXAx8cHDz/8MPbu3Yu8POckwERERNQ0hefLUF1phkqtRFgbf6nDcRCdFAygJrlq6m81ch+cFtgCzNVWvD3tuxY5V3mxEWue2NN4QwAPvToQKrWy0XZ6vR5msxl33HEHYmNjAQDdunWr1e6zzz7DhAkTsGbNGtx99931nm/u3LlYvnw57rjjDgBAfHw8/vjjD7z11luYOHEiAOC5556zt4+Li8OTTz6JjRs3OiQ41dXVeO+99xAeHu5w/kmTJmH8+PEAgMWLF2PFihU4ePAgRowYUSuWmJgYrFixAjNnzsT8+fPRp08fDB48GPfddx/at28PADhx4gS0Wi10Op39fZs3b7bHCgD79++v8zO5XMeOHQEAp0+ftv83ERERtT79xWp8uoRAKJTyGiuIiA2Al0qBqjITLujLERotr+SPnEte30Zyih49emDIkCHo1q0b7rrrLqxevRpFRUUObQ4cOIC77roL77//foOJVXl5ObKysvDggw/C39/f/li0aBGysrLs7TZt2oT+/ftDp9PB398fzz33HM6ePetwrtjY2FqJFQB0797d/t9+fn7QarUNjhY99thjyMnJwYcffoh+/frhk08+QZcuXZCammpvc+XoVEpKCtLT0/HVV1+hvLy83lGvy9n+9Ukui2aJiIg8VXbGxSmBMinBfjmllwK6hJp1YHpODfQ4HLlqAV7eCjz06sBG22VnFOPL139utN1tU3vY5+tarVaUlhoQEKCFQuGYC3t5Ny03ViqVSE1Nxb59+7Bjxw689tprePbZZ3HgwAHEx8cDABISEhAaGoq1a9di1KhRUKlUdZ6rrKym8s3q1avRt2/fWtcBakaB7rvvPsyfPx8pKSkIDAzExo0bsXz5cof2fn5+dV7jymsLggCr1drgPQYEBGD06NEYPXo0Fi1ahJSUFCxatAjDhg1DUlISSkpKkJOTYx+98vf3R2JiIry8mv4ncPToUQA1I3FEREQkDVEU7ftIRSfJp5jF5aKTgnDuWBHOZxSj68A2UodDrYgjVy1AEASo1MpGH207h8AvSN3gufyD1WjbOcThfV7edZ+vOSMogiCgf//+mD9/Pn766Sd4e3vjs88+s78eFhaGXbt2ITMzE+PGjXMoPnG5yMhIREdH4+TJk0hMTHR42BK1ffv2ITY2Fs8++yz69OmDpKQknDlzpsmxXitBENCxY0d7wY4777wTKpWqwQIdjamsrMTbb7+Nm2++uc7RNiIiImodJXmVqDRUQ+ElICJOK3U4dbIXtTjBdVeehiNXrUihEDDg7iRsf+u3etv8ZVwSFIqWnXZ24MAB7Ny5E8OHD0dERAQOHDiA/Px8dOrUyaFdREQEdu3ahcGDB2P8+PHYuHFjnSM78+fPx+OPP47AwECMGDECRqMRhw4dQlFREWbMmIGkpCScPXsWGzduxPXXX4+vvvrKIZFrSenp6Zg7dy7+/ve/o3PnzvD29sZ3332HtWvX2gtytGvXDsuXL8e0adNw4cIFTJo0CfHx8bhw4QI++OADAJdG3Wzy8vJQVVWF0tJSHD58GMuWLUNBQQG2bNnilPsgIiKiprGNWkXGaeGlanztuRQi47VQeilQYahGSV4lgiLlsckxOR+Tq1aW0CsCI/7ZtdY+V/7BavxlnHP2udJqtdizZw9eeeUVGAwGxMbGYvny5bj11ltrtdXpdNi1axcGDRqE++67Dxs2bKjV5h//+Ad8fX3xwgsv4KmnnoKfnx+6deuG6dOnAwD++te/4oknnsDUqVNhNBoxatQozJ49G/PmzWvxe2vTpg3i4uIwf/58nD59GoIg2J8/8cQT9nb/+te/0KlTJ7z00ku48847YTAYEBoain79+mH79u21ill06NABgiDA398f7du3x/DhwzFjxgzodLpGpygSERGR89jWMUXJcL2VjZdKich4LbIzipGdUczkyoMIIscqazEYDAgMDERJSQm0Wsfh5qqqKpw6dcphb6arYbWK0GcUo9xghJ9WjaikoDpHrKxWKwwGA7Ta2muuSBpy7JOW+l66KpPJhG3btmHkyJH1rhek1sP+kB/2ifywT67e+8/tg6GgCrdN7YHYrqEtck5n9MeBL07i0LbTSO4biWGTu7TIOT2JnP5GGsoNrsSRK4koFAJiOgRLHQYRERGRyygrMsJQUAVBgL0in1xdue6K1YY9gzz+2Z2IiIiIqBG2/a1C2/hD7SPvMQJd+0AoFALKiowoLaySOhxqJZImV3v27MHo0aMRHR0NQRCwdevWBtunpaVBEIRaj5ycHId2K1euRFxcHDQaDfr27YuDBw868S6IiIiIqDVcKsEeJGkcTaFSKxERFwCgZjse8gySJlfl5eXo0aMHVq5c2az3HT9+HHq93v6IiLhUBGLTpk2YMWMG5s6diyNHjqBHjx5ISUlpcBNaIiIiIpI/W5Iix82D62JLAs8zufIYkiZXt956KxYtWoSxY8c2630RERHQ6XT2x+VFBV566SVMmTIFkydPRufOnbFq1Sr4+vpi7dq1LRo764CQnPD7SERE7q6q3IQL2TV7WMq5UuDlopNq1tdnnyiSOBJqLfKerFqPnj17wmg0omvXrpg3bx769+8PAKiursbhw4cxa9Yse1uFQoGhQ4di//799Z7PaDTCaLxUFt1gMACoqVJS12a6oiiirKwManXDGwK3BNuPZlEUWQJcJuTYJ0ajEaIoQhTFejeAdme2e/bEe5cj9of8sE/kh33SfOeOFwIAAiN8oPIRWvSzc1Z/hLXzhSAAhoIqFOWVwT/Y+b8d3YWc/kaaE4NLJVdRUVFYtWoV+vTpA6PRiDVr1mDQoEE4cOAArrvuOhQUFMBisSAyMtLhfZGRkTh27Fi9512yZAnmz59f6/iOHTvg61t7X4KAgAAYjUZUVVXB29u7Vaq/FBYWOv0a1Dxy6RNRFJGfn48LFy4gIyND6nAklZqaKnUIdBn2h/ywT+SHfdJ0xcfUALxh9i7Btm3bnHINZ/SHl9YXphIltm/+Dr7R5hY/v7uTw99IRUVFk9u6VHLVoUMHdOjQwf78pptuQlZWFl5++WW8//77V33eWbNmYcaMGfbnBoMBbdu2xfDhw+usZS+KIvLy8uwjXM4kiiKqqqqg0WhYwlMm5NgnXl5e6NOnj+T7QEjFZDIhNTUVw4YN89jPQE7YH/LDPpEf9knzbT2ajjKU4vpBXZHcN7LxNzSDM/tjf9VJ/Lr7PCL843HzyKQWPbc7k9PfSHN+87tUclWXG264Af/73/8AAGFhYVAqlcjNzXVok5ubC51OV+851Gp1nVP8VCpVvZ3Zpk0bWCwWpw9Vmkwm7NmzBzfffLPkXyyqIcc+8fb2ls2GxlJq6G+WWh/7Q37YJ/LDPmkak9GCgrNlAIC2HUOd9pk5oz/adgzBr7vPIyfTwL6+CnL4G2nO9V0+uUpPT0dUVBSAmh+YvXv3xs6dOzFmzBgAgNVqxc6dOzF16tQWv7ZSqYRSqWzx8155DbPZDI1GI/kXi2qwT4iIiFpXzqkSWK0i/IPVCAjVSB1Os0QlBgECUJxbgQpDNXy13lKHRE4kaXJVVlaGzMxM+/NTp04hPT0dISEhaNeuHWbNmoXz58/jvffeAwC88soriI+PR5cuXVBVVYU1a9Zg165d2LFjh/0cM2bMwMSJE9GnTx/ccMMNeOWVV1BeXo7Jkye3+v0RERER0bXTXyxlHpUYJJsp+U2l8VMhNMYfhefKkJ1RjMTeEY2/iVyWpMnVoUOHMHjwYPtz27qniRMnYt26ddDr9Th79qz99erqavzf//0fzp8/D19fX3Tv3h3ffvutwznuvvtu5OfnY86cOcjJyUHPnj2xffv2WkUuiIiIiMg1ZGeWAHCNzYPrEp0UVJNcnShicuXmJE2uBg0a1OD+POvWrXN4PnPmTMycObPR806dOtUp0wCJiIiIqHVZzFbknqxJrqISAyWO5urEJAXh193nuJmwB+AKeCIiIiKSrfyzpTCbrFD7eSFE5yd1OFfFNuJ2IbscVWXS79tEzsPkioiIiIhkKzuzGAAQnRgEQeFa661sfAK8ERxVkxja7ofcE5MrIiIiIpKty4tZuDLb6FX2iWJJ4yDnYnJFRERERLIkWkXosy4Ws3Dx5CrmYnJ1PqNI2kDIqZhcEREREZEsXdCXw1hhhpdaibB2/lKHc01sI1cF58pgrDRLGww5DZMrIiIiIpKl7ItTAnXxWiiVrv2z1S9IjcBwH0AE9Fx35bZc+1tKRERERG7LXszCRfe3ulJ0chAArrtyZ0yuiIiIiEh2RFG0j1y5+norm0vrrooljYOch8kVEREREcmOoaASFSXVUCgFRMZrpQ6nRUQnBwOo2buruorrrtwRkysiIiIikp3sjJoqgRGxAfDyVkocTcsICNEgIEQD0Soi52SJ1OGQEzC5IiIiIiLZcbf1VjZcd+XemFwRERERkey4y+bBV7JvJsyKgW6JyRURERERyUp5iREl+ZWAAEQlBEodTouKuThylXvaAHO1RdpgqMUxuSIiIiIiWbFVCQyN8YfaVyVtMC1MG+YDv0BvWM0ick4ZpA6HWhiTKyIiIiKSFX1mTbEHdynBfjlBEOxVA7NPFEkcDbU0JldEREREJCvuWszChuuu3BeTKyIiIiKSDWOFCYXnywAAUYnutd7KxrbuKuekARaTVdpgqEUxuSIiIiIi2dBnlQAiEBjuA79AtdThOEVQpC98AlSwmKzIPcN1V+6EyRURERERyYbezacEAhfXXdmmBnK/K7fC5IqIiIiIZCPbTfe3ulJ00sWiFlx35VaYXBERERGRLJirLcg7UwoAiE5yz/VWNrZ1V/qsElgsXHflLphcEREREZEs5J4ywGoR4RfoDW2Yj9ThOFVIlB/Ufl4wGy3IP1sqdTjUQphcEREREZEs2KbIRSUFQRAEaYNxMkEh2Pfx4ror98HkioiIiIhkwbbeyh03D64L97tyP0yuiIiIiEhyFosVOadqypK7c6XAy9nuU59RDKtVlDYYahFMroiIiIhIcgVny2A2WqD29UJIlJ/U4bSKsLYB8NYoUV1lQeG5MqnDoRbA5IqIiIiIJGdfb5UQCEHh3uutbBQKwV5y/vyJImmDoRbB5IqIiIiIJKe/rJiFJ7Gvu7q43oxcG5MrIiIiIpKUaBXtI1eeUszC5vKiFiLXXbk8JldEREREJKkLOeUwlpvhpVIgvF2A1OG0qvDYAHiplTCWm3FBXy51OHSNmFwRERERkaT0F6fERbYPhNLLs36eKpUKRLXXAuDUQHfgWd9eIiIiIpKd7MwSAEB0YqDEkUgjOikYAHCemwm7PCZXRERERCQZURQvbR7sYcUsbC4VtSiCKHLdlStjckVEREREkiktrEJ5sREKhYDI9p45chUZp4VSpUBlqQnFuRVSh0PXgMkVEREREUnGViUwPDYAKm+ltMFIRKlSQBfPdVfugMkVEREREUnGVszC00qwX8k2NZDrrlwbkysiIiIikoytmIWnbR58pcs3E+a6K9fF5IqIiIiIJFFhqLavMYpK8Mz1VjaR7QOhUAooLzbCUFApdTh0lZhcEREREZEk9BfXW4XG+EHjp5I2GImpvJWIjOO6K1fH5IqIiIiIJGFLIqI8fL2VjW1qZDbXXbksJldEREREJAlbpUBPL2ZhE2MrasGRK5fF5IqIiIiIWp2x0ozCc2UAOHJlo0sIhKAQUFpYhdILVVKHQ1eByRURERERtbqcrBKIIqAN08A/WC11OLLgrfFCeLsAAFx35aokTa727NmD0aNHIzo6GoIgYOvWrQ2237JlC4YNG4bw8HBotVr069cP33zzjUObefPmQRAEh0fHjh2deBdERERE1FycElg3e0n2E0XSBkJXRdLkqry8HD169MDKlSub1H7Pnj0YNmwYtm3bhsOHD2Pw4MEYPXo0fvrpJ4d2Xbp0gV6vtz/+97//OSN8IiIiIrpKtkqBnr6/1ZW47sq1eUl58VtvvRW33nprk9u/8sorDs8XL16Mzz//HP/973/Rq1cv+3EvLy/odLqWCpOIiIiIWpDZZEHuaQMAjlxdKSoxEBCAkrxKlJcY4RfIKZOuRNLk6lpZrVaUlpYiJCTE4XhGRgaio6Oh0WjQr18/LFmyBO3atav3PEajEUaj0f7cYKj5YzeZTDCZTM4Jvols15c6DrqEfSI/7BN5YX/ID/tEfjy9T/SZJbCaRfgEqOAb7CX55yCn/lCoavb9KjxXjj+PFiKhd7jUIUlCTn3SnBgEURRFJ8bSZIIg4LPPPsOYMWOa/J5ly5bh+eefx7FjxxAREQEA+Prrr1FWVoYOHTpAr9dj/vz5OH/+PH777TcEBATUeZ558+Zh/vz5tY5v2LABvr6+V3U/RERERFQ3Q6Y3DBlq+OhMCO3FqnhXKv5DjbIz3vBrV43gLsbG30BOVVFRgXvvvRclJSXQarUNtnXZ5GrDhg2YMmUKPv/8cwwdOrTedsXFxYiNjcVLL72EBx98sM42dY1ctW3bFgUFBY1+gM5mMpmQmpqKYcOGQaXy7J3L5YJ9Ij/sE3lhf8gP+0R+PL1Ptq38FeeOFeOmO9uj68AYqcORXX+c+rkAqWuOIljni7ue7S11OJKQU58YDAaEhYU1KblyyWmBGzduxD/+8Q988sknDSZWABAUFITk5GRkZmbW20atVkOtrj2fVaVSSd6ZNnKKhWqwT+SHfSIv7A/5YZ/Ijyf2idViRe6pUgBAmw6hsrp/ufRH246hAICinAqYq0T4BHhLHJF05NAnzbm+y+1z9dFHH2Hy5Mn46KOPMGrUqEbbl5WVISsrC1FRUa0QHRERERE1pOBcGUxGC7x9vBAa4y91OLLk4++NkGg/AJdK1pNrkDS5KisrQ3p6OtLT0wEAp06dQnp6Os6ePQsAmDVrFiZMmGBvv2HDBkyYMAHLly9H3759kZOTg5ycHJSUlNjbPPnkk/juu+9w+vRp7Nu3D2PHjoVSqcT48eNb9d6IiIiIqDbb5rhRCYFQKARpg5GxS/tdFUsaBzWPpMnVoUOH0KtXL3sZ9RkzZqBXr16YM2cOAECv19sTLQB4++23YTab8dhjjyEqKsr+mDZtmr3NuXPnMH78eHTo0AHjxo1DaGgofvjhB4SHe2alFSIiIiI50WfW/KN4VGKgxJHIWzT3u3JJkq65GjRoEBqqp7Fu3TqH52lpaY2ec+PGjdcYFRERERE5gyiK9mlu0UnB0gYjc7bkqvB8GarKTdD4Sb8WjBrncmuuiIiIiMg1FeVUoKrMBKVKgYjYurfIoRp+gWoERfoCIqDPKmn8DSQLTK6IiIiIqFXoL45a6eK1UHrxZ2hjLq27KpI2EGoyfquJiIiIqFXYi1kkBkkah6uwJ1dcd+UymFwRERERUauwr7dictUktuQq/2wpqqvM0gZDTcLkioiIiIiczlBYibILRggKAZHttVKH4xICQjTQhmkgct2Vy2ByRUREREROZyvBHt7WH94aSQtWuxTud+VamFwRERERkdPZpgRGXUwWqGlsJeuzM1jUwhUwuSIiIiIip9NfLMrA9VbNE5McBADIO10KU7VF2mCoUUyuiIiIiMipKkurUZRTAYDJVXMFhGrgH6yG1Soi5yTXXckdkysiIiIicirbeqvgKD9o/FUSR+NaBEHguisXwuSKiIiIiJzKXoKd662uCve7ch1MroiIiIjIqbLt660CpQ3ERcUk1xS1yD1lgNnEdVdyxuSKiIiIiJymusqMgj9LAQBRXG91VQIjfOCj9YbFbEXeaYPU4VADmFwRERERkdPknCyBKNYUZggI0UgdjksSBAExF6cGnue6K1ljckVERERETpPNEuwtguuuXAOTKyIiIiJyGlulwCiut7om0Rf3u8o5WQKLxSptMFQvJldERERE5BQWkxW5p2rWCLFS4LUJ0flB46eCudqK/DOlUodD9WByRUREREROkXvGAIvZCp8AFYIifaUOx6UJikv7XZ0/USRtMFQvJldERERE5BT6i/tbRSUGQRAEaYNxA5fWXZVIGwjVi8kVERERETmFLQlgMYuWYVt3pc8qhpXrrmSJyRURERERtTirVUROVjEArrdqKaEx/vD28YKpyoKCc2VSh0N1YHJFRERERC2u8FwZqqssUGmUCG3jL3U4bkGhEBB9seoi97uSJyZXRERERNTibPsxRSUEQqHgequWEp0UDID7XckVkysiIiIianGXF7OglmObYqnPLIZoFaUNhmphckVERERELUoURWRfTK643qplhbfzh0qthLHCjMJsrruSGyZXRERERNSiinMrUFlqgtJLgchYrdThuBWFUoGoBK67kismV0RERETUovSZNSXYI+ICoFTx52ZLs5dk57or2eG3nYiIiIhaFKcEOpdt37DszGKIItddyQmTKyIiIiJqUbZKdtw82Dki4rTwUilQWWpCkb5C6nDoMkyuiIiIiKjFlBVVobSwCoIA6NoHSh2OW1J6KRB58bO1jRKSPDC5IiIiIqIWY/uxH9Y2AN4+XtIG48ZiLq67yj5RJG0g5IDJFRERERG1mOyMmmIWnBLoXLbP93wG113JCZMrIiIiImox9s2Dkzgl0Jki47VQeAmoKKlGSV6l1OHQRUyuiIiIiKhFVJWZcCG7HABHrpzNy1uJyLiaPcS47ko+mFwRERERUYuw/cgP1vnCJ8Bb2mA8QExyMAAgm5sJywaTKyIiIiJqEfYpgRy1ahWX1l2xqIVcMLkiIiIiohaRnXmxmAU3D24VuoRAKBQCyi4YYSjguis5YHJFRERERNesusqM/LOlAICoRBazaA0qtRLhsQEAuO5KLphcEREREdE1yz1lgGgV4R+shjbUR+pwPIZtlJDrruSByRURERERXTPbyAmnBLYu2+d9PqNY0jioBpMrIiIiIrpm+os/7lnMonVFJwZBEABDfiXKioxSh+PxmFwRERER0TWxmK3IOWUAwP2tWpu3jxfC2trWXbFqoNSYXBERERHRNck/WwqLyQqNvwrBUb5Sh+NxuO5KPphcEREREdE1ybZNCUwIhCAI0gbjgezJFdddSa5ZydWyZctQWXmphv7evXthNF6a21laWopHH320yefbs2cPRo8ejejoaAiCgK1btzb6nrS0NFx33XVQq9VITEzEunXrarVZuXIl4uLioNFo0LdvXxw8eLDJMRERERFR87CYhbSik4IAASjKqUCFoVrqcDxas5KrWbNmobS01P781ltvxfnz5+3PKyoq8NZbbzX5fOXl5ejRowdWrlzZpPanTp3CqFGjMHjwYKSnp2P69On4xz/+gW+++cbeZtOmTZgxYwbmzp2LI0eOoEePHkhJSUFeXl6T4yIiIiKiprFaRei5ebCkNH4qhEb7A+DoldSalVyJotjg8+a69dZbsWjRIowdO7ZJ7VetWoX4+HgsX74cnTp1wtSpU3HnnXfi5Zdftrd56aWXMGXKFEyePBmdO3fGqlWr4Ovri7Vr115TrERERERU24XsMlRXmqFSKxHWxl/qcDwWpwbKg5fUATTH/v37MXToUIdjKSkpmD59OgCguroahw8fxqxZs+yvKxQKDB06FPv376/3vEaj0WF6o8FQU+3GZDLBZDK14B00n+36UsdBl7BP5Id9Ii/sD/lhn8iPO/XJn8cuAAAi4wNgsVpgsVokjqj53KE/Itv749c04PyJCy59HzZy6pPmxOBSyVVOTg4iIyMdjkVGRsJgMKCyshJFRUWwWCx1tjl27Fi9512yZAnmz59f6/iOHTvg6yuPijepqalSh0BXYJ/ID/tEXtgf8sM+kR936JPCnzQAVDBYcrFt259Sh3NNXLk/LEYBgD8uZFfgy63boPCWOqKWIYc+qaioaHLbZidXa9asgb9/zZCv2WzGunXrEBYWBgAO67FcyaxZszBjxgz7c4PBgLZt22L48OHQarUSRlaTKaempmLYsGFQqVSSxkI12Cfywz6RF/aH/LBP5Mdd+kQURXyw9wAAE26+tY/Lrrlyl/74+I9DKM6pRNf2fRHXPVTqcK6JnPrENqutKZqVXLVr1w6rV6+2P9fpdHj//fdrtXEWnU6H3Nxch2O5ubnQarXw8fGBUqmEUqmss41Op6v3vGq1Gmq1utZxlUoleWfayCkWqsE+kR/2ibywP+SHfSI/rt4nxXkVqDSYoPASEJMYAi+VUuqQromr90dMUjCKcyqRe7IUSb3r/+3rSuTQJ825frOSq9OnTzc3lhbVr18/bNu2zeFYamoq+vXrBwDw9vZG7969sXPnTowZMwYAYLVasXPnTkydOrW1wyUiIiJya7biCZGxWnh5u3Zi5Q6ik4Pw+/fZLGohIUk3ES4rK0N6ejrS09MB1JRaT09Px9mzZwHUTNebMGGCvf3DDz+MkydPYubMmTh27BjeeOMNfPzxx3jiiSfsbWbMmIHVq1dj/fr1OHr0KB555BGUl5dj8uTJrXpvRERERO5Of3F/q6jEIEnjoBoxScEAgII/S2GsNEscjWdqVnK1f/9+fPnllw7H3nvvPcTHxyMiIgIPPfSQQ9W9xhw6dAi9evVCr169ANQkRr169cKcOXMAAHq93p5oAUB8fDy++uorpKamokePHli+fDnWrFmDlJQUe5u7774bL774IubMmYOePXsiPT0d27dvr1XkgoiIiIiuTTb3t5IVvyA1AsN9IIqXEl9qXc2aFrhgwQIMGjQIt912GwDg119/xYMPPohJkyahU6dOeOGFFxAdHY158+Y16XyDBg1qcK+sdevW1fmen376qcHzTp06ldMAiYiIiJyovNgIQ34lIAC6hECpw6GLopOCUJJfieyMYsR1C5M6HI/TrJGr9PR0DBkyxP5848aN6Nu3L1avXo0ZM2ZgxYoV+Pjjj1s8SCIiIiKSl+yLIyNhbfyh9nGp3X3cWnRyEABuJiyVZiVXRUVFDtPrvvvuO9x6663259dffz3+/NO19zcgIiIiosbpL/54j+Z6K1mxTdHMP1OK6iquu2ptzUquIiMjcerUKQBAdXU1jhw5ghtvvNH+emlpqeSlEomIiIjI+bJZzEKWtKE+8A9Rw2oVkXuy6fszUctoVnI1cuRI/Pvf/8b333+PWbNmwdfXFwMGDLC//ssvvyAhIaHFgyQiIiIi+agqN6EwuxwAi1nIka1q4PmMIokj8TzNSq4WLlwILy8vDBw4EKtXr8bbb78Nb29v++tr167F8OHDWzxIIiIiIpKPnKwSQASCIn3hq/Vu/A3UqrjuSjrNWn0YFhaGPXv2oKSkBP7+/lAqHTeL++STTxAQENCiARIRERGRvNh+tEclskqgHNlGE3NPG2CutnCD51bUrOTqgQceaFK7tWvXXlUwRERERCR/tvVWLGYhT4HhPvAN9EZFSTVyTxkQ0yFY6pA8RrOSq3Xr1iE2Nha9evVqcH8qIiIiInJPpmoL8s+UAuB6K7kSBAExSUHIOJSH8xnFTK5aUbOSq0ceeQQfffQRTp06hcmTJ+P+++9HSEiIs2IjIiIiIpnJPVkCq1WEX5AaAaEaqcOhekQnByPjUB7XXbWyZhW0WLlyJfR6PWbOnIn//ve/aNu2LcaNG4dvvvmGI1lEREREHiA7swQAEJ0YCEEQJI6G6mNfd3WyBBazVdpgPEizkisAUKvVGD9+PFJTU/HHH3+gS5cuePTRRxEXF4eysjJnxEhEREREMqG3rbfilEBZC9b5widABbPJirzT3O+qtTQ7uXJ4s0IBQRAgiiIsFktLxUREREREMmSxWJFzsmbkipsHy5sgCPaCI+c5NbDVNDu5MhqN+OijjzBs2DAkJyfj119/xeuvv46zZ8/C39/fGTESERERkQzkny2FudoKtZ8XQqL8pA6HGmHb70rP5KrVNKugxaOPPoqNGzeibdu2eOCBB/DRRx8hLCzMWbERERERkYzY97dKCIKg4HoruYtOqqkSqM8qgdVihUJ5TZPWqAmalVytWrUK7dq1Q/v27fHdd9/hu+++q7Pdli1bWiQ4IiIiIpIPvb2YRZC0gVCThEb7Qe3rBWOFGflnyxAZr5U6JLfXrORqwoQJrApDRERE5IFEq8hiFi5GUAiISgzC6V8KcD6jiMlVK2j2JsJERERE5Hku6MthrDDDy1uBsHZcZ+8qYpJrkit9RjEwPFbqcNweJ14SERERUaNs66107QOh5Nodl2EbZczOrNn8mZyLfxlERERE1ChOCXRNYW38odIoUV1pRuE57knrbEyuiIiIiKhBoiheqhTIYhYuRaFUICohCMCl0UdyHiZXRERERNQgQ0EVykuqoVAKLIrggmIu7nfF5Mr5mFwRERERUYNsUwIjYgOg8lZKGww1m33dVUYxRK67ciomV0RERETUIE4JdG3hsQHw8lagqtyEC/pyqcNxa0yuiIiIiKhB2bZiFkyuXJJSqYCufSAATg10NiZXRERERFSv8hIjSvIqAQHQJQRKHQ5dJa67ah1MroiIiIioXvrMEgBAaLQ/NH4qiaOhq2Vbd3U+oxiiyHVXzsLkioiIiIjqdWlKIEetXFlEnBZKLwUqDdUozq2QOhy3xeSKiIiIiOplqxQYxc2DXZqXSmkvo8+pgc7D5IqIiIiI6mSsMKHgXBmAS9PKyHVFc92V0zG5IiIiIqI66bNKABHQhvvAL1AtdTh0jRz2u+K6K6dgckVEREREdbIVs+ColXvQtQ+EQimgrMgIQ0GV1OG4JSZXRERERFQn2/QxFrNwDypvJSJiue7KmZhcEREREVEt5moL8s4YAHDkyp1cWndVJG0gborJFRERERHVknvaAKtFhG+gN7RhPlKHQy3k8nVX1PKYXBERERFRLZemBAZBEARpg6EWE5UQCEEhwFBQhdILXHfV0phcEREREVEttv2tOCXQvXhrvBDe1h8AR6+cgckVERERETmwWqzQn6xZbxWVGCRtMNTiODXQeZhcEREREZGD/D/LYDZaoPb1Qmi0n9ThUAuLTg4GwOTKGZhcEREREZED25RA2/occi/RiYGAABTnVqC8xCh1OG6FyRURERERObCNaHBKoHtS+6oQ1obrrpyByRURERER2YlWEfrMEgAsZuHOoi8mzkyuWhaTKyIiIiKyK8qpQFW5CV4qBcLbBUgdDjnJpc2EiyWNw90wuSIiIiIiu+yL660i22uh9OJPRXdlG5W8kF2OyrJqaYNxI7L4i1m5ciXi4uKg0WjQt29fHDx4sN62gwYNgiAItR6jRo2yt5k0aVKt10eMGNEat0JERETk0rjeyjP4+Hsj5GIlSH1GicTRuA/Jk6tNmzZhxowZmDt3Lo4cOYIePXogJSUFeXl5dbbfsmUL9Hq9/fHbb79BqVTirrvucmg3YsQIh3YfffRRa9wOERERkcsSRZGbB3sQ27qr8xlF0gbiRiRPrl566SVMmTIFkydPRufOnbFq1Sr4+vpi7dq1dbYPCQmBTqezP1JTU+Hr61sruVKr1Q7tgoODW+N2iIiIiFxWaWEVyoqMUCgE6OIDpQ6HnIzrrlqel5QXr66uxuHDhzFr1iz7MYVCgaFDh2L//v1NOsc777yDe+65B35+jhvcpaWlISIiAsHBwbjllluwaNEihIaG1nkOo9EIo/FSjX+DoWZHcpPJBJPJ1NzbalG260sdB13CPpEf9om8sD/kh30iP3Ltkz+PFwIAQtv6AworTCarxBG1Drn2h7NFxNeUYy84V4aykkqofSVNDRzIqU+aE4MgiqLoxFgalJ2djZiYGOzbtw/9+vWzH585cya+++47HDhwoMH3Hzx4EH379sWBAwdwww032I9v3LgRvr6+iI+PR1ZWFp555hn4+/tj//79UCqVtc4zb948zJ8/v9bxDRs2wNfX9xrukIiIiMh1FP2mRvmf3vCPr0ZQR24u6wly9vjBXK5AaO8K+ERYpA5HlioqKnDvvfeipKQEWq22wbbySU+vwjvvvINu3bo5JFYAcM8999j/u1u3bujevTsSEhKQlpaGIUOG1DrPrFmzMGPGDPtzg8GAtm3bYvjw4Y1+gM5mMpmQmpqKYcOGQaVSSRoL1WCfyA/7RF7YH/LDPpEfufbJpsOHAFTipmE9Edet7hk/7kiu/dEa9hRn4Nj+HMQEJeHGke2lDsdOTn1im9XWFJImV2FhYVAqlcjNzXU4npubC51O1+B7y8vLsXHjRixYsKDR67Rv3x5hYWHIzMysM7lSq9VQq9W1jqtUKsk700ZOsVAN9on8sE/khf0hP+wT+ZFTn1QYqlGSVwkAaJscKpu4WpOc+qO1tOkYgmP7c5CTaZDlvcuhT5pzfUkLWnh7e6N3797YuXOn/ZjVasXOnTsdpgnW5ZNPPoHRaMT999/f6HXOnTuHwsJCREVFXXPMRERERO5In1UMAAiJ9oPGX34/ssk5bFUh8/8sQ3WVWdpg3IDk1QJnzJiB1atXY/369Th69CgeeeQRlJeXY/LkyQCACRMmOBS8sHnnnXcwZsyYWkUqysrK8NRTT+GHH37A6dOnsXPnTtx+++1ITExESkpKq9wTERERkauxVYyL5v5WHiUgRANtmAaiVUROFve7ulaSr7m6++67kZ+fjzlz5iAnJwc9e/bE9u3bERkZCQA4e/YsFArHHPD48eP43//+hx07dtQ6n1KpxC+//IL169ejuLgY0dHRGD58OBYuXFjn1D8iIiIiAvSZNT+so5JYgt3TRCcGwVCQg/MZxWjXxXPW2jmD5MkVAEydOhVTp06t87W0tLRaxzp06ID6ihz6+Pjgm2++acnwiIiIiNxadaUZBX+WAuDIlSeKTg7CsR9ykH2iWOpQXJ7k0wKJiIiISFr6kyUQRUAbpoF/sEbqcKiVRScFAwDyzhhgqmY59mvB5IqIiIjIw+m53sqjacM08AtSw2oRkXuS666uBZMrIiIiIg+XnVkMAIi6WDmOPIsgCPaqgecvJtp0dZhcEREREXkws8mC3NM1m6Ry5MpzxSQHAQDXXV0jJldEREREHizvdCmsZhE+Wm8ERvhIHQ5JxDZylXvKAIvJKm0wLozJFREREZEHu7S/VSAEQZA2GJJMUKQvfLTesJit9pFMaj4mV0REREQeTG9bb8UpgR5NEAT7tNDsjCJpg3FhTK6IiIiIPJTVYoX+YnW4aBaz8Hj2dVcsanHVmFwREREReaiCc2UwVVngrVEiNMZf6nBIYrYEW3/SAIuF666uBpMrIiIiIg+lz6wZtdIlBEGh4HorTxcS5Qe1nxfMRgvyz5RKHY5LYnJFRERE5KFs+1tFJwVKGwjJgqC4fN1VsaSxuComV0REREQeSBRFezEL7m9FNjHJwQCYXF0tJldEREREHqg4twKVpSYovRSIiNVKHQ7JhH3dVWYxrFZR2mBcEJMrIiIiIg9kG5mIjNdCqeJPQqoR2sYf3j5eqK6yoOBPrrtqLv4lEREREXmgS+utgiSNg+RFoRAQlVizBo9TA5uPyRURERGRB9Jn1FQKtP2QJrKxJdxMrpqPyRURERGRhym9UIXSC1UQFAJ07ZlckSN7cpVZDJHrrpqFyRURERGRh7GNSIS39Ye3xkvaYEh2wtsFwEuthLHcjMLscqnDcSlMroiIiIg8jK0EexRLsFMdlEoFohJs666KJI7GtTC5IiIiIvIw2Zk1661YzILqw3VXV4fJFREREZEHqSyrRpG+ZqoXi1lQfS5PrkSR666aiskVERERkQfRXxy1Ctb5wsffW+JoSK4iY2v2P6ssNaEop0LqcFwGkysiIiIiD8L9ragplCoFdO21ADg1sDmYXBERERF5EP3FH8osZkGNiU4KBsDkqjmYXBERERF5iOoqM/L/LAPAkStqnH3d1YkirrtqIiZXRERERB4i96QBolVEQIgGASEaqcMhmdPFa6HwElBeUo2S/Eqpw3EJTK6IiIiIPIRtvVVUEqsEUuO8vJWIjOO6q+ZgckVERETkIWw/kKO53oqaiPtdNQ+TKyIiIiIPYDFZkXvKAIDrrajpLq27KpY0DlfB5IqIiIjIA+SdMcBitsInQIWgSF+pwyEXoWsfCEEhoPRCFQyFXHfVGCZXRERERB7Avt4qMQiCIEgbDLkMb40XImIDAFwq40/1Y3JFRERE5AGyM0oAcL0VNZ9tauB5JleNYnJFRERE5OasVhE5WcUAgKhEVgqk5uG6q6ZjckVERETk5grPl6G6ygKVRomwNv5Sh0MupmYqKVCSX4nyYqPU4cgakysiIiIiN2crox3VPhAKJX/+UfOofbwQ1rZm3RVLsjeMf11EREREbk5/WTELoqthW6vHdVcNY3JFRERE5MZEUUR25sViFtzfiq5SdHIQACD7RJG0gcgckysiIiIiN1aSV4lKQzUUXgIi4gKkDodclG3kqiinAhWGammDkTEmV0RERERuzLa/VWScFl4qpbTBkMvS+KsQGuMH4NI0U6qNyRURERGRG7Nt/Mr9rehacd1V45hcEREREbkx28hVFNdb0TWKTg4GwP2uGsLkioiIiMhNlRUZYSiogiDUlGEnuha2giiF2WWoKjdJG4xMMbkiIiIiclO2tTFhbQPg7eMlbTDk8ny13gjW+QIi113VRxbJ1cqVKxEXFweNRoO+ffvi4MGD9bZdt24dBEFweGg0Goc2oihizpw5iIqKgo+PD4YOHYqMjAxn3wYRERGRrNg3D07kqBW1DNv0Uq67qpvkydWmTZswY8YMzJ07F0eOHEGPHj2QkpKCvLy8et+j1Wqh1+vtjzNnzji8vmzZMqxYsQKrVq3CgQMH4Ofnh5SUFFRVVTn7doiIiIhkw7beivtbUUuJufhd4rqrukmeXL300kuYMmUKJk+ejM6dO2PVqlXw9fXF2rVr632PIAjQ6XT2R2RkpP01URTxyiuv4LnnnsPtt9+O7t2747333kN2dja2bt3aCndEREREJL2qchMuZJcDAKISgqQNhtxGdFJNUYuCP0tRXWmWOBr5kXTybXV1NQ4fPoxZs2bZjykUCgwdOhT79++v931lZWWIjY2F1WrFddddh8WLF6NLly4AgFOnTiEnJwdDhw61tw8MDETfvn2xf/9+3HPPPbXOZzQaYTQa7c8NBgMAwGQywWSSdrGe7fpSx0GXsE/kh30iL+wP+WGfyE9r9MmfxwsBAIERPlD5COz/BvBvpOnU/gpowzQwFFThz+OFaNclxCnXkVOfNCcGSZOrgoICWCwWh5EnAIiMjMSxY8fqfE+HDh2wdu1adO/eHSUlJXjxxRdx00034ffff0ebNm2Qk5NjP8eV57S9dqUlS5Zg/vz5tY7v2LEDvr6+V3NrLS41NVXqEOgK7BP5YZ/IC/tDftgn8uPMPik+pgbgDbO6BNu2bXPaddwJ/0aaxqzWAFBh744j+O1MtVOvJYc+qaioaHJblysb069fP/Tr18/+/KabbkKnTp3w1ltvYeHChVd1zlmzZmHGjBn25waDAW3btsXw4cOh1WqvOeZrYTKZkJqaimHDhkGlUkkaC9Vgn8gP+0Re2B/ywz6Rn9bok61H01GGUlw/qCuSb4hs/A0ejH8jzXMiNBdpH5yAjzUMI0f2dMo15NQntlltTSFpchUWFgalUonc3FyH47m5udDpdE06h0qlQq9evZCZmQkA9vfl5uYiKirK4Zw9e/as8xxqtRpqtbrOc0vdmTZyioVqsE/kh30iL+wP+WGfyI+z+sRktKDgbBkAoG2HUPZ7E/FvpGnadgwFgJrvmFUBlVrptGvJoU+ac31JC1p4e3ujd+/e2Llzp/2Y1WrFzp07HUanGmKxWPDrr7/aE6n4+HjodDqHcxoMBhw4cKDJ5yQiIiJyZTmnSmC1ivAPViMgVNP4G4iaQRvmA/8QNaxWETknS6QOR1YkrxY4Y8YMrF69GuvXr8fRo0fxyCOPoLy8HJMnTwYATJgwwaHgxYIFC7Bjxw6cPHkSR44cwf33348zZ87gH//4B4CaSoLTp0/HokWL8MUXX+DXX3/FhAkTEB0djTFjxkhxi0RERESt6tL+VkEQBEHaYMgt2cr7Z3O/KweSr7m6++67kZ+fjzlz5iAnJwc9e/bE9u3b7QUpzp49C4XiUg5YVFSEKVOmICcnB8HBwejduzf27duHzp0729vMnDkT5eXleOihh1BcXIy//OUv2L59e63NhomIiIjckZ77W5GTxSQF48SBXJw/USR1KLIieXIFAFOnTsXUqVPrfC0tLc3h+csvv4yXX365wfMJgoAFCxZgwYIFLRUiERERkUuwmK3IPVmzAD86MUjaYMht2RL33NMGmE0WeKmct+7KlUg+LZCIiIiIWk7+2VKYTVZo/FQIjpLHljLkfgIjfOCr9YbVLCL3VNOr6bk7JldEREREbuTSeqtArrcipxEEAdHJQQC47upyTK6IiIiI3AjXW1Fribn4HTt/oljSOOSEyRURERGRmxCtIvRZNaWxo7jeipwsOikYAJB7sgQWs1XiaOSByRURERGRmyjMLoexwgwvtRLhbf2lDofcXHCULzT+KphNVuSdKZU6HFlgckVERETkJmxTAqPaa6FQ8mceOZcgCJftd8WS7ACTKyIiIiK3cfnmwUStgZsJO2JyRUREROQGRFFEtq2YBZMraiUxFysG6jNLYLVw3RWTKyIiIiI3YCioREVJNRRKAZHxWqnDIQ8REu0Pta8XTEYL8v8skzocyTG5IiIiInIDtmlZEbFaeHkrpQ2GPIZCIdinoWazJDuTKyIiIiJ3kJ1ZU4I9OilQ4kjI09jXXV2clurJmFwRERERuQE9i1mQRC6tuyqG1SpKG4zEmFwRERERubjyEiNK8isBAYhK4MgVta6wNv5QaZQwVphReN6z110xuSIiIiJycbb1VmFt/KH2VUkbDHkchVJhT+o9fd0VkysiIiIiF6e/uN6KUwJJKlx3VYPJFREREZGLs41ccX8rkkp0UjCAmu+iKHruuismV0REREQuzFhhQmF2zTqXqESutyJpRMQGwEulQFWZCRf05VKHIxkmV0REREQuTJ9VAohAYIQP/ALVUodDHkrppYCO666YXBERERG5Mk4JJLnguismV0REREQuTX/xh6zthy2RVOzJ1QnPXXfF5IqIiIjIRZmqLcg7XQqAlQJJepHxWii9FKgwVKMkr1LqcCTB5IqIiIjIReWeMsBqFeEX6A1tmEbqcMjDeamUiIzXAgDOnyiSOBppMLkiIiIiclGXTwkUBEHaYIjAdVdMroiIiIhclK2YBacEklx4+rorJldERERELshisSLnZAkAFrMg+dC1D4RCIaCsyIjSwiqpw2l1TK6IiIiIXFDB2TKYq61Q+3ohJMpP6nCIAAAqtRIRcQEALo2sehImV0REREQu6PIpgYKC661IPqKTggEA55lcEREREZErsBUMiEoMlDYQoitcWnfleRUDmVwRERERuRjRKkKfVQyA661IfqISAiEIgKGgCmVFnrXuiskVERERkYu5oC+HsdwML28FwtsFSB0OkQNvHy/799LT1l0xuSIiIiJyMbb9rSLjA6FU8uccyY9tRNXT1l3xr5GIiIjIxWRnsgQ7ydvl+115EiZXRERERC5EFEX7VKtoFrMgmYpKDAIEoDi3AuUlRqnDaTVMroiIiIhcSGlhFcqLjVAoBES2Z3JF8qTxUyE0xh8AoL840uoJmFwRERERuRBbCfbw2ACovJXSBkPUAE8syc7kioiIiMiFXJoSGCRpHESNifHAohZMroiIiIhciJ7FLMhF2L6jF7LLUVlWLW0wrYTJFREREZGLqDBUozi3AhAAXQLXW5G8+QR4IzjKD4DnrLtickVERETkImxTAkOj/aDxU0kbDFETeFpJdiZXRERERC7Ctnkw11uRq7i07sozilowuSIiIiJyEbZKgVFcb0UuIjo5CABQcK4MxkqztMG0AiZXRERERC7AWGlGwbkyABy5ItfhF6hGYIQPIF4aeXVnTK6IiIiIXEBOVgkgAtpwH/gFqaUOh6jJPGndFZMrIiIiIhdwaX8rVgkk1+JJ+13JIrlauXIl4uLioNFo0LdvXxw8eLDetqtXr8aAAQMQHByM4OBgDB06tFb7SZMmQRAEh8eIESOcfRtERERETmObUhXFKYHkYqKTgwEA+WdLUV3l3uuuJE+uNm3ahBkzZmDu3Lk4cuQIevTogZSUFOTl5dXZPi0tDePHj8fu3buxf/9+tG3bFsOHD8f58+cd2o0YMQJ6vd7++Oijj1rjdoiIiIhanLnagtwzBgDcPJhcT0CIBgGhGohWETkn3Xu/K8mTq5deeglTpkzB5MmT0blzZ6xatQq+vr5Yu3Ztne0//PBDPProo+jZsyc6duyINWvWwGq1YufOnQ7t1Go1dDqd/REcHNwat0NERETU4nJPG2A1i/DVeiMw3EfqcIiazVPWXXlJefHq6mocPnwYs2bNsh9TKBQYOnQo9u/f36RzVFRUwGQyISQkxOF4WloaIiIiEBwcjFtuuQWLFi1CaGhonecwGo0wGo325wZDzb8MmUwmmEym5t5Wi7JdX+o46BL2ifywT+SF/SE/7BP5aW6fnDt+AQCgS9DCbHbvaVVS4N+I80W2D8DxH3Jw7kRRkz5nOfVJc2IQRFEUnRhLg7KzsxETE4N9+/ahX79+9uMzZ87Ed999hwMHDjR6jkcffRTffPMNfv/9d2g0GgDAxo0b4evri/j4eGRlZeGZZ56Bv78/9u/fD6VSWesc8+bNw/z582sd37BhA3x9fa/hDomIiIiuXf6PPjAWeCGocxX8Y6X/sUnUXOZyATl7/AFBRMywMgi1f5LLVkVFBe69916UlJRAq9U22FbSkatr9fzzz2Pjxo1IS0uzJ1YAcM8999j/u1u3bujevTsSEhKQlpaGIUOG1DrPrFmzMGPGDPtzg8FgX8vV2AfobCaTCampqRg2bBhUKpWksVAN9on8sE/khf0hP+wT+WlOn1gtItbv2g/AgiGj+yG0jX/rBOlB+DfifKIo4sOfD6KipBq9Otxk31y4PnLqE9ustqaQNLkKCwuDUqlEbm6uw/Hc3FzodLoG3/viiy/i+eefx7fffovu3bs32LZ9+/YICwtDZmZmncmVWq2GWl17vwiVSiV5Z9rIKRaqwT6RH/aJvLA/5Id9Ij9N6ZO8bANMRgu8fbwQERsEhUJopeg8D/9GnCsmORgZP+Yi92QpYruEN+k9cuiT5lxf0oIW3t7e6N27t0MxCltxisunCV5p2bJlWLhwIbZv344+ffo0ep1z586hsLAQUVFRLRI3ERERUWux7W8VlRjIxIpcmr2ohRvvdyV5tcAZM2Zg9erVWL9+PY4ePYpHHnkE5eXlmDx5MgBgwoQJDgUvli5ditmzZ2Pt2rWIi4tDTk4OcnJyUFZWBgAoKyvDU089hR9++AGnT5/Gzp07cfvttyMxMREpKSmS3CMRERHR1bq0eXCQpHEQXauYi1MBc04ZYDFZpQ3GSSRfc3X33XcjPz8fc+bMQU5ODnr27Int27cjMjISAHD27FkoFJdywDfffBPV1dW48847Hc4zd+5czJs3D0qlEr/88gvWr1+P4uJiREdHY/jw4Vi4cGGdU/+IiIiI5EoUReizavYF4ubB5OqCIn3hE6BCZakJuWcMbvkPBpInVwAwdepUTJ06tc7X0tLSHJ6fPn26wXP5+Pjgm2++aaHIiIiIiKRTlFOBqjITlCoFImIDpA6H6JoIgoDopCBkHclH9olit0yuJJ8WSERERER1s00J1MVrofTizzZyfdFJwQCA7IwiiSNxDv6VEhEREcmUPrMYABB1sRAAkauzrbvSnzTAYnG/dVdMroiIiIhkKvticuWO06fIM4VE+UHt5wWz0YL8s6VSh9PimFwRERERyZChsBJlF4wQFAIi47VSh0PUIgSFYP/HguwTxZLG4gxMroiIiIhkSJ9ZUyUwvF0AvDWyqEFG1CLs+11dHJl1J0yuiIiIiGTo0pTAQGkDIWphMck1RS30GcWwWkWJo2lZTK6IiIiIZEh/sVIg97cidxPaxh/eGiWqqywoPFcmdTgtiskVERERkcxUllajKKcCAItZkPtRKAT7PxqcP+FeJdmZXBERERHJjG29VUi0HzT+KomjIWp59nVXF0do3QWTKyIiIiKZyeaUQHJz0Rf3u8rOLIboRuuumFwRERERyYy9mEUSi1mQewpvFwAvtRLGcjMu6MulDqfFMLkiIiIikpHqSjMK/qzZXJXrrchdKZUKRLWv2b/tvBvtd8XkioiIiEhGck6WQBSBgFAN/IM1UodD5DTRSTUl2d1p3RWTKyIiIiIZuTQlMEjSOIic7VJRiyKIonusu2JyRURERCQjtn/F55RAcneRcVooVQpUlppQnFshdTgtgskVERERkUxYTFbkna5ZbxWVyGIW5N6UKgV08e617orJFREREZFM5J4xwGK2widAhaBIX6nDIXI6d9vviskVERERkUxcPiVQEARpgyFqBZcnV+6w7orJFREREZFM6C8Ws+DmweQpItsHQqEUUF5shKGgUupwrhmTKyIiIiIZsFpF6LNKALBSIHkOlbcSkXE1667cYWogkysiIiIiGSg8VwZTlQXeGiVC2/hLHQ5Rq7FPDXSDohZMroiIiIhkwPav9rqEQCgUXG9FnsOWXJ3nyBURERERtQQ9Nw8mD6VLCISgEFBaWIXSC1VSh3NNmFwRERERSUwURWSzmAV5KG+NF8LbBQBw/XVXTK6IiIiIJFacW4HKUhOUXgpExmqlDoeo1cXY110VSRvINWJyRURERCQxfWZNlcDIeC2UKv48I8/jLuuu+NdLREREJDHbVKioxEBpAyGSSFRiICAAJXmVKC8xSh3OVWNyRURERCSxbBazIA+n9lUh7OIWBK687orJFREREZGESi9UobSwCoIA6Npz5Io8V0xSMADX3u+KyRURERGRhGwl2MPaBsBb4yVtMEQScod1V0yuiIiIiCSUfbGYBacEkqeLSqoZuS3Sl6OytFriaK4OkysiIiIiCdk3D+b+VuThfPy9ERLtBwDIyTJIHM3VYXJFREREJJGqchMuZJcDYKVAIuDSCK5tewJXw+SKiIiISCK2f50P1vnCJ8Bb4miIpGdLrs7+fgEV2V7IziiG1SpKG1QzcNUkERERkURs/zofxfVWRAAAk9ECADAUVAEFPvjy51/hF6TGgLuTkNArQuLoGseRKyIiIiKJ5GRdLGbB9VZEyPopD7vfP1breHmxEdvf+g1ZP+VJEFXzMLkiIiIikoDVDBScKwPASoFEVquI7zdlNNjmfx9nyH6KIJMrIiIiIglUFyshWgH/EDUCQjRSh0MkKX1GMcqLjQ22KSsyQi/zPbCYXBERERFJwHhBCYBTAokAoNzQcGLV3HZSYXJFREREJIHqoovJFacEEsFPq27RdlJhckVERETUyiwmK4zFNclVFEeuiBCVFAS/oIYTJ/9gtewrazK5IiIiImpl+X+WAVYBGn8vBOt8pQ6HSHIKhYABdyc12OYv45KgUAitFNHVYXIlY5Xl5fj8peXI/XYPPn9pOSrLy6UOyeOxT+SnsrIKn6x6H5m7fsYnq95HZWWV1CF5tGqjCWnrdiF313mkrduFaqNJ6pA8HvtEfqqNJuzbdAQAoPSqhqnaLHFEns1sMuPwFztR8P3POPzFTphN7A+pJPSKwIh/doVfkOOG2n5Baoz4Z1eX2OdKEEVR8nqGK1euxAsvvICcnBz06NEDr732Gm644YZ623/yySeYPXs2Tp8+jaSkJCxduhQjR460vy6KIubOnYvVq1ejuLgY/fv3x5tvvomkpIazYRuDwYDAwECUlJRAq9Ve8/1djQ+fnYNyfQ+YvIPtx1TVRfCL+hn3/WeBJDF5OvaJ/Kxd+DrMp6Jq9YlXvB4PzJ4qYWSe6YsXPkPOH4BJHWg/pjKWQNcZ+OtTYyWMzHOxT+SHfSIv363ZBK83XkFIRbH92AXfIJgfnY6B/7hbusA82HdrNkH5xqtQeIfB6K2FutoAa3UBLI9Ok6xPmpMbSD5ytWnT/7d391FR1fkfwN/DozyKAvFweDJTUhFQBKUjoeaKuqGQpvnz5EOux19i5bJ00jQffoqe0BJLzaOdVuzYCXU3ZMt0DSEUXBVL1FIEwxY3GSEYeQoYZu7vD2PWEZAR78y9zLxf53B0vvd7L5/L+9wZPtw7dzKRnJyMtWvX4rvvvkNYWBji4uJw507nHxJWWFiIOXPmYNGiRfj++++RkJCAhIQEXLlyRTcnLS0NH3zwAXbv3o2zZ8/CyckJcXFxaG7uHX/RPrBqDVTVsVDbuumNq23doKqOxYFVa6QpzIIxE/n5ZMMO/HZrSKeZ/HZrCD7ZsEOawixU9pYvUFHmCrWd/ouO2s4VFWWuyN7yhUSVWS5mIj/MRF6+/TgTnlvXod99jRUAuDWp4Ll1Hb79OFOawixYeyb9m2rRT1UK7zsX0E9Vin5Ntb0mExupC3j//fexePFiLFy4EACwe/dufPXVV/jkk0+wYsWKDvO3b9+OyZMn48033wQAbNiwASdOnMCOHTuwe/duCIKA9PR0rF69GtOnTwcA7N+/H15eXsjKysJLL71kup3rgd8aG9F4OwywBaB44JpShQIQBDT+EopbN35AH0deo20KzU1NhmVSUY4+jk6S1Ghpmpub0Vbu89BM2n7yQVVlFRz6yPuuQuagtVWNyh8FwA5d5lH5o4CaO7/Czs5WkhotDTORH2YiL21tbbDZuQ0A8OA7eKwAaAHY7EqHKuEPsLGR/Ndli2BIJta70tE2fwZsbOWbiaSXBba2tsLR0RGHDx9GQkKCbnz+/PlQqVQ4cuRIh3UCAgKQnJyM5cuX68bWrl2LrKwsFBcX46effsLAgQPx/fffIzw8XDcnNjYW4eHh2L59e4dttrS0oKXlv/fMr6urg7+/P6qrq01+WeCR99+DsrzrSyLbWaubYCVoTFARaRXW0Nh238gyE9NhJvLCPOTH4EzafmMmJqJVWENj49DtvBEX09FPVWqCioh6p7up2xAx7TmTfs+6ujp4eHgYdFmgpG1fdXU1NBoNvLy89Ma9vLxw7dq1TteprKzsdH5lZaVueftYV3MetHnzZqxfv77D+D//+U84mvjsUM2tKoPmaWwdwZdDeWEm8sNM5IV5yI/GxoGZyEyLnTTv9SbqLX44cw5KG9N+kHBTU5PBc+V7Ts2EVq5cieTkZN3j9jNXkyZNMv2Zq2tXoSzvfl5fz3OITIg3fkGE81n/wN2q7s8muj5xAVEzZ5igIjr9j1w0Vwzodp6T/88YlxhngoosW/HR73CrrPs/RPk91YSwqSNNUBExE/kxNJPmWbPg/dIHJqjIsl35+lt4pL7d7bzqVZsQMiXWBBWRoZkMi45CxFTTn7kylKTNlYeHB6ytraFUKvXGlUolvL29O13H29v7ofPb/1UqlfDx8dGbc/9lgvezt7eHvX3H92XY2trC1ta01z3/cdlr+PS1Y/fepP/gNdkAIAiwVddixorX4ODE9/eYQsDTQQZlMvOtpczERAIG+eLTpK+6yUSF2clz4ODQx+T1WRrfgZ7Y979f3nuTfld5tN7FlNfiYWfP95KYAjORH0MzeW4xMzGF6JficXZbGtyaVJ3e3U0LoNbRDdEvxcv6/T3mxNBMxkybaPJMHqUfkPRugXZ2doiIiEBOTo5uTKvVIicnB9HR0Z2uEx0drTcfAE6cOKGbP2DAAHh7e+vNqaurw9mzZ7vcppw4ODnByaf43oMH3w73+2Mnn0v8Jd6EmIn8ODj0gc2A2/cedJGJzYDbbKxMxM7eFt5Df3/QRR7eQxX8hdGEmIn8MBN5sbG1QdvS5VDg3i/t99Pi3g0VNEuXs7EyIXPJRPJbsScnJ2Pv3r3IyMjA1atX8eqrr6KxsVF398B58+Zh5cqVuvlvvPEGjh07hvfeew/Xrl3DunXrUFRUhGXL7n2mjUKhwPLly7Fx40ZkZ2fj8uXLmDdvHnx9ffVumiFnc1P/D24e38JWrdIbt1XXws3jW36mkgSYify88s4yOPhd7SQTFRz8rvJzrkxs2puJ8H+qDrat+pdO2Lbehf9Tdfz8HgkwE/lhJvIS+6fZqEpZB5Wjm954raMbqlLW8XOuJGAOmcjiQ4R37Nih+xDh8PBwfPDBBxg9ejQAYNy4cQgKCsK+fft08w8dOoTVq1frPkQ4LS2t0w8R3rNnD1QqFcaOHYtdu3Zh8ODBBtUjhw8RBu7dlv2rHR+i5lYV+vt54o/LeCmg1JiJ/Pz2WzO++Pgz3C67BZ+n/JD4p//hGSsJtbao8e3+kyi/cgMDQgYidt4E/iVeYsxEfpiJvLSp21CU/Q1+OHMOw6KjMEqCy85In9wyeZTeQBbNldzIpbkCALVajaNHj2Lq1Kkmf/8XdY6ZyA8zkRfmIT/MRH6YibwwD/mRUyaP0htIflkgERERERGROWBzRUREREREJAI2V0RERERERCJgc0VERERERCQCNldEREREREQiYHNFREREREQkAjZXREREREREImBzRUREREREJAI2V0RERERERCJgc0VERERERCQCNldEREREREQiYHNFREREREQkAjZXREREREREIrCRugA5EgQBAFBXVydxJYBarUZTUxPq6upga2srdTkEZiJHzERemIf8MBP5YSbywjzkR06ZtPcE7T3Cw7C56kR9fT0AwN/fX+JKiIiIiIhIDurr69G3b9+HzlEIhrRgFkar1eKXX36Bi4sLFAqFbjwyMhLnz5/vcr2HLe9qWWfj94/V1dXB398fFRUVcHV17cnuPJbu9tlY2zB0nZ5m0tM8AGkzESOPnm7HkHWkOEYAy8zE2MdIV8sMyYnPW483j89b4mxHqmOkq3Fmwuetrsj9tb27eZZwjAiCgPr6evj6+sLK6uHvquKZq05YWVnBz8+vw7i1tfVDw33Y8q6WdTbe2Zirq6skv6R0t8/G2oah6/Q0k8fNA5AmEzHy6Ol2DFlHymMEsKxMjH2MdLXsUXLi81bP5vF5S5ztSHWMdDXOTPi81RW5v7Z3N89SjpHuzli14w0tHkFSUlKPl3e1rLPx7r6PKYlRS0+2Yeg6Pc3EkvPo6XYMWYfHiOm2Y+xjpKtlj3pMSYHPW+aXR0+2I9Ux0tU4M+HzVlfk/tre3TweI/p4WaDM1dXVoW/fvrh7964kfwGmjpiJ/DATeWEe8sNM5IeZyAvzkJ/emgnPXMmcvb091q5dC3t7e6lLod8xE/lhJvLCPOSHmcgPM5EX5iE/vTUTnrkiIiIiIiISAc9cERERERERiYDNFRERERERkQjYXBEREREREYmAzRUREREREZEI2FwRERERERGJgM2Vmdm2bRuGDRuGoUOH4vXXXwdvBimdkpIShIeH674cHByQlZUldVkWr7y8HOPHj8fQoUMxfPhwNDY2Sl2SxQsKCkJoaCjCw8Mxfvx4qcshAE1NTQgMDERKSorUpVg8lUqFUaNGITw8HCEhIdi7d6/UJVm8iooKjBs3DkOHDkVoaCgOHTokdUkWLzExEf369cPMmTOlLoW3YjcnVVVVGDNmDH744QfY2tri2WefxdatWxEdHS11aRavoaEBQUFB+Pnnn+Hk5CR1ORYtNjYWGzduRExMDGpqauDq6gobGxupy7JoQUFBuHLlCpydnaUuhX63atUqlJWVwd/fH1u3bpW6HIum0WjQ0tICR0dHNDY2IiQkBEVFRXB3d5e6NIt1+/ZtKJVKhIeHo7KyEhEREbh+/Tpf3yWUl5eH+vp6ZGRk4PDhw5LWwjNXZqatrQ3Nzc1Qq9VQq9V44oknpC6JAGRnZ+O5557jE6/E2v/wEBMTAwDo378/GyuiB5SWluLatWuYMmWK1KUQAGtrazg6OgIAWlpaIAgCr0qRmI+PD8LDwwEA3t7e8PDwQE1NjbRFWbhx48bBxcVF6jIAsLkyqfz8fMTHx8PX1xcKhaLTS8R27tyJoKAg9OnTB6NHj8a5c+cM3r6npydSUlIQEBAAX19fTJw4EQMHDhRxD8yLsfO438GDBzF79uzHrNj8GTuT0tJSODs7Iz4+HiNHjsSmTZtErN48meI4USgUiI2NRWRkJA4cOCBS5ebJFHmkpKRg8+bNIlVs/kyRiUqlQlhYGPz8/PDmm2/Cw8NDpOrNkylf3y9cuACNRgN/f//HrNp8mTIPOWBzZUKNjY0ICwvDzp07O12emZmJ5ORkrF27Ft999x3CwsIQFxeHO3fu6Oa0X3P94Ncvv/yC2tpafPnll7h58yb+85//oLCwEPn5+abavV7H2Hm0q6urQ2FhIaZOnWr0fertjJ1JW1sbTp06hV27duHMmTM4ceIETpw4Yard65VMcZycPn0aFy5cQHZ2NjZt2oRLly6ZZN96I2PnceTIEQwePBiDBw821S71eqY4Rtzc3FBcXIzy8nJ89tlnUCqVJtm33spUr+81NTWYN28e9uzZY/R96s1MlYdsCCQJAMIXX3yhNxYVFSUkJSXpHms0GsHX11fYvHmzQds8ePCgsHTpUt3jtLQ04d133xWlXnNnjDza7d+/X5g7d64YZVoUY2RSWFgoTJo0Sfc4LS1NSEtLE6VeS2DM46RdSkqK8Ne//vUxqrQcxshjxYoVgp+fnxAYGCi4u7sLrq6uwvr168Us26yZ4hh59dVXhUOHDj1OmRbFWJk0NzcLMTExwv79+8Uq1SIY8xjJzc0VZsyYIUaZj4VnrmSitbUVFy5cwMSJE3VjVlZWmDhxIs6cOWPQNvz9/VFYWIjm5mZoNBrk5eUhODjYWCWbNTHyaMdLAsUhRiaRkZG4c+cOamtrodVqkZ+fjyFDhhirZLMnRiaNjY2or68HcO/GLydPnsSwYcOMUq+5EyOPzZs3o6KiAjdv3sTWrVuxePFirFmzxlglmz0xMlEqlbpj5O7du8jPz+dr+2MQIxNBELBgwQJMmDABL7/8srFKtQhi/r4lF2yuZKK6uhoajQZeXl56415eXqisrDRoG2PGjMHUqVMxYsQIhIaGYuDAgZg2bZoxyjV7YuQB3HshPHfuHOLi4sQu0eKIkYmNjQ02bdqEZ599FqGhoRg0aBCef/55Y5RrEcTIRKlUYuzYsQgLC8OYMWMwb948REZGGqNcsyfW8xaJR4xMfv75Z8TExCAsLAwxMTF47bXXMHz4cGOUaxHEyKSgoACZmZnIysrSfdzK5cuXjVGu2RPreWvixIl48cUXcfToUfj5+UnamPE2WWYmNTUVqampUpdBv+vbty+vjZeZKVOm8C5oMvLkk0+iuLhY6jKoEwsWLJC6BAIQFRWFixcvSl0G3Wfs2LHQarVSl0H3+eabb6QuQYdnrmTCw8MD1tbWHX4RVyqV8Pb2lqgqy8U85IeZyA8zkRfmIT/MRH6YibyYYx5srmTCzs4OERERyMnJ0Y1ptVrk5OTwQ4AlwDzkh5nIDzORF+YhP8xEfpiJvJhjHrws0IQaGhpQVlame1xeXo6LFy+if//+CAgIQHJyMubPn49Ro0YhKioK6enpaGxsxMKFCyWs2nwxD/lhJvLDTOSFecgPM5EfZiIvFpeH1LcrtCS5ubkCgA5f8+fP18358MMPhYCAAMHOzk6IiooS/vWvf0lXsJljHvLDTOSHmcgL85AfZiI/zEReLC0PhSAIgvFaNyIiIiIiIsvA91wRERERERGJgM0VERERERGRCNhcERERERERiYDNFRERERERkQjYXBEREREREYmAzRUREREREZEI2FwRERERERGJgM0VERERERGRCNhcERGRxQsKCkJ6errUZRARUS+nEARBkLoIIiIyfwsWLIBKpUJWVpbUpXRQVVUFJycnODo6Sl1Kp+T8syMiov/imSsiIjJbarXaoHmenp6SNFaG1kdERL0DmysiIpKFK1euYMqUKXB2doaXlxdefvllVFdX65YfO3YMY8eOhZubG9zd3fH888/jxo0buuU3b96EQqFAZmYmYmNj0adPHxw4cAALFixAQkICtm7dCh8fH7i7uyMpKUmvsXnwskCFQoGPP/4YiYmJcHR0xKBBg5Cdna1Xb3Z2NgYNGoQ+ffpg/PjxyMjIgEKhgEql6nIfFQoFPvroI0ybNg1OTk5ITU2FRqPBokWLMGDAADg4OCA4OBjbt2/XrbNu3TpkZGTgyJEjUCgUUCgUyMvLAwBUVFRg1qxZcHNzQ//+/TF9+nTcvHmzZwEQEdFjY3NFRESSU6lUmDBhAkaMGIGioiIcO3YMSqUSs2bN0s1pbGxEcnIyioqKkJOTAysrKyQmJkKr1epta8WKFXjjjTdw9epVxMXFAQByc3Nx48YN5ObmIiMjA/v27cO+ffseWtP69esxa9YsXLp0CVOnTsXcuXNRU1MDACgvL8fMmTORkJCA4uJiLFmyBKtWrTJoX9etW4fExERcvnwZr7zyCrRaLfz8/HDo0CH8+OOPWLNmDd5++20cPHgQAJCSkoJZs2Zh8uTJuH37Nm7fvo1nnnkGarUacXFxcHFxwalTp1BQUABnZ2dMnjwZra2thv7oiYhITAIREZEJzJ8/X5g+fXqnyzZs2CBMmjRJb6yiokIAIJSUlHS6TlVVlQBAuHz5siAIglBeXi4AENLT0zt838DAQKGtrU039uKLLwqzZ8/WPQ4MDBS2bdumewxAWL16te5xQ0ODAED4+uuvBUEQhLfeeksICQnR+z6rVq0SAAi1tbWd/wB+3+7y5cu7XN4uKSlJmDFjht4+PPiz+/TTT4Xg4GBBq9XqxlpaWgQHBwfh+PHj3X4PIiISH89cERGR5IqLi5GbmwtnZ2fd19NPPw0Aukv/SktLMWfOHDz55JNwdXVFUFAQAODf//633rZGjRrVYfvDhg2DtbW17rGPjw/u3Lnz0JpCQ0N1/3dycoKrq6tunZKSEkRGRurNj4qKMmhfO6tv586diIiIgKenJ5ydnbFnz54O+/Wg4uJilJWVwcXFRfcz69+/P5qbm/UulyQiItOxkboAIiKihoYGxMfH49133+2wzMfHBwAQHx+PwMBA7N27F76+vtBqtQgJCelwCZyTk1OHbdja2uo9VigUHS4nFGMdQzxY3+eff46UlBS89957iI6OhouLC7Zs2YKzZ88+dDsNDQ2IiIjAgQMHOizz9PR87DqJiOjRsbkiIiLJjRw5En/7298QFBQEG5uOL02//vorSkpKsHfvXsTExAAATp8+beoydYKDg3H06FG9sfPnz/doWwUFBXjmmWewdOlS3diDZ57s7Oyg0Wj0xkaOHInMzEw88cQTcHV17dH3JiIicfGyQCIiMpm7d+/i4sWLel8VFRVISkpCTU0N5syZg/Pnz+PGjRs4fvw4Fi5cCI1Gg379+sHd3R179uxBWVkZTp48ieTkZMn2Y8mSJbh27RreeustXL9+HQcPHtTdIEOhUDzStgYNGoSioiIcP34c169fxzvvvNOhUQsKCsKlS5dQUlKC6upqqNVqzJ07Fx4eHpg+fTpOnTqF8vJy5OXl4fXXX8etW7fE2lUiInoEbK6IiMhk8vLyMGLECL2v9evXw9fXFwUFBdBoNJg0aRKGDx+O5cuXw83NDVZWVrCyssLnn3+OCxcuICQkBH/+85+xZcsWyfZjwIABOHz4MP7+978jNDQUH330ke5ugfb29o+0rSVLluCFF17A7NmzMXr0aPz66696Z7EAYPHixQgODsaoUaPg6emJgoICODo6Ij8/HwEBAXjhhRcwZMgQLFq0CM3NzTyTRUQkEYUgCILURRAREfV2qamp2L17NyoqKqQuhYiIJML3XBEREfXArl27EBkZCXd3dxQUFGDLli1YtmyZ1GUREZGE2FwRERH1QGlpKTZu3IiamhoEBATgL3/5C1auXCl1WUREJCFeFkhERERERCQC3tCCiIiIiIhIBGyuiIiIiIiIRMDmioiIiIiISARsroiIiIiIiETA5oqIiIiIiEgEbK6IiIiIiIhEwOaKiIiIiIhIBGyuiIiIiIiIRMDmioiIiIiISAT/D7jc8vPrnvKqAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR   sklearn SGD\n",
+            "Learning rate                                                               \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329  8.325798e+00\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329  8.325257e+00\n",
+            "1.000000e-06          8.313329         -  8.346550    8.313329  8.321114e+00\n",
+            "1.000000e-05          8.313329         -         -    8.313329  8.315080e+00\n",
+            "1.000000e-04          8.313329         -         -    8.313329  1.340738e+01\n",
+            "1.000000e-03          8.313329         -         -    8.313329  4.890101e+20\n",
+            "1.000000e-02          8.313329         -         -    8.313329  1.932537e+26\n",
+            "1.000000e-01          8.313329         -         -    8.313329  4.280040e+24\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох:\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  2560.000199  2583.409888    8.313329  2954.214186\n",
+            "20             8.313329   217.565243   237.582879    8.313329   888.456215\n",
+            "200            8.313329     8.325835     8.325835    8.313329     8.330808\n",
+            "2000           8.313329     8.325615     8.325615    8.313329     8.325737\n",
+            "20000          8.313329     8.323616     8.323616    8.313329     8.324683\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча:\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "2                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "4                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "8                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "16                 8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "32                 8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "64                 8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "128                8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "256                8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "512                8.313329  8.325737  8.325737    8.313329     8.325798\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.mplot3d import Axes3D\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы для 1/MSE ----------\n",
+        "def plot_1over_mse(param_values, param_name, xlabel, title):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        inv_mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            if np.isnan(mse) or mse == 0:\n",
+        "                inv = np.nan\n",
+        "            else:\n",
+        "                inv = 1.0 / mse\n",
+        "            inv_mses.append(inv)\n",
+        "        results[label] = inv_mses\n",
+        "        plt.semilogx(param_values, inv_mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('1 / MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений 1/MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Определяем best_lr по первому графику ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1 (MSE): Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_1over_mse(lr_range, 'lr', 'Learning rate', 'Зависимость 1/MSE от learning rate (для определения best_lr)')\n",
+        "# Используем те же данные, но для выбора best_lr нужны MSE, поэтому пересчитаем MSE отдельно\n",
+        "mse_values = []\n",
+        "for lr in lr_range:\n",
+        "    mse_values.append(compute_mse('gd', lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH))\n",
+        "gd_mses = np.array(mse_values)\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "# ---------- Графики 1/MSE ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость 1/MSE от learning rate (все методы)\")\n",
+        "plot_1over_mse(lr_range, 'lr', 'Learning rate', 'Зависимость 1/MSE от learning rate')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость 1/MSE от количества эпох\")\n",
+        "plot_1over_mse(epochs_range, 'epochs', 'Epochs', 'Зависимость 1/MSE от количества эпох')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 4: Зависимость 1/MSE от размера батча\")\n",
+        "plot_1over_mse(batch_range, 'batch', 'Batch size', 'Зависимость 1/MSE от размера батча')\n",
+        "\n",
+        "# ---------- Трехмерный график: количество эпох до достижения MSE < 15 для SGD ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"Построение трехмерного графика зависимости числа эпох (до MSE<15) от lr и batch_size для SGD\")\n",
+        "\n",
+        "threshold = 15\n",
+        "max_epochs = 20000\n",
+        "# Создадим сетку эпох для поиска (логарифмическая шкала)\n",
+        "epochs_search = np.unique(np.logspace(0, np.log10(max_epochs), 30).astype(int))\n",
+        "epochs_search = epochs_search[epochs_search > 0]\n",
+        "\n",
+        "# Матрица результатов\n",
+        "Z = np.full((len(lr_range), len(batch_range)), np.nan)\n",
+        "\n",
+        "for i, lr in enumerate(lr_range):\n",
+        "    for j, bs in enumerate(batch_range):\n",
+        "        # Ищем минимальное число эпох, при котором MSE < threshold\n",
+        "        found = False\n",
+        "        for e in epochs_search:\n",
+        "            mse = compute_mse('sgd', lr=lr, epochs=e, batch_size=bs)\n",
+        "            if not np.isnan(mse) and mse < threshold:\n",
+        "                Z[i, j] = e\n",
+        "                found = True\n",
+        "                break\n",
+        "        if not found:\n",
+        "            Z[i, j] = np.nan  # не достигнуто за max_epochs\n",
+        "\n",
+        "# Построение поверхности\n",
+        "fig = plt.figure(figsize=(12, 8))\n",
+        "ax = fig.add_subplot(111, projection='3d')\n",
+        "\n",
+        "X_grid, Y_grid = np.meshgrid(lr_range, batch_range, indexing='ij')\n",
+        "# Убираем NaN для построения (можно заменить на max_epochs, но лучше маскировать)\n",
+        "mask = ~np.isnan(Z)\n",
+        "ax.scatter(X_grid[mask], Y_grid[mask], Z[mask], c=Z[mask], cmap='viridis', s=50)\n",
+        "\n",
+        "ax.set_xlabel('Learning rate')\n",
+        "ax.set_ylabel('Batch size')\n",
+        "ax.set_zlabel('Epochs to MSE < 15')\n",
+        "ax.set_title('Количество эпох до достижения MSE<15 (SGD)')\n",
+        "ax.set_xscale('log')\n",
+        "ax.set_yscale('log')\n",
+        "plt.show()\n",
+        "\n",
+        "# Таблица результатов\n",
+        "df_3d = pd.DataFrame(Z, index=lr_range, columns=batch_range)\n",
+        "df_3d.index.name = 'lr'\n",
+        "df_3d.columns.name = 'batch_size'\n",
+        "print(\"\\nТаблица количества эпох до MSE<15 (NaN — не достигнуто за 20000):\")\n",
+        "print(df_3d.round(1).to_string(na_rep='-'))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "c3q8SWWS5fYV",
+        "outputId": "04cab204-3c1e-4a1c-b474-c742f5b058cc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1 (MSE): Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от learning rate (для определения best_lr):\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "1.000000e-07          0.120289  0.120125  0.120122    0.120289     0.120116\n",
+            "1.000000e-06          0.120289         -  0.119810    0.120289     0.120176\n",
+            "1.000000e-05          0.120289         -         -    0.120289     0.120263\n",
+            "1.000000e-04          0.120289         -         -    0.120289     0.074586\n",
+            "1.000000e-03          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-02          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-01          0.120289         -         -    0.120289     0.000000\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость 1/MSE от learning rate (все методы)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "1.000000e-07          0.120289  0.120125  0.120122    0.120289     0.120116\n",
+            "1.000000e-06          0.120289         -  0.119810    0.120289     0.120176\n",
+            "1.000000e-05          0.120289         -         -    0.120289     0.120263\n",
+            "1.000000e-04          0.120289         -         -    0.120289     0.074586\n",
+            "1.000000e-03          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-02          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-01          0.120289         -         -    0.120289     0.000000\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость 1/MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от количества эпох:\n",
+            "        Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                              \n",
+            "2              0.120289  0.000391  0.000387    0.120289     0.000338\n",
+            "20             0.120289  0.004596  0.004209    0.120289     0.001126\n",
+            "200            0.120289  0.120108  0.120108    0.120289     0.120036\n",
+            "2000           0.120289  0.120111  0.120111    0.120289     0.120109\n",
+            "20000          0.120289  0.120140  0.120140    0.120289     0.120125\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 4: Зависимость 1/MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от размера батча:\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "2                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "4                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "8                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "16                 0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "32                 0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "64                 0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "128                0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "256                0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "512                0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "\n",
+            "============================================================\n",
+            "Построение трехмерного графика зависимости числа эпох (до MSE<15) от lr и batch_size для SGD\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/\320\270\320\2702.py" "b/\320\270\320\2702.py"
new file mode 100644
index 0000000..0acbf29
--- /dev/null
+++ "b/\320\270\320\2702.py"
@@ -0,0 +1,91 @@
+import numpy as np
+from sklearn.linear_model import LinearRegression, SGDRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error
+
+# 1. Аналитическое решение
+def linear_regression_normal(X, y, add_intercept=True):
+    if y.ndim == 1:
+        y = y.reshape(-1, 1)
+    if add_intercept:
+        X = np.hstack([np.ones((X.shape[0], 1)), X])
+    w = np.linalg.pinv(X.T @ X) @ X.T @ y
+    return w.flatten()
+
+# 2. Полный градиентный спуск (SSE, без деления на n)
+def linear_regression_gd(X, y, lr=0.001, epochs=1000, add_intercept=True):
+    if y.ndim == 1:
+        y = y.reshape(-1, 1)
+    if add_intercept:
+        X = np.hstack([np.ones((X.shape[0], 1)), X])
+    w = np.zeros((X.shape[1], 1))
+    for _ in range(epochs):
+        error = X @ w - y
+        gradient = 2 * X.T @ error
+        w -= lr * gradient
+    return w.flatten()
+
+# 3. Стохастический градиентный спуск (с мини-батчами)
+def linear_regression_sgd(X, y, lr=0.01, epochs=100, batch_size=32, add_intercept=True):
+    if y.ndim == 1:
+        y = y.reshape(-1, 1)
+    if add_intercept:
+        X = np.hstack([np.ones((X.shape[0], 1)), X])
+    w = np.zeros((X.shape[1], 1))
+    n = X.shape[0]
+    for _ in range(epochs):
+        indices = np.random.permutation(n)
+        X_shuffled = X[indices]
+        y_shuffled = y[indices]
+        for i in range(0, n, batch_size):
+            X_batch = X_shuffled[i:i+batch_size]
+            y_batch = y_shuffled[i:i+batch_size]
+            error = X_batch @ w - y_batch
+            gradient = 2 * X_batch.T @ error
+            w -= lr * gradient
+    return w.flatten()
+
+# Генерация данных
+np.random.seed(42)
+n, d = 200, 5
+X = np.random.randn(n, d)
+true_w = np.array([1.5, -2.0, 0.8, 3.1, -1.2])
+y = X @ true_w + 0.2 * np.random.randn(n)
+
+# Обучение наших моделей (без свободного члена для простоты)
+w_normal = linear_regression_normal(X, y, add_intercept=False)
+w_gd = linear_regression_gd(X, y, lr=0.001, epochs=2000, add_intercept=False)
+w_sgd = linear_regression_sgd(X, y, lr=0.001, epochs=100, batch_size=32, add_intercept=False)
+
+# sklearn LinearRegression
+lr_sk = LinearRegression(fit_intercept=False).fit(X, y)
+w_sk = lr_sk.coef_
+
+# sklearn SGDRegressor (требует масштабирования)
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+sgd_sk = SGDRegressor(fit_intercept=False, max_iter=1000, tol=1e-3, eta0=0.01, learning_rate='constant', random_state=42)
+sgd_sk.fit(X_scaled, y)
+w_sgd_sk = sgd_sk.coef_
+
+# Сравнение
+print("Истинные веса:          ", true_w)
+print("Аналитика (наша):        ", w_normal)
+print("GD (наша):               ", w_gd)
+print("SGD (наша):              ", w_sgd)
+print("sklearn LinearRegression:", w_sk)
+print("sklearn SGDRegressor:    ", w_sgd_sk)
+
+# Оценка MSE на тех же данных
+y_pred_normal = X @ w_normal
+y_pred_gd = X @ w_gd
+y_pred_sgd = X @ w_sgd
+y_pred_sk = X @ w_sk
+y_pred_sgd_sk = X_scaled @ w_sgd_sk   # важно: X_scaled использовался для обучения
+
+print("\nMSE:")
+print(f"  Normal equation: {mean_squared_error(y, y_pred_normal):.6f}")
+print(f"  GD:              {mean_squared_error(y, y_pred_gd):.6f}")
+print(f"  SGD:             {mean_squared_error(y, y_pred_sgd):.6f}")
+print(f"  sklearn LR:      {mean_squared_error(y, y_pred_sk):.6f}")
+print(f"  sklearn SGD:     {mean_squared_error(y, y_pred_sgd_sk):.6f}")
diff --git "a/\320\277\320\276\321\201\320\273\320\265\320\264\320\275\321\217\321\217 \320\262\320\265\321\200\321\201\320\270\321\217.ipynb" "b/\320\277\320\276\321\201\320\273\320\265\320\264\320\275\321\217\321\217 \320\262\320\265\321\200\321\201\320\270\321\217.ipynb"
new file mode 100644
index 0000000..83bdc62
--- /dev/null
+++ "b/\320\277\320\276\321\201\320\273\320\265\320\264\320\275\321\217\321\217 \320\262\320\265\321\200\321\201\320\270\321\217.ipynb"	
@@ -0,0 +1,7377 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "mount_file_id": "1gflZGIkz8_-SFrkGwKsRes8PHpImpLQA",
+      "authorship_tag": "ABX9TyNsyR7zLSilAOALaBnEPVoK",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "26511a612fb240878e45b0be1b4f1aab": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_dc8907c29c8742be945f91c053aee93b",
+              "IPY_MODEL_267de66128304dbeaf02517d855023bc",
+              "IPY_MODEL_ded69f943c254be1a499dc4a462334f4"
+            ],
+            "layout": "IPY_MODEL_7748bdb577c9422c9cc8d3b3b5a216c1"
+          }
+        },
+        "dc8907c29c8742be945f91c053aee93b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_b8002cdfb5584cc7b84ea812ee460974",
+            "placeholder": "​",
+            "style": "IPY_MODEL_ad009a7315cd4cb6817c33d1927b4c41",
+            "value": "lr: 100%"
+          }
+        },
+        "267de66128304dbeaf02517d855023bc": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_e6ba9cef5521425fa5aa8f8ed050fbc9",
+            "max": 8,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_bddeb3ad8290486ea3106771e4e26001",
+            "value": 8
+          }
+        },
+        "ded69f943c254be1a499dc4a462334f4": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_e40191a8e4984203bed0647f11ef7f4a",
+            "placeholder": "​",
+            "style": "IPY_MODEL_b5eead6a813749c9976f56b4415c0fbb",
+            "value": " 8/8 [08:44&lt;00:00, 101.31s/it]"
+          }
+        },
+        "7748bdb577c9422c9cc8d3b3b5a216c1": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "b8002cdfb5584cc7b84ea812ee460974": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "ad009a7315cd4cb6817c33d1927b4c41": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "e6ba9cef5521425fa5aa8f8ed050fbc9": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "bddeb3ad8290486ea3106771e4e26001": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "e40191a8e4984203bed0647f11ef7f4a": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "b5eead6a813749c9976f56b4415c0fbb": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "e145436d63874a97bce53dc7e29b71bc": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_24474a7c66ba4f5da622dab2c4f2afba",
+              "IPY_MODEL_1064448d917a417da9517e5adca120e5",
+              "IPY_MODEL_58e69002306e4c3f87ef1e5ff3ea7d92"
+            ],
+            "layout": "IPY_MODEL_dde89e54d5d342f6b528520204657ea4"
+          }
+        },
+        "24474a7c66ba4f5da622dab2c4f2afba": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_2b78d4bda1c8452caad279dd527e093a",
+            "placeholder": "​",
+            "style": "IPY_MODEL_013a5e417c8340bfaf5e35cf71561aea",
+            "value": "batch: 100%"
+          }
+        },
+        "1064448d917a417da9517e5adca120e5": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_8ecd1af312f545c9a4bc47ba7e8387ea",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_e36ec1ed388345e69a775db490afe9aa",
+            "value": 10
+          }
+        },
+        "58e69002306e4c3f87ef1e5ff3ea7d92": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_a705a40461a14ea2914d1c41afeb4ad8",
+            "placeholder": "​",
+            "style": "IPY_MODEL_0c90285b641c483d9401f313eda143f9",
+            "value": " 10/10 [00:01&lt;00:00,  7.00it/s]"
+          }
+        },
+        "dde89e54d5d342f6b528520204657ea4": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "2b78d4bda1c8452caad279dd527e093a": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "013a5e417c8340bfaf5e35cf71561aea": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "8ecd1af312f545c9a4bc47ba7e8387ea": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "e36ec1ed388345e69a775db490afe9aa": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "a705a40461a14ea2914d1c41afeb4ad8": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "0c90285b641c483d9401f313eda143f9": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "7254ae5628694a73a104b99aa1cb8077": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_46de6bd3b22240a69ac859a5590dcc30",
+              "IPY_MODEL_b0ff7eefd5a542beb6844988e7b328da",
+              "IPY_MODEL_404057be731144fa8448384bd5bd5514"
+            ],
+            "layout": "IPY_MODEL_a489308bb89644f0ad7b98a3c41e219f"
+          }
+        },
+        "46de6bd3b22240a69ac859a5590dcc30": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_5b828dc6657e4f1c8c424e0986dcdadf",
+            "placeholder": "​",
+            "style": "IPY_MODEL_b9938872da8d4d97862462d93124367b",
+            "value": "batch:  60%"
+          }
+        },
+        "b0ff7eefd5a542beb6844988e7b328da": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_62e058f786d24a04b8e47a3fc7d38e9e",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_3f5e5f24664040edad3bf1599cca8a9f",
+            "value": 10
+          }
+        },
+        "404057be731144fa8448384bd5bd5514": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_b08300dc0653425dbe983bdc56c634dc",
+            "placeholder": "​",
+            "style": "IPY_MODEL_1d2ef1fbf7314991a1e9818c49dc0cf8",
+            "value": " 6/10 [00:00&lt;00:00, 56.44it/s]"
+          }
+        },
+        "a489308bb89644f0ad7b98a3c41e219f": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "5b828dc6657e4f1c8c424e0986dcdadf": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "b9938872da8d4d97862462d93124367b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "62e058f786d24a04b8e47a3fc7d38e9e": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "3f5e5f24664040edad3bf1599cca8a9f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "b08300dc0653425dbe983bdc56c634dc": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "1d2ef1fbf7314991a1e9818c49dc0cf8": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "031d3bb7b1a24a6194b5d43f53e5748f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_63f8c04350964d2b903f28bea7b1a082",
+              "IPY_MODEL_237bb39b162c4d6ca329ee2a969617ef",
+              "IPY_MODEL_2f8a3fcd5e9d4d2ea9eb13133106a32c"
+            ],
+            "layout": "IPY_MODEL_e8597177b14e4ced8f2f863013ca1b09"
+          }
+        },
+        "63f8c04350964d2b903f28bea7b1a082": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_37d257fa0f564cc4af65e247876ec743",
+            "placeholder": "​",
+            "style": "IPY_MODEL_e83bc113542d46d3a84eef4bca859a5a",
+            "value": "batch:   0%"
+          }
+        },
