import numpy as np
import random
from typing import Callable, Dict, Any

class ParameterOptimizer:
    """
    지능형 파라미터 최적화 엔진 (Algorithmic Review 1.2 반영)
    브루트 포스 대신 시뮬레이티드 어닐링 또는 경사 하강 초기화를 활용함.
    """

    def __init__(self, objective_function: Callable):
        self.objective_function = objective_function

    def simulated_annealing(self, initial_params: np.ndarray, iterations: int = 1000, temp: float = 1.0, cooling_rate: float = 0.95):
        """
        시뮬레이티드 어닐링(Simulated Annealing) 기반 최적화
        지역 최적점(Local Optima) 탈출이 가능하며 브루트 포스보다 압도적으로 빠름.
        """
        current_params = initial_params
        current_score = self.objective_function(current_params)
        
        best_params = current_params
        best_score = current_score
        
        for i in range(iterations):
            # 이웃 해(Neighbor) 탐색
            neighbor_params = current_params + np.random.normal(0, 0.1, size=current_params.shape)
            neighbor_score = self.objective_function(neighbor_params)
            
            # 수락 확률 계산 (Metropolis Criterion)
            if neighbor_score > current_score or random.random() < np.exp((neighbor_score - current_score) / temp):
                current_params = neighbor_params
                current_score = neighbor_score
                
                if current_score > best_score:
                    best_score = current_score
                    best_params = neighbor_params
            
            # 냉각 (Cooling)
            temp *= cooling_rate
            
        print(f"[Optimizer] Best Score Found: {best_score:.4f}")
        return best_params

# Example Objective Function (e.g., Accuracy based on threshold and weights)
def dummy_objective(params):
    # 가상의 성능 평가 함수 (파라미터가 0.5에 가까울수록 높은 점수)
    return -np.sum((params - 0.5)**2)

if __name__ == "__main__":
    optimizer = ParameterOptimizer(dummy_objective)
    initial = np.array([0.1, 0.9, 0.2])
    print(f"Starting Intelligent Optimization from {initial}...")
    best = optimizer.simulated_annealing(initial)
    print(f"Optimized Parameters: {best}")
