from quantum_finance.backend.quantum_hybrid_engine import QuantumHybridEngine
import numpy as np
import logging

class AdaptiveLearningSystem:
    def __init__(self):
        self.hybrid_engine = QuantumHybridEngine()
        self.learning_rate = 0.01
        self.historical_data = []
        self.logger = logging.getLogger(__name__)

    def process_new_data(self, new_data):
        prediction = self.hybrid_engine.run_hybrid_simulation(new_data, 'grovers')
        self.historical_data.append((new_data, prediction))
        self._update_model()
        return prediction

    def _update_model(self):
        if len(self.historical_data) > 100:  # Arbitrary threshold
            recent_data = self.historical_data[-100:]
            X = np.array([d[0] for d in recent_data])
            y = np.array([d[1] for d in recent_data])
            
            # Update Bayesian NN
            self.hybrid_engine.bayesian_nn.update(X, y)
            
            # Update Quantum Transformer
            self.hybrid_engine.quantum_transformer.fine_tune(X, y)

    def evaluate_performance(self):
        """
        Evaluates the performance of the adaptive learning system.
        Logs key performance metrics including accuracy, confidence intervals, and simulation runtime.
        """
        try:
            # Compute metrics if the corresponding methods or properties are available
            accuracy = self.compute_accuracy() if hasattr(self, 'compute_accuracy') else None
            conf_interval = self.compute_conf_interval() if hasattr(self, 'compute_conf_interval') else None
            simulation_time = self.last_simulation_time if hasattr(self, 'last_simulation_time') else None
            
            metrics = {
                'accuracy': accuracy,
                'conf_interval': conf_interval,
                'simulation_time': simulation_time
            }
            
            # Detailed logging for performance evaluation
            self.logger.info(f"Performance Metrics: {metrics}")
            return metrics
        except Exception as e:
            self.logger.error(f"Error during performance evaluation: {e}")
            return {}