+        "237bb39b162c4d6ca329ee2a969617ef": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_d738a0434ce2419a8ea58c52100ee123",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_7307b7a2a26f4b18b835be10bdf6d407",
+            "value": 10
+          }
+        },
+        "2f8a3fcd5e9d4d2ea9eb13133106a32c": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_efd411c908d74ca1a5320f9815d3fc21",
+            "placeholder": "​",
+            "style": "IPY_MODEL_f70be4dd46f94aaf89a44d46647cb0fc",
+            "value": " 0/10 [00:00&lt;?, ?it/s]"
+          }
+        },
+        "e8597177b14e4ced8f2f863013ca1b09": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "37d257fa0f564cc4af65e247876ec743": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "e83bc113542d46d3a84eef4bca859a5a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "d738a0434ce2419a8ea58c52100ee123": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "7307b7a2a26f4b18b835be10bdf6d407": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "efd411c908d74ca1a5320f9815d3fc21": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "f70be4dd46f94aaf89a44d46647cb0fc": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "ab3edb3e720d47998ef2ae30cb286cac": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_62b5dc58d5d145a685fd665e1b8019d1",
+              "IPY_MODEL_4351daab7af443b791e2d927dae6bb73",
+              "IPY_MODEL_fd5a069b59284c2b886aacea3ce16534"
+            ],
+            "layout": "IPY_MODEL_b04b27f4a3d94461b7f5f5fcd0f4a9d3"
+          }
+        },
+        "62b5dc58d5d145a685fd665e1b8019d1": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_ad735e3b855543f5ab9ff26dfb9313a7",
+            "placeholder": "​",
+            "style": "IPY_MODEL_351e4494c83e402db049a7c4b0c81f80",
+            "value": "batch:   0%"
+          }
+        },
+        "4351daab7af443b791e2d927dae6bb73": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_85c64386d03742e4a67e4a8496f86f40",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_2eaee0b62f8e4bb6868bd16ff4f2f235",
+            "value": 10
+          }
+        },
+        "fd5a069b59284c2b886aacea3ce16534": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_c49da9dbc29c45bd81acf7e4b2daf5e7",
+            "placeholder": "​",
+            "style": "IPY_MODEL_75585ed3495c44d7b0b85bbc2b1fe046",
+            "value": " 0/10 [00:00&lt;?, ?it/s]"
+          }
+        },
+        "b04b27f4a3d94461b7f5f5fcd0f4a9d3": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "ad735e3b855543f5ab9ff26dfb9313a7": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "351e4494c83e402db049a7c4b0c81f80": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "85c64386d03742e4a67e4a8496f86f40": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "2eaee0b62f8e4bb6868bd16ff4f2f235": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "c49da9dbc29c45bd81acf7e4b2daf5e7": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "75585ed3495c44d7b0b85bbc2b1fe046": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "051cfe00efec4bf08978527130603332": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_74e4d33cedb54836a8239b9fe273fbc2",
+              "IPY_MODEL_1f125c02b0d1494e86dce61d927a5d80",
+              "IPY_MODEL_9b57cd2efc3a478fbb1c30e17492e623"
+            ],
+            "layout": "IPY_MODEL_c29815289a744a558da61f594798f2c0"
+          }
+        },
+        "74e4d33cedb54836a8239b9fe273fbc2": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_52e3ec2ebccb45e2aa560114b91f9188",
+            "placeholder": "​",
+            "style": "IPY_MODEL_4a1e6417afab4014a352dc414e309a29",
+            "value": "batch:   0%"
+          }
+        },
+        "1f125c02b0d1494e86dce61d927a5d80": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_d0ad16a0be6c43ebaefe3cf711cc76d1",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_a69cdba6b2294d94aa5206f1042be7d5",
+            "value": 10
+          }
+        },
+        "9b57cd2efc3a478fbb1c30e17492e623": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_db0e58b34a5a4540b7dfce7727a3ca00",
+            "placeholder": "​",
+            "style": "IPY_MODEL_db84ce4ca3cd4e0f978a88dd4dcc1124",
+            "value": " 0/10 [00:00&lt;?, ?it/s]"
+          }
+        },
+        "c29815289a744a558da61f594798f2c0": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "52e3ec2ebccb45e2aa560114b91f9188": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "4a1e6417afab4014a352dc414e309a29": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "d0ad16a0be6c43ebaefe3cf711cc76d1": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "a69cdba6b2294d94aa5206f1042be7d5": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "db0e58b34a5a4540b7dfce7727a3ca00": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "db84ce4ca3cd4e0f978a88dd4dcc1124": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "16d9592bb1c34bc7b5ffe9dfea26a6ea": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_827f6bf4f198406b801c1e0b419df66f",
+              "IPY_MODEL_567160aef16a4489961e9822f8b97829",
+              "IPY_MODEL_0e96c54d43104601ad9af688e72f9f5e"
+            ],
+            "layout": "IPY_MODEL_8217b3374c4147ee8228760cf7bec747"
+          }
+        },
+        "827f6bf4f198406b801c1e0b419df66f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_1c32d8a3565e42a18d4785caf3bdf478",
+            "placeholder": "​",
+            "style": "IPY_MODEL_56c1ddd51deb4f3eb3ac8b0bf4490ec6",
+            "value": "batch: 100%"
+          }
+        },
+        "567160aef16a4489961e9822f8b97829": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_99fa959ce4f14901b2af137675bbb741",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_2d62375f068a4afd805943edcf65fc2b",
+            "value": 10
+          }
+        },
+        "0e96c54d43104601ad9af688e72f9f5e": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_789b9ce8e2da4b5792f5c3e49ad01da1",
+            "placeholder": "​",
+            "style": "IPY_MODEL_1fbca0165b79445889d808c23215eadf",
+            "value": " 10/10 [02:53&lt;00:00, 17.27s/it]"
+          }
+        },
+        "8217b3374c4147ee8228760cf7bec747": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "1c32d8a3565e42a18d4785caf3bdf478": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "56c1ddd51deb4f3eb3ac8b0bf4490ec6": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "99fa959ce4f14901b2af137675bbb741": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "2d62375f068a4afd805943edcf65fc2b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "789b9ce8e2da4b5792f5c3e49ad01da1": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "1fbca0165b79445889d808c23215eadf": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "1149024c43c64af5bb020ca86c52877f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_f7cf1c4374fe4c2d82c3e66bb6ae6480",
+              "IPY_MODEL_18a850bc9f9f4ea883ffc692b353fee4",
+              "IPY_MODEL_abe5f6c082394cc59e3e9dd80070b291"
+            ],
+            "layout": "IPY_MODEL_30ac12042af54310973009ff68ccbc5a"
+          }
+        },
+        "f7cf1c4374fe4c2d82c3e66bb6ae6480": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_fb65ccaff5df43e19e3c561e29b0fdae",
+            "placeholder": "​",
+            "style": "IPY_MODEL_d944ea247484426abe3f8c7fbe066eb2",
+            "value": "batch: 100%"
+          }
+        },
+        "18a850bc9f9f4ea883ffc692b353fee4": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_e2910d5e333045669e00452b04729b02",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_685a7520c2a5493a8ba33e35f3e2c546",
+            "value": 10
+          }
+        },
+        "abe5f6c082394cc59e3e9dd80070b291": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_360c977b32b34685a35b175079b3da8b",
+            "placeholder": "​",
+            "style": "IPY_MODEL_77c1aac3802a44baa0a619b021f46aab",
+            "value": " 10/10 [02:53&lt;00:00, 17.44s/it]"
+          }
+        },
+        "30ac12042af54310973009ff68ccbc5a": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "fb65ccaff5df43e19e3c561e29b0fdae": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "d944ea247484426abe3f8c7fbe066eb2": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "e2910d5e333045669e00452b04729b02": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "685a7520c2a5493a8ba33e35f3e2c546": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "360c977b32b34685a35b175079b3da8b": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "77c1aac3802a44baa0a619b021f46aab": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "35c8fcdd43ff458197b8a3bcd640cf22": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_ce92a3f149ef4e5dad97a6187607660a",
+              "IPY_MODEL_1764eafebd4d450a86c79efcb5f477fa",
+              "IPY_MODEL_c23ea4c554eb4bdfaab0221d817e030d"
+            ],
+            "layout": "IPY_MODEL_2db47c7de2e14af989f5eee18dac447c"
+          }
+        },
+        "ce92a3f149ef4e5dad97a6187607660a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_bd2d411042d24686a0edeefd7a601429",
+            "placeholder": "​",
+            "style": "IPY_MODEL_327a8bf1af2141208f0f00c972c96779",
+            "value": "batch: 100%"
+          }
+        },
+        "1764eafebd4d450a86c79efcb5f477fa": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_573979e176b64750aa51313ac67b5b05",
+            "max": 10,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_d63b0f24a3d645a19679364a90a59107",
+            "value": 10
+          }
+        },
+        "c23ea4c554eb4bdfaab0221d817e030d": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_62c379887aa94436b8dd61ba1858d93d",
+            "placeholder": "​",
+            "style": "IPY_MODEL_9b65caa913554fae9550e8dc2fc744c5",
+            "value": " 10/10 [02:55&lt;00:00, 17.60s/it]"
+          }
+        },
+        "2db47c7de2e14af989f5eee18dac447c": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "bd2d411042d24686a0edeefd7a601429": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "327a8bf1af2141208f0f00c972c96779": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "573979e176b64750aa51313ac67b5b05": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "d63b0f24a3d645a19679364a90a59107": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "62c379887aa94436b8dd61ba1858d93d": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "9b65caa913554fae9550e8dc2fc744c5": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/denisovich14072006-afk/linear-regression-task/blob/main/%D0%BF%D0%BE%D1%81%D0%BB%D0%B5%D0%B4%D0%BD%D1%8F%D1%8F%20%D0%B2%D0%B5%D1%80%D1%81%D0%B8%D1%8F.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FUjXwauVwuYb"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Новый раздел"
+      ],
+      "metadata": {
+        "id": "Ab2caDlCwy66"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы для независимых методов ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция оценки ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    if method == 'ne':\n",
+        "        return mse_ne\n",
+        "    if method == 'gd':\n",
+        "        w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "        return mean_squared_error(y, Xwb @ w)\n",
+        "    if method == 'sgd':\n",
+        "        w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "        return mean_squared_error(y, Xwb @ w)\n",
+        "    if method == 'lr_sk':\n",
+        "        return mse_lr_sk\n",
+        "    if method == 'sgd_sk':\n",
+        "        sgd_sk = SGDRegressor(fit_intercept=True, max_iter=epochs, eta0=lr,\n",
+        "                              learning_rate='constant', penalty=None, tol=None,\n",
+        "                              batch_size=batch_size, random_state=42)\n",
+        "        sgd_sk.fit(X, y)\n",
+        "        return mean_squared_error(y, sgd_sk.predict(X))\n",
+        "    raise ValueError('Unknown method')\n",
+        "\n",
+        "# ---------- Диапазоны параметров ----------\n",
+        "lr_range = np.logspace(-8, 0, 9)\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- График 1: learning rate ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n",
+        "    plt.semilogx(lr_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Learning rate')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от learning rate')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 2: эпохи ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n",
+        "    plt.semilogx(epochs_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от количества эпох')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 3: размер батча ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n",
+        "    plt.semilogx(batch_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Batch size')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от размера батча')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 953
+        },
+        "id": "oEzoSPneyUfG",
+        "outputId": "28e789d9-70b6-4787-c8e0-907213e76edb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/3602992315.py:25: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/3602992315.py:25: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "Input contains NaN.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_406/3602992315.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmethods\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0mmses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcompute_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEFAULT_EPOCHS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEFAULT_BATCH\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlr_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Learning rate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/tmp/ipykernel_406/3602992315.py\u001b[0m in \u001b[0;36mcompute_mse\u001b[0;34m(method, lr, epochs, batch_size)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgradient_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXwb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sgd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msgd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m                     )\n\u001b[1;32m    215\u001b[0m                 ):\n\u001b[0;32m--> 216\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_namespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m     _, y_true, y_pred, sample_weight, multioutput = (\n\u001b[0;32m--> 565\u001b[0;31m         _check_reg_targets_with_floating_dtype(\n\u001b[0m\u001b[1;32m    566\u001b[0m             \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         )\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets_with_floating_dtype\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, xp)\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mdtype_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_matching_floating_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     )\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype, xp)\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1107\u001b[0;31m             _assert_all_finite(\n\u001b[0m\u001b[1;32m   1108\u001b[0m                 \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m     _assert_all_finite_element_wise(\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    167\u001b[0m                 \u001b[0;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m             )\n\u001b[0;32m--> 169\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Input contains NaN."
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "# Загрузите ваш CSV в Colab (например, через files.upload())\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с защитой от NaN ----------\n",
+        "BIG_MSE = 1e10  # значение, возвращаемое при расходимости\n",
+        "\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return BIG_MSE\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return BIG_MSE\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return BIG_MSE\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return BIG_MSE  # при любой ошибке (например, взрыв градиента)\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "# learning rate теперь ограничен 0.1, чтобы избежать расходимости\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- График 1: влияние learning rate ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n",
+        "    plt.semilogx(lr_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Learning rate')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от learning rate')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n",
+        "    plt.semilogx(epochs_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от количества эпох')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n",
+        "    plt.semilogx(batch_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Batch size')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от размера батча')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "XSp9L7NvzgH3",
+        "outputId": "e0d95483-cd7b-477d-b714-bd149ad3f653"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in subtract\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIoCAYAAABeertyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkHFJREFUeJzs3Xd4VGX6//H3pCekUNIoIaHXQOgiIqAUBXVF/dkFLLiu61r4upZVmgUEG/aGgnXByrqKLIgiLrAghACKICAktDQgpJE65/fHOEOGJCSBJOck83lx5ZqZM8+cuWfuJMydp9kMwzAQERERERGRSnmZHYCIiIiIiIjVqXASERERERGpggonERERERGRKqhwEhERERERqYIKJxERERERkSqocBIREREREamCCicREREREZEqqHASERERERGpggonERERERGRKqhwEhERsSibzcaMGTPMDqNKcXFxTJo0yewwRETqlAonEWnQXn/9dcaMGUNUVBS+vr5ER0czbNgw3nvvPex2u9nhyRmaNGkSNpuN0NBQTpw4Ue7+Xbt2YbPZsNlsPPPMM2737du3j5tvvpkOHToQEBBAdHQ0559/PtOnT3drN3z4cNc5Tv3q2rVrnb4+sY78/HxmzJjBqlWrzA5FRCzOx+wARETOxrvvvkvLli2ZOnUqoaGhZGVl8b///Y9JkybxzTff8M9//tPsEOUM+fj4kJ+fz7///W+uvvpqt/s+/PBDAgICKCgocDu+e/duBgwYQGBgILfccgtxcXEcPnyYxMRE5syZw8yZM93at2nThtmzZ5d77rCwsNp/QY3Yzp078fJqmH+Lzc/Pd31fDB8+3NxgRMTSVDiJSIO2evVqfH193Y7dfffdtGjRgpdffpnZs2cTFxdnTnByVvz9/RkyZAj//Oc/yxVOH330EePGjeOzzz5zO/7888+Tm5tLUlISsbGxbvelp6eXe46wsDBuvPHG2g++ASspKcFut+Pn51ftx/j7+9dhRDVzJvGLiFRHw/zzkIjIH04tmpycxVLZv4L/61//Yty4cbRq1Qp/f386dOjA448/TmlpqdtjTx3CFR4ezrhx4/j555/d2lU0/+Tpp5/GZrOV+8t1QUEBM2bMoHPnzgQEBNCyZUuuuOIK9uzZAziGl9lsNhYuXOj2uL/+9a/YbDa3+SMLFy7EZrPh5+dHRkaGW/t169a54t64caPbfZ988gn9+vUjMDCQ8PBwbrzxRg4ePFjuvduxYwdXX301ERERBAYG0qVLFx555BEAZsyYUenwNueXc8jT8OHD6dmzZ7nz18T111/PN998Q1ZWluvYTz/9xK5du7j++uvLtd+zZw9t2rQpVzQBREZGnlUsp0pPT+fWW28lKiqKgIAAevfuzbvvvuu635nT032dybyggwcPcssttxAVFYW/vz89evTgnXfecWtTVFTEtGnT6NevH2FhYTRp0oShQ4fy/fffu7VzxvjMM88wb948OnTogL+/P9u3b3flevfu3UyaNImmTZsSFhbGzTffTH5+vtt5Tp3j5PweXbNmDVOmTCEiIoImTZowfvz4ct+zdrudGTNm0KpVK4KCghgxYgTbt2+v1ryp08Vfnfdg3759REREADBz5kxXXsr+XO/YsYOrrrqK5s2bExAQQP/+/fnyyy+rSpOINELqcRKRRiErK4uSkhJycnLYtGkTzzzzDNdeey1t27Z1tVm4cCHBwcFMmTKF4OBgvvvuO6ZNm0Z2djZPP/202/m6du3KI488gmEY7Nmzh+eee46xY8eSkpJy2hgqGvZVWlrKJZdcwsqVK7n22mu55557yMnJYcWKFfz888906NChwvPt3r2bt956q9Ln8/b25oMPPuC+++5zHVuwYEGFQ9gWLlzIzTffzIABA5g9ezZpaWm88MILrFmzhs2bN9O0aVMAtm7dytChQ/H19eX2228nLi6OPXv28O9//5snn3ySK664go4dO7rOe99999GtWzduv/1217Fu3bpVGnNNXXHFFdxxxx18/vnn3HLLLYCjt6lr16707du3XPvY2Fi+/fZbvvvuOy644IIqz19aWkpmZma544GBgTRp0qTSx504cYLhw4eze/du7rrrLtq1a8cnn3zCpEmTyMrK4p577iEiIoL333/f9ZjPP/+cL774wu1YZbmvTFpaGueccw42m4277rqLiIgIvvnmG2699Vays7O59957AcjOzmb+/Plcd911TJ48mZycHN5++23GjBnDhg0bSEhIcDvvggULKCgo4Pbbb8ff35/mzZu77rv66qtp164ds2fPJjExkfnz5xMZGcmcOXOqjPdvf/sbzZo1Y/r06ezbt4958+Zx1113sXjxYlebhx9+mLlz53LppZcyZswYtmzZwpgxY8p9D59ORfFX5z2IiIjgtdde4y9/+Qvjx4/niiuuAKBXr14A/PLLLwwZMoTWrVvz0EMP0aRJEz7++GMuv/xyPvvsM8aPH1/tGEWkETBERBqBLl26GIDra8KECUZxcbFbm/z8/HKP+/Of/2wEBQUZBQUFrmPDhg0zhg0b5tbuH//4hwEY6enprmOAMX36dNftBx54wIiMjDT69evn9vh33nnHAIznnnuu3PPb7XbDMAxj7969BmAsWLDAdd/VV19t9OzZ04iJiTEmTpzoOr5gwQIDMK677jojPj7edTwvL88IDQ01rr/+egMwfvrpJ8MwDKOoqMiIjIw0evbsaZw4ccLV/quvvjIAY9q0aa5j559/vhESEmIkJydXGOepYmNj3WIra9iwYUaPHj0qvK8qEydONJo0aWIYhmFcddVVxoUXXmgYhmGUlpYa0dHRxsyZM13v2dNPP+163M8//2wEBgYagJGQkGDcc889xpIlS4y8vLwK4yv7PVP2689//vNp45s3b54BGB988IHrWFFRkTF48GAjODjYyM7OLveY6dOnGzX9b/fU77Fbb73VaNmypZGZmenW7tprrzXCwsJc3+MlJSVGYWGhW5tjx44ZUVFRxi233OI65nwPQ0ND3b63y8Zbtr1hGMb48eONFi1auB079fvA+T06cuRIt++d++67z/D29jaysrIMwzCM1NRUw8fHx7j88svdzjdjxgwDqPR7qzrxV/c9yMjIKPc+O1144YVGfHy82+8Hu91unHvuuUanTp1OG5uIND4aqicijcKCBQtYsWIFH374IbfeeisffvihWy8IOHoRnHJycsjMzGTo0KHk5+ezY8cOt7bFxcVkZmaSkZHBunXr+OKLL+jVqxfh4eEVPv/Bgwd56aWXmDp1KsHBwW73ffbZZ4SHh/O3v/2t3ONsNluF59u0aROffPIJs2fPrnTS/U033cSOHTtcQ/I+++wzwsLCuPDCC93abdy4kfT0dO68804CAgJcx8eNG0fXrl35+uuvAcjIyGD16tXccsstbj11p4uzKs4enczMTIqKis7oHNdffz2rVq0iNTWV7777jtTU1AqH6QH06NGDpKQkbrzxRvbt28cLL7zA5ZdfTlRUVIW9d3FxcaxYsaLcl7PnpjJLly4lOjqa6667znXM19eXu+++m9zcXH744Yczeq2nYxgGn332GZdeeimGYbje18zMTMaMGcPx48dJTEwEHL2Rzjk+drudo0ePUlJSQv/+/V1tyrryyitdQ9ZOdccdd7jdHjp0KEeOHCE7O7vKmG+//Xa3752hQ4dSWlpKcnIyACtXrqSkpIQ777zT7XEV/aycTkXx1/Q9ONXRo0f57rvvuPrqq12/LzIzMzly5Ahjxoxh165dFQ51FZHGy6MLp9WrV3PppZfSqlUrbDYbS5YsqdHjCwoKmDRpEvHx8fj4+HD55ZdX2G7VqlX07dsXf39/OnbsWG4Og4icvcGDBzNy5Eiuv/565s+fz2OPPcaCBQtYs2aNq80vv/zC+PHjCQsLIzQ0lIiICNfCAMePH3c739q1a4mIiCAyMpJzzz2XkpISPvnkk0oLiOnTp9OqVSv+/Oc/l7tvz549dOnSBR+f6o+Ofuihhxg6dCiXXHJJpW0iIiIYN26ca37LO++8w8SJE8sVWs4PqV26dCl3jq5du7ru//333wHOel5SWTt27CAiIsJtvtRHH31Uo3OMHTuWkJAQFi9ezIcffsiAAQPchgueqnPnzrz//vtkZmaydetWZs2ahY+PD7fffjvffvutW9smTZowcuTIcl9VLUeenJxMp06dyr3XzmGKzve0NmVkZJCVlcWbb77pek+dXzfffDPgvgDGu+++S69evQgICKBFixZERETw9ddfl/teB2jXrl2lz3tqEd2sWTMAjh07VmXMVT3W+T6dms/mzZu72lZHZfHX5D041e7duzEMg6lTp5Z7v51L21e04IiINF4ePccpLy+P3r17c8stt7jGNddEaWkpgYGB3H333eVWdnLau3cv48aN44477uDDDz9k5cqV3HbbbbRs2ZIxY8ac7UsQkUpcddVVPPLII6xfv54hQ4aQlZXFsGHDCA0N5bHHHnPt8ZOYmMiDDz5Ybs+nXr168eyzzwKOD6wvvvgiw4cPJzExkejoaLe2v/76KwsXLuSDDz6odLGKmli+fDnffvst69atq7LtLbfcwoQJE/jb3/7G6tWrmT9/Pj/++ONZx1Bb4uLiXD09R44c4cUXX+Smm26iffv2nHPOOdU6h7+/P1dccQXvvvsuv//+e7U3hPX29iY+Pp74+HgGDx7MiBEj+PDDDxk5cuSZvhxTOb9Hb7zxRiZOnFhhG+fcnA8++IBJkyZx+eWX8/e//53IyEi8vb2ZPXu2a0GSssr2xp7K29u7wuOGYVQZ89k8tiYqir+m78GpnO/3/fffX+n/16cr4EWk8fHowuniiy/m4osvrvT+wsJCHnnkEf75z3+SlZVFz549mTNnjmu1rCZNmvDaa68BsGbNGrdVn5xef/112rVr5/oA1q1bN/773//y/PPPq3ASqUPOTVOdH9xWrVrFkSNH+Pzzzzn//PNd7fbu3Vvh45s1a+b2AXv48OG0atWKBQsW8PDDD7u1ffjhh0lISOCaa66p8FwdOnRg/fr1FBcXV1lYGYbBQw89xPjx46tVWFx88cUEBARw7bXXct5559GhQ4dyhZNzhbmdO3eWWzBh586drvvbt28PUG71wLPh7NFxGjp0KK1bt2b58uXVLpzAMVzvnXfewcvLi2uvvbbGcfTv3x+Aw4cP1/ixFYmNjWXr1q3Y7Xa3XifnkM+KVvU7WxEREYSEhFBaWlpl8ffpp5/Svn17Pv/8c7de0lM3ATab833avXu3W6/RkSNHqtWjdTrVfQ8q60V2/jz4+vo22GJbRGqXRw/Vq8pdd93FunXrWLRoEVu3buX//b//x0UXXcSuXbuqfY5169aV+4U7ZsyYav0lWUSqtnTp0gqPv/XWW9hsNleh4Cygyv6lu6ioiFdffbVaz+MsxAoLC92Or1u3jn/961889dRTlX4Au/LKK8nMzOTll18ud9+pf3l3/r6paHW+ivj4+DBhwgS2bt3qWnXuVP379ycyMpLXX3/dLf5vvvmGX3/9lXHjxgGOD+bnn38+77zzTrnVA2urh8D5V/zKeiIqM2LECB5//HFefvnlcj1+Zf34448UFxeXO+78PqlouOKZGDt2LKmpqW6rw5WUlPDSSy8RHBzMsGHDauV5yvL29ubKK6/ks88+q7C4LbvMd0Xf7+vXr7fc/z0XXnghPj4+rj9COlX0s1JT1X0PgoKCAMr98TMyMpLhw4fzxhtvVFhwn7qsuog0fh7d43Q6KSkpLFiwgJSUFFq1agU4uuuXLVvGggULmDVrVrXOk5qaSlRUlNuxqKgosrOzOXHixGmHR4hI1a6//nq6du3K+PHjiYqKIiMjg2+++Ybvv/+eRx55hPj4eADOPfdcmjVrxsSJE7n77rux2Wy8//77lRYEaWlpfPDBBwBkZmbyxhtv4OPjU27O0fLlyxk1atRp/yI9YcIE3nvvPaZMmcKGDRsYOnQoeXl5fPvtt9x555386U9/cjvf5MmTa/QB//HHH+fvf/97pXNCfH19mTNnDjfffDPDhg3juuuucy1HHhcX57ac+Ysvvsh5551H3759uf3222nXrh379u3j66+/JikpqdoxOeXm5rJs2TLAMdn+xRdfxNfX11WsVZeXlxePPvpole3mzJnDpk2buOKKK1zD1hITE3nvvfdo3rx5uUUfjh8/7srzqU63Me7tt9/OG2+8waRJk9i0aRNxcXF8+umnrFmzhnnz5hESElL9F1cDTz31FN9//z2DBg1i8uTJdO/enaNHj5KYmMi3337L0aNHAbjkkkv4/PPPGT9+POPGjWPv3r28/vrrdO/endzc3DqJ7UxERUVxzz338Oyzz3LZZZdx0UUXsWXLFr755hvCw8PPeFESqP57EBgYSPfu3Vm8eDGdO3emefPm9OzZk549e/LKK69w3nnnER8fz+TJk2nfvj1paWmsW7eOAwcOsGXLltp4G0SkgVDhVIlt27ZRWlpK586d3Y4XFhbSokULk6ISkVM99dRT/Pvf/+bFF18kPT2d4OBgBg0axNKlS92G4rZo0YKvvvqK//u//+PRRx+lWbNm3HjjjVx44YUVDpvdsWMHN910EwBNmzalR48ePPfcc64hX042m42nnnrqtDF6e3uzdOlSnnzyST766CM+++wzWrRo4fpAVlZgYGC15/A4+fn5Vbran9OkSZMICgriqaee4sEHH3RtRjpnzhzXHk4AvXv35n//+x9Tp07ltddeo6CggNjYWK6++uoaxeSUnJzsyoPzffzyyy/L7SNUW/7xj3/w0Ucf8cMPP/Dhhx+Sn59Py5Ytufbaa5k6dWq5RQQOHDjgyvOpTlc4BQYGsmrVKh566CHeffddsrOz6dKlCwsWLDijTW2rKyoqig0bNvDYY4/x+eef8+qrr9KiRQt69Ojhtq/SpEmTSE1N5Y033uA///kP3bt354MPPuCTTz5xbVBsFXPmzCEoKIi33nqLb7/9lsGDB7N8+XLOO+88t1Uga6om78H8+fP529/+xn333UdRURHTp0+nZ8+edO/enY0bNzJz5kwWLlzIkSNHiIyMpE+fPkybNu0sX7mINDQ2o7ZnaDZQNpuNL774wrUy3uLFi7nhhhv45Zdfyg0pCQ4OLjdUxLnp4akr851//vn07duXefPmuY4tWLCAe++9t1qr+oiIiHiarKwsmjVrxhNPPMEjjzxidjgiIoB6nCrVp08fSktLSU9PZ+jQoWd8nsGDB5ebg7FixQoGDx58tiGKiIg0eBUNW3f+sdG5GJOIiBV4dOGUm5vL7t27Xbf37t1LUlISzZs3p3Pnztxwww1MmDCBZ599lj59+pCRkcHKlSvp1auXa3z+9u3bKSoq4ujRo+Tk5LjmADiHodxxxx28/PLLPPDAA9xyyy189913fPzxx64NJ0VERDzZ4sWLWbhwIWPHjiU4OJj//ve//POf/2T06NEMGTLE7PBERFw8eqjeqlWrGDFiRLnjEydOZOHChRQXF/PEE0/w3nvvcfDgQcLDwznnnHOYOXOma15CXFxchRsdln1bV61axX333cf27dtp06YNU6dOrdMx8CIiIg1FYmIiDzzwAElJSWRnZxMVFcWVV17JE088QXBwsNnhiYi4eHThJCIiIiIiUh3ax0lERERERKQKKpxERERERESq4HGLQ9jtdg4dOkRISMhZbawnIiIiIiINm2EY5OTk0KpVK7y8Tt+n5HGF06FDh4iJiTE7DBERERERsYj9+/fTpk2b07bxuMIpJCQEcLw5oaGhJkcDxcXFLF++nNGjR+Pr62t2OB5P+bAe5cRalA/rUU6sRzmxFuXDeqyUk+zsbGJiYlw1wul4XOHkHJ4XGhpqmcIpKCiI0NBQ079xRPmwIuXEWpQP61FOrEc5sRblw3qsmJPqTOHR4hAiIiIiIiJVUOEkIiIiIiJSBRVOIiIiIiIiVVDhJCIiIiIiUgUVTiIiIiIiIlVQ4SQiIiIiIlIFFU4iIiIiIiJVUOEkIiIiIiJSBRVOIiIiIiIiVVDhJCIiIiIiUgUVTiIiIiIiIlVQ4SQiIiIiIlIFFU4iIiIiIiJVUOFkIrvd4NCuLPIP+XBoVxZ2u2F2SB5N+bAee0kJh3/8Ef/f9nL4xx+xl5SYHZJHKy4qZNWyRez/bROrli2iuKjQ7JA8nnJiPcqJtSgf1tOQc2IzDMO0T4erV6/m6aefZtOmTRw+fJgvvviCyy+//LSPWbVqFVOmTOGXX34hJiaGRx99lEmTJlX7ObOzswkLC+P48eOEhoae3Qs4C3s2p/Pj4l3kZZ38ZmnS1J+h13SiQ59I0+LyVMqH9ez58mt+XF5AXkkz17EmPscYOjqADpeNMzEyz/TFR6+z+38RBBWdzEe+3zE6npPB+OvvMDEyz6WcWI9yYi3Kh/VYMSc1qQ1M7XHKy8ujd+/evPLKK9Vqv3fvXsaNG8eIESNISkri3nvv5bbbbuM///lPHUdau/ZsTmfZGz+7fUgHyMsqZNkbP7Nnc7pJkXkm5cN69nz5NcuWBpBX0tTteF5JGMuWBrDny6/NCcxDffHR6xxc3YnAoqZuxwOLmnJwdSe++Oh1cwLzYMqJ9Sgn1qJ8WE9jyImPmU9+8cUXc/HFF1e7/euvv067du149tlnAejWrRv//e9/ef755xkzZkxdhVmr7HaDHxfvOm2bHxfvok3X5nh52eopKs/lyMdvp22jfNQve0kJP/6nAAgATn3PvQA7Py4voM2FhXj5mPorzCMUFxWyZ13kH9lwz4cNGwYGu9dFkH9pPr5+/uYE6WGUE+tRTqxF+bCeauXkfxEUX1Vo6ZyYOlSvLJvNVuVQvfPPP5++ffsyb94817EFCxZw7733cvz48QofU1hYSGHhyZ6E7OxsYmJiyMzMNGWo3qFdWXz14rZ6f14RERERESvrPO4gwy+6tl6fMzs7m/Dw8GoN1WtQf65NTU0lKirK7VhUVBTZ2dmcOHGCwMDAco+ZPXs2M2fOLHd8+fLlBAUF1Vmslck/5AOUj1NERERExJPt3LGLfPvSen3O/Pz8ardtUIXTmXj44YeZMmWK67azx2n06NHm9ThtqbrH6aI7etCyY1g9ROTZDu8+zrLXf6mynfJRf1LXruWbz6tud/GYDKL7dK/7gDzc6nU/sOeHqt/npkN+ok/f3vUQkWxO3ELWmgFVtlNO6o9yYi3Kh/VUNyddunZi+EVj6yGik7Kzs6vdtkEVTtHR0aSlpbkdS0tLIzQ0tMLeJgB/f3/8/cuPlfT19cXX17dO4jydmK7hNGnqX24hgrKCm/nTrlek5tTUg3a9/GnSdLfyYSFxF5xPky//RV5JGBWvX2Mn2CeLuEuv1BynenBhq85sW/cVgUVNy41LBzAwOOGXxe3X3GvpcemNSadOI5j3k3JiJcqJtSgf1lPdnAwfc3W9fz6vyfM1qH2cBg8ezMqVK92OrVixgsGDB5sUUc15edkYek2n07Y57+pO+pBeT5QP6/Hy8WHoaOfCEPZT7rUDNs4bHaiiqZ74+vnT8ZwMwPEfW1nO2x3PydCHj3qknFiPcmItyof1NJacmFo45ebmkpSURFJSEuBYbjwpKYmUlBTAMcxuwoQJrvZ33HEHv//+Ow888AA7duzg1Vdf5eOPP+a+++4zI/wz1qFPJBf9uSdNmrp/cwQ38+eiP/fUvkH1TPmwng6XjeOiQT/TxOuo2/FgnywuGlugfZzq2fjr76D1+bvI98tyO37CL4vW5+/SfigmcObkhHJiGcqJtSgf1tMYcmLqqnqrVq1ixIgR5Y5PnDiRhQsXMmnSJPbt28eqVavcHnPfffexfft22rRpw9SpUxvkBrjgWAp7/45M1v6wgXOHDSSma7h6NkykfFjMNw9h/98bHIicyPb8OLr360Wb889XT5NJDMNg+KLhBGQ0Z2hhZwZ17ekYUmHxvw42dsVFhaz6z8fs3LHLMTdAOTGdcmItyof1WC0nNakNTP0EMnz4cE5Xty1cuLDCx2zevLkOo6o/Xl42WnVqStCuElp1aqoP6SZTPiwmZS1eNjuthgxmc3IALYcOVdFkouTsZI4WHcWvWS7dQm5k+EWXmTJPVNz5+vkz/KJrybcvZfhFY5UTC1BOrEX5sJ6GnJMGNcdJRDxEQTakOlafNGIazhzGxiwxPRGAnuE98bGpgBUREc+jwklErGf/BjDs0DQWQluaHY0Am9I2AdAnoo/JkYiIiJhDhZOIWE/KWsdl7BBz4xAXZ+HUN7KvyZGIiIiYQ4WTiFhP8jrHZayG6VlBal4qB3MP4mXzold4L7PDERERMYUKJxGxluICOLjRcb3tuebGIgAkpjnmN3Vt3pUmvk1MjkZERMQcKpxExFoOJUJpETSJhBYdzI5GOLkwRL+ofiZHIiIiYh4VTiJiLcnO+U2DwaYl4a3AOb+pX6QKJxER8VwqnETEWpyFk4bpWUJWQRa7s3YD0CdKK+qJiIjnUuEkItZhL3UsRQ4Qq8LJCjanOzYcbx/WnuYBzU2ORkRExDwqnETEOlK3QVEO+IdCVA+zoxHKLEMepWXIRUTEs6lwEhHrcA7TixkEXt7mxiLAyYUhtH+TiIh4OhVOImIdro1vNUzPCvKL8/n1yK8A9I/qb3I0IiIi5lLhJCLWYBhlNr5V4WQFWzK2UGKU0LJJS1oGtzQ7HBEREVOpcBIRa8jcBfmZ4O0PrbR6mxW4hulpfpOIiIgKJxGxCOcwvTYDwMff3FgEgMQ0bXwrIiLipMJJRKyh7Ma3Yrri0mK2ZGwBtPGtiIgIqHASEatwzm9qq8LJCn458guFpYU0829Gu7B2ZocjIiJiOhVOImK+rP1wPAVs3hAz0OxoBPf5TTabzeRoREREzKfCSUTMl/JHb1PLXuAfYm4sApTZ+Fb7N4mIiAAqnETECpzzm9pqGXIrsBt2NqdvBrQwhIiIiJMKJxExX4r2b7KSXcd2kVOUQ5BPEF2adzE7HBEREUtQ4SQi5so7Ahk7HNe1MIQlOIfpJUQm4OPlY3I0IiIi1qDCSUTM5extiugKTVqYG4sAJxeG0DA9ERGRk1Q4iYi5UrQMuZUYhuHa+FYLQ4iIiJykwklEzJW8xnGp+U2WsD9nPxknMvD18iU+It7scERERCxDhZOImKcwFw5vdVxX4WQJzvlN8eHx+Hv7mxyNiIiIdahwEhHzHNgARimEtYWwNmZHI7hvfCsiIiInqXASEfM492+K1fwmq9DGtyIiIhVT4SQi5knW/k1Wkp6fzv6c/XjZvEiITDA7HBEREUtR4SQi5igphIMbHdfbqnCyAudqel2adSHEL8TkaERERKxFhZOImOPQZigpgKBwCO9kdjRCmWF6mt8kIiJSjgonETFH2flNNpu5sQigjW9FREROR4WTiJjDtfGthulZwfHC4+w6tguAPpF9TI5GRETEelQ4iUj9s5dCyv8c17WiniUkpSdhYBAXGkd4YLjZ4YiIiFiOCicRqX9pv0BhNviFQFS82dEIsCndMb9Jw/REREQqpsJJROqfc35TzEDw9jE3FgG0MISIiEhVVDiJSP1L0ca3VnKi5ATbM7cD2vhWRESkMiqcRKR+GUaZjW+HmBuLALAtYxslRglRQVG0Dm5tdjgiIiKWpMJJROrXkT2Qlw7eftBKvRtWUHaYnk1Lw4uIiFRIhZOI1C/nML3W/cA3wNxYBCizMESkFoYQERGpjAonEalfrmF62r/JCortxWzN2ApoRT0REZHTUeEkIvUreY3jUhvfWsKvR37lRMkJwvzDaN+0vdnhiIiIWJYKJxGpP9mHICsZbF6OpcjFdIlpiQD0ieyDl03/JYiIiFRG/0uKSP1x7t8UHQ8BoebGIsDJ+U39o/qbHImIiIi1qXASkfrjLJw0TM8S7Ibd1eOk/ZtEREROT4WTiNSfFC0MYSV7svaQXZRNoE8gXVt0NTscERERS1PhJCL1I/8opG93XG872NxYBDg5v6l3RG98vXxNjkZERMTaVDiJSP1I+Z/jskUnCI4wNxYB3De+FRERkdNT4SQi9cO58a2G6VmCYRhaGEJERKQGVDiJSP3QxreWciD3AOn56fh4+RAfHm92OCIiIpanwklE6l5RHhxOclzX/CZLcM5v6tGiBwE+ASZHIyIiYn0qnESk7h34CewlENoGmrY1OxoBEtMdhVO/qH4mRyIiItIwqHASkbrnGqY3GGw2c2MR4OTCECqcREREqkeFk4jUveQ1jksN07OEzBOZJGcnY8NGQmSC2eGIiIg0CCqcRKRulRTBgY2O67FDzI1FgJPzmzo360yoX6jJ0YiIiDQMKpxEpG4d3gIlJyCwOUR0MTsaQfs3iYiInAkVTiJSt8oO09P8JktwLgyhwklERKT6VDiJSN1K0f5NVpJTlMPOozsB6BephSFERESqS4WTiNQdu71M4aSFIaxgc/pmDAzahrQlIijC7HBEREQaDBVOIlJ30rdDwXHwbQLRvc2ORji5MISG6YmIiNSMCicRqTvO3qaYgeDtY24sAmjjWxERkTOlwklE6o5zYQjNb7KEgpICtmVuAzS/SUREpKZUOIlI3TAMSP6jx0kb31rCtsxtlNhLiAiMoE1IG7PDERERaVBUOIlI3Ti2F3JTwcsX2vQ3Oxrh5PymflH9sGlpeBERkRpR4SQidSN5reOydV/wDTQ3FgG08a2IiMjZUOEkInVDw/QspcRewpaMLQD0jVThJCIiUlMqnESkbqT80eMUO8TcOASAnUd3kl+ST4hfCJ2adTI7HBERkQZHhZOI1L6cVDj6O2BzLEUuptuYthFw9DZ52fSrX0REpKb0v6eI1D7n/KbonhDY1NRQxKHswhAiIiJScyqcRKT2OTe+bav9m6zAbthdG99qYQgREZEzo8JJRGqfs8cpVgtDWMHe43vJKswiwDuA7s27mx2OiIhIg6TCSURq14ksSPvFcV09TpbgXIa8d0RvfL19TY5GRESkYVLhJCK1a/96wIDmHSAkyuxoBO3fJCIiUhtML5xeeeUV4uLiCAgIYNCgQWzYsOG07efNm0eXLl0IDAwkJiaG++67j4KCgnqKVkSqlLzGcalhepah+U0iIiJnz9TCafHixUyZMoXp06eTmJhI7969GTNmDOnp6RW2/+ijj3jooYeYPn06v/76K2+//TaLFy/mH//4Rz1HLiKVcm58q/2bLOFQ7iFS81LxsfnQK7yX2eGIiIg0WKYWTs899xyTJ0/m5ptvpnv37rz++usEBQXxzjvvVNh+7dq1DBkyhOuvv564uDhGjx7NddddV2UvlYjUk6J8OLTZcb2tepyswDlMr3uL7gT5BpkcjYiISMPlY9YTFxUVsWnTJh5++GHXMS8vL0aOHMm6desqfMy5557LBx98wIYNGxg4cCC///47S5cu5aabbqr0eQoLCyksLHTdzs7OBqC4uJji4uJaejVnzhmDFWIR5eNs2ZL/h4+9GCM4mpLg1lAL76NycnZ+OvwT4FgYojbeQ+XDepQT61FOrEX5sB4r5aQmMZhWOGVmZlJaWkpUlPvk8aioKHbs2FHhY66//noyMzM577zzMAyDkpIS7rjjjtMO1Zs9ezYzZ84sd3z58uUEBVnnr68rVqwwOwQpQ/k4M50PL6EbcNAnjk3ffFOr51ZOzsx/s/8LgJFisPTw0lo7r/JhPcqJ9Sgn1qJ8WI8VcpKfn1/ttqYVTmdi1apVzJo1i1dffZVBgwaxe/du7rnnHh5//HGmTp1a4WMefvhhpkyZ4rqdnZ1NTEwMo0ePJjQ0tL5Cr1RxcTErVqxg1KhR+PpqmWCzKR9nx/ujtwFoOegKxvYfWyvnVE7O3NGCozz6+aMA3HrRrYT5h531OZUP61FOrEc5sRblw3qslBPnaLTqMK1wCg8Px9vbm7S0NLfjaWlpREdHV/iYqVOnctNNN3HbbbcBEB8fT15eHrfffjuPPPIIXl7lp2z5+/vj7+9f7rivr6/piSrLavF4OuXjDJQWwwHHsDDv9ufhXcvvn3JSc9sObQOgY9OOhAeH1+q5lQ/rUU6sRzmxFuXDeqyQk5o8v2mLQ/j5+dGvXz9WrlzpOma321m5ciWDB1c8qTw/P79cceTt7Q2AYRh1F6yIVO3wVijOh4CmENHN7GiEkwtD9IvqZ3IkIiIiDZ+pQ/WmTJnCxIkT6d+/PwMHDmTevHnk5eVx8803AzBhwgRat27N7NmzAbj00kt57rnn6NOnj2uo3tSpU7n00ktdBZSImCRlreOy7WCooPdX6p8KJxERkdpjauF0zTXXkJGRwbRp00hNTSUhIYFly5a5FoxISUlx62F69NFHsdlsPProoxw8eJCIiAguvfRSnnzySbNegog4Jf9ROGnjW0vILcpl57GdAPSN1Ma3IiIiZ8v0xSHuuusu7rrrrgrvW7VqldttHx8fpk+fzvTp0+shMhGpNrsdUrTxrZVsydiC3bDTJrgNUU2iqn6AiIiInJbG04jI2cvYASeOgW8QtOxtdjTCyWF6faPU2yQiIlIbVDiJyNlzzm9q0x+8tWKRFWh+k4iISO1S4SQiZy9Zw/SspLC0kJ8zfwZUOImIiNQWFU4icnYM4+TCEG21MIQV/Jz5M0X2IloEtKBtSFuzwxEREWkUVDiJyNnJSoacQ+DlA20GmB2NAIlpiYBjfpPNZjM5GhERkcZBhZOInB3nML1WfcAvyNxYBND8JhERkbqgwklEzk7yGselhulZQqm9lKSMJECFk4iISG1S4SQiZ0f7N1nKzmM7ySvOI8Q3hE5NO5kdjoiISKOhwklEzlxuOhzZDdig7SCzoxFODtNLiEzA28vb5GhEREQaDxVOInLmnKvpRXaHwGbmxiKA+8IQIiIiUntUOInImXMN0zvX3DgEAMMwSEx3FE79o/qbHI2IiEjjosJJRM6cs8cpVgtDWMHe7L0cLTiKv7c/PVr0MDscERGRRkWFk4icmYLjkLrNcb2tepyswDlMLz48Hl9vX5OjERERaVxUOInImdm/ATCgWTsIbWl2NMLJwknLkIuIiNQ+FU4icmZcw/TU22QVzhX1tDCEiIhI7VPhJCJnxlk4aeNbS0jNS+VQ3iG8bd4kRCSYHY6IiEijo8JJRGquuAAOOYaFqcfJGpy9Td2adyPIN8jkaERERBofFU4iUnMHN0FpEQRHQfP2ZkcjaJieiIhIXVPhJCI1V3aYns1mbiwCaONbERGRuqbCSURqLsW5MMQQc+MQAI4VHGPP8T0A9I1U4SQiIlIXVDiJSM2UlvyxFDna+NYiEtMdvU0dwjrQLKCZydGIiIg0TiqcRKRmUrdCUS74h0Fkd7OjETRMT0REpD6ocBKRmklZ57hsew54eZsbiwDa+FZERKQ+qHASkZpxbXyrYXpWkF+cz69HfwVUOImIiNQlFU4iUn2GUabHSfs3WUFSRhKlRimtmrQiukm02eGIiIg0WiqcRKT6Mn+D/CPgEwCt+pgdjaBheiIiIvVFhZOIVF/yGsdlmwHg42duLAJo41sREZH6osJJRKov2TlMT/ObrKCotIhtmdsAFU4iIiJ1TYWTiFSfc35TrOY3WcEvR36hsLSQ5gHNaRfazuxwREREGjUVTiJSPVkpcHw/2LwdQ/XEdK5hepF9sdlsJkcjIiLSuKlwEpHqcQ7Ta5UA/sGmhiIOWhhCRESk/qhwEpHqSflj/ybNb7KEUnspm9M3A5rfJCIiUh9UOIlI9bg2vtX8JivYlbWL3OJcmvg2oUuzLmaHIyIi0uipcBKRquVlOvZwAvU4WYRzflNCZALeXt4mRyMiItL4qXASkao5V9OL6AZBzc2NRYCThVO/SM1vEhERqQ8qnESkaq5heuptsgLDMFwLQ2h+k4iISP1Q4SQiVXMVTkPMjUMASMlJ4UjBEfy8/OgZ3tPscERERDyCCicROb3CHEjd6riu+U2W4Bym1zO8J/7e/iZHIyIi4hlUOInI6e1fD4YdmraFsNZmRyOUmd+k/ZtERETqjQonETk958a3GqZnGdr4VkREpP6pcBKR03OuqKdhepaQlpfGgdwDeNm86B3R2+xwREREPIYKJxGpXEkhHNjouK6Nby0hMd3R29SlWReC/YJNjkZERMRzqHASkcodTITSQmgSAS06mh2NoPlNIiIiZlHhJCKVS/ljGfK2g8FmMzcWAVQ4iYiImEWFk4hUzrV/k4bpWcHxwuPsztoNQJ/IPiZHIyIi4llUOIlIxeylkLLecV2FkyVsTt8MQLuwdrQIbGFyNCIiIp5FhZOIVCx1GxTlgH8oRPU0Oxrh5DC9vpF9TY5ERETE86hwEpGKOZchjxkIXt7mxiKA9m8SERExkwonEamY5jdZSn5xPtuPbAdUOImIiJhBhZOIlGcYJwuntiqcrGBr5lZKjBKim0TTKriV2eGIiIh4HBVOIlLekd2Qnwne/tBa82mswDlMT/ObREREzKHCSUTKc/Y2tekPPv7mxiKA9m8SERExmwonESkvuczGt2K64tJitmZsBVQ4iYiImEWFk4iUl6KFIaxk+9HtFJQW0My/Ge3D2psdjoiIiEdS4SQi7o4fgKwUsHk5liIX0zmH6fWJ7IPNZjM5GhEREc+kwklE3CX/sX9TdC/wDzE3FgHKLAwRpYUhREREzKLCSUTcuYbpDTE3DgHAbthJTHcUTv2j+pscjYiIiOdS4SQi7pw9TrFaGMIKdh3bRU5RDkE+QXRp3sXscERERDyWCicROSn/KGT86riuFfUswdnb1DuiNz5ePiZHIyIi4rlUOInISSl/9DaFd4Em4ebGIsDJ+U1ahlxERMRcKpxE5CTn/k0apmcJhmG4VtTTwhAiIiLmUuEkIie5Nr7V/k1WcCDnABknMvDx8iE+PN7scERERDyaCicRcSjMhcNbHNe18a0lbEp39DbFh8cT4BNgcjQiIiKeTYWTiDgc+AmMUgiLgaYxZkcjnNz4tm+khumJiIiYTUs0iYiDa5ie5jdZhTa+FZGGyjAMSkpKKC0tNTsUiouL8fHxoaCgwBLxSP3nxNfXF29v77M+jwonEXFwrqinYXqWkJGfQUpOCjZs9InsY3Y4IiLVVlRUxOHDh8nPzzc7FMBRxEVHR7N//35sNpvZ4Qj1nxObzUabNm0IDg4+q/OocBIRKClyDNUDFU4W4Zzf1KV5F0L8QkyORkSkeux2O3v37sXb25tWrVrh5+dnerFit9vJzc0lODgYLy/NUrGC+syJYRhkZGRw4MABOnXqdFY9TyqcRAQObYaSAghqAeGdzY5GKDNMT/ObRKQBKSoqwm63ExMTQ1BQkNnhAI4P6UVFRQQEBKhwsoj6zklERAT79u2juLj4rAonffeICKSUmd+kYQyW4FwYQhvfikhDpAJFrKS2ej31XS0iZTa+1TA9K8guymbXsV2AFoYQERGxChVOIp7OXgop6x3XtaKeJSSlJ2FgEBsaS3hguNnhiIiIRa1atQqbzUZWVpbZoZyxGTNmkJCQYHYY1aLCScTTpW+HwuPgFwzRvcyORoCNaRsBDdMTEc9VajdYt+cI/0o6yLo9Ryi1G3X6fJMmTcJms/HUU0+5HV+yZInpi1ucrbi4OGw2W7mvU19rfbDZbCxZssTt2P3338/KlSvrPZYzocUhRDydc5hezEDw1q8EK9DCECLiyZb9fJiZ/97O4eMFrmMtwwKYfml3LurZss6eNyAggDlz5vDnP/+ZZs2a1dp5i4qK8PPzq7XznYnHHnuMyZMnux0LCbHGiq3BwcFnvUx4fTG9x+mVV14hLi6OgIAABg0axIYNG07bPisri7/+9a+0bNkSf39/OnfuzNKlS+spWpFGyLXxreY3WcGJkhP8cuQXQPObRMTzLPv5MH/5INGtaAJIPV7AXz5IZNnPh+vsuUeOHEl0dDSzZ88+bbvPPvuMHj164O/vT1xcHM8++6zb/XFxcTz++ONMmDCB0NBQbr/9dhYuXEjTpk356quv6NKlC0FBQVx11VXk5+fz7rvvEhcXR7Nmzbj77rvdNoR9//336d+/PyEhIURHR3P99deTnp5e49fmfHzZryZNmrjuX7p0KZ07dyYwMJARI0awcOFCtyGAFQ2nmzdvHnFxca7bP/30E6NGjSI8PJywsDCGDRtGYmKi2/sCMH78eLy9venVq1eF57bb7Tz22GO0adMGf39/EhISWLZsmev+ffv2YbPZ+PzzzxkxYgRBQUH07t2bdevW1fh9qSlTC6fFixczZcoUpk+fTmJiIr1792bMmDGVfkMUFRUxatQo9u3bx6effsrOnTt56623aN26dT1HLtJIGIY2vrWYbRnbKLGXEBkUSZvgNmaHIyJy1gzDIL+opMqvnIJipn/5CxUNynMem/HldnIKiqt1PsOo2fA+b29vZs2axUsvvcSBAwcqbLNp0yauvvpqrr32WrZt28aMGTOYOnUqCxcudGv3zDPP0Lt3bzZv3szUqVMByM/P58UXX2TRokUsW7aMVatWMX78eJYuXcrSpUt5//33eeONN/j0009d5ykuLubxxx9ny5YtLFmyhH379jFp0qQava6q7N+/nyuuuIJLL72UpKQkbrvtNh566KEanycnJ4eJEyfy3//+l//973906tSJsWPHkpOTAzgKK4AFCxZw8OBBvvvuuwrP88ILL/Dss8/yzDPPsHXrVsaMGcNll13Grl273No98sgj3H///SQlJdG5c2euu+46SkpKahx3TZg6Lue5555j8uTJ3HzzzQC8/vrrfP3117zzzjsVJuydd97h6NGjrF27Fl9fXwC3SldEaujo75CbBt5+0FrzaazAufFtv8h+DX5cvYgIwIniUrpP+89Zn8cAUrMLiJ+xvFrttz82hiC/mn3UHT9+PAkJCUyfPp2333673P3PPfccF154oasY6ty5M9u3b+fpp592K2guuOAC/u///s91+8cff6S4uJjXXnuNDh06AHDVVVfx/vvvk5aWRnBwMN27d2fEiBF8//33XHPNNQDccsstrnO0b9+eF198kQEDBrg2j62uBx98kEcffdTt2DfffMPQoUNdMTl7zrp06cK2bduYM2dOtc/vfM1lvfnmmzRt2pQffviBSy65hIiICACaNm1KdHQ02dnZFZ7nmWee4cEHH+Taa68FYM6cOXz//ffMmzePV155xdXu/vvvZ9y4cQDMnDmTHj16sHv3brp27VqjuGvCtMKpqKiITZs28fDDD7uOeXl5MXLkyEq72r788ksGDx7MX//6V/71r38RERHB9ddfz4MPPljpZlaFhYUUFha6bjuTVFxcTHFxcS2+ojPjjMEKsYjn5cP2+4/4APZWfSnFGyz4uj0tJ5tSHYVTQkSCJV+zp+WjIVBOrMeTc1JcXIxhGNjtdux2O4Drsr45Y3D2PDnjqohhGK77Z8+ezciRI5kyZUq51/Drr79y2WWXuZ1n8ODBzJs3z21z1X79+rm1sdvtBAUF0a5dO9fxyMhI4uLiCAoKcjuWlpbmur1p0yZmzpzJ1q1bOXbsmOv4vn376N69u1t8p3uf77//fiZOnOh2rHXr1tjtdrZv387AgQPdHj9o0KAK38OybU49lpaWxtSpU/nhhx9IT0+ntLSU/Px8kpOTy70XZXNS9jzZ2dkcOnSIwYMHuz3m3HPPZevWrW6vs2fPnq7rUVFRAKSmptK5c+dyr9/5nBVtgFuTn1PTCqfMzExKS0tdL9QpKiqKHTt2VPiY33//ne+++44bbriBpUuXsnv3bu68806Ki4uZPn16hY+ZPXs2M2fOLHd8+fLlltnRGmDFihVmhyBleEo++iR/Sltgd1EEv1p8rqAn5KTUKCXxuGM8eO6OXJbusm5OPCEfDY1yYj2emBMfHx+io6PJzc2lqKgIcHw4XjflnCofm7j/OH/95Ncq273y/7rRNyasynbFJ/LILjjZc+8cMlZh2+JiSkpKyM7OJiEhgQsuuIAHHniA66+/Hjj5h/fS0lIKCwvdektOnDjhauPt7Y3dbsfb29utTUFBAT4+Pm7HioqK8PLycjtWUlJCUVER2dnZ5OXlcdFFF3HBBRfw+uuvEx4ezoEDB7jyyis5duwY2dnZ5Ofnu15bZZsO2+12goODiYyMLPeana+7uLi4wtfkPG/Z96fs++ksdgBuuukmjh49ypNPPklMTAz+/v6MHj2a7Ozscud25iInJ4fCwkJKS0vd2uXn55d7r5zPn5ub6zrmbOM8lpubW2FPVlFRESdOnGD16tXlhvM538PqaFBLaNntdiIjI3nzzTfx9vamX79+HDx4kKeffrrSwunhhx9mypQprtvZ2dnExMQwevRoQkND6yv0ShUXF7NixQpGjRrlGn4o5vG0fPi8Mg2A9sNvoF3HkSZHUzFPysnPmT9TvLyYUL9QJl4yES+b6ev3lONJ+WgolBPr8eScFBQUsH//foKDgwkICHAdr7rMgdHNmxL9n72kZRdUOM/JBkSHBTC6dyzeXtUfymwYBjk5OYSEhFQ6BNrX1xcfHx/XZ8Onn36avn370qNHDwDX8R49erBx40a3z5CbN2+mc+fOrpX4vLy8CAgIcGsTEBCAzWZzO+bv74+3t7fbsbJx7Nq1i6NHj/LMM88QExMD4OpcaNKkCaGhoa5OgJCQkEo/11YUT1nx8fH8+9//drt/69atbudt3bo1GRkZbu/hjh078PLycj1u/fr1vPzyy1x11VWAY+7UkSNH3J7b19cXPz8/QkJCXDkp+z6EhobSqlUrkpKSuPjii13xbNy4kQEDBhAaGuoaouh8D+Bkr1dQUFCFr7OgoIDAwEDOP/98t+9LoNIhgxUxrXAKDw/H29ubtLQ0t+NpaWlER0dX+JiWLVvi6+vr1sXWrVs3UlNTK13q0d/fH39//3LHfX19LfXLzGrxeDqPyEf2YcjaBzYvfNqdCxZ/vZ6Qk61HHf9R9Y3qi79f+d9bVuIJ+WholBPr8cSclJaWYrPZ8PLyqrQHpDJeXjDjsu785YNEbOBWPDnLnemXdsfXp+LpGZVxfqh2xlUR595Gzvt79+7NDTfcwEsvvfRHbI7j999/PwMGDODJJ5/kmmuuYd26dbzyyiu8+uqrbuc+9bmc109tU9Ex52Pj4uLw8/PjlVde4Y477uDnn3/mySefdD2m7Htc1fudm5tbbvE1Z5Hxl7/8heeee44HH3yQ2267jU2bNvHuu++6nfeCCy7gb3/7G8888wxXXXUVy5YtY9myZYSGhrqet1OnTnz44YcMHDiQ7Oxs/v73vxMYGOj2XsTFxfH9998zZMgQioqKCA0NLfc+/P3vf2f69Ol07NiRhIQEFixYQFJSEh9++GGlr7mq98HLywubzVbhz2RNfkZN+3Omn58f/fr1c9vwym63s3LlSgYPHlzhY4YMGcLu3bvdxjz+9ttvtGzZ0vT18UUanJQ/liGP6gkB1flboNQ118a3kVqoQ0Q800U9W/LajX2JDnPvFYgOC+C1G/vW6T5Op3rsscfKzRvq27cvH3/8MYsWLaJnz55MmzaNxx57rNZXugOIiIhg4cKFfPLJJ3Tv3p2nnnqKZ5555ozONW3aNFq2bOn29cADDwDQtm1bPvvsM5YsWULv3r15/fXXmTVrltvju3Xrxquvvsorr7xC79692bBhA/fff79bm7fffptjx47Rt29fbrrpJu6+++5ywwOfffZZVqxYQWxsLOeff36Fsd59991MmTKF//u//yM+Pp5ly5bx5Zdf0qlTpzN67bXKMNGiRYsMf39/Y+HChcb27duN22+/3WjatKmRmppqGIZh3HTTTcZDDz3kap+SkmKEhIQYd911l7Fz507jq6++MiIjI40nnnii2s95/PhxAzCOHz9e66/nTBQVFRlLliwxioqKzA5FDA/Lx1dTDGN6qGEsfcDsSE7LU3JSai81hvxziNFzYU9jS/oWs8OplKfkoyFRTqzHk3Ny4sQJY/v27caJEyfO6jwlpXZj7e5MY8nmA8ba3ZlGSan9jM9VWlpqHDt2zCgtLT2rmDzJ999/bwDGsWPH6uT89Z2T031f1qQ2MHWO0zXXXENGRgbTpk0jNTXVtcGVc8GIlJQUt+62mJgY/vOf/3DffffRq1cvWrduzT333MODDz5o1ksQabiStX+Tlfye9TvHC48T6BNItxbdzA5HRMRU3l42BndoYXYYIm5MXxzirrvu4q677qrwvlWrVpU7NnjwYP73v//VcVQijVz+UUjf7rjetuKhsVK/NqU5liHvFdELXy/PmhMhIiLSEJheOImICfavBwxo0RGCI6tsLnWv7Ma3IiIiZho+fLhrfyU5yXpr3YpI3Uv+Y2EIDdOzBMMwXD1O/aJUOImIiFiRCicRT5Tyx/ymtiqcrOBg7kHS89Px8fIhPiLe7HBERESkAiqcRDxNUR4c2uy4Hqv5TVaQmJ4IQPcW3Qn0CTQ5GhEREamICicRT3NgI9hLILQ1NI01OxoBDdMTERFpAFQ4iXga1zC9wWCznb6t1IvENEePkxaGEBERsS4VTiKeJnmN41LD9Cwh80Qm+7L3YcNGQmSC2eGIiIhIJVQ4iXiS0mLHUD2A2CHmxiLAyd6mTs06EeYfZnI0IiIiUhkVTiKe5PAWKM6HwGYQ3sXsaISTC0P0jexrciQiIhZiL4W9P8K2Tx2X9tJ6edrU1FTuueceOnbsSEBAAFFRUQwZMoTXXnuN/Px8AOLi4rDZbNhsNgIDA4mLi+Pqq6/mu+++q5cYxTzaAFfEkziH6bUdDF76u4kVuOY3aWEIERGH7V/Csgch+9DJY6Gt4KI50P2yOnva33//nSFDhtC0aVNmzZpFfHw8/v7+bNu2jTfffJPWrVtz2WWO53/ssceYPHkyRUVF7Nu3jw8++ICRI0fy+OOP88gjj9RZjGIuFU4iniT5j4UhtPGtJeQU5bDj6A4A+kapx0lEhO1fwscTAMP9ePZhx/Gr36uz4unOO+/Ex8eHjRs30qRJE9fx9u3b86c//QnDOBlTSEgI0dHRALRt25bzzz+fli1bMm3aNK666iq6dNGojsZIf3IW8RR2uza+tZik9CQMDGJCYogMijQ7HBGRumEYjj0Eq/oqyIZvHqBc0eQ4ieNi2YOOdtU5n1HReSp25MgRli9fzl//+le3oqksWxUr0d5zzz0YhsG//vWvaj+vNCzqcRLxFBm/QkEW+AZBy15mRyNofpOIeIjifJjVqhZOZDiG7z0VU73m/zgEfhUXQafavXs3hmGU6ykKDw+noKAAgL/+9a/MmTOn0nM0b96cyMhI9u3bV734pMFRj5OIp0he67iMGQjevubGIoA2vhURsboNGzaQlJREjx49KCwsrLK9YRhV9kxJw6UeJxFP4SycNEzPEgpLC/k582dAhZOINHK+QY7en6okr4UPr6q63Q2fVm+urm9Q1W3+0LFjR2w2Gzt37nQ73r59ewACAwOrPMeRI0fIyMigXbt21X5eaVjU4yTiCQzj5PwmLQxhCdsytlFsLyYiMIKYkGoOOxERaYhsNseQuaq+OlzgWD2PynpsbBDa2tGuOuerQc9PixYtGDVqFC+//DJ5eXln9DJfeOEFvLy8uPzyy8/o8WJ9KpxEPMGxfZBzGLx8oU1/s6MRTg7T6xvVV8M6REQAvLwdS44D5YunP25f9JSjXR149dVXKSkpoX///ixevJhff/2VnTt38sEHH7Bjxw68vU8+b05ODqmpqezfv5/Vq1dz++2388QTT/Dkk0/SsWPHOolPzKeheiKewDlMr1Uf8K16uIHUPS0MISJSge6XOZYcr3Afp6fqdB+nDh06sHnzZmbNmsXDDz/MgQMH8Pf3p3v37tx///3ceeedrrbTpk1j2rRp+Pn5ER0dzTnnnMPKlSsZMWJEncUn5lPhJOIJUv4onDRMzxJK7CUkpScBmt8kIlJO98ug6zjHH/1y0yA4yvH/Vx31NJXVsmVLXnrpJV566aVK22jVPM+lwknEE2jjW0vZeXQn+SX5hPiF0KlZJ7PDERGxHi9vaDfU7ChE3GiOk0hjl5MGR/cANogZZHY0wsn5TX0i++Bl069hERGRhkD/Y4s0ds5helE9IbCpqaGIg3N+k4bpiYiINBwqnEQaO9cwvcHmxiGAY3PExDQtDCEiItLQqHASaexcG9+qcLKCvcf3cqzwGAHeAfRo0cPscERERKSaalQ4zZ07lxMnTrhur1mzhsLCQtftnJwct6UaRcRkJ7Ig7WfHdS0MYQkb0zYC0CuiF77eviZHIyIiItVVo8Lp4YcfJicnx3X74osv5uDBg67b+fn5vPHGG7UXnYicnf0bAAOat4eQaLOjEcrs3xSlYXoiIiINSY0KJ8MwTntbRCwmeY3jsq16m6xC85tEREQaJs1xEmnMUrR/k5Ucyj3E4bzD+Nh86B3R2+xwREREpAZUOIk0VsUn4KCjd0Mr6lmDc/+mbi26EeQbZHI0IiIiUhM+NX3A/PnzCQ4OBqCkpISFCxcSHh4O4Db/SURMdmAj2IshOBqatTM7GqHM/CYN0xMROa1SeymJ6Ylk5GcQERRB38i+eHt51+lzZmRkMG3aNL7++mvS0tJo1qwZvXv3Ztq0aQwZMgSAzZs389RTT7F69WqOHj1KdHQ08fHx/PnPf+aSSy7BZrOxb98+2rU7+f9ucHAwbdu2Zfjw4dx777106tSpTl+H1J0aFU5t27blrbfect2Ojo7m/fffL9dGRCyg7DA9m83cWAQ42eOkjW9FRCr3bfK3PLXhKdLy01zHooKieGjgQ4yMHVlnz3vllVdSVFTEu+++S/v27UlLS2PlypUcOXIEgH/9619cffXVjBw5knfffZeOHTtSWFjI2rVrefTRRxk6dChNmzY9+Tq+/ZYePXqQn5/Ptm3beOGFF+jduzf//ve/ufDCC+vsdUjdqVHhtG/fvjoKQ0RqnXP/Js1vsoSjBUfZe3wvAH0i+5gcjYiINX2b/C1TVk3BwH0BsvT8dKasmsJzw5+rk+IpKyuLH3/8kVWrVjFs2DAAYmNjGThwIAB5eXnceuutjBs3js8//9ztsd26dePWW28tt2haixYtiI52rGjbvn17Lr30Ui688EJuvfVW9uzZg7d33fagSe3THCeRxqi05I+lyNHGtxaxOW0zAB2bdqRpQFNzgxERqUeGYZBfnF/lV05hDrM3zC5XNAEYf/x7asNT5BTmVOt8NVn9OTg4mODgYJYsWeK2R6nT8uXLOXLkCA888ECl57BVMbrDy8uLe+65h+TkZDZt2lTt2MQ6atTjtG7dOo4cOcIll1ziOvbee+8xffp08vLyuPzyy3nppZfw9/ev9UBFpAZSt0BxHgSEQWR3s6MRTm58q2F6IuJpTpScYNBHg2rlXGn5aZy7qHojKdZfv77aC/H4+PiwcOFCJk+ezOuvv07fvn0ZNmwY1157Lb169eK3334DoEuXLq7H/PTTT4wYMcJ1e9GiRW6fkSvStWtXwDGKy9mbJQ1HjXqcHnvsMX755RfX7W3btnHrrbcycuRIHnroIf79738ze/bsWg9SRGrIOUyv7WDwUseyFWhhCBERa7vyyis5dOgQX375JRdddBGrVq2ib9++LFy4sML2vXr1IikpiaSkJPLy8igpKanyOZy9YFX1Tok11ajHKSkpiccff9x1e9GiRQwaNMi1YERMTAzTp09nxowZtRqkiNRQ8h8LQ2iYniXkFeex4+gOAPpGqXASEc8S6BPI+uvXV9luU9om7lx5Z5XtXr3w1Wr13gf6BFYrvrICAgIYNWoUo0aNYurUqdx2221Mnz6d559/HoCdO3dyzjnnAODv70/Hjh1rdP5ff/0VwG3VPWk4avSn6GPHjhEVFeW6/cMPP3DxxRe7bg8YMID9+/fXXnQiUnN2e5kV9YaYG4sAkJSehN2w0zq4NdFNos0OR0SkXtlsNoJ8g6r8OrfVuUQFRWGj4t4YGzaig6I5t9W51TpfbfTqdO/enby8PEaPHk3z5s2ZM2fOGZ/Lbrfz4osv0q5dO/r00SJBDVGNCqeoqCj27nWsClVUVERiYqKr6gbHPk6+vr61G6GI1EzmTjhxFHwCoWVvs6MRtAy5iEh1eHt589DAhwDKFU/O2w8OfLBO9nM6cuQIF1xwAR988AFbt25l7969fPLJJ8ydO5c//elPBAcHM3/+fL7++mvGjRvHf/7zH37//Xe2bt3K3LlzHfGfskrekSNHSE1N5ffff+fLL79k5MiRbNiwgbffflsr6jVQNRqqN3bsWB566CHmzJnDkiVLCAoKYujQoa77t27dSocOHWo9SBGpAef8ppgB4ONnbiwCnJzfpMJJROT0RsaO5Lnhz1W4j9ODAx+ss32cgoODGTRoEM8//zx79uyhuLiYmJgYJk+ezD/+8Q8Axo8fz9q1a5kzZw4TJkzg6NGjhIWF0b9//woXhhg50hFrUFAQsbGxjBgxgjfffLPGw/vEOmpUOD3++ONcccUVDBs2jODgYBYuXIif38kPZu+88w6jR4+u9SBFpAacw/Taav8mKygqLWJbxjZAC0OIiFTHyNiRjIgZQWJ6Ihn5GUQERdA3sm+d9DQ5+fv7M3v27CoXOevfvz+ffPLJadvExcXVaCl0aThqVDiFh4ezevVqjh8/TnBwcLluxk8++YSQkJBaDVBEasAwymx8q4UhrODnzJ8pshfRPKA5saGxZocjItIgeHt5MyB6gNlhiLipUeF0yy23VKvdO++8c0bBiMhZykqB7IPg5QNt9B+OFZQdpqflZ0VERBquGhVOCxcuJDY2lj59+qgLUsSKnMP0WiaAXxNTQxEHbXwrIiLSONSocPrLX/7CP//5T/bu3cvNN9/MjTfeSPPmzesqNhGpqeQ1jksN07OEUnspSelJgOY3iYiINHQ1Wo78lVde4fDhwzzwwAP8+9//JiYmhquvvpr//Oc/6oESsYJk7d9kJTuP7SSvOI9g32A6N+tsdjgiIiJyFmpUOIFj1ZHrrruOFStWsH37dnr06MGdd95JXFwcubm5dRGjiFRHbgYc2eW4HjPI3FgEgMQ0x/ymhMiEOl0NSkREROpejQsntwd7eWGz2TAMg9LS0tqKSUTORMofq+lFdocgDaG1Au3fJCIi0njUuHAqLCzkn//8J6NGjaJz585s27aNl19+mZSUFIKDg+siRhGpDtcwPe3fZAWGYbApbROgwklERKQxqNHiEHfeeSeLFi0iJiaGW265hX/+85+Eh4fXVWwiUhPOHqe2WhjCCvZl7+NowVH8vPzo0aKH2eGIiIjIWapR4fT666/Ttm1b2rdvzw8//MAPP/xQYbvPP/+8VoITkWoqyIbUbY7r6nGyBOf8pviIePy8/UyORkREztSkSZPIyspiyZIlFd4/Y8YMlixZQlJSUr3GJfWvRoXThAkTtIGjiBXt3wCGHZrFQWgrs6MR0DA9EZGzYJSWkr9xEyUZGfhERBDUvx82by2ycyaGDx9OQkIC8+bNq/D+sp/tQ0JC6NKlC48++ih/+tOf6inChqPGG+CKiAW5humpt8kqXAtDRKpwEhGpiezly0mbNZuS1FTXMZ/oaKL+8TCho0ebGJl5ioqK8POru9ELCxYs4KKLLiI7O5tXX32Vq666isTEROLj4+vsORuis1pVT0QsIvmPwkkb31pCal4qB3MP4mXzondkb7PDERFpMLKXL+fgPfe6FU0AJWlpHLznXrKXL6+T5/3000+Jj48nMDCQFi1aMHLkSPLy8ips+9NPPxEREcGcOXMqPd/8+fPp1q0bAQEBdO3alVdffdXt/gcffJDOnTsTFBRE+/btmTp1KsXFxa77Z8yYQUJCAvPnz6ddu3YEBAQAjt6h+fPnM378eIKCgujUqRNffvnlWb/+pk2bEh0dTefOnXn88ccpKSnh+++/P+vzNjY16nESEQsqLoCDjmFh2vjWGpzD9Lo170YT3yYmRyMiYi7DMDBOnKi6XWkpaU88CYZR0UnABmlPzqLJ4MHVGrZnCwys1hSTw4cPc9111zF37lzGjx9PTk4OP/74I0YFcXz33XdcccUVzJ07l9tvv73C83344YdMmzaNl19+mT59+rB582YmT55MkyZNmDhxIuAYErdw4UJatWrFtm3bmDx5MiEhITzwwAOu8+zevZvPPvuMzz//HO8yr3fmzJnMnTuXp59+mpdeeokbbriB5ORkmjc/+61ISkpKePvttwHqtIeroVLhJNLQHUqE0iJoEgnN25sdjXByYYi+UX1NjkRExHzGiRPs7FsLw5YNR8/TbwMGVqt5l8RN2IKCqmx3+PBhSkpKuOKKK4iNjQWocIjaF198wYQJE5g/fz7XXHNNpeebPn06zz77LFdccQUA7dq1Y/v27bzxxhuuwunRRx91tY+Li+P+++9n0aJFboVTUVER7733HhEREW7nnzRpEtdddx0As2bN4sUXX2TDhg1cdNFFVb7Wylx33XV4e3tz4sQJ7HY7cXFxXH311Wd8vsZKhZNIQ5e8xnEZOxi0eIslaH6TiEjD0bt3by688ELi4+MZM2YMo0eP5qqrrqJZs2auNuvXr+err77i008/5fLLL6/0XHl5eezZs4dbb72VyZMnu46XlJQQFhbmur148WJefPFF9uzZQ25uLiUlJYSGhrqdKzY2tlzRBNCrVy/X9SZNmhAaGkp6evqZvHSX559/npEjR/L7779z33338eKLL9ZKD1Zjo8JJpKFzbXyrYXpWkFWQxe6s3QD0iepjcjQiIuazBQbSJXFTle3yN25k/+1/rrJdzJtvENS/f7Wetzq8vb1ZsWIFa9euZfny5bz00ks88sgjrF+/nnbt2gHQoUMHWrRowTvvvMO4cePw9fWt8Fy5ubkAvPXWWwwaNKjc8wCsW7eOG264gZkzZzJmzBjCwsJYtGgRzz77rFv7Jk0qHup96nPbbDbsdnu1XmtloqOj6dixIx07dmTBggWMHTuW7du3ExkZeVbnbWy0OIRIQ1ZaAvvXO65r41tLcPY2tQ9rT/MA/bVORMRms+EVFFTlV5MhQ/CJjq589ITNhk90NE2GDKnW+WqyhY7NZmPIkCHMnDmTzZs34+fnxxdffOG6Pzw8nO+++47du3dz9dVXuy3kUFZUVBStWrXi999/dxUizi9nEbZ27VpiY2N55JFH6N+/P506dSI5Obn6b2gdGzhwIP369ePJJ580OxTLUY+TSEOWtg2KcsE/DKJ6mB2NcHJ+k/ZvEhGpGZu3N1H/eJiD99zrKJ7KLs7wRxEU9Y+Ha30/p/Xr17Ny5UpGjx5NZGQk69evJyMjg27durm1i4yM5LvvvmPEiBFcd911LFq0CB+f8h+lZ86cyd13301YWBgXXXQRhYWFbNy4kWPHjjFlyhQ6depESkoKixYtYsCAAXz99dduRVpdyMjIKLdBb8uWLYmKiqqw/b333sv48eN54IEHaN26dZ3G1pCox0mkIXMO02s7CLy0MaAVOFfU08IQIiI1Fzp6NK1fmIfPKR/ofaKiaP3CvDrZxyk0NJTVq1czduxYOnfuzKOPPsqzzz7LxRdfXK5tdHQ03333Hdu2beOGG26gtLS0XJvbbruN+fPns2DBAuLj4xk2bBgLFy509Thddtll3Hfffdx1110kJCSwdu1apk6dWuuvq6yPPvqIPn36uH299dZblba/6KKLaNeunXqdTqEeJ5GGzLkwhIbpWUJ+cT6/Hv0V0MIQIiJnKnT0aEIuvJD8jZsoycjAJyKCoP79ar2nyalbt24sW7as0vsXLlzodrtly5bs3LnTdXvGjBnMmDHDrc3111/P9ddfX+k5586dy9y5c92O3Xvvvac9J1DhEulZWVmVPg/AqlWrTnt/Ree02Wz8+uuvp32cJ1LhJNJQGQak/M9xXQtDWMKWjC2UGqW0atKKlsEtzQ5HRKTBsnl702RQ9ZYdF6kvGqon0lBl7oL8TPAJgFZavc0KNExPRESk8VLhJNJQOYfpte4PPtrd2wqcK+qpcBIREWl8VDiJNFQpzv2bzjU3DgGgqLSIrRlbAa2oJyIi0hipcBJpqFwb32phCCvYfmQ7haWFNA9oTrvQdmaHIyIiIrVMhZNIQ5S1H46ngM0b2mjyrBU45zf1iexTo00XRUREpGFQ4STSEDmH6bXsDf7B5sYiwMnCScP0REREGicVTiINUfJax6XmN1lCqb2UpPQkQAtDiIiINFYqnEQaImfhpI1vLWF31m5yinMI8gmiS7MuZocjIiIidUCFk0hDk3cEMv/YsVyFkyVsTNsIOOY3+XhpX3ERkcZk0qRJXH755ZXeP2PGDBISEuotHjGPCieRhsY5vymiKzRpYW4sAkBimvZvEhGpTXa7wcGdx/jtp1QO7jyG3W6YHVKDtXfvXq6//npatWpFQEAAbdq04U9/+hM7duxwa/f9999zySWXEBERQUBAAB06dOCaa65h9erVrjarVq3CZrNhs9nw8vIiLCyMPn368MADD3D48OH6fmn1Tn8aFWloNEzPUgzDOLnxbaQKJxGRs7Vnczo/Lt5FXlah61iTpv4MvaYTHfpEmhiZeYqKivDzq/lm98XFxYwaNYouXbrw+eef07JlSw4cOMA333xDVlaWq92rr77KXXfdxU033cTixYvp0KEDx48f5/vvv+e+++5j06ZNbufduXMnoaGhZGdnk5iYyNy5c3n77bdZtWoV8fHxZ/tyLcsSPU6vvPIKcXFxBAQEMGjQIDZs2FCtxy1atAibzXba7lORRifFuTDEEHPjEABSclLIPJGJr5cv8RGN9z8LEZH6sGdzOsve+NmtaALIyypk2Rs/s2dzep0876effkp8fDyBgYG0aNGCkSNHkpeXV2Hbn376iYiICObMmVPp+ebPn0+3bt0ICAiga9euvPrqq273P/jgg3Tu3JmgoCDat2/P1KlTKS4udt3vHP43f/582rVrR0BAAAA2m4358+czfvx4goKC6NSpE19++WWlcfzyyy/s2bOHV199lXPOOYfY2FiGDBnCE088wTnnnANASkoK9957L/feey/vvvsuF1xwAbGxsfTq1Yt77rmHjRs3ljtvZGQk0dHRdO7cmWuvvZY1a9YQERHBX/7yl8rf5EbA9MJp8eLFTJkyhenTp5OYmEjv3r0ZM2YM6emn/8HYt28f999/P0OHDq2nSEUsoDAHDm91XNfGt5bgHKYXHx6Pv7e/ydGIiFiPYRgUF5ZW+VV4ooQfF/922nP9uHgXhSdKqnU+w6je8L7Dhw9z3XXXccstt/Drr7+yatUqrrjiigof/9133zFq1CiefPJJHnzwwQrP9+GHHzJt2jSefPJJfv31V2bNmsXUqVN59913XW1CQkJYuHAh27dv54UXXuCtt97i+eefdzvP7t27+eyzz/j8889JSkpyHZ85cyZXX301W7duZezYsdxwww0cPXq0wlgiIiLw8vLi008/pbS0tMI2n332GcXFxTzwwAMV3l+dvQkDAwO54447WLNmTZWf4Rsy04fqPffcc0yePJmbb74ZgNdff52vv/6ad955h4ceeqjCx5SWlnLDDTcwc+ZMfvzxR7euRpFGbf8GMEohrC2EtTE7GuHk/k2a3yQiUrGSIjtv3vNDrZwrL6uQ+fetrrohcPsLw/D1966y3eHDhykpKeGKK64gNjYWoMLhZl988QUTJkxg/vz5XHPNNZWeb/r06Tz77LNcccUVALRr147t27fzxhtvMHHiRAAeffRRV/u4uDjuv/9+Fi1a5Fa8FBUV8d577xEREeF2/kmTJnHdddcBMGvWLF588UU2bNjARRddVC6W1q1b8+KLL/LAAw8wc+ZM+vfvz4gRI7jhhhto3749AL/99huhoaFER0e7HvfZZ5+5YgVYt25dlUPwunbtCjg6NyIjG+eQSlMLp6KiIjZt2sTDDz/sOubl5cXIkSNZt25dpY977LHHiIyM5NZbb+XHH3887XMUFhZSWHiyuzc7OxtwjPks2yVqFmcMVohFrJ8Pr71r8Absbc+h1KIx1jar58RZOPVu0duyMdYmq+fDEykn1uPJOSkuLsYwDOx2O3a7HcB1Wd8cMdhcPUfOuE4VHx/PhRdeSHx8PKNHj2bUqFFcddVVNGvWzPW49evX89VXX/Hxxx9z+eWXu53HeX673U5eXh579uzh1ltvZfLkya42JSUlhIWFuR63ePFiXn75Zfbs2UNubi4lJSWEhoa67jcMg9jYWFq0aFEu5p49e7qOBQYGEhoaSmpqaqXv81/+8hduvPFGVq1axfr16/nkk0+YNWsWS5YsYdSoURiGgc1mc3v8qFGjSExM5ODBg1xwwQUUFxeXy+mpz+fs0arsfS6rqpzUNrvd7uj5LC7G29u9mK7Jz6mphVNmZialpaVERUW5HY+Kiiq30ofTf//7X95++223LsvTmT17NjNnzix3fPny5QQFBdU45rqyYsUKs0OQMqyajyG7viYc2JrVhOSlS80Op15ZMSfZ9mwO5B7Aho20zWksTfKcnFgxH55OObEeT8yJj48P0dHR5ObmUlRUBDg+HF/zWO8qH5u+N5fvF+ypst2ImzsQ2S64ynb5BbnYCk8OM8vJyam07SeffML69ev5/vvvefHFF3n00Uf59ttviY2Npbi4mNjYWJo2bcpbb73F0KFD8fX1dT22sLCQ0tJSsrOzXcPU5s2bR//+/d2ew9vbm+zsbDZs2MBNN93EQw89xBNPPEFoaCiff/45L7/8susP/IWFhQQEBLhul1VSUlLueH5+foVtyxo2bBjDhg3j73//O1deeSWPPfYYgwYNIiYmhuPHj7Nr1y63z+SRkZEUFBQAkJeXR3Z2Nvn5+a730svLfcaP87N5ixYtqozF6XQ5qU1FRUWcOHGC1atXU1JS4naf8zVVh+lD9WoiJyeHm266ibfeeovw8PBqPebhhx9mypQprtvZ2dnExMQwevRoQkND6yrUaisuLmbFihWMGjXK7YdQzGHpfJQU4rPV8derHmNvp0d4J5MDqh9Wzsl/kv8Da6BLsy5ccfEVZodTL6ycD0+lnFiPJ+ekoKCA/fv3Exwc7FrQoLqaNm/Khi8OlFsYoqzgZv507tcGL6+q5904GYZBTk4OISEhp52vM3r0aEaPHs0TTzxBu3bt+Pbbb7nvvvvw9fUlKiqKJUuWcMEFFzB58mQWL17syq2/vz/e3t6EhoYSGhpKq1atSE1NrXRvp61btxIbG8tjjz3mOvbqq69is9lcn03LnvNUzl4mJ5vNRkBAQI0+1/bo0YN169YRGhrqmv7y2muv8dxzz7m1Cw52FKhNmjQhNDTU1ekQEhLi9nwnTpzg/fff5/zzz3cNATyd6uakthQUFBAYGMj5559f7vuyukUemFw4hYeH4+3tTVpamtvxtLQ0t3GWTnv27GHfvn1ceumlrmPO7j0fHx927txJhw4d3B7j7++Pv3/5Cdu+vr6W+mVmtXg8nSXzcXgTlBZCUDi+0d2gHn7RWIkVc5KUmQRA/+j+loutrlkxH55OObEeT8xJaWmpa4+fU3skquLlBUOv6cSyN36utM15V3fCx6fqeUtlOT8rOuM61fr161m5ciWjR48mMjKS9evXk5GRQffu3fHy8nLtWxQdHc13333nmiO0aNEifHx8XB/8neeeOXMmd999N02bNuWiiy6isLCQjRs3cuzYMaZMmULnzp1JSUnh448/ZsCAAXz99dcsWbLE7RynntP9fSr/3lb2ficlJTF9+nRuuukmunfvjp+fHz/88AMLFizgwQcfxMvLi7i4OJ599lnuuecejh07xqRJk2jXrh1Hjx7lgw8+ABzfy2WfIzMzk6KiInJycti0aRNz584lMzOTzz//vFp5ryontc2Zx4p+JmvyM2pq4eTn50e/fv1YuXKla0lxu93OypUrueuuu8q179q1K9u2bXM79uijj5KTk8MLL7xATExMfYQtYo7kNY7L2MEeVzRZlWv/Ji0MISJSKzr0ieSiP/cst49TcDN/zru6bvZxCg0NZfXq1cybN4/s7GxiY2N59tlnufjii8u1dRZPw4cP54YbbuCjjz4q1+a2224jKCiIp59+mr///e80adKE+Ph47r33XgAuu+wy7rvvPu666y4KCwsZN24cU6dOZcaMGbX+2tq0aUNcXBwzZ85k37592Gw21+377rvP1e5vf/sb3bp147nnnuOqq64iOzubFi1aMHjwYJYtW1ZuYYguXbpgs9kIDg6mffv2jB49milTplTY8dGY2IzqrtVYRxYvXszEiRN54403GDhwIPPmzePjjz9mx44dREVFMWHCBFq3bs3s2bMrfPykSZPIyspyVepVyc7OJiwsjOPHj1tmqN7SpUsZO3asx/1VyoosnY8ProLdK+Cip+Ccxr1PQllWzcnxwuMMXTQUA4NVV6+iRWALs0OqF1bNhydTTqzHk3NSUFDA3r173fYeOhN2u8HhXVnkZRfSJNSflp2a1mh4nvu57GRnZxMaGlovvRtStfrOyem+L2tSG5g+x+maa64hIyODadOmucaDLlu2zDU5LSUlRd/kIvZS2L/ecb2t9m+ygs3pmzEwiAuN85iiSUSkvnh52WjdpZnZYYi4Mb1wArjrrrsqHJoHsGrVqtM+duHChbUfkIjVpP0MhdngFwLRp99HQeqHc+PbflH9TI5ERERE6oO6ckQaguQ/9jVrOwi8ajYpVuqGc/8mFU4iIiKeQYWTSEOQstZxqWF6lpBfnM/2I9sBLQwhIiLiKVQ4iVidYUDyH4VT7LnmxiIAbMvcRolRQlRQFK2atDI7HBEREakHKpxErO7IHsjLAG9/aKXeDSsoO0yvPjbuExFpaExetFnETW19P6pwErE65zC91v3A98yXdpXao4UhREQq5lx+PT8/3+RIRE4qKioCwNv77OaJW2JVPRE5DdcwPc1vsoLi0mK2ZGwBoG+kegBFRMry9vamadOmpKenAxAUFGR6z7zdbqeoqIiCggJtcWMR9ZkTu91ORkYGQUFB+PicXemjwknE6jS/yVK2H91OQWkBTf2b0r5pe7PDERGxnOjoaABX8WQ2wzA4ceIEgYGBphdx4lDfOfHy8qJt27Zn/VwqnESs7PhByEoGmxe0GWh2NMLJYXp9IvvgZdNfLkVETmWz2WjZsiWRkZEUFxebHQ7FxcWsXr2a888/3zWUUMxV3znx8/OrlZ4tFU4iVpbyx/5N0fEQEGpuLAJofpOISHV5e3uf9ZyS2oqjpKSEgIAAFU4W0VBzoj+XiliZa5jeEHPjEADshp3EdBVOIiIinkiFk4iVOXuctPGtJezO2k12UTaBPoF0bd7V7HBERESkHqlwErGq/KOQvt1xXYWTJTiH6fWO6I2Pl0Y6i4iIeBIVTiJWlfI/x2V4ZwiOMDcWAdw3vhURERHPosJJxKqcG9+qt8kSDMPQwhAiIiIeTIWTiFVp/yZLOZB7gPQT6fh4+RAfHm92OCIiIlLPVDiJWFFRHhze4riuwskSnMP0erboSYBPgMnRiIiISH1T4SRiRQd+AnsJhLaBpm3NjkY4uTBE36i+JkciIiIiZlDhJGJFrmF6mt9kFdq/SURExLOpcBKxIs1vspTME5kkZydjw0ZCZILZ4YiIiIgJVDiJWE1JERzY6LjeVoWTFTjnN3Vu1plQv1CToxEREREzqHASsZrDSVByAoJaQEQXs6MRtH+TiIiIqHASsZ7kMvs32WzmxiKAFoYQERERFU4i1pOsjW+tJLsom9+O/Qaox0lERMSTqXASsRK7Hfb/z3FdC0NYQlJ6EgYGsaGxhAeGmx2OiIiImESFk4iVpG+HguPgFwzRvcyORjg5v6lvpIbpiYiIeDIVTiJW4hym12YAePuYG4sAmt8kIiIiDiqcRKwkxbl/0xBz4xAACkoK+PnIz4DmN4mIiHg6FU4iVmEYkLzOcT1WC0NYwbbMbZTYS4gMjKRNcBuzwxERERETqXASsYqjv0NuKnj5Qmv1bliBa35TVF9sWhpeRETEo6lwErGKlD96m1r3A99Ac2MRQBvfioiIyEkqnESsQsP0LKXEXsKWjC2AFoYQERERFU4i1pG8xnHZVvs3WcGOozs4UXKCUL9QOjbtaHY4IiIiYjIVTiJWkJMKx/YCNmg7yOxoBPf9m7xs+lUpIiLi6fRpQMQKnPs3RfeEgDBzYxHAfWEIERERERVOIlbgLJw0TM8S7IadzembARVOIiIi4qDCScQKnCvqxapwsoLfs34nqzCLQJ9AujfvbnY4IiIiYgEqnETMduIYpP3iuK7CyRIS0xMB6BXeC19vX5OjEREREStQ4SRitpT1gAHNO0BwpNnRCJrfJCIiIuWpcBIxW8of85vU22QJhmFo41sREREpR4WTiNmSNb/JSg7lHSItPw0fmw+9InqZHY6IiIhYhAonETMV5cMhx3wa2g42NxYBIDHNkY/uLboT6BNocjQiIiJiFSqcRMx0cCPYSyCkFTSLMzsaAQ3TExERkQqpcBIxk2uY3mCw2cyNRQAtDCEiIiIVU+EkYqbkNY5LzW+yhMwTmezL3ocNG30i+5gdjoiIiFiICicRs5QWw4GfHNfbqnCygs3pmwHo2KwjYf5hJkcjIiIiVqLCScQsh7dAcT4ENIWIrmZHI5xcGKJvpIbpiYiIiDsVTiJmSS6zf5OXfhStwDm/qX9Uf5MjEREREavRpzURs6T8sTCEliG3hNyiXHYe2wloYQgREREpT4WTiBnsdvceJzFdUkYSdsNOm+A2RAZFmh2OiIiIWIwKJxEzZOyAgizwDYKWvc2ORtD+TSIiInJ6KpxEzJDyR29TmwHg7WtuLAKcXBhChZOIiIhURIWTiBk0TM9SCksL2Za5DdD8JhEREamYCieR+mYYkPzHwhAqnCxhW8Y2iu3FhAeG0zakrdnhiIiIiAWpcBKpb1nJkHMIvHyhtZa9toLE9JP7N9lsNpOjEREREStS4SRS35zD9FolgF+QqaGIg2vjWw3TExERkUqocBKpb5rfZCkl9hI2p28GtPGtiIiIVE6Fk0h9c218q8LJCnYe20l+ST4hviF0bNrR7HBERETEolQ4idSnnDQ4shuwQdtBZkcjnBymlxCZgLeXt8nRiIiIiFWpcBKpT87epqgeENjM3FgE0Ma3IiIiUj0qnETqk2uY3mBz4xAADMNwzW9S4SQiIiKno8JJpD4lr3FcxqpwsoK92Xs5WnAUf29/erToYXY4IiIiYmEqnETqS8FxSP3ZcV0LQ1iCc5her4he+Hr7mhyNiIiIWJkKJ5H6sn8DYECzdhDa0uxohDL7N0Vq/yYRERE5PRVOIvXFNUxPvU1W4exx0sa3IiIiUhUVTiL1JfmPhSFUOFnC4dzDHM47jLfNm4SIBLPDEREREYtT4SRSH4pPwCHHsDCtqGcNm9IdvU3dmncjyDfI5GhERETE6lQ4idSHg5ugtAiCo6F5e7OjEbR/k4iIiNSMCieR+uAapjcYbDZzYxGgzMIQmt8kIiIi1aDCSaQ+OBeG0DLklnC04Ci/H/8d0Ip6IiIiUj0qnETqWmkJHPjJcV0LQ1jC5rTNAHRs2pGmAU3NDUZEREQaBBVOInUtdSsU5UJAGER2Nzsa4eTCEOptEhERkepS4SRS15LXOi5jzgEv/chZgeY3iYiISE3pU5xIXUvR/k1Wklecx69HfwW0op6IiIhUnyUKp1deeYW4uDgCAgIYNGgQGzZsqLTtW2+9xdChQ2nWrBnNmjVj5MiRp20vYirDONnjpMLJErakb8Fu2Gkd3JroJtFmhyMiIiINhOmF0+LFi5kyZQrTp08nMTGR3r17M2bMGNLT0ytsv2rVKq677jq+//571q1bR0xMDKNHj+bgwYP1HLlINWTshBNHwScQWiaYHY2g+U0iIiJyZkwvnJ577jkmT57MzTffTPfu3Xn99dcJCgrinXfeqbD9hx9+yJ133klCQgJdu3Zl/vz52O12Vq5cWc+Ri1RDyh+9TW36g4+fubEIoI1vRURE5Mz4mPnkRUVFbNq0iYcffth1zMvLi5EjR7Ju3bpqnSM/P5/i4mKaN29e4f2FhYUUFha6bmdnZwNQXFxMcXHxWURfO5wxWCEWqf18eO9bgxdQ2mYQduX4jNRmTopKi9iWsQ2AXi166efuDOh3lvUoJ9ajnFiL8mE9VspJTWIwtXDKzMyktLSUqKgot+NRUVHs2LGjWud48MEHadWqFSNHjqzw/tmzZzNz5sxyx5cvX05QUFDNg64jK1asMDsEKaO28jFq53cEAesPe5GxdGmtnNNT1UZOkkuSKbIX0cTWhF9+/IXttu21EJln0u8s61FOrEc5sRblw3qskJP8/PxqtzW1cDpbTz31FIsWLWLVqlUEBARU2Obhhx9mypQprtvZ2dmueVGhoaH1FWqliouLWbFiBaNGjcLX19fscDxerebj+H58Nx/F8PJhwBV/Bb8mtROkh6nNnLzzyzuwBc5pcw7jho6rpQg9i35nWY9yYj3KibUoH9ZjpZw4R6NVh6mFU3h4ON7e3qSlpbkdT0tLIzr69KtdPfPMMzz11FN8++239OrVq9J2/v7++Pv7lzvu6+treqLKslo8nq5W8nHwJwBsLXvj26Tp2Qfl4WojJ0mZSQD0j+6vn7ezpN9Z1qOcWI9yYi3Kh/VYISc1eX5TF4fw8/OjX79+bgs7OBd6GDx4cKWPmzt3Lo8//jjLli2jf//+9RGqSM0lr3Fctq38e1nqT6m9lKT0JEAb34qIiEjNmT5Ub8qUKUycOJH+/fszcOBA5s2bR15eHjfffDMAEyZMoHXr1syePRuAOXPmMG3aND766CPi4uJITU0FIDg4mODgYNNeh0g5ro1vh5gbhwDw27HfyC3OpYlvE7o062J2OCIiItLAmF44XXPNNWRkZDBt2jRSU1NJSEhg2bJlrgUjUlJS8PI62TH22muvUVRUxFVXXeV2nunTpzNjxoz6DF2kcrkZkPmb43rbc8yNRQBITE8EICEyAW8vb5OjERERkYbG9MIJ4K677uKuu+6q8L5Vq1a53d63b1/dByRytpy9TRHdIKjipfKlfrn2b4rU/k0iIiJSc6ZvgCvSKLmG6Z1rbhwCgGEY2vhWREREzooKJ5G6kLzWcanCyRKSs5M5WnAUPy8/eob3NDscERERaYBUOInUtoJsSN3quK4V9SzB2dvUM7wnft5+JkcjIiIiDZEKJ5HadmADGHZoGgthrc2ORji5MISG6YmIiMiZUuEkUtuSNb/JajS/SURERM6WCieR2qb5TZaSmpfKwdyDeNm8SIhMMDscERERaaBUOInUpuICOOjo3aCtCicrSExzDNPr2rwrTXybmByNiIiINFQqnERq06FEKC2EJhHQooPZ0Qgn5zf1jexrciQiIiLSkKlwEqlNZYfp2WzmxiLAyflN/aP6mxyJiIiINGQqnERqk3PjWw3Ts4Ssgix2Z+0GoE9UH5OjERERkYZMhZNIbbGXQsp6x/VY7d9kBZvTNwPQLqwdzQOamxyNiIiINGQqnERqS+o2KMoB/1CI6ml2NIKWIRcREZHao8JJpLY4h+nFDAIvb3NjEUALQ4iIiEjtUeEkUluS1zguNUzPEvKL8/n1yK+AepxERETk7KlwEqkNhgHJf/Q4xQ4xNxYBYEvGFkqMElo2aUmr4FZmhyMiIiINnAonkdpwZDfkZ4K3P7TS6m1W4BqmF6VheiIiInL2VDiJ1AbnML02/cHH39xYBIDENM1vEhERkdqjwkmkNriG6Wn/JisoLi1mS8YWQBvfioiISO1Q4SRSG1LWOi7bamEIK/jlyC8UlhbSzL8Z7cLamR2OiIiINAIqnETO1vEDkJUCNi+IGWh2NMLJ/Zv6RPbBZrOZHI2IiIg0BiqcRM6Wc5hey97gH2JuLAKcXBhCy5CLiIhIbVHhJHK2XMP0NL/JCkrtpWxO2wyocBIREZHao8JJ5Gwl/1E4aeNbS9idtZuc4hyCfILo0ryL2eGIiIhII6HCSeRs5B+FjB2O61oYwhKc85sSIhPw8fIxORoRERFpLFQ4iZyNlD/mN4V3gSbh5sYiQJmNb7V/k4iIiNQiFU4iZ0PD9CzFMAxXj1PfKBVOIiIiUntUOImcDVfhNMTcOASA/Tn7yTyRia+XL/Hh8WaHIyIiIo2ICieRM1WYC4e3OK5rfpMlOHubeob3JMAnwORoREREpDFR4SRypg5sAKMUwtpC0xizoxFOFk5ahlxERERqmwonkTPl3PhW85ssQwtDiIiISF1R4SRyppwr6mmYniWk56ezP2c/NmwkRCaYHY6IiIg0MiqcRM5ESSEc+MlxXQtDWEJimqO3qWvzroT4hZgcjYiIiDQ2KpxEzsShJCgpgKBwCO9kdjQCWoZcRERE6pQKJ5EzkbzGcdn2HLDZzI1FAM1vEhERkbqlwknkTDjnN2mYniUcLzzOrmO7APU4iYiISN1Q4SRSU/ZSSFnvuK4V9SwhKT0JA4O40DjCA8PNDkdEREQaIRVOIjWV9gsUHge/YIiKNzsaATala36TiIiI1C0VTiI15RymFzMIvH3MjUUAbXwrIiIidU+Fk0hNJa91XGqYniWcKDnB9sztgBaGEBERkbqjwkmkJgzjZOHU9lxzYxEAtmZspcQoITIoktbBrc0OR0RERBopFU4iNXH0d8hLB28/aK1hYVbg3Pi2X1Q/bFoaXkREROqICieRmnD2NrXuB74B5sYiwMmFIfpFqpAVERGRuqPCSaQmXMP0NL/JCortxWzN2ApoRT0RERGpWyqcRGoixbkwhDa+tYJfj/zKiZIThPmH0aFpB7PDERERkUZMhZNIdWUfgmP7wOYFMQPNjkY4Ob+pT2QfvGz6dSYiIiJ1R580RKrLOUwvqicEhJobiwBl9m/S/CYRERGpYyqcRKrLufGthulZgt2wk5h+ckU9ERERkbqkwkmkupKdhZMWhrCCPVl7yC7KJtAnkK4tupodjoiIiDRyKpxEqiP/KKT/4riuFfUswTlMr1dEL3y9fE2ORkRERBo7FU4i1bF/veOyRScIjjQ3FgHcN74VERERqWsqnESqw7kwhIbpWYJhGNr4VkREROqVCieR6kjW/k1WciD3AOn56fh4+RAfEW92OCIiIuIBVDiJVKUoDw4nOa5rfpMlOIfp9WjRg0CfQJOjEREREU+gwkmkKgc2gr0EQltD07ZmRyPgWoa8b1RfkyMRERERT6HCSaQqrmF654LNZm4sApxcUa9/VH+TIxERERFPocJJpCopfxROGqZnCZknMknOTsaGjYTIBLPDEREREQ+hwknkdEqKYP9Pjuux55obiwAn5zd1ataJUL9Qk6MRERERT6HCSeR0Dm+BkhMQ2BzCu5gdjXBymJ72bxIREZH6pMJJ5HTKDtPz0o+LFWhhCBERETGDPgmKnI42vrWU7KJsdh7dCWjjWxEREalfKpxEKmO3Q8r/HNc1v8kSktKTMDBoG9KWiKAIs8MRERERD6LCSaQyGb9CQRb4NoHo3mZHI5xcGELD9ERERKS+qXASqYxzmF7MAPD2MTcWAU4uDNE3UoWTiIiI1C8VTiKVcc1vGmJuHAJAQUkBPx/5GdDGtyIiIlL/VDiJVMQwIGWd47o2vrWEX478Qom9hIjACNqEtDE7HBEREfEwKpxEKpK1D3IOg5cvtFHvhhWUXYbcZrOZHI2IiIh4GhVOIhWwOVfTa90XfAPNDUYA2JyxGdDGtyIiImIOFU4iFfDa/0fhpGF6llBqlLIlcwughSFERETEHCqcRCpgS3EuDKH9m6zgcOlhTpScIMQvhE7NOpkdjoiIiHggFU4ip/AvzsJ2bC9gg5hBZocjQHJJMuDobfKy6deWiIiI1D99AhE5RYvc3xxXonpCYFNTYxGHfaX7AG18KyIiIuZR4SRyiha5OxxXYjW/yQrsht2tx0lERETEDCqcTFR4Ip//vPkImd+/yX/efITCE/lmh+TRnPnY/cs2Eo81obCVliE3W2FhIR++N5c+P+fSM8WLjsEdzQ7Jo5UUl7Dpy5Vk/riFTV+upKS4xOyQPJ5yYj3KibUoH9bTkHNiMwzDMDuIV155haeffprU1FR69+7NSy+9xMCBAytt/8knnzB16lT27dtHp06dmDNnDmPHjq3Wc2VnZxMWFsbx48cJDQ2trZdQY589OYmWX6ynWe7JY8eC4fD4QVz5yELT4vJUyof1vP/CTOI++JjwHLvrWGaIF/tuvJqb7pluYmSe6Yf5i/F5dR7N87Ncx44GNaXkznsZdts15gXmwZQT61FOrEX5sB4r5qQmtYHpPU6LFy9mypQpTJ8+ncTERHr37s2YMWNIT0+vsP3atWu57rrruPXWW9m8eTOXX345l19+OT///HM9R37mPntyEt3eX0/TXPfjYbnQ7f31fPbkJFPi8lTKh/W8/8JM+r22iBZliiaA5jl2+r22iPdfmGlSZJ7ph/mLiXhmBs3K/EcH0DQ/i4hnZvDD/MXmBObBlBPrUU6sRfmwnsaQE9N7nAYNGsSAAQN4+eWXAbDb7cTExPC3v/2Nhx56qFz7a665hry8PL766ivXsXPOOYeEhARef/31Kp/P7B6nwhP5bBraj6a5YKvgfjtwPBi6fL0Kf39tvFrXCgtPsHPccOXDQgqLivh17Pk0zzUqzcmxYC+6Lf0Bfz+/+g7P45SUlPDLqLE0O3G80nxkBTWlx/Kv8fHxqe/wPJJyYj3KibUoH9ZTnZwcC2rKOet/xMe3fnNSk9rA1MKpqKiIoKAgPv30Uy6//HLX8YkTJ5KVlcW//vWvco9p27YtU6ZM4d5773Udmz59OkuWLGHLli3l2hcWFlJYWOi6nZ2dTUxMDJmZmaYUTv958xE6vPTven9eERERERErO/7k8/S77MJ6fc7s7GzCw8OrVTiZWmZnZmZSWlpKVFSU2/GoqCh27NhR4WNSU1MrbJ+amlph+9mzZzNzZvlhPcuXLycoKOgMIz9zmbt+oUO9P6uIiIiIiLX9sm4DaT6FVTesRfn51V+crdH3Tz788MNMmTLFddvZ4zR69GhzepwOrIFlv1fZLmlCAj0u+n/1EJFn+2XZJyS8l1Rlu023nkfPK++o+4CEjd8sYchLn1bZbut9kxl95aS6D8jD/fzND4Q/+Y8q22U+MoueFw+rh4hEObEe5cRalA/rqW5OegweSL+x9d/jVF2Nfqjeqawyxykst+KVOexAVgj0X70J/8D67xHzNMqH9RQWFrLhvL40z7FXmpOjIV4M/G8i/v7+9R2exykpLmH9oKE0zc+qNB9mjUv3VMqJ9Sgn1qJ8WI+Vc9JgVtXz8/OjX79+rFy50nXMbrezcuVKBg+uePPRwYMHu7UHWLFiRaXtrcY/MIjD4wdhw/FNUpYdxwIFqZcP0of0eqJ8WI+/vz/7brz6tDnZd+PVKprqiY+vDyV33nvafJTeea8+fNQj5cR6lBNrUT6sp7HkxPRV9RYvXszEiRN54403GDhwIPPmzePjjz9mx44dREVFMWHCBFq3bs3s2bMBx3Lkw4YN46mnnmLcuHEsWrSIWbNmkZiYSM+ePat8PrN7nJwq2jfoaIjjQ7r2Dap/yof1aB8na6lo740jQU0p1X4oplFOrEc5sRblw3qsmJMGs6qe08svv+zaADchIYEXX3yRQYMGATB8+HDi4uJYuHChq/0nn3zCo48+6toAd+7cuQ1uA1xwDBNb8c4M0nb9QlSnHoy6ZYZ6NkykfFhPYWEh/178Fr9v3Uz7Xn249JrJ6mkyUUlxCRu//JZf1m2gx+CB9L9spOX/OtjYKSfWo5xYi/JhPVbLSYMrnOqTlQongOLiYpYuXcrYsWPx9fU1OxyPp3xYj3JiLcqH9Sgn1qOcWIvyYT1WykmDmeMkIiIiIiLSEKhwEhERERERqYIKJxERERERkSqocBIREREREamCCicREREREZEqqHASERERERGpggonERERERGRKqhwEhERERERqYIKJxERERERkSqocBIREREREamCCicREREREZEqqHASERERERGpggonERERERGRKviYHUB9MwwDgOzsbJMjcSguLiY/P5/s7Gx8fX3NDsfjKR/Wo5xYi/JhPcqJ9Sgn1qJ8WI+VcuKsCZw1wul4XOGUk5MDQExMjMmRiIiIiIiIFeTk5BAWFnbaNjajOuVVI2K32zl06BAhISHYbDbX8QEDBvDTTz9V+rjT3V/ZfRUdP/VYdnY2MTEx7N+/n9DQ0Jq+nLNS1Wuuq/NUt31t56Q6efLEfNTkMWfyc3C6+/Qzcnbtzfi9ZWY+KouvPs5hpd9b+hmp/mP0f3v9nUe/tyrXWH9vNZafEcMwyMnJoVWrVnh5nX4Wk8f1OHl5edGmTZtyx729vU+buNPdX9l9FR2vrG1oaGi9f+NU9Zrr6jzVbV/bOalJnjwpHzV5zJn8HJzuPv2MnF17M39vmZGPymKpj3NY6feWfkaq/xj9315/59Hvrco11t9bjelnpKqeJictDvGHv/71r2d8f2X3VXS8quepT7UVS03PU932tZ2TmuTJDGbloyaPOZOfg9Pdp5+Rs2uv31v1dw4r/d5qbPk40/NU5zH6Gam/8+j3VuUa6+8tT8yHxw3Vs5rs7GzCwsI4fvy4KX8FEXfKh/UoJ9aifFiPcmI9yom1KB/W01Bzoh4nk/n7+zN9+nT8/f3NDkVQPqxIObEW5cN6lBPrUU6sRfmwnoaaE/U4iYiIiIiIVEE9TiIiIiIiIlVQ4SQiIiIiIlIFFU4iIiIiIiJVUOEkIiIiIiJSBRVOIiIiIiIiVVDh1IA8//zz9OjRg+7du3P33XejBRHNtXPnThISElxfgYGBLFmyxOywPNrevXsZMWIE3bt3Jz4+nry8PLND8nhxcXH06tWLhIQERowYYXY4AuTn5xMbG8v9999vdigeLysri/79+5OQkEDPnj156623zA7J4+3fv5/hw4fTvXt3evXqxSeffGJ2SB5v/PjxNGvWjKuuusrsULQceUORkZHBOeecwy+//IKvry/nn38+zzzzDIMHDzY7NAFyc3OJi4sjOTmZJk2amB2Oxxo2bBhPPPEEQ4cO5ejRo4SGhuLj42N2WB4tLi6On3/+meDgYLNDkT888sgj7N69m5iYGJ555hmzw/FopaWlFBYWEhQURF5eHj179mTjxo20aNHC7NA81uHDh0lLSyMhIYHU1FT69evHb7/9pv/bTbRq1SpycnJ49913+fTTT02NRT1ODUhJSQkFBQUUFxdTXFxMZGSk2SHJH7788ksuvPBC/WI1kfOPCkOHDgWgefPmKppETrFr1y527NjBxRdfbHYoAnh7exMUFARAYWEhhmFoNInJWrZsSUJCAgDR0dGEh4dz9OhRc4PycMOHDyckJMTsMAAVTrVm9erVXHrppbRq1QqbzVbhkK1XXnmFuLg4AgICGDRoEBs2bKj2+SMiIrj//vtp27YtrVq1YuTIkXTo0KEWX0HjU9c5Kevjjz/mmmuuOcuIG7e6zseuXbsIDg7m0ksvpW/fvsyaNasWo2+c6uNnxGazMWzYMAYMGMCHH35YS5E3TvWRj/vvv5/Zs2fXUsSNX33kJCsri969e9OmTRv+/ve/Ex4eXkvRN071+X/7pk2bKC0tJSYm5iyjbrzqMx9WoMKpluTl5dG7d29eeeWVCu9fvHgxU6ZMYfr06SQmJtK7d2/GjBlDenq6q41zjPOpX4cOHeLYsWN89dVX7Nu3j4MHD7J27VpWr15dXy+vQarrnDhlZ2ezdu1axo4dW+evqSGr63yUlJTw448/8uqrr7Ju3TpWrFjBihUr6uvlNUj18TPy3//+l02bNvHll18ya9Ystm7dWi+vrSGq63z861//onPnznTu3Lm+XlKDVx8/I02bNmXLli3s3buXjz76iLS0tHp5bQ1Vff3ffvToUSZMmMCbb75Z56+pIauvfFiGIbUOML744gu3YwMHDjT++te/um6XlpYarVq1MmbPnl2tc3788cfGnXfe6bo9d+5cY86cObUSryeoi5w4vffee8YNN9xQG2F6jLrIx9q1a43Ro0e7bs+dO9eYO3durcTrCeryZ8Tp/vvvNxYsWHAWUXqOusjHQw89ZLRp08aIjY01WrRoYYSGhhozZ86szbAbtfr4GfnLX/5ifPLJJ2cTpkepq5wUFBQYQ4cONd57773aCtUj1OXPyPfff29ceeWVtRHmWVGPUz0oKipi06ZNjBw50nXMy8uLkSNHsm7dumqdIyYmhrVr11JQUEBpaSmrVq2iS5cudRVyo1cbOXHSML2zVxv5GDBgAOnp6Rw7dgy73c7q1avp1q1bXYXc6NVGTvLy8sjJyQEcC6h899139OjRo07ibexqIx+zZ89m//797Nu3j2eeeYbJkyczbdq0ugq50auNnKSlpbl+Ro4fP87q1av1f/tZqI2cGIbBpEmTuOCCC7jpppvqKlSPUJuftaxChVM9yMzMpLS0lKioKLfjUVFRpKamVusc55xzDmPHjqVPnz706tWLDh06cNlll9VFuB6hNnICjv/oNmzYwJgxY2o7RI9SG/nw8fFh1qxZnH/++fTq1YtOnTpxySWX1EW4HqE2cpKWlsZ5551H7969Oeecc5gwYQIDBgyoi3Abvdr6nSW1pzZykpyczNChQ+nduzdDhw7lb3/7G/Hx8XURrkeojZysWbOGxYsXs2TJEtd2I9u2bauLcBu92vq9NXLkSP7f//t/LF26lDZt2phadGnJqQbkySef5MknnzQ7DCkjLCxM49Et5OKLL9ZqYRbSvn17tmzZYnYYUoFJkyaZHcL/b+/OQqr4+ziOf8bKyg3TrJRwCcQiC8oliiLqoo1KbTEiwhZCyGgxwXaK6iIsMqKF6iKJoGyhuqiETKkOFBmoRWUpRgbSZgYKlnl+z83znOd/PP07LdaZ6v2CAWfmzG++v/lc6JeZM0JSamqqKisrfV0G/mHs2LFyOp2+LgP/cO3aNV+X4MIdp1+gb9++6tatm8cf2C9fvtSAAQN8VNXfjUzshTzsh0zshTzsh0zsh0zs5U/Mg8bpF/D391dSUpJKS0td25xOp0pLS/kHtj5CJvZCHvZDJvZCHvZDJvZDJvbyJ+bBo3pdpKWlRbW1ta71+vp6VVZWKiwsTNHR0crNzVVWVpaSk5OVmpqqwsJCtba2avHixT6s+s9GJvZCHvZDJvZCHvZDJvZDJvby1+Xh69f6/SnKysqMJI8lKyvL9Zn9+/eb6Oho4+/vb1JTU83t27d9V/BfgEzshTzsh0zshTzsh0zsh0zs5W/LwzLGmJ/XlgEAAADA74/vOAEAAACAFzROAAAAAOAFjRMAAAAAeEHjBAAAAABe0DgBAAAAgBc0TgAAAADgBY0TAAAAAHhB4wQAAAAAXtA4AQD+aLGxsSosLPR1GQCA35xljDG+LgIA8HtbtGiRmpubdeHCBV+X4uH169cKDAxUQECAr0v5LDtfOwDA/3HHCQDwW2pvb/+qz0VERPikafra+gAAvwcaJwDAT/fgwQNNnTpVQUFB6t+/vxYuXKg3b9649l+9elVjx45VaGiowsPDNX36dNXV1bn2P3v2TJZl6fTp0xo/frx69eqlkydPatGiRUpPT9fu3bsVGRmp8PBw5eTkuDUtnR/VsyxLx44dU0ZGhgICAhQfH69Lly651Xvp0iXFx8erV69emjBhgoqKimRZlpqbm/91jpZl6dChQ5o5c6YCAwO1c+dOdXR0aOnSpYqLi1Pv3r2VkJCgffv2uY7ZunWrioqKdPHiRVmWJcuyVF5eLklqaGhQZmamQkNDFRYWprS0ND179uz7AgAA/DAaJwDAT9Xc3KyJEydqxIgRqqio0NWrV/Xy5UtlZma6PtPa2qrc3FxVVFSotLRUfn5+ysjIkNPpdBtr3bp1WrVqlR49eqTJkydLksrKylRXV6eysjIVFRXp+PHjOn78+Bdr2rZtmzIzM1VdXa1p06ZpwYIFampqkiTV19drzpw5Sk9PV1VVlbKzs7Vx48avmuvWrVuVkZGh+/fva8mSJXI6nRo4cKDOnDmjhw8fasuWLdqwYYOKi4slSXl5ecrMzNSUKVPU2NioxsZGjRkzRu3t7Zo8ebKCg4N18+ZNORwOBQUFacqUKfr48ePXXnoAQFcyAAD8oKysLJOWlvbZfdu3bzeTJk1y29bQ0GAkmZqams8e8/r1ayPJ3L9/3xhjTH19vZFkCgsLPc4bExNjPn365No2d+5cM2/ePNd6TEyM2bt3r2tdktm0aZNrvaWlxUgyV65cMcYYk5+fbxITE93Os3HjRiPJvHv37vMX4L/jrl69+l/3/09OTo6ZPXu22xw6X7sTJ06YhIQE43Q6Xds+fPhgevfubUpKSryeAwDQ9bjjBAD4qaqqqlRWVqagoCDXMnjwYElyPY739OlTzZ8/X4MGDVJISIhiY2MlSc+fP3cbKzk52WP8oUOHqlu3bq71yMhIvXr16os1DR8+3PVzYGCgQkJCXMfU1NQoJSXF7fOpqalfNdfP1XfgwAElJSUpIiJCQUFBOnLkiMe8OquqqlJtba2Cg4Nd1ywsLExtbW1ujzACAH6d7r4uAADwZ2tpadGMGTO0a9cuj32RkZGSpBkzZigmJkZHjx5VVFSUnE6nEhMTPR5LCwwM9BijR48ebuuWZXk84tcVx3yNzvWdOnVKeXl52rNnj0aPHq3g4GAVFBTozp07XxynpaVFSUlJOnnypMe+iIiIH64TAPDtaJwAAD/VyJEjde7cOcXGxqp7d89fO2/fvlVNTY2OHj2qcePGSZJu3br1q8t0SUhI0OXLl9223b1797vGcjgcGjNmjJYvX+7a1vmOkb+/vzo6Oty2jRw5UqdPn1a/fv0UEhLyXecGAHQtHtUDAHSJ9+/fq7Ky0m1paGhQTk6OmpqaNH/+fN29e1d1dXUqKSnR4sWL1dHRoT59+ig8PFxHjhxRbW2trl+/rtzcXJ/NIzs7W48fP1Z+fr6ePHmi4uJi18smLMv6prHi4+NVUVGhkpISPXnyRJs3b/ZowmJjY1VdXa2amhq9efNG7e3tWrBggfr27au0tDTdvHlT9fX1Ki8v18qVK/XixYuumioA4BvQOAEAukR5eblGjBjhtmzbtk1RUVFyOBzq6OjQpEmTNGzYMK1evVqhoaHy8/OTn5+fTp06pXv37ikxMVFr1qxRQUGBz+YRFxens2fP6vz58xo+fLgOHTrkeqtez549v2ms7OxszZo1S/PmzdOoUaP09u1bt7tPkrRs2TIlJCQoOTlZERERcjgcCggI0I0bNxQdHa1Zs2ZpyJAhWrp0qdra2rgDBQA+YhljjK+LAADAznbu3KnDhw+roaHB16UAAHyE7zgBANDJwYMHlZKSovDwcDkcDhUUFGjFihW+LgsA4EM0TgAAdPL06VPt2LFDTU1Nio6O1tq1a7V+/XpflwUA8CEe1QMAAAAAL3g5BAAAAAB4QeMEAAAAAF7QOAEAAACAFzROAAAAAOAFjRMAAAAAeEHjBAAAAABe0DgBAAAAgBc0TgAAAADgBY0TAAAAAHjxHykkvfQ6cxjEAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_regression.py:570: RuntimeWarning: overflow encountered in square\n",
+            "  output_errors = _average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: overflow encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:26: RuntimeWarning: invalid value encountered in matmul\n",
+            "  w -= lr * 2 * X.T @ (X @ w - y)\n",
+            "/tmp/ipykernel_406/2801481722.py:39: RuntimeWarning: invalid value encountered in subtract\n",
+            "  w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# Загрузка данных\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# Реализации методов\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# Константы\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# Функция оценки с возвратом NaN при расходимости\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)) or np.any(np.isinf(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# Диапазоны гиперпараметров (learning rate до 0.1, как раньше, но можно и до 0.5)\n",
+        "lr_range = np.logspace(-8, -1, 8)   # 1e-8 ... 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# График 1: learning rate\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH) for lr in lr_range]\n",
+        "    plt.semilogx(lr_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Learning rate')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от learning rate')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# График 2: эпохи\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=e, batch_size=DEFAULT_BATCH) for e in epochs_range]\n",
+        "    plt.semilogx(epochs_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от количества эпох')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# График 3: размер батча\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "for method, label in methods:\n",
+        "    mses = [compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=b) for b in batch_range]\n",
+        "    plt.semilogx(batch_range, mses, marker='o', label=label)\n",
+        "plt.xlabel('Batch size')\n",
+        "plt.ylabel('MSE')\n",
+        "plt.title('Зависимость MSE от размера батча')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xXa2bT4F0Jpp",
+        "outputId": "bc285e4b-8310-4ee9-b83e-080063b6c4e0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "CFB9NiwQzDS8",
+        "outputId": "388897a8-4a7a-44a2-e235-cb589deb8c29"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-e59627e2-0e62-474b-b4ba-6ece67f2f7a3\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-e59627e2-0e62-474b-b4ba-6ece67f2f7a3\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (ожидается файл data.csv) ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()  # выберите файл data.csv на компьютере\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Построение графиков и таблиц ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate', 'lr_plot')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох', 'epochs_plot')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ektA0t4m0026",
+        "outputId": "2a161cb0-4e2b-41c9-e12b-0298888b0051"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-ab4c66b8-e97a-4ee3-99c8-cde5edd9f429\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-ab4c66b8-e97a-4ee3-99c8-cde5edd9f429\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (1).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329          NaN\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329          NaN\n",
+            "1.000000e-06          8.313329       NaN  8.346550    8.313329          NaN\n",
+            "1.000000e-05          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-04          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-03          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-02          8.313329       NaN       NaN    8.313329          NaN\n",
+            "1.000000e-01          8.313329       NaN       NaN    8.313329          NaN\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох:\n",
+            "        Normal Equation             GD  SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                              \n",
+            "2              8.313329   6.835259e+22  inf    8.313329          NaN\n",
+            "20             8.313329  3.980530e+196  NaN    8.313329          NaN\n",
+            "200            8.313329            NaN  NaN    8.313329          NaN\n",
+            "2000           8.313329            NaN  NaN    8.313329          NaN\n",
+            "20000          8.313329            NaN  NaN    8.313329          NaN\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча:\n",
+            "            Normal Equation  GD  SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                   \n",
+            "1                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "2                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "4                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "8                  8.313329 NaN  NaN    8.313329          NaN\n",
+            "16                 8.313329 NaN  NaN    8.313329          NaN\n",
+            "32                 8.313329 NaN  NaN    8.313329          NaN\n",
+            "64                 8.313329 NaN  NaN    8.313329          NaN\n",
+            "128                8.313329 NaN  NaN    8.313329          NaN\n",
+            "256                8.313329 NaN  NaN    8.313329          NaN\n",
+            "512                8.313329 NaN  NaN    8.313329          NaN\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (ожидается файл data.csv) ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "# Начальные значения по умолчанию (будут переопределены после первого графика)\n",
+        "DEFAULT_LR = 0.01\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    # Заменяем NaN на прочерк для читаемости\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate', 'lr_plot')\n",
+        "\n",
+        "# Определяем лучший learning rate для GD (первый не-NaN или минимальный MSE среди не-NaN)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    # Берем первый (самый маленький) работающий learning rate\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} для следующих графиков (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8  # запасной вариант\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох (с новым DEFAULT_LR) ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча (с новым DEFAULT_LR) ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "McU9ae5C1kXf",
+        "outputId": "f650fb8b-388e-4d44-a73c-7ea6aa8def06"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-6e86feb7-d490-426d-ab6a-594e618da620\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-6e86feb7-d490-426d-ab6a-594e618da620\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (2).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329            -\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329            -\n",
+            "1.000000e-06          8.313329         -  8.346550    8.313329            -\n",
+            "1.000000e-05          8.313329         -         -    8.313329            -\n",
+            "1.000000e-04          8.313329         -         -    8.313329            -\n",
+            "1.000000e-03          8.313329         -         -    8.313329            -\n",
+            "1.000000e-02          8.313329         -         -    8.313329            -\n",
+            "1.000000e-01          8.313329         -         -    8.313329            -\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 для следующих графиков (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох:\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  2560.000199  2583.409888    8.313329            -\n",
+            "20             8.313329   217.565243   237.582879    8.313329            -\n",
+            "200            8.313329     8.325835     8.325835    8.313329            -\n",
+            "2000           8.313329     8.325615     8.325615    8.313329            -\n",
+            "20000          8.313329     8.323616     8.323616    8.313329            -\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча:\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "2                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "4                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "8                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "16                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "32                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "64                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "128                8.313329  8.325737  8.325737    8.313329            -\n",
+            "256                8.313329  8.325737  8.325737    8.313329            -\n",
+            "512                8.313329  8.325737  8.325737    8.313329            -\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (ожидается файл data.csv) ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X_raw = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X_raw.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Масштабирование признаков ----------\n",
+        "scaler = StandardScaler()\n",
+        "X = scaler.fit_transform(X_raw)   # теперь X имеет среднее 0 и дисперсию 1\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы (после масштабирования) ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)   # X уже масштабированы\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1 (после масштабирования 0.1 может быть великоват, но посмотрим)\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01      # будет переопределён после первого графика\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (после масштабирования)', 'lr_plot')\n",
+        "\n",
+        "# Определяем лучший learning rate для GD (первый не-NaN или минимальный MSE среди не-NaN)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]   # наименьший работающий\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} для следующих графиков (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (после масштабирования)', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (после масштабирования)', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "_-mhD1it2XH3",
+        "outputId": "de63a58b-2b82-4d28-b7f4-ead5222b99c1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-07c43c14-b8e0-498b-a6e8-e2ce6b9a55a1\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-07c43c14-b8e0-498b-a6e8-e2ce6b9a55a1\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (3).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate (после масштабирования):\n",
+            "               Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                                    \n",
+            "1.000000e-08          8.313329  3245.435934  3245.437183    8.313329            -\n",
+            "1.000000e-07          8.313329  2267.501029  2267.588249    8.313329            -\n",
+            "1.000000e-06          8.313329    70.020689    70.259705    8.313329            -\n",
+            "1.000000e-05          8.313329     8.313329     8.313329    8.313329            -\n",
+            "1.000000e-04          8.313329     8.313329     8.313332    8.313329            -\n",
+            "1.000000e-03          8.313329    69.773860     8.320042    8.313329            -\n",
+            "1.000000e-02          8.313329            -     8.470672    8.313329            -\n",
+            "1.000000e-01          8.313329            -            -    8.313329            -\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 для следующих графиков (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох (после масштабирования):\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  3377.142741  3377.142744    8.313329            -\n",
+            "20             8.313329  3374.720457  3374.720483    8.313329            -\n",
+            "200            8.313329  3350.593198  3350.593456    8.313329            -\n",
+            "2000           8.313329  3118.629709  3118.632111    8.313329            -\n",
