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from typing import Dict, Any, List
from collections import defaultdict
import json # For optional persistence
class AdaptiveLearningEnvironment:
"""Creates environments for real-time learning and adaptation"""
def __init__(self):
# Use defaultdict for simpler logic
self.environment_state = defaultdict(list)
def update_environment(self, user_id: int, interaction: Dict[str, Any]) -> None:
"""
Update the environment based on user interactions.
Args:
user_id (int): The ID of the user.
interaction (Dict[str, Any]): Details of the user interaction.
"""
if not isinstance(interaction, dict):
raise ValueError("Interaction must be a dictionary.")
# Append interaction to the user's state
self.environment_state[user_id].append(interaction)
def adapt_to_user(self, user_id: int) -> List[Dict[str, Any]]:
"""
Adapt the environment to the user's preferences and interactions.
Args:
user_id (int): The ID of the user.
Returns:
List[Dict[str, Any]]: The user's interaction history.
"""
return self.environment_state.get(user_id, [])
def save_state(self, file_path: str) -> None:
"""
Save the environment state to a file (optional persistence).
Args:
file_path (str): The file path to save the state.
"""
with open(file_path, 'w') as file:
json.dump(self.environment_state, file)
def load_state(self, file_path: str) -> None:
"""
Load the environment state from a file.
Args:
file_path (str): The file path to load the state from.
"""
try:
with open(file_path, 'r') as file:
data = json.load(file)
self.environment_state = defaultdict(list, data)
except FileNotFoundError:
print(f"File '{file_path}' not found. Starting with an empty state.")
# Example usage
if __name__ == "__main__":
ale = AdaptiveLearningEnvironment()
# Update environment with user interactions
ale.update_environment(1, {"action": "click", "page": "homepage"}) |