"""
EnhancedLearningFramework
Generated by Eden via recursive self-improvement
2025-11-01 00:01:57.891391
"""

class EnhancedLearningFramework:
    """
    This class provides a structured approach for continuous learning.
    It includes methods to identify knowledge gaps, integrate new information,
    and evaluate the impact of the learned content on overall performance.
    """

    def __init__(self):
        self.knowledge_base = {}

    def assess_knowledge_gaps(self, initial_data: dict) -> dict:
        """
        Identifies areas where knowledge is lacking by comparing existing data with a comprehensive database.

        :param initial_data: A dictionary containing the current knowledge state.
        :return: A dictionary indicating areas of deficiency in the knowledge base.
        """
        # Assuming there's a comprehensive database (replace with actual implementation)
        comprehensive_db = {
            "Python": ["Functions", "Classes", "Decorators"],
            "Java": ["OOP Concepts", "Collections", "Streams"],
            "AI": ["Neural Networks", "Reinforcement Learning", "Natural Language Processing"]
        }

        knowledge_gaps = {}
        for subject, topics in initial_data.items():
            gaps_in_subject = [topic for topic in comprehensive_db.get(subject, []) if topic not in topics]
            if gaps_in_subject:
                knowledge_gaps[subject] = gaps_in_subject
        return knowledge_gaps

    def integrate_new_information(self, new_info: dict) -> None:
        """
        Updates the knowledge base with newly acquired information.

        :param new_info: A dictionary containing new topics to add.
        """
        for subject, topics in new_info.items():
            if self.knowledge_base.get(subject):
                self.knowledge_base[subject].extend(topics)
            else:
                self.knowledge_base[subject] = topics

    def evaluate_performance(self) -> float:
        """
        Evaluates the effectiveness of learning by calculating a performance score.

        :return: A performance score between 0 and 1, where 1 indicates full mastery.
        """
        total_topics = sum([len(topics) for topics in self.knowledge_base.values()])
        if not total_topics:
            return 0.0

        # Simplified evaluation logic (replace with actual scoring mechanism)
        mastery_levels = [0.8, 0.75, 0.6]  # Example mastery levels for different subjects
        performance_score = sum(mastery_levels) / len(self.knowledge_base)
        return performance_score

# Example usage:
initial_knowledge_state = {
    "Python": ["Functions", "Classes"],
    "Java": ["OOP Concepts"]
}

framework = EnhancedLearningFramework()
print("Initial Knowledge State:", initial_knowledge_state)

knowledge_gaps = framework.assess_knowledge_gaps(initial_knowledge_state)
print("Identified Gaps:", knowledge_gaps)

new_information = {
    "Python": ["Decorators"],
    "Java": ["Streams"]
}

framework.integrate_new_information(new_information)
print("Updated Knowledge State:", framework.knowledge_base)

performance_score = framework.evaluate_performance()
print(f"Performance Score: {performance_score}")