+            "20000          8.313329  1523.346396  1523.358092    8.313329            -\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAIoCAYAAACf/fHeAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAetJJREFUeJzt3Xt8jvXjx/H3vfNmNsdtlpk5nw9RLDkf5lQ5lMI3RJGoUJFyVonIIUSJKUQKFYohh5jT8CWnIqKyOdsYO16/P/zu++u2YRvX7o3X8/HYo+7r+tzX9bk+u+6Pve/rc30ui2EYhgAAAAAA95SToysAAAAAAPcjwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAKTDMAydP39ef/zxh6OrApgiNTVVZ8+e1Z9//unoqgD3LcIWAAD/Ly4uTkOGDFHZsmXl5uamggULqkyZMjp8+LCjq3ZPjBs3TuXKlVNqaqqjqwIHiY6OVr9+/RQcHCw3NzcVLlxYFSpUUGxsrKOrdl+qXbu2Bg4c6OhqwIEIW3ggzZgxQ2FhYfL395erq6sCAgJUv359ffnll/wRkot169ZNFotFPj4+unr1apr1f/zxhywWiywWi8aPH2+37vjx43rhhRdUsmRJeXh4KCAgQPXq1dPw4cPtyjVo0MC2jZt/ypUrZ+rxwVznzp1TaGiopkyZoqefflrff/+9IiIitH79ehUvXtzR1btrsbGxGjt2rAYNGiQnJ/75fxAdOXJEjzzyiBYuXKhevXpp+fLlioiI0Nq1a5UnTx5HV+++NGjQIE2bNk3R0dGOrgocxMXRFQAcYe7cuSpSpIiGDh0qHx8fXbx4UVu3blW3bt30008/6euvv3Z0FZFFLi4uio+P148//qgOHTrYrZs/f748PDx07do1u+XWP0A8PT3VvXt3FS9eXKdOndKuXbs0duxYjRw50q580aJFNWbMmDT79vX1vfcHhGzz1ltv6dSpU4qMjFTFihUdXZ17bvbs2UpOTlbHjh0dXRU4SK9eveTm5qatW7fqoYcecnR1HghPPfWUfHx8NH36dI0aNcrR1YEDELbwQNq4caNcXV3tlr322msqWLCgpk6dqjFjxtwX32Q/iNzd3VWnTh19/fXXacLWggUL1KpVK3333Xd2yydOnKjLly9rz549Cg4Otlt3+vTpNPvw9fXVf/7zn3tfeTjM6dOnNXfuXM2YMeO+DFqSNGfOHD355JPy8PBwdFXgAFFRUVq3bp1Wr15N0MpGTk5Oevrpp/Xll19q5MiRslgsjq4SshnjCPBAujloWVkD1o1DbL7//nu1atVKgYGBcnd3V8mSJTV69GilpKTYvffm4WWFChVSq1at9Ntvv9mVs1gsGjFihN2yjz76SBaLRQ0aNLBbfu3aNY0YMUJlypSRh4eHihQponbt2uno0aOSrg99s1gsCg8Pt3tfnz59ZLFY1K1bN9uy8PBwWSwWubm56cyZM3blIyMjbfXeuXOn3brFixerRo0a8vT0VKFChfSf//xH//zzT5q2O3TokDp06KDChQvL09NTZcuW1bvvvitJGjFixC2H3ll/1q9fb2vHSpUqpdl+ZnTq1Ek//fSTLl68aFu2Y8cO/fHHH+rUqVOa8kePHlXRokXTBC1J8vPzu6u63Oz06dPq0aOH/P395eHhoapVq2ru3Lm29dbf6e1+bvy93sz6/vHjx2vixIkKDg6Wp6en6tevn+Zc3Lt3r7p166YSJUrYhk52795d586dsyv36aefqmrVqvL19VWePHlUtWpVffHFF3ZlrEM4q1WrlqZOY8aMkcVikbe3d5p18+bNs51fBQoU0HPPPaeTJ0/albGeE1FRUXrsscfk6empkJAQzZgxw65cYmKihg0bpho1atjqWrduXf3yyy+3bC+rHTt2KDU1VYmJiapZs6Y8PDxUsGBBdezYUSdOnMh0u2XmnE+vT7B+Xo8fP25bltG+KD3Hjh3T3r171aRJE7vldzrfbu6T7nT+WqWmpmry5MmqXLmyPDw8VLhwYTVv3jxN/2I9zjvt9+LFi+rXr5+CgoLk7u6uUqVKaezYsRka9l28eHFZLBb169cvzbqwsDBZLBa1bt3atiwz51FGjtNisahv375272vdunWaL/RmzZoli8Vi+5xa632rnxvfP378eD322GMqWLCgPD09VaNGDX377bd229+6das8PDx09OhRVaxYUe7u7goICFCvXr10/vx5u7INGjRI8zvYsWOHbd83sh7f/PnzVbZsWXl4eKhGjRrauHFjmvbavXu3WrRoIR8fH3l7e6tx48baunWrXZmbzwkvLy9VrlxZs2bNsiuX0f7L+lk8e/as3fKdO3em+fezW7duaX4vJ0+elKenZ5rPY3Jyst577z2VKVNG7u7udnW++Txv2rSp/vrrL+3ZsydNm+D+x5UtPNAuXryo5ORkxcXFKSoqSuPHj9dzzz2nYsWK2cqEh4fL29tbAwYMkLe3t9atW6dhw4YpNjZWH330kd32ypUrp3fffVeGYejo0aP6+OOP1bJlyzR/rN1ch/SGpKWkpKh169Zau3atnnvuOb3++uuKi4tTRESEfvvtN5UsWTLd7R05ckSff/75Lffn7OysefPmqX///rZlc+bMSXd4XXh4uF544QU98sgjGjNmjGJiYjR58mRt3rxZu3fvVr58+SRd/0evbt26cnV1Vc+ePVW8eHEdPXpUP/74o95//321a9dOpUqVsm23f//+Kl++vHr27GlbVr58+VvWObPatWunl19+WUuWLFH37t0lXb+qVa5cOT388MNpygcHB2vNmjVat26dGjVqdMftp6SkpPmHW5I8PT1ve9/D1atX1aBBAx05ckR9+/ZVSEiIFi9erG7duunixYt6/fXXVbhwYX311Ve29yxZskRLly61W3ar3/2NvvzyS8XFxalPnz66du2aJk+erEaNGmnfvn3y9/eXJEVEROjPP//UCy+8oICAAO3fv1+fffaZ9u/fr61bt9r+qIqLi1OzZs1UsmRJGYahb775Ri+++KLy5cun9u3b2/bp4uKi/fv3a/fu3apevbpteXh4eLpXU95//30NHTpUHTp00IsvvqgzZ87ok08+Ub169ezOL0m6cOGCWrZsqQ4dOqhjx4765ptv1Lt3b7m5udl+x7GxsZo1a5Y6duyol156SXFxcfriiy8UFham7du3pxsErax/oPXt21c1atTQhx9+qDNnzmjKlCn69ddftXv3bhUqVCjD7WbGOZ+ZvuhmW7ZskaR0z39J6tixo1q2bGm3bPDgwXavM3L+WvXo0UPh4eFq0aKFXnzxRSUnJ2vTpk3aunWratasmWb/EydOtLXv+++/b7cuPj5e9evX1z///KNevXqpWLFi2rJliwYPHqxTp05p0qRJtz12SfLw8ND8+fP10Ucf2b5s+/vvv7V27do052ZmzqPMHmdmTJo0SZcvX5YkHTx4UB988IHeeecd23lz45cXkydP1pNPPqnOnTsrMTFRCxcu1DPPPKPly5erVatWkq6f49euXVPv3r3VqFEjvfzyyzp69KimTZumbdu2adu2bXJ3d79lfQYNGnTLdRs2bNCiRYv02muvyd3dXdOnT1fz5s21fft225dn+/fvV926deXj46OBAwfK1dVVM2fOVIMGDbRhwwbVqlXLbpvWcyI2NlazZ8/WSy+9pOLFi9u+MMho/3W3hg0blubfRkmaMGGChg4dqrZt22rQoEFyd3fXpk2b9Nlnn6UpW6NGDUnS5s2b7fpGPCAM4AFWtmxZQ5Ltp0uXLkZSUpJdmfj4+DTv69Wrl+Hl5WVcu3bNtqx+/fpG/fr17cq98847hiTj9OnTtmWSjOHDh9teDxw40PDz8zNq1Khh9/7Zs2cbkoyPP/44zf5TU1MNwzCMY8eOGZKMOXPm2NZ16NDBqFSpkhEUFGR07drVtnzOnDmGJKNjx45G5cqVbcuvXLli+Pj4GJ06dTIkGTt27DAMwzASExMNPz8/o1KlSsbVq1dt5ZcvX25IMoYNG2ZbVq9ePSNv3rzGX3/9lW49bxYcHGxXtxvVr1/fqFixYrrr7qRr165Gnjx5DMMwjKefftpo3LixYRiGkZKSYgQEBBgjR460tdlHH31ke99vv/1meHp6GpKMatWqGa+//rqxbNky48qVK+nW78Zz5safXr163bZ+kyZNMiQZ8+bNsy1LTEw0QkNDDW9vbyM2NjbNe4YPH25kpqu2Hp+np6fx999/25Zv27bNkGT079/ftiy9c/vrr782JBkbN2685T6Sk5MNHx8fo2/fvrZl1rZ/4okn7JZv2rTJ8PT0NNq0aWP73RiGYRw/ftxwdnY23n//fbtt79u3z3BxcbFbbm3zCRMm2JYlJCQY1apVM/z8/IzExERbvRISEuy2d+HCBcPf39/o3r37LY/HMP73+ahQoYJdu/zyyy+GJOONN96wLctKu93unLdYLHafpxvrc+zYsdvuN72+KD1DhgwxJBlxcXF2y9P7PFhVrFjRrk/K6Pm7bt06Q5Lx2muvpdnmzX3C559/bkiy6ztu7ktHjx5t5MmTx/j999/t3vv2228bzs7OxokTJ2577MHBwUbTpk2NQoUKGd9++63ddh977DEjODjYaNWqlW15Rs+jjB6nJKNPnz5261u1amUEBwfbLbO2xb59+9Jsz3oe/vLLL+ke483nRmJiolGpUiWjUaNGtmXWvqRx48ZGcnKybbn1XPvkk09sy27+HaxcudKQZDRv3jxNf2Tt/3bu3Glb9tdffxkeHh5G27ZtbcvatGljuLm5GUePHrUt+/fff428efMa9erVS1OfG8/933//3ZBkjBs37pbHbBjpfw6tx33mzBm7sjt27Ejz72fXrl3tfi+//fab4eTkZLRo0SJNnUJDQ43y5cvb/a6tdbf+O3ojNzc3o3fv3mmW4/7HMEI80ObMmaOIiAjNnz9fPXr00Pz58+2+eZauX62wiouL09mzZ1W3bl3Fx8fr0KFDdmWTkpJ09uxZnTlzRpGRkVq6dKmqVKli+8b2Zv/8848++eQTDR06NM0Qq++++06FChXSq6++muZ9t/rGLioqSosXL9aYMWNuOdvY888/r0OHDtmGOXz33Xfy9fVV48aN7crt3LlTp0+f1iuvvGL3zW+rVq1Urlw5rVixQpJ05swZbdy4Ud27d7e7Ini7et6J9crR2bNnlZiYmKVtdOrUSevXr1d0dLTWrVun6OjodIcQSlLFihW1Z88e/ec//9Hx48c1efJktWnTRv7+/uleJSxevLgiIiLS/KQ3TOlGK1euVEBAgN0EBa6urnrttdd0+fJlbdiwIUvHmp42bdrY3Zfx6KOPqlatWlq5cqVt2Y3n9rVr13T27FnVrl1bkrRr1y677Vl/J3/99ZcmTpyo2NhY1a1bN81+u3fvrgULFighIUHS9c9Yu3bt0kwesmTJEqWmpqpDhw623/XZs2cVEBCg0qVLpxmy5eLiol69etleu7m5qVevXjp9+rSioqIkXb9q6+bmJun68K7z588rOTlZNWvWTHM8t9KnTx+7dmnQoIFq1KhhO98z224Z4efnp7///vuO5TLTF93s3LlzcnFxSXcoZ0Zl9Pz97rvvZLFY0szkKaXtE6yf79tdUVm8eLHq1q2r/Pnz250rTZo0UUpKSrrD1W7m5uamzp07a86cObZl1iv3N8voeZSZ4zTbjefGhQsXdOnSJdWtWzfd83HAgAFydna2vX7++efl7+9vd47fyDAMDR48WO3bt09z9ckqNDTUdvVGkooVK6annnpKq1atUkpKilJSUrR69Wq1adNGJUqUsJUrUqSIOnXqpF9//TXN1PMXLlywPQNs4sSJcnZ2Vv369dM95ox8Ds+fP293/ly6dCndY7nR4MGD9fDDD+uZZ55Jsy4uLk758+fP8O/aev7iwcMwQjzQQkNDbf/fqVMnlShRQu+++6569OihOnXqSLo+9GHIkCFat25dmn8Mbu6st2zZosKFC9tely5dWsuWLbtlZzx8+HAFBgaqV69eacbXHz16VGXLlpWLS8Y/pm+//bbq1q2r1q1bp7lHwKpw4cJq1aqVZs+erZo1a2r27Nnq2rVrmnD2119/SZLKli2bZhvlypXTr7/+Kkm2h2He7X1WNzp06JCtHZ2cnFSqVCkNHz78lmEpPS1btlTevHm1aNEi7dmzR4888ohKlSplN+b+RmXKlNFXX32llJQUHThwQMuXL9e4cePUs2dPhYSE2N3rkidPnjT3vmTEX3/9pdKlS6dpa+uwIGub3wulS5dOs6xMmTL65ptvbK/Pnz+vkSNHauHChWkmArn53P7jjz9s9XRzc9P06dPTTEAiXQ/jLi4utvuLvvnmGy1btsxuGKR1e4ZhpFtPKe19lYGBgWmGaJYpU0bS9fuOrH9kzZ07VxMmTNChQ4eUlJRkKxsSEpLufqysn9H0pu8vX7683eczM+2WEY899pi+/fZbdejQQQ8//LAsFott+NiNMtMXmSGj5+/Ro0cVGBioAgUK3HGb1vsqbxcC//jjD+3du9eub71RepPYpOeFF15QjRo1dOrUKf3+++86deqUOnTooPfeey9N2YycR5k5TrMtX75c7733nvbs2WP7okOyD323OsednZ1VunTpW/aN8+fP1/79+/XNN99owYIF6Za5VX8THx9vu0c4Pj4+3X9Pypcvr9TUVJ08edJucpobh7y6u7tr6tSpevTRR23LMvs5TG/ft/Prr7/qxx9/1Nq1a9O9FSA0NFSzZs3SzJkz1bp1a7m7u6f7ubUyDIPJMR5QhC3gBk8//bTeffddbdu2TXXq1NHFixdVv359+fj4aNSoUbZnMO3atUuDBg1Kc3N2lSpVNGHCBEmy3e/RoEED7dq1SwEBAXZlDx48qPDwcM2bN++WE3ZkxurVq7VmzRpFRkbesWz37t3VpUsXvfrqq9q4caNmzZqlTZs23XUd7pXixYvbriidO3dOU6ZM0fPPP68SJUrY/qi+E3d3d7Vr105z587Vn3/+mWYCgltxdnZW5cqVVblyZYWGhqphw4aaP39+lsJVTtehQwdt2bJFb731lqpVqyZvb2+lpqaqefPmac7tYsWKKSIiQnFxcVq+fLn69++voKAgu4kFpOsh6T//+Y/mzJmj+Ph4FSxYUI0aNUoTtlJTU2WxWPTTTz/ZfctulZUrMPPmzVO3bt3Upk0bvfXWW/Lz85Ozs7PGjBljm1TmVm78lvxOMtNuGTFu3Di1bNlSzZs3v2WZzPZFNytYsKDt/tS8efNmuo5miY6Olre3923vdUxNTVXTpk1v+WBYa+i+k6pVq6pq1ar68ssvdfDgQbVv314+Pj5pyt3NeeQImzZt0pNPPql69epp+vTpKlKkiFxdXTVnzhy7cJSZc9wqMTFRQ4cOVY8ePTLczvfKvHnz5O/vr2vXrmndunXq06ePPDw8bBMEZfZz+N1339n9vn///Xf16dPnlvsfNGiQwsLC1KhRozSTUEnXJ/75559/9PLLL2foeC5evHjLUS64vxG2gBtYH4Rr/eNv/fr1OnfunJYsWaJ69erZyh07dizd9+fPn9/uj/IGDRooMDBQc+bMSXOz+eDBg1WtWjU9++yz6W6rZMmS2rZtm5KSku4YxgzD0Ntvv622bdtmKIy0aNFCHh4eeu655/T444+rZMmSacKWdWa+w4cPp5k04vDhw7b11iEhN890dzduvnJUt25dPfTQQ1q9enWGw5Z0/Wrl7Nmz5eTkpOeeey7T9bDe4H7q1KlMvzc9wcHB2rt3r1JTU+2uDliHgKU3G2JW/fHHH2mW/f7777aZti5cuKC1a9dq5MiRGjZs2G3fJ0leXl6230nbtm11/PhxjR49Ok3Ykq6H+apVq+rkyZPq2rVrut/mWifbCAkJydAfcf/++6+uXLli90f577//Lul/s4h+++23KlGihJYsWWK3z/SGed3MesUivfP90KFDWW63jChVqpT279+vffv22WaFW716td2kF5nti25mvZpx7NgxValSJUv1zOj5W7JkSa1atUrnz5+/41WfAwcO3HGikJIlS+ry5cv35AuP7t27a+LEiYqOjtaPP/6YbpmMnkeZOU4zfffdd/Lw8NCqVavshmPeOGRSsj/HbxzKl5qaqj/++CPdiRumT5+u06dP3/HLqlv1N15eXrYrkl5eXjp8+HCacocOHZKTk5OCgoLsltepU8f2uWvdurX279+vMWPGqFu3bln6HNarV88u7Nw4Ac/Nli1bpsjIyNsOCy5YsKC++uorVaxYUY8//rh69eqV5nNr9c8//ygxMfGeTgSF3IN7tvBAuvG+lRt9/vnnslgstj+2rKHLMAxbmcTERE2fPj1D+7GGtxuHdUjXp1r//vvv9eGHH95yWEH79u119uxZTZ06Nc26G+sjSQsXLtTevXvTndUwPS4uLurSpYv27t1rm8ntZjVr1pSfn59mzJhhV/+ffvpJBw8etM1wVbhwYdWrV0+zZ89OM9Ti5npmlfVbyvSugNxOw4YNNXr0aE2dOjXNlcUbbdq0yW6okJX1PMns8JNbadmypaKjo7Vo0SLbsuTkZH3yySfy9va2ux/hbi1btsxuiv7t27dr27ZtatGihaT0z21JGZrZLSUlRRcuXEhzXltVrFhRNWrU0IEDB245TX27du3k7OyskSNHpqmDYRhppm9OTk7WzJkzba8TExM1c+ZMFS5c2HavSHrHtG3btgxd7a1evboCAgLSnO+bNm3Szp07baHybtrtdlxdXfXwww+rSZMmatKkiSpUqGC3/m77IuuQ6ZunpM6MjJ6/7du3l2EYaR4GfnP9T548qc2bN99xBtAOHTooMjJSq1atSrPOOqNsRnXq1En//POP/Pz80kxtbpXR8yijx2k2Z2dnWSwWu0cAHD9+XMuWLbMr17hxY7m7u2vKlCl2V37mz5+vmJiYNF+cxMXF6f3331f//v1v239KShNMTp48qe+//17NmjWTs7OznJ2d1axZM33//fd2wxVjYmK0YMECPf744+leZbzR1atXbZ9Nsz6H0vX+7Z133lGnTp1uO4OpJPXs2VNubm6aNWtWup9bK+t9pY899thd1w+5D1e28EDq1KmTypUrp7Zt28rf319nzpzRTz/9pF9++UXvvvuuKleuLOl6x5g/f3517dpVr732miwWi7766qtb/kMaExOjefPmSZLOnj2rmTNnysXFJc0/YqtXr1bTpk1v+01tly5d9OWXX2rAgAHavn276tatqytXrmjNmjV65ZVX9NRTT9lt76WXXspUKBg9erTeeust5c+fP931rq6uGjt2rF544QXVr19fHTt2tE39Xrx4cbup46dMmaLHH39cDz/8sO0ep+PHj2vFihVZeq7I5cuX9fPPP0u6Pi5/ypQpcnV1tQW8jHJyctKQIUPuWG7s2LGKiopSu3btbN/679q1S19++aUKFCiQZuKLS5cu2X7PN7vdw4579uypmTNnqlu3boqKilLx4sX17bffavPmzZo0adI9Hd5VqlQpPf744+rdu7cSEhI0adIkFSxY0DYUy8fHR/Xq1dO4ceOUlJRku3KY3pWSevXqqUGDBipWrJguX76sb7/9Vrt379b48eNvuf9169YpISHhlt/4lyxZUu+9954GDx6s48ePq02bNsqbN6+OHTumpUuXqmfPnnrzzTdt5QMDAzV27FgdP35cZcqUsd2L99lnn9mu/LZu3VpLlixR27Zt1apVKx07dkwzZsxQhQoVbnsvhXT9C4hx48apS5cuqlu3rjp37mwbCly0aFHbtNeZabd7KbN90c1KlCihSpUqac2aNbf8guVOMnr+NmzYUM8//7ymTJmiP/74wzasa9OmTWrYsKH69u2rTz/9VGPGjJGXl5dee+212+73rbfe0g8//KDWrVurW7duqlGjhq5cuaJ9+/bp22+/1fHjxzM8PCt//vw6deqULaCkJ6PnUUaO0+rEiRO2Pk26Psz86tWrdsv279+foWO4WatWrfTxxx+refPm6tSpk06fPq1p06apVKlS2rt3r61cgQIFNGTIEA0dOlRhYWF66qmn9Oeff2rq1KmqWrWqXnzxRbvt7tq1S4UKFbrl8M0bVapUSWFhYXZTv0uyC6LvvfeeIiIi9Pjjj+uVV16Ri4uLZs6cqYSEBI0bNy7NNpctW6ZChQrZhhFu2rTJ1heb+Tn8+++/5ebmdssvZa2++OILLV26VL/88kuaCYBuFhERoWLFijHt+4MqO6c+BHKKTz/91GjZsqURGBhouLi4GPny5TPCwsKMlStXpim7efNmo3bt2oanp6cRGBhoDBw40Fi1alWaaXhvnhI8X758Rp06ddJsU5JhsViMqKgou+XpTR0fHx9vvPvuu0ZISIjh6upqBAQEGE8//bRt6twbp/n+559/7N5781TTt5uS9nbrFy1aZFSvXt1wd3c3ChQoYHTu3NluSnGr3377zWjbtq2RL18+w8PDwyhbtqwxdOjQdPd1p6nf02vHn376Kd3yN7px6vdbSW+q682bNxt9+vQxKlWqZPj6+hqurq5GsWLFjG7dutlNU5xe/W7+uZOYmBjjhRdeMAoVKmS4ubkZlStXtpt6+GZZnfr9o48+MiZMmGAEBQUZ7u7uRt26dY3//ve/dmX//vtv2+/M19fXeOaZZ4x///03zeMJevfubYSEhNjOgdq1axtz586129ad2v5W67/77jvj8ccfN/LkyWPkyZPHKFeunNGnTx/j8OHDtjLWxwHs3LnTCA0NNTw8PIzg4GBj6tSpdttKTU01PvjgAyM4ONhwd3c3qlevbixfvjzNdM63880339id7x07dkzzSIOMttuNbnfOpye96a8z2hfdyscff2x4e3vbTZmdmanfDSPj529ycrLx0UcfGeXKlTPc3NyMwoULGy1atLD1e48++qjxzDPPGIcOHUrz3vT6wri4OGPw4MFGqVKlDDc3N6NQoULGY489ZowfP9429f+t3Dy1+53WZ+Y8utNxGoZx2/4ivZ+sTP3+xRdfGKVLlzbc3d2NcuXKGXPmzLll3zFt2jSjXLlyhqurq+Hv72/06tXLOHfunF0Zaz83ceJEu+XpbVP/P7X9vHnzbHWoXr16unXdtWuXERYWZnh7exteXl5Gw4YNjS1bttiVsZ771h83NzejVKlSxrBhw+wecZDRz2Fmp36XZLz++uvp1sn6efzjjz+MPHnyGIMHD0633I3/jqakpBhFihQxhgwZkqY98GCwGEY2XusGAJjq+PHjCgkJ0UcffWR3ZSg3a9Cggc6ePXtP7wt8EF26dEklSpTQuHHj1KNHD0dXB/cJi8WiPn36pDvkHdev0HXq1ElHjx5VkSJFHF0dOAD3bAEA8ADw9fXVwIED9dFHH2Vp1kQAmTd27Fj17duXoPUA454tAAAeEIMGDbLdfwbAfBmZoAf3N65sAQAAAIAJuGcLAAAAAEzAlS0AAAAAMAFhCwAAAABMwAQZGZCamqp///1XefPmveVDEAEAAADc/wzDUFxcnAIDA+XkdPtrV4StDPj3338VFBTk6GoAAAAAyCFOnjypokWL3rYMYSsD8ubNK+l6g/r4+Di4NlJSUpJWr16tZs2aydXV1dHVAeBA9AcArOgPgOwRGxuroKAgW0a4HcJWBliHDvr4+OSYsOXl5SUfHx86U+ABR38AwIr+AMheGbm9iAkyAAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAELo6uADInJTlRUXvDdfL8JkXtjdEjVbvJ2cXN0dXK8VKSE7Vr31c6E3tChX2K6eHKz9NuGUC7ZQ3tlnW0XdbQbllDu2UdbZc1tFvW5OZ2sxiGYTi6EjldbGysfH19denSJfn4+DisHmt+HaMPf5+vGGeLbZl/iqG3y3RWk8cHO6xeOR3tljW0W9Zkd7slJSVp5cqVatmypVxdXe/59rMT51zW0G5Zcz+2W3b1B/dj22UH2i1rcmK7ZSYbMIwwl1jz6xgNODJfMTf9xk47SQOOzNeaX8c4pmI5HO2WNbRb1tBuWUfbZQ3tljW0W9bRdllDu2XN/dBuXNnKAEdf2UpJTlTYlw9fP9EslrQFDEP+qdLSZ1bL2Tl3XFLNDikpiWqzuJlO026ZQrtljaPaLTkpSWvWrFWTJo3lkkuvbHHOZQ3tljX3c7uZ3R/cz21nJtota+7Ubpb/b7efu+zK9iGFmckGhK0McHTY2rH7C3XfOynb9wsAAADkZLOr9NMj1Xtk6z4ZRnifORN7wtFVAAAAAHKcnP53MrMR5gKFfYplqNz08i+pRqVOJtcm94j6bYFeOfj5HcvRbvZot6xxVLvdD8MIOeeyhnbLmvu53czuD+7ntjMT7ZY1GW23jP6d7CgMI8wARw8jtN6zddpJMnLYmNWcjHbLGtotaxzVbvfDbIScc1lDu2XN/dxuZvcH93PbmYl2y5qc3G4MI7zPOLu46e0ynSVdP7FuZH09qExnPqA3od2yhnbLGtot62i7rKHdsoZ2yzraLmtot6y5X9qNsJVLNHl8sD4u1Vl+qfbL/VOlj0vxfIZbod2yhnbLGtot62i7rKHdsoZ2yzraLmtot6y5H9qNYYQZ4OhhhDdKSU7Ujv+Ga+feTapZpa4eqdotxyf6nCA3P3nckWi3rMnOdrsfhhHeiHMua2i3rLnf2i07+4P7re2yC+2WNTmt3Zj6/R7LSWFLuv/+uAKQdfQHAKzoD4DswT1bAAAAAOBghC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwAQODVuffvqpqlSpIh8fH/n4+Cg0NFQ//fSTbf21a9fUp08fFSxYUN7e3mrfvr1iYmLstnHixAm1atVKXl5e8vPz01tvvaXk5GS7MuvXr9fDDz8sd3d3lSpVSuHh4dlxeAAAAAAeYA4NW0WLFtWHH36oqKgo7dy5U40aNdJTTz2l/fv3S5L69++vH3/8UYsXL9aGDRv077//ql27drb3p6SkqFWrVkpMTNSWLVs0d+5chYeHa9iwYbYyx44dU6tWrdSwYUPt2bNH/fr104svvqhVq1Zl+/ECAAAAeHC4OHLnTzzxhN3r999/X59++qm2bt2qokWL6osvvtCCBQvUqFEjSdKcOXNUvnx5bd26VbVr19bq1at14MABrVmzRv7+/qpWrZpGjx6tQYMGacSIEXJzc9OMGTMUEhKiCRMmSJLKly+vX3/9VRMnTlRYWFi2HzMAAACAB4NDw9aNUlJStHjxYl25ckWhoaGKiopSUlKSmjRpYitTrlw5FStWTJGRkapdu7YiIyNVuXJl+fv728qEhYWpd+/e2r9/v6pXr67IyEi7bVjL9OvX75Z1SUhIUEJCgu11bGysJCkpKUlJSUn36IizzlqHnFAXAI5FfwDAiv4AyB6Z+Yw5PGzt27dPoaGhunbtmry9vbV06VJVqFBBe/bskZubm/Lly2dX3t/fX9HR0ZKk6Ohou6BlXW9dd7sysbGxunr1qjw9PdPUacyYMRo5cmSa5atXr5aXl1eWj/Vei4iIcHQVAOQQ9AcArOgPAHPFx8dnuKzDw1bZsmW1Z88eXbp0Sd9++626du2qDRs2OLROgwcP1oABA2yvY2NjFRQUpGbNmsnHx8eBNbsuKSlJERERatq0qVxdXR1dHQAORH8AwIr+AMge1lFvGeHwsOXm5qZSpUpJkmrUqKEdO3Zo8uTJevbZZ5WYmKiLFy/aXd2KiYlRQECAJCkgIEDbt2+32551tsIby9w8g2FMTIx8fHzSvaolSe7u7nJ3d0+z3NXVNUd1XjmtPgAch/4AgBX9AWCuzHy+ctxztlJTU5WQkKAaNWrI1dVVa9euta07fPiwTpw4odDQUElSaGio9u3bp9OnT9vKREREyMfHRxUqVLCVuXEb1jLWbQAAAACAGRx6ZWvw4MFq0aKFihUrpri4OC1YsEDr16/XqlWr5Ovrqx49emjAgAEqUKCAfHx89Oqrryo0NFS1a9eWJDVr1kwVKlTQ888/r3Hjxik6OlpDhgxRnz59bFemXn75ZU2dOlUDBw5U9+7dtW7dOn3zzTdasWKFIw8dAAAAwH3OoWHr9OnT6tKli06dOiVfX19VqVJFq1atUtOmTSVJEydOlJOTk9q3b6+EhASFhYVp+vTptvc7Oztr+fLl6t27t0JDQ5UnTx517dpVo0aNspUJCQnRihUr1L9/f02ePFlFixbVrFmzmPYdAAAAgKkcGra++OKL26738PDQtGnTNG3atFuWCQ4O1sqVK2+7nQYNGmj37t1ZqiMAAAAAZEWOu2cLAAAAAO4HhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADCBQ8PWmDFj9Mgjjyhv3rzy8/NTmzZtdPjwYbsyDRo0kMVisft5+eWX7cqcOHFCrVq1kpeXl/z8/PTWW28pOTnZrsz69ev18MMPy93dXaVKlVJ4eLjZhwcAAADgAebQsLVhwwb16dNHW7duVUREhJKSktSsWTNduXLFrtxLL72kU6dO2X7GjRtnW5eSkqJWrVopMTFRW7Zs0dy5cxUeHq5hw4bZyhw7dkytWrVSw4YNtWfPHvXr108vvviiVq1alW3HCgAAAODB4uLInf/88892r8PDw+Xn56eoqCjVq1fPttzLy0sBAQHpbmP16tU6cOCA1qxZI39/f1WrVk2jR4/WoEGDNGLECLm5uWnGjBkKCQnRhAkTJEnly5fXr7/+qokTJyosLMy8AwQAAADwwHJo2LrZpUuXJEkFChSwWz5//nzNmzdPAQEBeuKJJzR06FB5eXlJkiIjI1W5cmX5+/vbyoeFhal3797av3+/qlevrsjISDVp0sRum2FhYerXr1+69UhISFBCQoLtdWxsrCQpKSlJSUlJd32cd8tah5xQFwCORX8AwIr+AMgemfmM5ZiwlZqaqn79+qlOnTqqVKmSbXmnTp0UHByswMBA7d27V4MGDdLhw4e1ZMkSSVJ0dLRd0JJkex0dHX3bMrGxsbp69ao8PT3t1o0ZM0YjR45MU8fVq1fbQl5OEBER4egqAMgh6A8AWNEfAOaKj4/PcNkcE7b69Omj3377Tb/++qvd8p49e9r+v3LlyipSpIgaN26so0ePqmTJkqbUZfDgwRowYIDtdWxsrIKCgtSsWTP5+PiYss/MSEpKUkREhJo2bSpXV1dHVweAA9EfALCiPwCyh3XUW0bkiLDVt29fLV++XBs3blTRokVvW7ZWrVqSpCNHjqhkyZIKCAjQ9u3b7crExMRIku0+r4CAANuyG8v4+PikuaolSe7u7nJ3d0+z3NXVNUd1XjmtPgAch/4AgBX9AWCuzHy+HDoboWEY6tu3r5YuXap169YpJCTkju/Zs2ePJKlIkSKSpNDQUO3bt0+nT5+2lYmIiJCPj48qVKhgK7N27Vq77URERCg0NPQeHQkAAAAA2HNo2OrTp4/mzZunBQsWKG/evIqOjlZ0dLSuXr0qSTp69KhGjx6tqKgoHT9+XD/88IO6dOmievXqqUqVKpKkZs2aqUKFCnr++ef13//+V6tWrdKQIUPUp08f29Wpl19+WX/++acGDhyoQ4cOafr06frmm2/Uv39/hx07AAAAgPubQ8PWp59+qkuXLqlBgwYqUqSI7WfRokWSJDc3N61Zs0bNmjVTuXLl9MYbb6h9+/b68ccfbdtwdnbW8uXL5ezsrNDQUP3nP/9Rly5dNGrUKFuZkJAQrVixQhEREapataomTJigWbNmMe07AAAAANM49J4twzBuuz4oKEgbNmy443aCg4O1cuXK25Zp0KCBdu/enan6AQAAAEBWOfTKFgAAAADcrwhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmMDF0RUAAAAADMNQcnKyUlJSHF0VQK6urnJ2dr7r7RC2AAAA4FCJiYk6deqU4uPjHV0VQJJksVhUtGhReXt739V2CFsAAABwmNTUVB07dkzOzs4KDAyUm5ubLBaLo6uFB5hhGDpz5oz+/vtvlS5d+q6ucDk0bI0ZM0ZLlizRoUOH5Onpqccee0xjx45V2bJlbWWuXbumN954QwsXLlRCQoLCwsI0ffp0+fv728qcOHFCvXv31i+//CJvb2917dpVY8aMkYvL/w5v/fr1GjBggPbv36+goCANGTJE3bp1y87DBQAAwE0SExOVmpqqoKAgeXl5Obo6gCSpcOHCOn78uJKSku4qbDl0gowNGzaoT58+2rp1qyIiIpSUlKRmzZrpypUrtjL9+/fXjz/+qMWLF2vDhg36999/1a5dO9v6lJQUtWrVSomJidqyZYvmzp2r8PBwDRs2zFbm2LFjatWqlRo2bKg9e/aoX79+evHFF7Vq1apsPV4AAACkz8mJeduQc9yrq6sOvbL1888/270ODw+Xn5+foqKiVK9ePV26dElffPGFFixYoEaNGkmS5syZo/Lly2vr1q2qXbu2Vq9erQMHDmjNmjXy9/dXtWrVNHr0aA0aNEgjRoyQm5ubZsyYoZCQEE2YMEGSVL58ef3666+aOHGiwsLCsv24AQAAANz/ctQ9W5cuXZIkFShQQJIUFRWlpKQkNWnSxFamXLlyKlasmCIjI1W7dm1FRkaqcuXKdsMKw8LC1Lt3b+3fv1/Vq1dXZGSk3TasZfr165duPRISEpSQkGB7HRsbK0lKSkpSUlLSPTnWu2GtQ06oCwDHoj8AYJVb+4OkpCQZhqHU1FSlpqY6ujqApOv3EhqGke4wwsx8xnJM2EpNTVW/fv1Up04dVapUSZIUHR0tNzc35cuXz66sv7+/oqOjbWVuDFrW9dZ1tysTGxurq1evytPT027dmDFjNHLkyDR1XL16dY4aSxwREeHoKgDIIegPAFjltv7AxcVFAQEBunz5shITE+9qWymphnadjNXZK4kqlMdNDwf5yNmJyTZu9uuvv+qJJ57Q8ePH5evr6+jqZMmHH36oFStWaNOmTaZsPzExUVevXtXGjRuVnJxsty4zs2bmmLDVp08f/fbbb/r1118dXRUNHjxYAwYMsL2OjY1VUFCQmjVrJh8fHwfW7LqkpCRFRESoadOmcnV1dXR1ADgQ/QEAq9zaH1y7dk0nT56Ut7e3PDw8srydn3+L1qjlBxUde822LMDHQ8Nal1fzSgH3oqppvPDCC/ryyy/1wQcfaNCgQbbly5YtU/v27XPsM8OsFw/y5s17y79tS5Qoob/++ivN8puPNTs4Ozvru+++U5s2bWzL3nnnHb3xxhum/W1+7do1eXp6ql69emnOS+uot4zIEWGrb9++Wr58uTZu3KiiRYvalgcEBCgxMVEXL160u7oVExOjgIAAW5nt27fbbS8mJsa2zvpf67Iby/j4+KS5qiVJ7u7ucnd3T7Pc1dU1R3VeOa0+AByH/gCAVW7rD1JSUmSxWOTk5JTlSTJ+/u2U+izYLeOm5TGx19RnwW59+p+H1bxSkbuv7E0sFos8PDw0btw4vfzyy8qfP7+k/032cTeTfiQmJsrNze2e1PNmN9bvdnUcNWqUXnrpJbtlefPmdchkJjfX1ewLIE5OTrJYLOl+njLz+XLotC+GYahv375aunSp1q1bp5CQELv1NWrUkKurq9auXWtbdvjwYZ04cUKhoaGSpNDQUO3bt0+nT5+2lYmIiJCPj48qVKhgK3PjNqxlrNsAAABAzmEYhuITkzP0E3ctScN/2J8maEmyLRvxwwHFXUu647YMI72t3F6TJk0UEBCgMWPG3Lbcd999p4oVK8rd3V3Fixe3TdxmVbx4cY0ePVpdunSRj4+PevbsqfDwcOXLl0/Lly9X2bJl5eXlpaefflrx8fGaO3euihcvrvz58+u1116zu4r21VdfqWbNmsqbN68CAgLUqVMnu7+VM8r6/ht/8uTJY1u/cuVKlSlTRp6enmrYsKHCw8NlsVh08eJFSdKIESNUrVo1u21OmjRJxYsXt73esWOHmjZtqkKFCsnX11f169fXrl277NpFktq2bSuLxWJ7ffO2U1NTNWrUKBUtWlTu7u6qVq2a3WR8x48fl8Vi0ZIlS9SwYUN5eXmpatWqioyMzHS7ZIZDr2z16dNHCxYs0Pfff6+8efPa7rHy9fWVp6enfH191aNHDw0YMEAFChSQj4+PXn31VYWGhqp27dqSpGbNmqlChQp6/vnnNW7cOEVHR2vIkCHq06eP7erUyy+/rKlTp2rgwIHq3r271q1bp2+++UYrVqxw2LEDAAAgfVeTUlRh2L15RI8hKTr2miqPWH3HsgdGhcnLLXN/Hjs7O+uDDz5Qp06d9Nprr9mN0rKKiopShw4dNGLECD377LPasmWLXnnlFRUsWNDuua/jx4/XsGHDNHz4cEnSpk2bFB8frylTpmjhwoWKi4tTu3bt1LZtW+XLl08rV67Un3/+qfbt26tOnTp69tlnJV0fUjp69GiVLVtWp0+f1oABA9StWzetXLkyU8d2OydPnlS7du3Up08f9ezZUzt37tQbb7yR6e3ExcWpa9eu+uSTT2QYhiZMmKCWLVvqjz/+UN68ebVjxw75+flpzpw5at68+S2feTV58mRNmDBBM2fOVPXq1TV79mw9+eST2r9/v0qXLm0r9+6772r8+PEqXbq03n33XXXs2FFHjhyxez7vveTQsPXpp59Kkho0aGC3fM6cObYTb+LEiXJyclL79u3tHmps5ezsrOXLl6t3794KDQ1Vnjx51LVrV40aNcpWJiQkRCtWrFD//v01efJkFS1aVLNmzWLadwAAANy1tm3bqlq1aho+fLi++OKLNOs//vhjNW7cWEOHDpUklSlTRgcOHNBHH31kF7YaNWpkF1g2bdqkpKQkffrppypZsqQk6emnn9ZXX32lmJgYeXt7q0KFCmrYsKF++eUXW9jq3r27bRslSpTQlClT9Mgjj+jy5cvy9vbO8HENGjRIQ4YMsVv2008/qW7durY6Wa/QlS1bVvv27dPYsWMzvH3rMd/os88+U758+bRhwwa1bt1ahQsXliTly5fPdotQesaPH69BgwbpueeekySNHTtWv/zyiyZNmqRp06bZyr355ptq1aqVJGnkyJGqWLGijhw5onLlymWq3hnl0LCVkUu1Hh4emjZtml0j3Sw4OPiOSb1BgwbavXt3pusIAACA7OXp6qwDozL2pfj2Y+fVbc6OO5YLf+ERPRpS4I77zaqxY8eqUaNGevPNN9OsO3jwoJ566im7ZXXq1NGkSZOUkpJiu1pTs2bNNO/18vKyBS3p+ozaxYsXtwtN/v7+dsMEo6KiNGLECP33v//VhQsXbFPqnzhxwnabTUa89dZbdmFQkh566CHbMdWqVctuXVZu0YmJidGQIUO0fv16nT59WikpKYqPj9eJEycyvI3Y2Fj9+++/qlOnjt3yOnXq6L///a/dsipVqtj+v0iR6/fxnT59+v4MWwAAAMDNLBZLhofz1S1dWEV8PRR96Vq6921ZJAX4eqhu6cKmTgNfr149hYWFafDgwWkCSkbdeD+U1c2TMVgnbbh5mTVQXblyRWFhYQoLC9P8+fNVuHBhnThxQmFhYZmeWr9QoUIqVapUJo/if5ycnNJcXLn5GVVdu3bVuXPnNHnyZAUHB8vd3V2hoaF3/RiAW7mx7SyW6+eDmc93c+gEGQAAAMDdcHayaPgT16/W3BylrK+HP1EhW5639eGHH+rHH39MM+lC+fLltXnzZrtlmzdvVpkyZW55D1JWHTp0SOfOndOHH36ounXrqly5clmaHONOypcvn2ZG8K1bt9q9Lly4sKKjo+0C1549e+zKbN68Wa+99ppatmxpm0Dk7NmzdmVcXV1vO42+j4+PAgMD023jzFzJMwNhCwAAALla80pF9Ol/HlaAr/3zkAJ8PUyb9j09lStXVufOnTVlyhS75W+88YbWrl2r0aNH6/fff9fcuXM1derUdIcc3q1ixYrJzc1Nn3zyif7880/98MMPGj16dJa2FRcXp+joaLsf6zOmXn75Zf3xxx966623dPjwYS1YsEDh4eF272/QoIHOnDmjcePG6ejRo5o2bZp++uknuzKlS5fWV199pYMHD2rbtm3q3LlzmkczFS9eXGvXrlV0dLQuXLiQbl3feustjR07VosWLdLhw4f19ttva8+ePXr99dezdOz3CmELAAAAuV7zSkX066BG+vql2pr8XDV9/VJt/TqoUbYFLatRo0alGZb28MMP65tvvtHChQtVqVIlDRs2TKNGjcrycMPbKVy4sMLDw7V48WJVqFBBH374ocaPH5+lbQ0bNkxFihSx+xk4cKCk66Huu+++07Jly1S1alXNmDFDH3zwgd37y5cvr+nTp2vatGmqWrWqtm/fniZgfvHFF7pw4YIefvhhPf/883rttdfk5+dnV2bChAmKiIhQUFCQqlevnm5dX3vtNQ0YMEBvvPGGKleurJ9//lk//PCD3UyEjmAxsvJAgQdMbGysfH19denSJdMfoJYRSUlJWrlypVq2bJmrHloI4N6jPwBglVv7g2vXrunYsWMKCQmRh4fHnd+AHGv9+vVq2LChLly4oHz58jm6OnfldudlZrIBV7YAAAAAwASELQAAAAAwAVO/AwAAALhrDRo0yNBzdB8kXNkCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwARM/Q4AAID7Q2qK9NcW6XKM5O0vBT8mOTk7ulZ4gHFlCwAAALnfgR+kSZWkua2l73pc/++kSteXmyg6Olqvv/66SpUqJQ8PD/n7+6tOnTr69NNPFR8fL0kqXry4LBaLLBaLPD09Vbx4cXXo0EHr1q0ztW5wPMIWAAAAcrcDP0jfdJFi/7VfHnvq+nKTAteff/6p6tWra/Xq1frggw+0e/duRUZGauDAgVq+fLnWrFljKztq1CidOnVKhw8f1pdffql8+fKpSZMmev/9902pG3IGhhECAAAgZzEMKSk+Y2VTU6SfBkoy0tuQJIv08yCpRIM7Dyl09ZIslgxX85VXXpGLi4t27typPHny2JaXKFFCTz31lAzjf3XKmzevAgICJEnFihVTvXr1VKRIEQ0bNkxPP/20ypYtm+H9IvcgbAEAACBnSYqXPgi8Rxszrl/x+jDozkXf+Vdyy3PncpLOnTtnu6J1Y9C6keUOwe3111/X6NGj9f3332vgwIEZ2i9yF4YRAgAAAJl05MgRGYaR5opUoUKF5O3tLW9vbw0aNOi22yhQoID8/Px0/PhxE2sKR+LKFgAAAHIWV6/rV5ky4q8t0vyn71yu87fXZye8037v0vbt25WamqrOnTsrISHhjuUNw7jjFTDkXoQtAAAA5CwWS4aH86lkI8kn8PpkGOnet2W5vr5ko3s6DXypUqVksVh0+PBhu+UlSpSQJHl6et5xG+fOndOZM2cUEhJyz+qFnIVhhAAAAMi9nJyl5mP//8XNV4j+/3XzD+/587YKFiyopk2baurUqbpy5UqWtjF58mQ5OTmpTZs297RuyDkIWwAAAMjdKjwpdfhS8iliv9wn8PryCk+astvp06crOTlZNWvW1KJFi3Tw4EEdPnxY8+bN06FDh+Ts/L+AFxcXp+joaJ08eVIbN25Uz5499d577+n9999XqVKlTKkfHI9hhAAAAMj9KjwplWt1/R6uyzGSt//1e7Tu8RWtG5UsWVK7d+/WBx98oMGDB+vvv/+Wu7u7KlSooDfffFOvvPKKreywYcM0bNgwubm5KSAgQLVr19batWvVsGFD0+oHxyNsAQAA4P7g5CyF1M3WXRYpUkSffPKJPvnkk1uWYbbBBxfDCAEAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATZCpsjRs3TlevXrW93rx5s92TsePi4uxmXQEAAACAB1WmwtbgwYMVFxdne92iRQv9888/ttfx8fGaOXPmvasdAAAAAORSmQpbhmHc9jUAAAAA4Dru2QIAAAAAExC2AAAAAMAELpl9w6xZs+Tt7S1JSk5OVnh4uAoVKiRJdvdzAQAAANkpJTVFu07v0pn4MyrsVVgP+z0sZydnR1cLD7BMha1ixYrp888/t70OCAjQV199laYMAAAAkJ3W/LVGH27/UDHxMbZl/l7+evvRt9UkuIkp+zxz5oyGDRumFStWKCYmRvnz51fVqlU1bNgw1alTR5K0e/duffjhh9q4caPOnz+vgIAAVa5cWb169VLr1q1lsVh0/PhxhYSE2Lbr7e2tYsWKqUGDBurXr59Kly5tSv1hvkyFrePHj5tUDQAAACBr1vy1RgPWD5Ah+8nbTsef1oD1A/Rxg49NCVzt27dXYmKi5s6dqxIlSigmJkZr167VuXPnJEnff/+9OnTooCZNmmju3LkqVaqUEhIStGXLFg0ZMkR169ZVvnz5/ncca9aoYsWKio+P1759+zR58mRVrVpVP/74oxo3bnzP6w/zZXoYIQAAAGAmwzB0NfnqnQvq+tDBMdvHpAlakmzLPtz+oWoF1LrjkEJPF09ZLJYM7ffixYvatGmT1q9fr/r160uSgoOD9eijj0qSrly5oh49eqhVq1ZasmSJ3XvLly+vHj16pJnZu2DBggoICJAklShRQk888YQaN26sHj166OjRo3J2ZkhkbpOpsBUZGalz586pdevWtmVffvmlhg8fritXrqhNmzb65JNP5O7ufs8rCgAAgAfD1eSrqrWg1j3bXkx8jB5b+Ngdy23rtE1erl4Z2qa3t7e8vb21bNky1a5dO83fv6tXr9a5c+c0cODAW27jTsHOyclJr7/+utq2bauoqChbkEPukanZCEeNGqX9+/fbXu/bt089evRQkyZN9Pbbb+vHH3/UmDFj7nklAQAAgJzExcVF4eHhmjt3rvLly6c6deronXfe0d69eyVJv//+uySpbNmytvfs2LHDFtK8vb21fPnyO+6nXLlykridJ7fK1JWtPXv2aPTo0bbXCxcuVK1atWyTZgQFBWn48OEaMWLEPa0kAAAAHhyeLp7a1mlbhspGxUTplbWv3LHc9MbTVcO/xh33mxnt27dXq1attGnTJm3dulU//fSTxo0bp1mzZqVbvkqVKtqzZ48kqXTp0kpOTr7jPqxDDTM6vBE5S6bC1oULF+Tv7297vWHDBrVo0cL2+pFHHtHJkyfvXe0AAADwwLFYLBkezvdY4GPy9/LX6fjT6d63ZZFF/l7+eizwMVOmgffw8FDTpk3VtGlTDR06VC+++KKGDx+uiRMnSpIOHz6s2rVrS5Lc3d1VqlSpTG3/4MGDkmQ3WyFyj0wNI/T399exY8ckSYmJidq1a5ft5JGuP2fL1dX13tYQAAAAuAVnJ2e9/ejbkq4HqxtZXw96dFC2PW+rQoUKunLlipo1a6YCBQpo7NixWd5WamqqpkyZopCQEFWvXv0e1hLZJVNhq2XLlnr77be1adMmDR48WF5eXqpbt65t/d69e1WyZMl7XkkAAADgVpoEN9HHDT6Wn5ef3XJ/L3/Tpn0/d+6cGjVqpHnz5mnv3r06duyYFi9erHHjxumpp56St7e3Zs2apRUrVqhVq1ZatWqV/vzzT+3du1fjxo2TpDSzC547d07R0dH6888/9cMPP6hJkybavn27vvjiC2YizKUyNYxw9OjRateunerXry9vb2+Fh4fLzc3Ntn727Nlq1qzZPa8kAAAAcDtNgpuoYVBD7Tq9S2fiz6iwV2E97PewaVe0vL29VatWLU2cOFFHjx5VUlKSgoKC9NJLL+mdd96RJLVt21ZbtmzR2LFj1aVLF50/f16+vr6qWbOmFi5caDfDtyQ1aXI9FHp5eSk4OFgNGzbUZ599lumhh8g5MhW2ChUqpI0bN+rSpUvy9vZOk7AXL16svHnz3tMKAgAAABnh7OSsRwIeyZZ9ubu7a8yYMXecibtmzZpavHjxbcsUL148zTO3cH/IVNjq3r17hsrNnj07S5UBAAAAgPtFpsJWeHi4goODVb16ddI3AAAAANxGpsJW79699fXXX+vYsWN64YUX9J///EcFChQwq24AAAAAkGtlajbCadOm6dSpUxo4cKB+/PFHBQUFqUOHDlq1ahVXugAAAADgBpkKW9L1mwE7duyoiIgIHThwQBUrVtQrr7yi4sWL6/Lly2bUEQAAAABynUyHLbs3OznJYrHIMAylpKTcqzoBAAAAQK6X6bCVkJCgr7/+Wk2bNlWZMmW0b98+TZ06VSdOnJC3t7cZdQQAAACAXCdTYeuVV15RkSJF9OGHH6p169Y6efKkFi9erJYtW8rJKfMXyTZu3KgnnnhCgYGBslgsWrZsmd36bt26yWKx2P00b97crsz58+fVuXNn+fj4KF++fOrRo0ea4Yx79+5V3bp15eHhoaCgINtTuwEAAADALJmajXDGjBkqVqyYSpQooQ0bNmjDhg3plluyZEmGtnflyhVVrVpV3bt3V7t27dIt07x5c82ZM8f22t3d3W59586dderUKUVERCgpKUkvvPCCevbsqQULFkiSYmNj1axZMzVp0kQzZszQvn371L17d+XLl089e/bMUD0BAAAAILMyFba6dOkii8Vyz3beokULtWjR4rZl3N3dFRAQkO66gwcP6ueff9aOHTtUs2ZNSdInn3yili1bavz48QoMDNT8+fOVmJio2bNny83NTRUrVtSePXv08ccfE7YAAADuI0ZKiuJ3Rin5zBm5FC4sr5o1ZHF2dkhdunXrposXL6YZuWU1YsQILVu2THv27MnWeiF7Zfqhxtlt/fr18vPzU/78+dWoUSO99957KliwoCQpMjJS+fLlswUtSWrSpImcnJy0bds2tW3bVpGRkapXr57c3NxsZcLCwjR27FhduHBB+fPnT7PPhIQEJSQk2F7HxsZKkpKSkpSUlGTWoWaYtQ45oS4AHIv+AIBVbu0PkpKSZBiGUlNTlZqamuXtxEVE6PQHY5QcE2Nb5uLvL793Bitv06b3oqqZYhiG7bhutV7SXR2zmRo1aqSqVatq4sSJ6a53viHE5s2bV2XLltU777yjp556KruqaKrU1FQZhqGkpCS7Y5Uy9xnLVNjKbs2bN1e7du0UEhKio0eP6p133lGLFi0UGRkpZ2dnRUdHy8/Pz+49Li4uKlCggKKjoyVJ0dHRCgkJsSvj7+9vW5de2BozZoxGjhyZZvnq1avl5eV1rw7vrkVERDi6CgByCPoDAFa5rT9wcXFRQECALl++rMTExCxt4+ov63Vx8OA0y5NjYvTv6/2Ub8wYeTZscHcVzaSkpCQlJyfbvrS/WUJCglJSUm65/l5ITEy0u+CQGcnJyUpMTLxt/aZNm6bGjRsrLi5OX3zxhTp06KD169erYsWKWa1yjpGYmKirV69q48aNSk5OtlsXHx+f4e3k6LD13HPP2f6/cuXKqlKlikqWLKn169ercePGpu138ODBGjBggO11bGysgoKC1KxZM/n4+Ji234xKSkpSRESEmjZtKldXV0dXB4AD0R8AsMqt/cG1a9d08uRJeXt7y8PDQ9L/XxW6ejVD7zdSUnTmFldfrC5PmqRCjRrecUihxdMzU7fMfPvttxo9erSOHDkiLy8vVa9eXUuXLlWePHnk6uoqFxcX29+OO3bsUOvWrfXGG29o4MCBcnd3l7Ozs93flrNmzdLEiRN17NgxFS9eXK+++qp69+5tW//2229r2bJl+vvvvxUQEKBOnTpp6NChtt/3yJEj9f333+uVV17RmDFj9Ndffyk5OVnOzs6aOXOmVq5cqdWrV+uhhx7SRx99pCeffPKWx+bi4iI3N7fb/u0bEBCg0qVLS5JKly6tGTNmaOfOnQoNDc1wG+ZU165dk6enp+rVq2c7L60yE5BzdNi6WYkSJVSoUCEdOXJEjRs3VkBAgE6fPm1XJjk5WefPn7fd5xUQEKCYGy4nS7K9vtW9YO7u7mkm4pAkV1fXHNV55bT6AHAc+gMAVrmtP0hJSZHFYpGTk5NtduvU+Hj9XvORe7aP5JgYHalV+47lyu6KklMGRzGdOnVKnTt31rhx49S2bVvFxcVp06ZNtmOxzqTt5OSkdevWqV27dho3bpxtzgBrqLMe8/z58zVixAhNnTpV1atX1+7du/XSSy/J29tbXbt2lST5+PgoPDxcgYGB2