VVP_2025/Probabilistic_Reasoning.py at main · Codegemini1/VVP_2025 · GitHub

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class ProbabilisticReasoner:
    def __init__(self, knowledge_base):
        self.kb = knowledge_base
        self.probability_tables = self._initialize_probability_tables()

    def _initialize_probability_tables(self):
        return {
            "skill_match_prob": {
                "AI Engineer": {"Python": 0.9, "Machine Learning": 0.85, "Deep Learning": 0.8, "Mathematics": 0.75},
                "Data Scientist": {"Python": 0.9, "Statistics": 0.85, "Data Analysis": 0.8, "Machine Learning": 0.75},
                "Web Developer": {"JavaScript": 0.9, "HTML/CSS": 0.85, "React": 0.8, "Node.js": 0.75},
                "Research Scientist": {"Research Methodology": 0.9, "Mathematics": 0.85, "Publications": 0.8}
            },
            "marks_probability": {
                "AI Engineer": {80: 0.9, 75: 0.8, 70: 0.6, 65: 0.4, 60: 0.2},
                "Data Scientist": {75: 0.9, 70: 0.8, 65: 0.6, 60: 0.4, 55: 0.2},
                "Web Developer": {70: 0.9, 65: 0.8, 60: 0.7, 55: 0.5, 50: 0.3},
                "Research Scientist": {85: 0.9, 80: 0.7, 75: 0.5, 70: 0.3, 65: 0.1}
            }
        }

    def calculate_career_probability(self, student: Student, career: Career) -> float:
        # Calculate skill match probability
        skill_match_prob = 1.0
        matched_skills = 0

        for skill in career.required_skills:
            if skill in student.skills:
                skill_prob = self.probability_tables["skill_match_prob"][career.name].get(
                    skill, 0.7)
                skill_match_prob *= skill_prob
                matched_skills += 1

        # Adjust for number of matched skills
        skill_ratio = matched_skills / len(career.required_skills)
        skill_probability = skill_match_prob * skill_ratio

        # Calculate marks probability
        marks_prob = 0.0
        for threshold, prob in self.probability_tables["marks_probability"][career.name].items():
            if student.marks >= threshold:
                marks_prob = prob
                break

        # Calculate interest match
        interest_match = len(set(student.interests) & set(
            career.required_interests)) / len(career.required_interests)

        # Combined probability (weighted average)
        final_probability = (
            0.4 * skill_probability +
            0.4 * marks_prob +
            0.2 * interest_match
        )

        return min(1.0, max(0.0, final_probability))

    def update_probability_with_new_skill(self, student: Student, new_skill: str, career: Career) -> float:
        # Simulate Bayesian update
        original_prob = self.calculate_career_probability(student, career)

        if new_skill in career.required_skills:
            skill_boost = self.probability_tables["skill_match_prob"][career.name].get(
                new_skill, 0.7)
            updated_prob = original_prob + \
                (1 - original_prob) * skill_boost * 0.3
        else:
            updated_prob = original_prob * 0.95  # Slight penalty for irrelevant skill

        return updated_prob