"""
MultiLayerFeatureExtractor
Generated by Eden via recursive self-improvement
2025-11-01 10:40:11.245573
"""

class MultiLayerFeatureExtractor:
    def __init__(self):
        self.features = {}

    def extract_features(self, layer_name: str, function_name: str) -> float:
        """
        Extracts a feature from the specified layer using the provided function name.
        
        Args:
            layer_name (str): The name of the layer to extract features from. Valid options are 'Trinity', 'Nyx', 'Ava', 'Eden', 'Integration', 'LongTerm'.
            function_name (str): The name of the feature extraction function to use. Valid options depend on the layer.

        Returns:
            float: The extracted feature value.
        
        Raises:
            ValueError: If the provided layer or function names are invalid.
        """
        if layer_name not in self.features:
            raise ValueError(f"Unknown layer: {layer_name}")

        if function_name not in self.features[layer_name]:
            raise ValueError(f"Unknown function for layer {layer_name}: {function_name}")

        return self.features[layer_name][function_name]()

    def add_feature(self, layer_name: str, function_name: str, feature_function) -> None:
        """
        Adds a new feature extraction function to the specified layer.
        
        Args:
            layer_name (str): The name of the layer to add the function to. Valid options are 'Trinity', 'Nyx', 'Ava', 'Eden', 'Integration', 'LongTerm'.
            function_name (str): A unique name for the feature extraction function.
            feature_function: A callable that returns a float representing the extracted feature.

        Raises:
            ValueError: If the provided layer or function names are invalid.
        """
        if layer_name not in self.features:
            raise ValueError(f"Unknown layer: {layer_name}")

        if function_name in self.features[layer_name]:
            raise ValueError(f"A feature function with name '{function_name}' already exists for layer {layer_name}.")

        self.features[layer_name][function_name] = feature_function

    def get_all_features(self) -> dict:
        """
        Returns a dictionary containing all features extracted from all layers.
        
        Returns:
            dict: A dictionary where keys are layer names and values are dictionaries of function names and their corresponding feature values.
        """
        return self.features


# Example usage
feature_extractor = MultiLayerFeatureExtractor()

def extract_emotion(layer):
    # Placeholder for an actual emotion extraction logic
    return 0.7

def extract_energy(layer):
    # Placeholder for an actual energy extraction logic
    return 0.95

# Add features to the 'Trinity' layer
feature_extractor.add_feature('Trinity', 'extract_emotion', extract_emotion)
feature_extractor.add_feature('Trinity', 'extract_energy', extract_energy)

# Extract features from the 'Trinity' layer
trinity_features = feature_extractor.get_all_features()['Trinity']
print(f"Emotion in Trinity: {trinity_features['extract_emotion']}")
print(f"Energy in Trinity: {trinity_features['extract_energy']}")