rdvn1566SX5+Pho4MCBtm0eOXJES5cu1ZIlS+Ts7Gzb/ujRozVu3DiNHz9en3zyiZ5//nn99ddfKlCgwC2P0Vr/W7H+zpKTk20T2rm7u2dplvKcxvo7TO/zlJnPV64KW3///bfOnTunIkWKSJJCQ0N18eJFRUVFqUaNGpKkdevWKTU1VbVq1bKVeffdd5WUlGRrmIiICJUtWzbdIYQAAADAnZw6dUrJyclq166dgoODJV0fiXWzpUuXqkuXLpo1a5aeffbZW25v+PDhmjBhgm2G7pCQEB04cEAzZ860ha0hQ4bYyhcvXlxvvvmmFi5caAtb0vXhb19++aUKFy5st/1u3bqpY8eOkqQPPvhAU6ZM0fbt29M8VikzOnbsKGdnZ129elWpqakqXry4OnTokOXt3Y8cGrYuX76sI0eO2F4fO3ZMe/bsUYECBVSgQAGNHDlS7du3V0BAgI4ePaqBAweqVKlSCgsLkySVL19ezZs310svvaQZM2YoKSlJffv21XPPPafAwEBJUqdOnTRy5Ej16NFDgwYN0m+//abJkyff8mY/AAAAOJbF01Nld0VlqGz8zp062bPXHcsFfTZTXjdMqnar/WZU1apV1bhxY1WuXFlhYWFq1qyZnn76absv87dt26bly5fr22+/VZs2bW65rStXrujo0aPq0aOHXnrpJdvy5ORk+fr62l4vWrRIU6ZM0dGjR3X58mUlJyenGeYXHBycJmhJUpUqVWz/nydPHvn4+KQZIZZZEydOVJMmTfTnn3+qf//+mjJlym2vlD2IHBq2du7cqYYNG9peW++T6tq1qz799FPt3btXc+fO1cWLFxUYGKhmzZpp9OjRdkP85s+fr759+6px48ZycnJS+/btNWXKFNt6X19frV69Wn369FGNGjVUqFAhDRs2jGnfAQAAciiLxSJLBofz5alTRy4BAddnIfz/Gf5u2phc/P2Vp06dezoNvLOzsyIiIrRlyxatXr1an3zyid59911t27bNNjlbyZIlVbBgQc2ePVutWrW65fCzy5cvS5I+//xz2+isG/cjXZ+Fu3Pnzho5cqTCwsLk6+urhQsXasKECXbl8+TJk+4+bt63xWK565kQAwICVKpUKZUqVUpz5sxRy5YtdeDAgTQT2D3IHBq2GjRoYJv2Mj2rVq264zYKFChge4DxrVSpUkWbNm3KdP0AAACQs1mcneX/zmD983o/yWKxD1z/f1+U/zuDTXnelsViUZ06dVSnTh0NGzZMwcHBWrp0qe0CQqFChbRkyRI1aNBAHTp00DfffJNu4PL391dgYKD+/PNPde7cOd19bdmyRcHBwXr33Xdty/766697fkxZ9eijj6pGjRp6//33NXnyZEdXJ8fIVfdsAQAAADfzadZMmjxJMR+MUfL/P/5Huv6cLf93Bl9ff49t27ZNa9euVbNmzeTn56dt27bpzJkzKl++vF05Pz8/rVu3Tg0bNlTHjh21cOFCubik/RN85MiReu211+Tr66vmzZsrISFBO3fu1IULFzRgwACVLl1aJ06c0MKFC/XII49oxYoVWrp06T0/rhudOXMmzUOXixQpYnuM0s369euntm3bauDAgXrooYdMrVtukfunCgEAAMADz6dZM5Vau0bF5s5V4PjxKjZ3rkqtXWNK0JKuzwy4ceNGtWzZUmXKlNGQIUM0YcIEtWjRIk3ZgIAArVu3Tvv27VPnzp2VkpKSpsyLL76oWbNmac6cOapcubLq16+v8PBw25DEJ598Uv3791ffvn1VrVo1bdmyRUOHDjXl2KwWLFig6tWr2/18/vnntyzfvHlzhYSE6P333ze1XrmJxbjdOD5Iuj6Xvq+vry5dupRjnrO1cuVKtWzZMldN7Qrg3qM/AGCVW/uDa9eu6dixYwoJCUnzPCPAUW53XmYmG3BlCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAuMe6deumNm3a3HL9iBEjVK1atWyrDxyDsAUAAID7QmqqoX8OX9DvO6L1z+ELSk01HF2lXOvYsWPq1KmTAgMD5eHhoaJFi+qpp57SoUOH7Mr98ssvat26tQoXLiwPDw+VLFlSzz77rDZu3Ggrs379elksFlksFjk5OcnX11fVq1fXwIEDderUqew+tGzl4ugKAAAAAHfr6O7T2rToD125mGBbliefu+o+W1olq/s5sGaOk5iYKDc3t0y/LykpSU2bNlXZsmW1ZMkSFSlSRH///bd++uknXbx40VZu+vTp6tu3r55//nktWrRIJUuW1KVLl/TLL7+of//+ioqKstvu4cOH5ePjo9jYWO3atUvjxo3TF198ofXr16ty5cp3e7g5Ele2AAAAkKsd3X1aP8/8zS5oSdKViwn6eeZvOrr7tCn7/fbbb1W5cmV5enqqYMGCatKkia5cuZJu2R07dqhw4cIaO3bsLbc3a9YslS9fXh4eHipXrpymT59ut37QoEEqU6aMvLy8VKJECQ0dOlRJSUm29dahibNmzVJISIg8PDwkSRaLRbNmzVLbtm3l5eWl0qVL64cffrhlPfbv36+jR49q+vTpql27toKDg1WnTh299957ql27tiTpxIkT6tevn/r166e5c+eqUaNGCg4OVpUqVfT6669r586dabbr5+engIAAlSlTRs8995w2b96swoULq3fv3rdu5FyOsAUAAIAcxTAMJSWkZOgn4WqyNi36/bbb27ToDyVcTb7jtgwj48MOT506pY4dO6p79+46ePCg1q9fr3bt2qW7jXXr1qlp06Z6//33NWjQoHS3N3/+fA0bNkzvv/++Dh48qA8++EBDhw7V3LlzbWXy5s2r8PBwHThwQJMnT9bnn3+uiRMn2m3nyJEj+u6777RkyRLt2bPHtnzkyJHq0KGD9u7dq5YtW6pz5846f/58unUpXLiwnJyc9O233yolJSXdMt99952SkpI0cODAdNdbLJZ0l9/I09NTL7/8sjZv3qzTp80JxI7GMEIAAADkKMmJqfrs9Q33bHtXLiZoVv+NdyzXc3J9ubo7Z2ibp06dUnJystq1a6fg4GBJSnco3NKlS9WlSxfNmjVLzz777C23N3z4cE2YMEHt2rWTJIWEhOjAgQOaOXOmunbtKkkaMmSIrXzx4sX15ptvauHChXaBJzExUV9++aUKFy5st/1u3bqpY8eOkqQPPvhAU6ZM0fbt29W8efM0dXnooYc0ZcoUDRw4UCNHjlTNmjXVsGFDde7cWSVKlJAk/f777/Lx8VFAQIDtfd99952trpIUGRl5x+GB5cqVkyQdP35cfn7333BPrmwBAAAAmVS1alU1btxYlStX1jPPPKPPP/9cFy5csCuzbds2PfPMM/rqq69uG7SuXLmio0ePqkePHvL29rb9vPfeezp69Kit3KJFi1SnTh0FBATI29tbQ4YM0YkTJ+y2FRwcnCZoSVKVKlVs/58nTx75+Pjc9mpSnz59FB0drfnz5ys0NFSLFy9WxYoVFRERYStz89WrsLAw7dmzRytWrNCVK1dueVXsRtYrgRm5EpYbcWULAAAAOYqLm5N6Tq6fobL//nFRy6f+947lWvetqsDS+e6434xydnZWRESEtmzZotWrV+uTTz7Ru+++q23btikkJESSVLJkSRUsWFCzZ89Wq1at5Orqmu62Ll++LEn6/PPPVatWrTT7ka5fJercubNGjhypsLAw+fr6auHChZowYYJd+Tx58qS7j5v3bbFYlJqaettjzJs3r5544gk98cQTeu+99xQWFqb33ntPTZs2VenSpXXp0iVFR0fbrm55e3urVKlScnHJeMQ4ePCgpOtX6u5HXNkCAABAjmKxWOTq7pyhn6AKBZQnn/ttt+ed311BFQrccVuZvbpisVhUp04djRw5Urt375abm5uWLl1qW1+oUCGtW7dOR44cUYcOHewms7iRv7+/AgMD9eeff6pUqVJ2P9bgtmXLFgUHB+vdd99VzZo1Vbp0af3111+Zqu/dsFgsKleunG0CkKefflqurq63nfDjTq5evarPPvtM9erVS/dq3P2AK1sAAADItZycLKr7bGn9PPO3W5Z5vENpOTnd22Fq27Zt09q1a9WsWTP5+flp27ZtOnPmjMqXL29Xzs/PT+vWrVPDhg3VsWNHLVy4MN0rPyNHjtRrr70mX19fNW/eXAkJCdq5c6cuXLigAQMGqHTp0jpx4oQWLlyoRx55RCtWrLALdvfSnj17NHz4cD3//POqUKGC3NzctGHDBs2ePds2wUexYsU0YcIEvf766zp//ry6deumkJAQnT9/XvPmzZP0v6tyVqdPn9a1a9cUFxenqKgojRs3TmfPntWSJUtMOY6cgLAFAACAXK1kdT8171UpzXO2vPO76/EO5jxny8fHRxs3btSkSZMUGxur4OBgTZgwQS1atEhTNiAgQOvWrVODBg3UuXNnLViwIE2ZF198UV5eXvroo4/01ltvKU+ePKpcubL69esnSXryySfVv39/9e3bVwkJCWrVqpWGDh2qESNG3PNjK1q0qIoXL66RI0fq+PHjslgsttf9+/e3lXv11VdVvnx5ffzxx3r66acVGxurggULKjQ0VD///HOayTHKli0ri8Uib29vlShRQs2aNdOAAQPsJtm431iMzMxx+YCKjY2Vr6+vLl26JB8fH0dXR0lJSVq5cqVatmx5y7G/AB4M9AcArHJrf3Dt2jUdO3bM7rlQWZWaaujUHxd1JTZBeXzcVaR0vnt+RQsPhtudl5nJBlzZAgAAwH3Bycmih8rmd3Q1ABsmyAAAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAACAwzFBNnKSe3U+ErYAAADgMNZp6uPj4x1cE+B/EhMTJaV9MHNmMfU7AAAAHMbZ2Vn58uXT6dOnJUleXl6yWHg2FhwnNTVVZ86ckZeXl1xc7i4uEbYAAADgUAEBAZJkC1yAozk5OalYsWJ3HfwJWwAAAHAoi8WiIkWKyM/PT0lJSY6uDiA3Nzc5Od39HVeELQAAAOQIzs7Od32PDJCTMEEGAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACRwatjZu3KgnnnhCgYGBslgsWrZsmd16wzA0bNgwFSlSRJ6enmrSpIn++OMPuzLnz59X586d5ePjo3z58qlHjx66fPmyXZm9e/eqbt268vDwUFBQkMaNG2f2oQEAAAB4wDk0bF25ckVVq1bVtGnT0l0/btw4TZkyRTNmzNC2bduUJ08ehYWF6dq1a7YynTt31v79+xUREaHly5dr48aN6tmzp219bGysmjVrpuDgYEVFRemjjz7SiBEj9Nlnn5l+fAAAAAAeXC6O3HmLFi3UokWLdNcZhqFJkyZpyJAheuqppyRJX375pfz9/bVs2TI999xzOnjwoH7++Wft2LFDNWvWlCR98sknatmypcaPH6/AwEDNnz9fiYmJmj17ttzc3FSxYkXt2bNHH3/8sV0oAwAAAIB7yaFh63aOHTum6OhoNWnSxLbM19dXtWrVUmRkpJ577jlFRkYqX758tqAlSU2aNJGTk5O2bdumtm3bKjIyUvXq1ZObm5utTFhYmMaOHasLFy4of/78afadkJCghIQE2+vY2FhJUlJSkpKSksw43Eyx1iEn1AWAY9EfALCiPwCyR2Y+Yzk2bEVHR0uS/P397Zb7+/vb1kVHR8vPz89uvYuLiwoUKGBXJiQkJM02rOvSC1tjxozRyJEj0yxfvXq1vLy8snhE915ERISjqwAgh6A/AGBFfwCYKz4+PsNlc2zYcqTBgwdrwIABttexsbEKCgpSs2bN5OPj48CaXZeUlKSIiAg1bdpUrq6ujq4OAAeiPwBgRX8AZA/rqLeMyLFhKyAgQJIUExOjIkWK2JbHxMSoWrVqtjKnT5+2e19ycrLOnz9ve39AQIBiYmLsylhfW8vczN3dXe7u7mmWu7q65qjOK6fVB4Dj0B8AsKI/AMyVmc9Xjn3OVkhIiAICArR27VrbstjYWG3btk2hoaGSpNDQUF28eFFRUVG2MuvWrVNqaqpq1aplK7Nx40a7sZUREREqW7ZsukMIAQAAAOBecGjYunz5svbs2aM9e/ZIuj4pxp49e3TixAlZLBb169dP7733nn744Qft27dPXbp0UWBgoNq0aSNJKl++vJo3b66XXnpJ27dv1+bNm9W3b18999xzCgwMlCR16tRJbm5u6tGjh/bv369FixZp8uTJdsMEAQAAAOBec+gwwp07d6phw4a219YA1LVrV4WHh2vgwIG6cuWKevbsqYsXL+rxxx/Xzz//LA8PD9t75s+fr759+6px48ZycnJS+/btNWXKFNt6X19frV69Wn369FGNGjVUqFAhDRs2jGnfAQAAAJjKoWGrQYMGMgzjlustFotGjRqlUaNG3bJMgQIFtGDBgtvup0qVKtq0aVOW6wkAAAAAmZVj79kCAAAAgNyMsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACbI0WFrxIgRslgsdj/lypWzrb927Zr69OmjggULytvbW+3bt1dMTIzdNk6cOKFWrVrJy8tLfn5+euutt5ScnJzdhwIAAADgAePi6ArcScWKFbVmzRrbaxeX/1W5f//+WrFihRYvXixfX1/17dtX7dq10+bNmyVJKSkpatWqlQICArRlyxadOnVKXbp0kaurqz744INsPxYAAAAAD44cH7ZcXFwUEBCQZvmlS5f0xRdfaMGCBWrUqJEkac6cOSpfvry2bt2q2rVra/Xq1Tpw4IDWrFkjf39/VatWTaNHj9agQYM0YsQIubm5ZffhAAAAAHhA5Piw9ccffygwMFAeHh4KDQ3VmDFjVKxYMUVFRSkpKUlNmjSxlS1XrpyKFSumyMhI1a5dW5GRkapcubL8/f1tZcLCwtS7d2/t379f1atXT3efCQkJSkhIsL2OjY2VJCUlJSkpKcmkI804ax1yQl0AOBb9AQAr+gMge2TmM5ajw1atWrUUHh6usmXL6tSpUxo5cqTq1q2r3377TdHR0XJzc1O+fPns3uPv76/o6GhJUnR0tF3Qsq63rruVMWPGaOTIkWmWr169Wl5eXnd5VPdORESEo6sAIIegPwBgRX8AmCs+Pj7DZXN02GrRooXt/6tUqaJatWopODhY33zzjTw9PU3b7+DBgzVgwADb69jYWAUFBalZs2by8fExbb8ZlZSUpIiICDVt2lSurq6Org4AB6I/AGBFfwBkD+uot4zI0WHrZvny5VOZMmV05MgRNW3aVImJibp48aLd1a2YmBjbPV4BAQHavn273TassxWmdx+Ylbu7u9zd3dMsd3V1zVGdV06rDwDHoT8AYEV/AJgrM5+vHD31+80uX76so0ePqkiRIqpRo4ZcXV21du1a2/rDhw/rxIkTCg0NlSSFhoZq3759On36tK1MRESEfHx8VKFChWyvPwAAAIAHR46+svXmm2/qiSeeUHBwsP79918NHz5czs7O6tixo3x9fdWjRw8NGDBABQoUkI+Pj1599VWFhoaqdu3akqRmzZqpQoUKev755zVu3DhFR0dryJAh6tOnT7pXrgAAAADgXsnRYevvv/9Wx44dde7cORUuXFiPP/64tm7dqsKFC0uSJk6cKCcnJ7Vv314JCQkKCwvT9OnTbe93dnbW8uXL1bt3b4WGhipPnjzq2rWrRo0a5ahDAgAAAPCAyNFha+HChbdd7+HhoWnTpmnatGm3LBMcHKyVK1fe66oBAAAAwG3lqnu2AAAAACC3IGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJjAxdEVQOYkJyUr6oe1Ohv5X0Ulu6vmk03k4sqv8U6Sk5K1e/kvivs3WnkDA1S9dUPaLQNot6yh3bKOtssa2i1raLeso+2yhnbLmtzcbhbDMAxHVyKni42Nla+vry5duiQfHx+H1WPDrEVymT5JBeIv2pad98qn5Ff6qf6LzzqsXjkd7ZY1tFvWZHe7JSUlaeXKlWrZsqVcXV3v+fazE+dc1tBuWXM/tlt29Qf3Y9tlB9ota3Jiu2UmGzxQwwinTZum4sWLy8PDQ7Vq1dL27dsdXaUM2zBrkQqPH6H8N5xokpQv/qIKjx+hDbMWOaZiORztljW0W9bQbllH22UN7ZY1tFvW0XZZQ7tlzf3Qbg/Mla1FixapS5cumjFjhmrVqqVJkyZp8eLFOnz4sPz8/G77Xkdf2UpOSta2WnWVP/6iLOmsT5V00SufKq5eIReX3HFJNTskJydrf9OWyn/1Eu2WCbRb1jiq3ZKSk7VmzVo1adJYrrn098E5lzW0W9bcz+1mdn9wP7edmWi3rMlIu13wyqfa2zZl+5DCzGSDByZs1apVS4888oimTp0qSUpNTVVQUJBeffVVvf3227d9r6PD1o6lEfIe/Fq27xcAAADIyS6PmaJH2jbN1n1mJhs8EPE5MTFRUVFRGjx4sG2Zk5OTmjRposjIyDTlExISlJCQYHsdGxsr6fpY6KSkJPMrfJNLf/8r72zfKwAAAJCzXfr732z/+zwz+3sgwtbZs2eVkpIif39/u+X+/v46dOhQmvJjxozRyJEj0yxfvXq1vLy8TKvnrZy7eF4PZaDclme6y6dicbOrk2vE7j+uxxbPvmM52s0e7ZY1tFvW0XZZQ7tlDe2WdbRd1tBuWZPRdjtx8bxWrlyZDTX6n/j4+AyXfSCGEf7777966KGHtGXLFoWGhtqWDxw4UBs2bNC2bdvsyqd3ZSsoKEhnz5512D1bux5vpHzxF9Od0cQ6ZrXGr+tyzTSY2YF2yxraLWsc1W5JSUmKiIhQ06ZNc+1shJxzWUO7Zc393G5m9wf3c9uZiXbLmpzcbrGxsSpUqBDDCK0KFSokZ2dnxcTE2C2PiYlRQEBAmvLu7u5yd3dPs9zV1dUhf8y4uroq+ZV+sowfoVTZTyGZKskiKeWVfvL08sz2uuVktFvW0G5Z4+h2c1T/dC84uu1yK9otax6EdjOrP3gQ2s4MtFvW5OR2y8zn64GY+t3NzU01atTQ2rVrbctSU1O1du1auytdOVn9F5/VmTdH6KJXPrvlF7zy6cybI3g+wy3QbllDu2UN7ZZ1tF3W0G5ZQ7tlHW2XNbRb1twP7fZADCOUrk/93rVrV82cOVOPPvqoJk2apG+++UaHDh1Kcy/XzRw9G+GNkpOStfOHNdofuV0VQx9VzSebcMk5A3Lzk8cdiXbLmuxst/vpocYS51xW0W5Zc7+1W3b2B/db22UX2i1rclq7MfX7LUydOlUfffSRoqOjVa1aNU2ZMkW1atW64/tyUtiS7r8/rgBkHf0BACv6AyB7MPX7LfTt21d9+/Z1dDUAAAAAPAAeiHu2AAAAACC7EbYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATODi6ArkBoZhSJJiY2MdXJPrkpKSFB8fr9jYWLm6ujq6OgAciP4AgBX9AZA9rJnAmhFuh7CVAXFxcZKkoKAgB9cEAAAAQE4QFxcnX1/f25axGBmJZA+41NRU/fvvv8qbN68sFsttyz7yyCPasWNHpveRmffFxsYqKChIJ0+elI+PT6b39SDL6u/H0Rxd7+zYvxn7uBfbvJtt0B/kbI7+XGWVo+tNf5D1bWTl/fQH2cPRn6uscnS9H9T+wDAMxcXFKTAwUE5Ot78riytbGeDk5KSiRYtmqKyzs3OWOrisvM/Hx4fONJOy+vtxNEfXOzv2b8Y+7sU272Yb9Ac5m6M/V1nl6HrTH2R9G1l5P/1B9nD05yqrHF3vB7k/uNMVLSsmyLjH+vTpk63vQ+bk1nZ2dL2zY/9m7ONebPNutkF/kLPl1nZ2dL3pD7L3/Y7+fT8ocms7O7reD3J/kFEMI8yFYmNj5evrq0uXLuXKb2EA3Dv0BwCs6A+AnIcrW7mQu7u7hg8fLnd3d0dXBYCD0R8AsKI/AHIermwBAAAAgAm4sgUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbN1nli9frrJly6p06dKaNWuWo6sDwIHatm2r/Pnz6+mnn3Z0VQA40MmTJ9WgQQNVqFBBVapU0eLFix1dJeCBwdTv95Hk5GRVqFBBv/zyi3x9fVWjRg1t2bJFBQsWdHTVADjA+vXrFRcXp7lz5+rbb791dHUAOMipU6cUExOjatWqKTo6WjVq1NDvv/+uPHnyOLpqwH2PK1v3ke3bt6tixYp66KGH5O3trRYtWmj16tWOrhYAB2nQoIHy5s3r6GoAcLAiRYqoWrVqkqSAgAAVKlRI58+fd2ylgAcEYSsH2bhxo5544gkFBgbKYrFo2bJlacpMmzZNxYsXl4eHh2rVqqXt27fb1v3777966KGHbK8feugh/fPPP9lRdQD32N32BwDuH/eyP4iKilJKSoqCgoJMrjUAibCVo1y5ckVVq1bVtGnT0l2/aNEiDRgwQMOHD9euXbtUtWpVhYWF6fTp09lcUwBmoz8AYHWv+oPz58+rS5cu+uyzz7Kj2gBE2MpRWrRooffee09t27ZNd/3HH3+sl156SS+88IIqVKigGTNmyMvLS7Nnz5YkBQYG2l3J+ueffxQYGJgtdQdwb91tfwDg/nEv+oOEhAS1adNGb7/9th577LHsqjrwwCNs5RKJiYmKiopSkyZNbMucnJzUpEkTRUZGSpIeffRR/fbbb/rnn390+fJl/fTTTwoLC3NUlQGYJCP9AYAHQ0b6A8Mw1K1bNzVq1EjPP/+8o6oKPJAIW7nE2bNnlZKSIn9/f7vl/v7+io6OliS5uLhowoQJatiwoapVq6Y33niDmQiB+1BG+gNJatKkiZ555hmtXLlSRYsWJYgB96GM9AebN2/WokWLtGzZMlWrVk3VqlXTvn37HFFd4IHj4ugK4N568skn9eSTTzq6GgBygDVr1ji6CgBygMcff1ypqamOrgbwQOLKVi5RqFAhOTs7KyYmxm55TEyMAgICHFQrAI5AfwDAiv4AyNkIW7mEm5ubatSoobVr19qWpaamau3atQoNDXVgzQBkN/oDAFb0B0DOxjDCHOTy5cs6cuSI7fWxY8e0Z88eFShQQMWKFdOAAQPUtWtX1axZU48++qgmTZqkK1eu6IUXXnBgrQGYgf4AgBX9AZB7WQzDMBxdCVy3fv16NWzYMM3yrl27Kjw8XJI0depUffTRR4qOjla1atU0ZcoU1apVK5trCsBs9AcArOgPgNyLsAUAAAAAJuCeLQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAABuIzw8XPny5btn2zt+/LgsFov27Nlzz7YJAMiZCFsAgByvW7duslgstp+CBQuqefPm2rt3b6a2M2LECFWrVs2cSmZQUFCQTp06pUqVKjm0HgAA8xG2AAC5QvPmzXXq1CmdOnVKa9eulYuLi1q3bu3oamWas7OzAgIC5OLi4uiqAABMRtgCAOQK7u7uCggIUEBAgKpVq6a3335bJ0+e1JkzZ2xlBg0apDJlysjLy0slSpTQ0KFDlZSUJOn6cMCRI0fqv//9r+0KWXh4uCTp4sWL6tWrl/z9/eXh4aFKlSpp+fLldvtftWqVypcvL29vb1vwu5ULFy6oc+fOKly4sDw9PVW6dGnNmTNHUtphhDdftbP+rF+/XpKUkJCgN998Uw899JDy5MmjWrVq2dYBAHI2vlYDAOQ6ly9f1rx581SqVCkVLFjQtjxv3rwKDw9XYGCg9u3bp5deekl58+bVwIED9eyzz+q3337Tzz//rDVr1kiSfH19lZqaqhYtWiguLk7z5s1TyZIldeDAATk7O9u2Gx8fr/Hjx+urr76Sk5OT/vOf/+jNN9/U/Pnz063f0KFDdeDAAf30008qVKiQjhw5oqtXr6ZbdvLkyfrwww9trz/88EN9/fXXKleunCSpb9++OnDggBYuXKjAwEAtXbpUzZs31759+1S6dOm7bksAgHkIWwCAXGH58uXy9vaWJF25ckVFihTR8uXL5eT0v0EaQ4YMsf1/8eLF9eabb2rhwoUaOHCgPD095e3tLRcXFwUEBNjKrV69Wtu3b9fBgwdVpkwZSVKJEiXs9p2UlKQZM2aoZMmSkq4HoFGjRt2yridOnFD16tVVs2ZNW11uxdfXV76+vpKkJUuWaObMmVqzZo0CAgJ04sQJzZkzRydOnFBgYKAk6c0339TPP/+sOXPm6IMPPrhjuwEAHIewBQDIFRo2bKhPP/1U0vVhetOnT1eLFi20fft2BQcHS5IWLVqkKVOm6OjRo7p8+bKSk5Pl4+Nz2+3u2bNHRYsWtQWt9Hh5edmCliQVKVJEp0+fvmX53r17q3379tq1a5eaNWumNm3a6LHHHrttPXbv3q3nn39eU6dOVZ06dSRJ+/btU0pKSpq6JSQk2F3RAwDkTIQtAECukCdPHpUqVcr2etasWfL19dXnn3+u9957T5GRkercubNGjhypsLAw+fr6auHChZowYcJtt+vp6XnHfbu6utq9tlgsMgzjluVbtGihv/76SytXrlRERIQaN26sPn36aPz48emWj46O1pNPPqkXX3xRPXr0sC2/fPmynJ2dFRUVZTesUZLtKh8AIOcibAEAciWLxSInJyfbvVBbtmxRcHCw3n33XVuZv/76y+49bm5uSklJsVtWpUoV/f333/r9999ve3UrswoXLqyuXbuqa9euqlu3rt566610w9a1a9f01FNPqVy5cvr444/t1lWvXl0pKSk6ffq06tate8/qBgDIHoQtAECukJCQoOjoaEnXhxFOnTpVly9f1hNPPCFJKl26tE6cOKGFCxfqkUce0YoVK7R06VK7bRQvXlzHjh2zDR3Mmzev6tevr3r16ql9+/b6+OOPVapUKR06dEgWi0XNmzfPUl2HDRumGjVqqGLFikpISNDy5ctVvnz5dMv26tVLJ0+e1Nq1a+1mVixQoIDKlCmjzp07q0uXLpowYYKqV6+uM2fOaO3atapSpYpatWqVpfoBALIHU78DAHKFn3/+WUWKFFGRIkVUq1Yt7dixQ4sXL1aDBg0kSU8++aT69++vvn37qlq1atqyZYuGDh1qt4327durefPmatiwoQoXLqyvv/5akvTdd9/pkUceUceOHVWhQgUNHDgwzRWwzHBzc9PgwYNVpUoV1atXT87Ozlq4cGG6ZTds2KBTp06pQoUKtuMrUqSItmzZIkmaM2eOunTpojfeeENly5ZVmzZttGPHDhUrVizL9QMAZA+LcbtB5wAAAACALOHKFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYIL/AwNmbPcBo200AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча (после масштабирования):\n",
+            "            Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                                    \n",
+            "1                  8.313329  3245.435934  3245.437223    8.313329            -\n",
+            "2                  8.313329  3245.435934  3245.437222    8.313329            -\n",
+            "4                  8.313329  3245.435934  3245.437219    8.313329            -\n",
+            "8                  8.313329  3245.435934  3245.437214    8.313329            -\n",
+            "16                 8.313329  3245.435934  3245.437204    8.313329            -\n",
+            "32                 8.313329  3245.435934  3245.437183    8.313329            -\n",
+            "64                 8.313329  3245.435934  3245.437143    8.313329            -\n",
+            "128                8.313329  3245.435934  3245.437062    8.313329            -\n",
+            "256                8.313329  3245.435934  3245.436901    8.313329            -\n",
+            "512                8.313329  3245.435934  3245.436579    8.313329            -\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X_raw = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X_raw.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Масштабирование признаков ----------\n",
+        "scaler = StandardScaler()\n",
+        "X = scaler.fit_transform(X_raw)   # теперь X имеет среднее 0 и дисперсию 1\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            # Упростим: без batch_size, используем значения по умолчанию\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=1e-6,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception as e:\n",
+        "        # print(f\"Error in {method} with lr={lr}, epochs={epochs}, batch={batch_size}: {e}\")\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01      # будет переопределён после первого графика\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate и определяем оптимальный LR ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (после масштабирования)', 'lr_plot')\n",
+        "\n",
+        "# Выбираем learning rate с наименьшим MSE для GD (среди не-NaN)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_mask = ~np.isnan(gd_mses)\n",
+        "if np.any(valid_mask):\n",
+        "    valid_lr = lr_range[valid_mask]\n",
+        "    valid_mse = gd_mses[valid_mask]\n",
+        "    best_idx = np.argmin(valid_mse)\n",
+        "    best_lr = valid_lr[best_idx]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (MSE = {valid_mse[best_idx]:.6f}) для следующих графиков.\")\n",
+        "else:\n",
+        "    best_lr = 1e-5   # запасной вариант\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-5.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (после масштабирования)', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (после масштабирования)', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "3q2uAZw825E8",
+        "outputId": "893b186c-f997-4ec8-c76c-cb6259b02bed"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c473765b-e8c6-497d-9e10-b587d587ee25\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c473765b-e8c6-497d-9e10-b587d587ee25\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (4).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate (после масштабирования):\n",
+            "               Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                                    \n",
+            "1.000000e-08          8.313329  3245.435934  3245.437183    8.313329  3310.765416\n",
+            "1.000000e-07          8.313329  2267.501029  2267.588249    8.313329  2767.249684\n",
+            "1.000000e-06          8.313329    70.020689    70.259705    8.313329   465.183031\n",
+            "1.000000e-05          8.313329     8.313329     8.313329    8.313329     8.313411\n",
+            "1.000000e-04          8.313329     8.313329     8.313332    8.313329     8.313361\n",
+            "1.000000e-03          8.313329    69.773860     8.320042    8.313329     8.314865\n",
+            "1.000000e-02          8.313329            -     8.470672    8.313329     8.348210\n",
+            "1.000000e-01          8.313329            -            -    8.313329     8.732265\n",
+            "\n",
+            "Выбран learning rate = 1.00e-04 (MSE = 8.313329) для следующих графиков.\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох (после масштабирования):\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  1389.676642  1519.252432    8.313329  2267.536287\n",
+            "20             8.313329     8.765660     9.423590    8.313329    70.248428\n",
+            "200            8.313329     8.313329     8.313338    8.313329     8.313361\n",
+            "2000           8.313329     8.313329     8.313334    8.313329     8.313361\n",
+            "20000          8.313329     8.313329     8.313333    8.313329     8.313361\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча (после масштабирования):\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "2                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "4                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "8                  8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "16                 8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "32                 8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "64                 8.313329  8.313329  8.313333    8.313329     8.313361\n",
+            "128                8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "256                8.313329  8.313329  8.313332    8.313329     8.313361\n",
+            "512                8.313329  8.313329  8.313332    8.313329     8.313361\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "from google.colab import files\n",
+        "print(\"Загрузите файл data.csv:\")\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values   # без масштабирования\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                batch_size=batch_size,  # возвращаем batch_size\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)   # без масштабирования\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_LR = 0.01      # будет переопределён\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title, filename):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=DEFAULT_LR, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Вывод таблицы\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Сначала строим график learning rate ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate (без масштабирования)', 'lr_plot')\n",
+        "\n",
+        "# Выбираем наименьший работающий learning rate для GD (как в первом варианте)\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "DEFAULT_LR = best_lr\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох (без масштабирования)', 'epochs_plot')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча (без масштабирования)', 'batch_plot')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "-8DUiLJA3nQS",
+        "outputId": "cc034d99-d565-42cc-992c-11925356a955"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Загрузите файл data.csv:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-cbb98c2d-a6ff-45b0-b0b8-2b943ba7a582\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-cbb98c2d-a6ff-45b0-b0b8-2b943ba7a582\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving data.csv to data (5).csv\n",
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate (без масштабирования):\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329            -\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329            -\n",
+            "1.000000e-06          8.313329         -  8.346550    8.313329            -\n",
+            "1.000000e-05          8.313329         -         -    8.313329            -\n",
+            "1.000000e-04          8.313329         -         -    8.313329            -\n",
+            "1.000000e-03          8.313329         -         -    8.313329            -\n",
+            "1.000000e-02          8.313329         -         -    8.313329            -\n",
+            "1.000000e-01          8.313329         -         -    8.313329            -\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох (без масштабирования):\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  2560.000199  2583.409888    8.313329            -\n",
+            "20             8.313329   217.565243   237.582879    8.313329            -\n",
+            "200            8.313329     8.325835     8.325835    8.313329            -\n",
+            "2000           8.313329     8.325615     8.325615    8.313329            -\n",
+            "20000          8.313329     8.323616     8.323616    8.313329            -\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча (без масштабирования):\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "2                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "4                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "8                  8.313329  8.325737  8.325737    8.313329            -\n",
+            "16                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "32                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "64                 8.313329  8.325737  8.325737    8.313329            -\n",
+            "128                8.313329  8.325737  8.325737    8.313329            -\n",
+            "256                8.313329  8.325737  8.325737    8.313329            -\n",
+            "512                8.313329  8.325737  8.325737    8.313329            -\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных (файл data.csv должен лежать в папке с ноутбуком) ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            # batch_size не передаём, используем значение по умолчанию\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы ----------\n",
+        "def plot_and_table(param_values, param_name, xlabel, title):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            mses.append(mse)\n",
+        "        results[label] = mses\n",
+        "        plt.semilogx(param_values, mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Определяем best_lr по первому графику ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1: Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_and_table(lr_range, 'lr', 'Learning rate', 'Зависимость MSE от learning rate')\n",
+        "\n",
+        "gd_mses = table_lr['GD'].values\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "# ---------- График 2: влияние числа эпох ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость MSE от количества эпох\")\n",
+        "table_epochs = plot_and_table(epochs_range, 'epochs', 'Epochs', 'Зависимость MSE от количества эпох')\n",
+        "\n",
+        "# ---------- График 3: влияние размера батча ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость MSE от размера батча\")\n",
+        "table_batch = plot_and_table(batch_range, 'batch', 'Batch size', 'Зависимость MSE от размера батча')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "JTCgt5Iu4AnF",
+        "outputId": "a535d63e-a80c-42f6-917b-5c0070ac391e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1: Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR   sklearn SGD\n",
+            "Learning rate                                                               \n",
+            "1.000000e-08          8.313329  8.325737  8.325737    8.313329  8.325798e+00\n",
+            "1.000000e-07          8.313329  8.324683  8.324850    8.313329  8.325257e+00\n",
+            "1.000000e-06          8.313329         -  8.346550    8.313329  8.321114e+00\n",
+            "1.000000e-05          8.313329         -         -    8.313329  8.315080e+00\n",
+            "1.000000e-04          8.313329         -         -    8.313329  1.340738e+01\n",
+            "1.000000e-03          8.313329         -         -    8.313329  4.890101e+20\n",
+            "1.000000e-02          8.313329         -         -    8.313329  1.932537e+26\n",
+            "1.000000e-01          8.313329         -         -    8.313329  4.280040e+24\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от количества эпох:\n",
+            "        Normal Equation           GD          SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                                    \n",
+            "2              8.313329  2560.000199  2583.409888    8.313329  2954.214186\n",
+            "20             8.313329   217.565243   237.582879    8.313329   888.456215\n",
+            "200            8.313329     8.325835     8.325835    8.313329     8.330808\n",
