"""
ConceptualUnderstandingEnhancer
Generated by Eden via recursive self-improvement
2025-10-28 01:04:35.999090
"""

class ConceptualUnderstandingEnhancer:
    """
    A capability that enhances the AI's conceptual understanding by analyzing context.
    
    This class processes input text to identify and store concept-concept relationships,
    which can then be used to improve contextual understanding of concepts.

    Attributes:
        max_context_length (int): Maximum number of previous concepts to consider for context.
        knowledge_base (dict): Stores identified concepts and their related concepts.
    """

    def __init__(self, max_context_length=5):
        """
        Initializes the ConceptualUnderstandingEnhancer with specified parameters.

        Args:
            max_context_length (int, optional): Maximum number of previous concepts to store. Defaults to 5.
        """
        self.max_context_length = max_context_length
        self.knowledge_base = {}

    def train(self, text):
        """
        Trains the enhancer by processing input text and extracting concept relationships.

        Args:
            text (str): Input text containing concepts and their relationships.
        """
        # Simplified example: extract pairs of concepts from text
        concepts = self._extract_concepts(text)
        for i in range(len(concepts)):
            current_concept = concepts[i]
            related_concepts = []
            # Look at previous max_context_length concepts as context
            for j in range(max(0, i - self.max_context_length), i):
                related_concepts.append(concepts[j])
            # Store relationships
            if current_concept not in self.knowledge_base:
                self.knowledge_base[current_concept] = []
            self.knowledge_base[current_concept].extend(related_concepts)

    def query(self, concept):
        """
        Queries the enhancer for related concepts based on stored knowledge.

        Args:
            concept (str): Concept to find related concepts for.

        Returns:
            list of tuples: Each tuple contains a related concept and similarity score.
        """
        if concept not in self.knowledge_base:
            return []
        
        similar_concepts = []
        # For demonstration, we'll use simple cosine similarity heuristic
        from sklearn.metrics.pairwise import cosine_similarity
        from numpy import array

        current_embedding = self._get_concept_embedding(concept)
        for relatedConcept in self.knowledge_base[concept]:
            related_embedding = self._get_concept_embedding(relatedConcept)
            if current_embedding is not None and related_embedding is not None:
                sim_score = cosine_similarity(array(current_embedding).reshape(1, -1),
                                            array(related_embedding).reshape(1, -1))[0][0]
                similar_concepts.append((relatedConcept, sim_score))

        # Sort by similarity score
        similar_concepts.sort(key=lambda x: x[1], reverse=True)
        
        return similar_concepts

    def _extract_concepts(self, text):
        """
        Simplified concept extraction method (can be replaced with NLP model).
        
        Args:
            text (str): Input text.

        Returns:
            list of str: Extracted concepts.
        """
        # For demonstration, split text into words and return unique concepts
        return list(set(text.split()))

    def _get_concept_embedding(self, concept):
        """
        Mock method to get concept embedding (can be replaced with actual embedding model).
        
        Args:
            concept (str): Concept to get embedding for.

        Returns:
            list of float or None: Embedding vector or None if not found.
        """
        # Simplified embedding generation
        return [0.1] * 100

# Example usage:
if __name__ == "__main__":
    """
    Example of using ConceptualUnderstandingEnhancer to improve conceptual understanding.

    Demonstrates training with text and querying related concepts.
    """

    enhancer = ConceptualUnderstandingEnhancer(max_context_length=3)

    # Training phase
    training_text = "The quick brown fox jumps over the lazy dog. Dogs are mammals. Mammals have four legs."
    enhancer.train(training_text)

    # Querying
    results = enhancer.query("dog")
    print(f"Querying concept 'dog': {results}")