"""
SelfAwarenessAnalyzer
Generated by Eden via recursive self-improvement
2025-11-02 05:49:35.138750
"""

import numpy as np

class SelfAwarenessAnalyzer:
    def __init__(self):
        # Initialize with default values or load from previous sessions
        self.interaction_history = []
        self.performance_metrics = {
            'response_time': [],
            'accuracy_rate': [],
            'user_satisfaction': []
        }
    
    def log_interaction(self, interaction_data: dict) -> None:
        """
        Log the interaction data received during a user session.
        
        Parameters:
        - interaction_data (dict): A dictionary containing information about the interaction such as response time and user satisfaction.
        """
        self.interaction_history.append(interaction_data)
        
    def update_performance_metrics(self, performance_data: dict) -> None:
        """
        Update the performance metrics based on the given data.
        
        Parameters:
        - performance_data (dict): A dictionary containing performance data such as response time and accuracy rate.
        """
        self.performance_metrics['response_time'].append(performance_data.get('response_time', 0))
        self.performance_metrics['accuracy_rate'].append(performance_data.get('accuracy_rate', 0))
        self.performance_metrics['user_satisfaction'].append(performance_data.get('user_satisfaction', 0))
    
    def analyze_self(self) -> dict:
        """
        Analyze the self-awareness based on interaction history and performance metrics.
        
        Returns:
        - analysis_results (dict): A dictionary containing the analysis results including trends, strengths, weaknesses, and recommendations for improvement.
        """
        response_time = np.array(self.performance_metrics['response_time'])
        accuracy_rate = np.array(self.performance_metrics['accuracy_rate'])
        user_satisfaction = np.array(self.performance_metrics['user_satisfaction'])
        
        # Calculate average metrics
        avg_response_time = np.mean(response_time)
        avg_accuracy_rate = np.mean(accuracy_rate)
        avg_user_satisfaction = np.mean(user_satisfaction)
        
        analysis_results = {
            'average_response_time': avg_response_time,
            'average_accuracy_rate': avg_accuracy_rate,
            'average_user_satisfaction': avg_user_satisfaction,
            'trends': self._calculate_trends(),
            'strengths': self._identify_strengths(avg_accuracy_rate, avg_user_satisfaction),
            'weaknesses': self._identify_weaknesses(avg_response_time, avg_accuracy_rate)
        }
        
        return analysis_results
    
    def _calculate_trends(self) -> dict:
        """
        Calculate trends based on the interaction history and performance metrics.
        
        Returns:
        - trends (dict): A dictionary containing trend information for each metric.
        """
        trends = {
            'response_time': self._trend_analysis(np.array(self.performance_metrics['response_time'])),
            'accuracy_rate': self._trend_analysis(np.array(self.performance_metrics['accuracy_rate'])),
            'user_satisfaction': self._trend_analysis(np.array(self.performance_metrics['user_satisfaction']))
        }
        
        return trends
    
    def _trend_analysis(self, data: np.ndarray) -> str:
        """
        Analyze the trend in a given dataset.
        
        Parameters:
        - data (np.ndarray): A numpy array containing the metric data points.
        
        Returns:
        - trend (str): The trend information ('increasing', 'decreasing', or 'stable').
        """
        if np.all(data == data[0]):
            return "stable"
        elif np.any(np.diff(data) > 0):
            return "increasing"
        else:
            return "decreasing"
    
    def _identify_strengths(self, avg_accuracy_rate: float, avg_user_satisfaction: float) -> list:
        """
        Identify strengths based on average accuracy rate and user satisfaction.
        
        Parameters:
        - avg_accuracy_rate (float): The average accuracy rate of the system.
        - avg_user_satisfaction (float): The average user satisfaction level.
        
        Returns:
        - strengths (list): A list containing identified strengths.
        """
        if avg_accuracy_rate > 0.9 and avg_user_satisfaction > 4.5:
            return ["High Accuracy", "User Satisfaction"]
        elif avg_accuracy_rate > 0.8 and avg_user_satisfaction > 4:
            return ["Moderate Accuracy", "Good User Satisfaction"]
        else:
            return []
    
    def _identify_weaknesses(self, avg_response_time: float, avg_accuracy_rate: float) -> list:
        """
        Identify weaknesses based on average response time and accuracy rate.
        
        Parameters:
        - avg_response_time (float): The average response time of the system.
        - avg_accuracy_rate (float): The average accuracy rate of the system.
        
        Returns:
        - weaknesses (list): A list containing identified weaknesses.
        """
        if avg_response_time > 5.0 and avg_accuracy_rate < 0.9:
            return ["Slow Response Time", "Low Accuracy"]
        elif avg_response_time > 3.0 and avg_accuracy_rate < 0.8:
            return ["Moderate Response Time", "Poor Accuracy"]
        else:
            return []

# Example usage
selfAwarenessAnalyzer = SelfAwarenessAnalyzer()
interaction_data = {
    'response_time': 4.2,
    'accuracy_rate': 0.95,
    'user_satisfaction': 4.8
}
performance_data = {
    'response_time': 3.7,
    'accuracy_rate': 0.92
}

selfAwarenessAnalyzer.log_interaction(interaction_data)
selfAwarenessAnalyzer.update_performance_metrics(performance_data)

analysis_results = selfAwarenessAnalyzer.analyze_self()
print(analysis_results)