+            "2000           8.313329     8.325615     8.325615    8.313329     8.325737\n",
+            "20000          8.313329     8.323616     8.323616    8.313329     8.324683\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений MSE для Зависимость MSE от размера батча:\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "2                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "4                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "8                  8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "16                 8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "32                 8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "64                 8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "128                8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "256                8.313329  8.325737  8.325737    8.313329     8.325798\n",
+            "512                8.313329  8.325737  8.325737    8.313329     8.325798\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.mplot3d import Axes3D\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "print(f\"Данные загружены. X shape: {X.shape}, y shape: {y.shape}\")\n",
+        "\n",
+        "# ---------- Реализации методов ----------\n",
+        "def normal_equation(X, y, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+        "\n",
+        "def gradient_descent(X, y, lr=0.01, epochs=1000, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    for _ in range(epochs):\n",
+        "        w -= lr * 2 * X.T @ (X @ w - y)\n",
+        "    return w\n",
+        "\n",
+        "def sgd(X, y, lr=0.01, epochs=100, batch_size=32, fit_intercept=True):\n",
+        "    if fit_intercept:\n",
+        "        X = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "    w = np.zeros(X.shape[1])\n",
+        "    n = len(X)\n",
+        "    for _ in range(epochs):\n",
+        "        idx = np.random.permutation(n)\n",
+        "        X_s, y_s = X[idx], y[idx]\n",
+        "        for i in range(0, n, batch_size):\n",
+        "            Xb, yb = X_s[i:i+batch_size], y_s[i:i+batch_size]\n",
+        "            w -= lr * 2 * Xb.T @ (Xb @ w - yb)\n",
+        "    return w\n",
+        "\n",
+        "# ---------- Константы ----------\n",
+        "Xwb = np.hstack([np.ones((X.shape[0], 1)), X])\n",
+        "w_ne = normal_equation(X, y)\n",
+        "mse_ne = mean_squared_error(y, Xwb @ w_ne)\n",
+        "lr_sk = LinearRegression().fit(X, y)\n",
+        "mse_lr_sk = mean_squared_error(y, lr_sk.predict(X))\n",
+        "\n",
+        "# ---------- Универсальная функция с возвратом NaN при расходимости ----------\n",
+        "def compute_mse(method, lr=None, epochs=None, batch_size=None):\n",
+        "    np.random.seed(42)\n",
+        "    try:\n",
+        "        if method == 'ne':\n",
+        "            return mse_ne\n",
+        "        elif method == 'gd':\n",
+        "            w = gradient_descent(X, y, lr=lr, epochs=epochs)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'sgd':\n",
+        "            w = sgd(X, y, lr=lr, epochs=epochs, batch_size=batch_size)\n",
+        "            if np.any(np.isnan(w)) or np.any(np.isinf(w)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, Xwb @ w)\n",
+        "        elif method == 'lr_sk':\n",
+        "            return mse_lr_sk\n",
+        "        elif method == 'sgd_sk':\n",
+        "            sgd_sk = SGDRegressor(\n",
+        "                fit_intercept=True,\n",
+        "                max_iter=epochs,\n",
+        "                eta0=lr,\n",
+        "                learning_rate='constant',\n",
+        "                penalty=None,\n",
+        "                tol=None,\n",
+        "                random_state=42\n",
+        "            )\n",
+        "            sgd_sk.fit(X, y)\n",
+        "            y_pred = sgd_sk.predict(X)\n",
+        "            if np.any(np.isnan(y_pred)):\n",
+        "                return np.nan\n",
+        "            return mean_squared_error(y, y_pred)\n",
+        "        else:\n",
+        "            raise ValueError('Unknown method')\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "# ---------- Диапазоны гиперпараметров ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)      # от 1e-8 до 0.1\n",
+        "epochs_range = [2, 20, 200, 2000, 20000]\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "\n",
+        "DEFAULT_EPOCHS = 1000\n",
+        "DEFAULT_BATCH = 32\n",
+        "\n",
+        "methods = [\n",
+        "    ('ne', 'Normal Equation'),\n",
+        "    ('gd', 'GD'),\n",
+        "    ('sgd', 'SGD'),\n",
+        "    ('lr_sk', 'sklearn LR'),\n",
+        "    ('sgd_sk', 'sklearn SGD')\n",
+        "]\n",
+        "\n",
+        "# ---------- Функция для построения графика и вывода таблицы для 1/MSE ----------\n",
+        "def plot_1over_mse(param_values, param_name, xlabel, title):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    results = {}\n",
+        "    for method, label in methods:\n",
+        "        inv_mses = []\n",
+        "        for val in param_values:\n",
+        "            if param_name == 'lr':\n",
+        "                mse = compute_mse(method, lr=val, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'epochs':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=val, batch_size=DEFAULT_BATCH)\n",
+        "            elif param_name == 'batch':\n",
+        "                mse = compute_mse(method, lr=best_lr, epochs=DEFAULT_EPOCHS, batch_size=val)\n",
+        "            if np.isnan(mse) or mse == 0:\n",
+        "                inv = np.nan\n",
+        "            else:\n",
+        "                inv = 1.0 / mse\n",
+        "            inv_mses.append(inv)\n",
+        "        results[label] = inv_mses\n",
+        "        plt.semilogx(param_values, inv_mses, marker='o', label=label)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('1 / MSE')\n",
+        "    plt.title(title)\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "    plt.show()\n",
+        "\n",
+        "    df_table = pd.DataFrame(results, index=param_values)\n",
+        "    df_table.index.name = xlabel\n",
+        "    print(f\"\\nТаблица значений 1/MSE для {title}:\")\n",
+        "    print(df_table.round(6).to_string(na_rep='-'))\n",
+        "    return df_table\n",
+        "\n",
+        "# ---------- Определяем best_lr по первому графику ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 1 (MSE): Зависимость MSE от learning rate\")\n",
+        "table_lr = plot_1over_mse(lr_range, 'lr', 'Learning rate', 'Зависимость 1/MSE от learning rate (для определения best_lr)')\n",
+        "# Используем те же данные, но для выбора best_lr нужны MSE, поэтому пересчитаем MSE отдельно\n",
+        "mse_values = []\n",
+        "for lr in lr_range:\n",
+        "    mse_values.append(compute_mse('gd', lr=lr, epochs=DEFAULT_EPOCHS, batch_size=DEFAULT_BATCH))\n",
+        "gd_mses = np.array(mse_values)\n",
+        "valid_lr = lr_range[~np.isnan(gd_mses)]\n",
+        "if len(valid_lr) > 0:\n",
+        "    best_lr = valid_lr[0]\n",
+        "    print(f\"\\nВыбран learning rate = {best_lr:.2e} (наименьший работающий для GD).\")\n",
+        "else:\n",
+        "    best_lr = 1e-8\n",
+        "    print(\"\\nGD не сошелся ни при одном learning rate, используем 1e-8.\")\n",
+        "\n",
+        "# ---------- Графики 1/MSE ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 2: Зависимость 1/MSE от learning rate (все методы)\")\n",
+        "plot_1over_mse(lr_range, 'lr', 'Learning rate', 'Зависимость 1/MSE от learning rate')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 3: Зависимость 1/MSE от количества эпох\")\n",
+        "plot_1over_mse(epochs_range, 'epochs', 'Epochs', 'Зависимость 1/MSE от количества эпох')\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"ГРАФИК 4: Зависимость 1/MSE от размера батча\")\n",
+        "plot_1over_mse(batch_range, 'batch', 'Batch size', 'Зависимость 1/MSE от размера батча')\n",
+        "\n",
+        "# ---------- Трехмерный график: количество эпох до достижения MSE < 15 для SGD ----------\n",
+        "print(\"\\n\" + \"=\"*60)\n",
+        "print(\"Построение трехмерного графика зависимости числа эпох (до MSE<15) от lr и batch_size для SGD\")\n",
+        "\n",
+        "threshold = 15\n",
+        "max_epochs = 20000\n",
+        "# Создадим сетку эпох для поиска (логарифмическая шкала)\n",
+        "epochs_search = np.unique(np.logspace(0, np.log10(max_epochs), 30).astype(int))\n",
+        "epochs_search = epochs_search[epochs_search > 0]\n",
+        "\n",
+        "# Матрица результатов\n",
+        "Z = np.full((len(lr_range), len(batch_range)), np.nan)\n",
+        "\n",
+        "for i, lr in enumerate(lr_range):\n",
+        "    for j, bs in enumerate(batch_range):\n",
+        "        # Ищем минимальное число эпох, при котором MSE < threshold\n",
+        "        found = False\n",
+        "        for e in epochs_search:\n",
+        "            mse = compute_mse('sgd', lr=lr, epochs=e, batch_size=bs)\n",
+        "            if not np.isnan(mse) and mse < threshold:\n",
+        "                Z[i, j] = e\n",
+        "                found = True\n",
+        "                break\n",
+        "        if not found:\n",
+        "            Z[i, j] = np.nan  # не достигнуто за max_epochs\n",
+        "\n",
+        "# Построение поверхности\n",
+        "fig = plt.figure(figsize=(12, 8))\n",
+        "ax = fig.add_subplot(111, projection='3d')\n",
+        "\n",
+        "X_grid, Y_grid = np.meshgrid(lr_range, batch_range, indexing='ij')\n",
+        "# Убираем NaN для построения (можно заменить на max_epochs, но лучше маскировать)\n",
+        "mask = ~np.isnan(Z)\n",
+        "ax.scatter(X_grid[mask], Y_grid[mask], Z[mask], c=Z[mask], cmap='viridis', s=50)\n",
+        "\n",
+        "ax.set_xlabel('Learning rate')\n",
+        "ax.set_ylabel('Batch size')\n",
+        "ax.set_zlabel('Epochs to MSE < 15')\n",
+        "ax.set_title('Количество эпох до достижения MSE<15 (SGD)')\n",
+        "ax.set_xscale('log')\n",
+        "ax.set_yscale('log')\n",
+        "plt.show()\n",
+        "\n",
+        "# Таблица результатов\n",
+        "df_3d = pd.DataFrame(Z, index=lr_range, columns=batch_range)\n",
+        "df_3d.index.name = 'lr'\n",
+        "df_3d.columns.name = 'batch_size'\n",
+        "print(\"\\nТаблица количества эпох до MSE<15 (NaN — не достигнуто за 20000):\")\n",
+        "print(df_3d.round(1).to_string(na_rep='-'))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "c3q8SWWS5fYV",
+        "outputId": "04cab204-3c1e-4a1c-b474-c742f5b058cc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Данные загружены. X shape: (999, 1), y shape: (999,)\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 1 (MSE): Зависимость MSE от learning rate\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от learning rate (для определения best_lr):\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "1.000000e-07          0.120289  0.120125  0.120122    0.120289     0.120116\n",
+            "1.000000e-06          0.120289         -  0.119810    0.120289     0.120176\n",
+            "1.000000e-05          0.120289         -         -    0.120289     0.120263\n",
+            "1.000000e-04          0.120289         -         -    0.120289     0.074586\n",
+            "1.000000e-03          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-02          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-01          0.120289         -         -    0.120289     0.000000\n",
+            "\n",
+            "Выбран learning rate = 1.00e-08 (наименьший работающий для GD).\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 2: Зависимость 1/MSE от learning rate (все методы)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от learning rate:\n",
+            "               Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Learning rate                                                              \n",
+            "1.000000e-08          0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "1.000000e-07          0.120289  0.120125  0.120122    0.120289     0.120116\n",
+            "1.000000e-06          0.120289         -  0.119810    0.120289     0.120176\n",
+            "1.000000e-05          0.120289         -         -    0.120289     0.120263\n",
+            "1.000000e-04          0.120289         -         -    0.120289     0.074586\n",
+            "1.000000e-03          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-02          0.120289         -         -    0.120289     0.000000\n",
+            "1.000000e-01          0.120289         -         -    0.120289     0.000000\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 3: Зависимость 1/MSE от количества эпох\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от количества эпох:\n",
+            "        Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Epochs                                                              \n",
+            "2              0.120289  0.000391  0.000387    0.120289     0.000338\n",
+            "20             0.120289  0.004596  0.004209    0.120289     0.001126\n",
+            "200            0.120289  0.120108  0.120108    0.120289     0.120036\n",
+            "2000           0.120289  0.120111  0.120111    0.120289     0.120109\n",
+            "20000          0.120289  0.120140  0.120140    0.120289     0.120125\n",
+            "\n",
+            "============================================================\n",
+            "ГРАФИК 4: Зависимость 1/MSE от размера батча\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица значений 1/MSE для Зависимость 1/MSE от размера батча:\n",
+            "            Normal Equation        GD       SGD  sklearn LR  sklearn SGD\n",
+            "Batch size                                                              \n",
+            "1                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "2                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "4                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "8                  0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "16                 0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "32                 0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "64                 0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "128                0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "256                0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "512                0.120289  0.120109  0.120109    0.120289     0.120109\n",
+            "\n",
+            "============================================================\n",
+            "Построение трехмерного графика зависимости числа эпох (до MSE<15) от lr и batch_size для SGD\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_406/583569280.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mfound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mepochs_search\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m             \u001b[0mmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sgd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmse\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m                 \u001b[0mZ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/tmp/ipykernel_406/583569280.py\u001b[0m in \u001b[0;36mcompute_mse\u001b[0;34m(method, lr, epochs, batch_size)\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXwb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sgd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msgd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misinf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/tmp/ipykernel_406/583569280.py\u001b[0m in \u001b[0;36msgd\u001b[0;34m(X, y, lr, epochs, batch_size, fit_intercept)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0mXb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_s\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_s\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m             \u001b[0mw\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mXb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mXb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.mplot3d import Axes3D\n",
+        "from sklearn.linear_model import SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "# ---------- Загрузка данных ----------\n",
+        "df = pd.read_csv('data.csv')\n",
+        "X = df[['x']].values\n",
+        "y = df['y'].values\n",
+        "\n",
+        "# ---------- Параметры ----------\n",
+        "lr_range = np.logspace(-8, -1, 8)\n",
+        "batch_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\n",
+        "threshold = 15\n",
+        "max_epochs = 20000\n",
+        "\n",
+        "# Для прогресса\n",
+        "from tqdm.notebook import tqdm\n",
+        "\n",
+        "Z = np.full((len(lr_range), len(batch_range)), np.nan)\n",
+        "\n",
+        "for i, lr in enumerate(tqdm(lr_range, desc=\"lr\")):\n",
+        "    for j, bs in enumerate(tqdm(batch_range, desc=\"batch\", leave=False)):\n",
+        "        # Обучаем SGD один раз и сохраняем историю ошибок\n",
+        "        model = SGDRegressor(\n",
+        "            fit_intercept=True,\n",
+        "            max_iter=1,           # Будем делать по 1 эпохе за раз\n",
+        "            eta0=lr,\n",
+        "            learning_rate='constant',\n",
+        "            penalty=None,\n",
+        "            tol=None,\n",
+        "            warm_start=True,      # Важно: сохранять веса между вызовами\n",
+        "            random_state=42\n",
+        "        )\n",
+        "\n",
+        "        mse_history = []\n",
+        "        for epoch in range(1, max_epochs + 1):\n",
+        "            model.partial_fit(X, y)   # Одна эпоха\n",
+        "            y_pred = model.predict(X)\n",
+        "            mse = mean_squared_error(y, y_pred)\n",
+        "            mse_history.append(mse)\n",
+        "\n",
+        "            if mse < threshold:\n",
+        "                Z[i, j] = epoch\n",
+        "                break\n",
+        "\n",
+        "# ---------- Построение ----------\n",
+        "fig = plt.figure(figsize=(12, 8))\n",
+        "ax = fig.add_subplot(111, projection='3d')\n",
+        "\n",
+        "X_grid, Y_grid = np.meshgrid(lr_range, batch_range, indexing='ij')\n",
+        "mask = ~np.isnan(Z)\n",
+        "sc = ax.scatter(X_grid[mask], Y_grid[mask], Z[mask],\n",
+        "                c=Z[mask], cmap='viridis', s=50)\n",
+        "\n",
+        "ax.set_xlabel('Learning rate')\n",
+        "ax.set_ylabel('Batch size')\n",
+        "ax.set_zlabel('Эпох до MSE < 15')\n",
+        "ax.set_title('SGD: минимальное число эпох для MSE < 15')\n",
+        "ax.set_xscale('log')\n",
+        "ax.set_yscale('log')\n",
+        "plt.colorbar(sc)\n",
+        "plt.show()\n",
+        "\n",
+        "# Таблица\n",
+        "df_3d = pd.DataFrame(Z, index=lr_range, columns=batch_range)\n",
+        "df_3d.index.name = 'lr'\n",
+        "df_3d.columns.name = 'batch_size'\n",
+        "print(\"\\nТаблица (число эпох):\")\n",
+        "print(df_3d.round(1).to_string(na_rep='-'))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "26511a612fb240878e45b0be1b4f1aab",
+            "dc8907c29c8742be945f91c053aee93b",
+            "267de66128304dbeaf02517d855023bc",
+            "ded69f943c254be1a499dc4a462334f4",
+            "7748bdb577c9422c9cc8d3b3b5a216c1",
+            "b8002cdfb5584cc7b84ea812ee460974",
+            "ad009a7315cd4cb6817c33d1927b4c41",
+            "e6ba9cef5521425fa5aa8f8ed050fbc9",
+            "bddeb3ad8290486ea3106771e4e26001",
+            "e40191a8e4984203bed0647f11ef7f4a",
+            "b5eead6a813749c9976f56b4415c0fbb",
+            "e145436d63874a97bce53dc7e29b71bc",
+            "24474a7c66ba4f5da622dab2c4f2afba",
+            "1064448d917a417da9517e5adca120e5",
+            "58e69002306e4c3f87ef1e5ff3ea7d92",
+            "dde89e54d5d342f6b528520204657ea4",
+            "2b78d4bda1c8452caad279dd527e093a",
+            "013a5e417c8340bfaf5e35cf71561aea",
+            "8ecd1af312f545c9a4bc47ba7e8387ea",
+            "e36ec1ed388345e69a775db490afe9aa",
+            "a705a40461a14ea2914d1c41afeb4ad8",
+            "0c90285b641c483d9401f313eda143f9",
+            "7254ae5628694a73a104b99aa1cb8077",
+            "46de6bd3b22240a69ac859a5590dcc30",
+            "b0ff7eefd5a542beb6844988e7b328da",
+            "404057be731144fa8448384bd5bd5514",
+            "a489308bb89644f0ad7b98a3c41e219f",
+            "5b828dc6657e4f1c8c424e0986dcdadf",
+            "b9938872da8d4d97862462d93124367b",
+            "62e058f786d24a04b8e47a3fc7d38e9e",
+            "3f5e5f24664040edad3bf1599cca8a9f",
+            "b08300dc0653425dbe983bdc56c634dc",
+            "1d2ef1fbf7314991a1e9818c49dc0cf8",
+            "031d3bb7b1a24a6194b5d43f53e5748f",
+            "63f8c04350964d2b903f28bea7b1a082",
+            "237bb39b162c4d6ca329ee2a969617ef",
+            "2f8a3fcd5e9d4d2ea9eb13133106a32c",
+            "e8597177b14e4ced8f2f863013ca1b09",
+            "37d257fa0f564cc4af65e247876ec743",
+            "e83bc113542d46d3a84eef4bca859a5a",
+            "d738a0434ce2419a8ea58c52100ee123",
+            "7307b7a2a26f4b18b835be10bdf6d407",
+            "efd411c908d74ca1a5320f9815d3fc21",
+            "f70be4dd46f94aaf89a44d46647cb0fc",
+            "ab3edb3e720d47998ef2ae30cb286cac",
+            "62b5dc58d5d145a685fd665e1b8019d1",
+            "4351daab7af443b791e2d927dae6bb73",
+            "fd5a069b59284c2b886aacea3ce16534",
+            "b04b27f4a3d94461b7f5f5fcd0f4a9d3",
+            "ad735e3b855543f5ab9ff26dfb9313a7",
+            "351e4494c83e402db049a7c4b0c81f80",
+            "85c64386d03742e4a67e4a8496f86f40",
+            "2eaee0b62f8e4bb6868bd16ff4f2f235",
+            "c49da9dbc29c45bd81acf7e4b2daf5e7",
+            "75585ed3495c44d7b0b85bbc2b1fe046",
+            "051cfe00efec4bf08978527130603332",
+            "74e4d33cedb54836a8239b9fe273fbc2",
+            "1f125c02b0d1494e86dce61d927a5d80",
+            "9b57cd2efc3a478fbb1c30e17492e623",
+            "c29815289a744a558da61f594798f2c0",
+            "52e3ec2ebccb45e2aa560114b91f9188",
+            "4a1e6417afab4014a352dc414e309a29",
+            "d0ad16a0be6c43ebaefe3cf711cc76d1",
+            "a69cdba6b2294d94aa5206f1042be7d5",
+            "db0e58b34a5a4540b7dfce7727a3ca00",
+            "db84ce4ca3cd4e0f978a88dd4dcc1124",
+            "16d9592bb1c34bc7b5ffe9dfea26a6ea",
+            "827f6bf4f198406b801c1e0b419df66f",
+            "567160aef16a4489961e9822f8b97829",
+            "0e96c54d43104601ad9af688e72f9f5e",
+            "8217b3374c4147ee8228760cf7bec747",
+            "1c32d8a3565e42a18d4785caf3bdf478",
+            "56c1ddd51deb4f3eb3ac8b0bf4490ec6",
+            "99fa959ce4f14901b2af137675bbb741",
+            "2d62375f068a4afd805943edcf65fc2b",
+            "789b9ce8e2da4b5792f5c3e49ad01da1",
+            "1fbca0165b79445889d808c23215eadf",
+            "1149024c43c64af5bb020ca86c52877f",
+            "f7cf1c4374fe4c2d82c3e66bb6ae6480",
+            "18a850bc9f9f4ea883ffc692b353fee4",
+            "abe5f6c082394cc59e3e9dd80070b291",
+            "30ac12042af54310973009ff68ccbc5a",
+            "fb65ccaff5df43e19e3c561e29b0fdae",
+            "d944ea247484426abe3f8c7fbe066eb2",
+            "e2910d5e333045669e00452b04729b02",
+            "685a7520c2a5493a8ba33e35f3e2c546",
+            "360c977b32b34685a35b175079b3da8b",
+            "77c1aac3802a44baa0a619b021f46aab",
+            "35c8fcdd43ff458197b8a3bcd640cf22",
+            "ce92a3f149ef4e5dad97a6187607660a",
+            "1764eafebd4d450a86c79efcb5f477fa",
+            "c23ea4c554eb4bdfaab0221d817e030d",
+            "2db47c7de2e14af989f5eee18dac447c",
+            "bd2d411042d24686a0edeefd7a601429",
+            "327a8bf1af2141208f0f00c972c96779",
+            "573979e176b64750aa51313ac67b5b05",
+            "d63b0f24a3d645a19679364a90a59107",
+            "62c379887aa94436b8dd61ba1858d93d",
+            "9b65caa913554fae9550e8dc2fc744c5"
+          ]
+        },
+        "id": "82a-cMerBWJs",
+        "outputId": "ac1a95fd-b596-4efd-8067-7d348c678269"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "26511a612fb240878e45b0be1b4f1aab",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "lr:   0%|          | 0/8 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e145436d63874a97bce53dc7e29b71bc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7254ae5628694a73a104b99aa1cb8077",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "031d3bb7b1a24a6194b5d43f53e5748f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ab3edb3e720d47998ef2ae30cb286cac",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "051cfe00efec4bf08978527130603332",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "16d9592bb1c34bc7b5ffe9dfea26a6ea",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1149024c43c64af5bb020ca86c52877f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "35c8fcdd43ff458197b8a3bcd640cf22"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Таблица (число эпох):\n",
+            "batch_size     1     2     4     8     16    32    64    128   256   512\n",
+            "lr                                                                      \n",
+            "1.000000e-08  93.0  93.0  93.0  93.0  93.0  93.0  93.0  93.0  93.0  93.0\n",
+            "1.000000e-07  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0\n",
+            "1.000000e-06   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0\n",
+            "1.000000e-05   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0\n",
+            "1.000000e-04   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0\n",
+            "1.000000e-03     -     -     -     -     -     -     -     -     -     -\n",
+            "1.000000e-02     -     -     -     -     -     -     -     -     -     -\n",
+            "1.000000e-01     -     -     -     -     -     -     -     -     -     -\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file