"""
FeedbackLearning
Generated by Eden via recursive self-improvement
2025-11-01 04:29:45.616360
"""

class FeedbackLearning:
    """
    Class for processing user feedback and adapting the system's functionality.
    
    Attributes:
        learning_rate (float): The rate at which new information is incorporated into the system.
        feedback_history (list): A list containing historical feedback entries.
        
    Methods:
        process_feedback(feedback: str) -> None:
            Processes the given feedback and adjusts the system accordingly.
        incorporate_new_info(new_info: str, importance: float = 1.0) -> None:
            Incorporates new information into the system with a specified level of importance.
        adjust_learning_rate(rate: float) -> None:
            Sets the learning rate for processing new feedback.
    """
    
    def __init__(self):
        self.learning_rate = 0.5
        self.feedback_history = []
        
    def process_feedback(self, feedback: str) -> None:
        """
        Processes user feedback and adjusts the system's behavior based on the feedback.

        Args:
            feedback (str): The user feedback received.
        """
        # Simulate processing of feedback
        print(f"Processing feedback: {feedback}")
        self.feedback_history.append(feedback)
        
    def incorporate_new_info(self, new_info: str, importance: float = 1.0) -> None:
        """
        Incorporates new information into the system with a specified level of importance.

        Args:
            new_info (str): The new information to be incorporated.
            importance (float): The importance given to this new information.
        """
        # Simulate incorporating new information
        print(f"Incorporating new information: {new_info} with importance={importance}")
        
    def adjust_learning_rate(self, rate: float) -> None:
        """
        Adjusts the learning rate for processing new feedback.

        Args:
            rate (float): The new learning rate value.
        """
        self.learning_rate = rate
        print(f"Learning rate adjusted to {rate}")

# Example Usage
feedback_system = FeedbackLearning()
feedback_system.process_feedback("Improve response time")
feedback_system.incorporate_new_info("Increase processing cycles", 2.0)
feedback_system.adjust_learning_rate(0.75)