import numpy as np

RI_DICT = {1: 0.00, 2: 0.00, 3: 0.58, 4: 0.90, 5: 1.12, 6: 1.24, 7: 1.32, 8: 1.41, 9: 1.45, 10: 1.49}

def get_weights_and_cr(matrix):
    n = matrix.shape[0]
    # Use the column normalization method (standard AHP approximation)
    col_sums = np.sum(matrix, axis=0)
    norm_matrix = matrix / col_sums
    weights = np.mean(norm_matrix, axis=1)
    
    # Calculate lambda_max
    aw = np.dot(matrix, weights)
    lambda_i = aw / weights
    lambda_max = np.mean(lambda_i)
    
    ci = (lambda_max - n) / (n - 1) if n > 1 else 0
    ri = RI_DICT.get(n, 0.90)
    cr = ci / ri if ri > 0 else 0
    return weights, lambda_max, ci, cr

allowed = [1, 2, 3, 1/2, 1/3]

def search_matrix(target_weights, label):
    best_diff = 999
    best_matrix = None
    best_weights = None
    best_cr = 999
    best_lambda_max = 0
    
    import itertools
    for comb in itertools.product(allowed, repeat=6):
        a12, a13, a14, a23, a24, a34 = comb
        
        M = np.array([
            [1.0, a12, a13, a14],
            [1/a12, 1.0, a23, a24],
            [1/a13, 1/a23, 1.0, a34],
            [1/a14, 1/a24, 1/a34, 1.0]
        ])
        
        w, l_max, ci, cr = get_weights_and_cr(M)
        if cr < 0.10:
            diff = np.sum(np.abs(w - target_weights))
            if diff < best_diff:
                best_diff = diff
                best_matrix = M
                best_weights = w
                best_cr = cr
                best_lambda_max = l_max
                
    print(f"=== {label} ===")
    print("Matrix:")
    for row in best_matrix:
        row_str = "  ".join([f"{val:.4f}" if val >= 1 else f"1/{int(1/val)}" for val in row])
        print(f"  [ {row_str} ]")
    print("Calculated Weights:", [round(x, 4) for x in best_weights])
    print("Target Weights:    ", target_weights)
    print(f"Lambda Max: {best_lambda_max:.4f}, CI: {best_lambda_max-4:.4f}/3 = {(best_lambda_max-4)/3:.4f}, CR: {best_cr:.4f}")
    print()

scenarios = [
    ("Outdoor - Day (Siang)", [0.30, 0.15, 0.25, 0.30]),
    ("Outdoor - Night (Malam)", [0.15, 0.50, 0.15, 0.20]),
    ("Indoor - Cerah", [0.35, 0.20, 0.20, 0.25]),
    ("Indoor - Gelap", [0.15, 0.55, 0.15, 0.15]),
    ("Semua Kondisi", [0.25, 0.30, 0.20, 0.25])
]

for name, target in scenarios:
    search_matrix(target, name)