import math
import numpy as np

# Cameras dataset
cameras = [
    {"name": "Canon M10", "price": 80000, "iso": 25600, "af": 49, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Sony A5000", "price": 90000, "iso": 25600, "af": 25, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon M3", "price": 100000, "iso": 25600, "af": 49, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Sony A6000", "price": 110000, "iso": 25600, "af": 179, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon M50", "price": 130000, "iso": 51200, "af": 143, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Sony A6300", "price": 155000, "iso": 51200, "af": 425, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Fujifilm XA3", "price": 80000, "iso": 25600, "af": 77, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Sony A6400", "price": 185000, "iso": 51200, "af": 425, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Fujifilm XA5", "price": 90000, "iso": 25600, "af": 77, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Nikon J5", "price": 75000, "iso": 12800, "af": 171, "sensor": "1-inch", "sensor_score": 0.5},
    {"name": "Fujifilm XT20", "price": 145000, "iso": 51200, "af": 325, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 500D", "price": 55000, "iso": 12800, "af": 9, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 60D", "price": 100000, "iso": 12800, "af": 9, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 1100D", "price": 55000, "iso": 12800, "af": 9, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 80D", "price": 140000, "price_spec": 140000, "iso": 25600, "af": 45, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 600D", "price": 80000, "iso": 12800, "af": 9, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 6D", "price": 165000, "iso": 102400, "af": 61, "sensor": "Full Frame", "sensor_score": 1.0},
    {"name": "Canon 550D", "price": 75000, "iso": 12800, "af": 9, "sensor": "APS-C", "sensor_score": 0.7},
    {"name": "Canon 5D Mark III", "price": 215000, "iso": 102400, "af": 61, "sensor": "Full Frame", "sensor_score": 1.0}
]

price_min = 55000
price_max = 215000
iso_max = 102400
af_max = 425

def php_round(val, decimals=2):
    multiplier = 10 ** decimals
    return math.floor(val * multiplier + 0.5) / multiplier

# Generate Decision Matrix X (0-1 scale)
X = []
for c in cameras:
    ps = php_round((price_max - c["price"]) / (price_max - price_min), 2)
    is_ = php_round(c["iso"] / iso_max, 2)
    af = php_round(c["af"] / af_max, 2)
    ss = php_round(c["sensor_score"], 2)
    X.append([ps, is_, af, ss])
X = np.array(X)

m, n = X.shape
dividers = [math.sqrt(sum(X[i][j]**2 for i in range(m))) for j in range(n)]

print("Dividers:", [round(d, 4) for d in dividers])

# Normalization Matrix R
R = np.zeros((m, n))
for i in range(m):
    for j in range(n):
        R[i][j] = X[i][j] / dividers[j]

scenarios = {
    "outdoor_day": {
        "name": "Outdoor - Day (Siang)",
        "bobot": [0.30, 0.15, 0.25, 0.30],
        "jenis": ["cost", "benefit", "benefit", "benefit"]
    },
    "outdoor_night": {
        "name": "Outdoor - Night (Malam)",
        "bobot": [0.15, 0.50, 0.15, 0.20],
        "jenis": ["cost", "benefit", "benefit", "benefit"]
    },
    "indoor_cerah": {
        "name": "Indoor - Cerah",
        "bobot": [0.35, 0.20, 0.20, 0.25],
        "jenis": ["cost", "benefit", "benefit", "benefit"]
    },
    "indoor_gelap": {
        "name": "Indoor - Gelap",
        "bobot": [0.15, 0.55, 0.15, 0.15],
        "jenis": ["cost", "benefit", "benefit", "benefit"]
    },
    "all": {
        "name": "Semua Kondisi",
        "bobot": [0.25, 0.30, 0.20, 0.25],
        "jenis": ["cost", "benefit", "benefit", "benefit"]
    }
}

for key, sc in scenarios.items():
    print(f"\n================ {sc['name']} ================")
    w = sc["bobot"]
    jenis = sc["jenis"]
    
    # Weighted normalized matrix Y
    Y = np.zeros((m, n))
    for i in range(m):
        for j in range(n):
            Y[i][j] = R[i][j] * w[j]
            
    # Ideal solutions
    ideal_pos = []
    ideal_neg = []
    for j in range(n):
        col = Y[:, j]
        if jenis[j] == 'benefit':
            ideal_pos.append(max(col))
            ideal_neg.append(min(col))
        else:
            ideal_pos.append(min(col))
            ideal_neg.append(max(col))
            
    print("A+:", [round(x, 6) for x in ideal_pos])
    print("A-:", [round(x, 6) for x in ideal_neg])
    
    # Jarak and Preferensi
    results = []
    for i in range(m):
        dp = math.sqrt(sum((Y[i][j] - ideal_pos[j])**2 for j in range(n)))
        dm = math.sqrt(sum((Y[i][j] - ideal_neg[j])**2 for j in range(n)))
        v = dm / (dp + dm) if (dp + dm) > 0 else 0.0
        results.append((cameras[i]["name"], X[i], dp, dm, v))
        
    results.sort(key=lambda x: x[4], reverse=True)
    
    print("Top 3:")
    for rank in range(3):
        res = results[rank]
        print(f"Rank {rank+1}: {res[0]} | Scores: {res[1]} | D+: {res[2]:.6f} | D-: {res[3]:.6f} | V: {res[4]:.6f}")
        
    print("Full ranking:")
    for rank, res in enumerate(results):
        print(f"{rank+1} | {res[0]} | {res[1][0]:.2f} | {res[1][1]:.2f} | {res[1][2]:.2f} | {res[1][3]:.2f} | {res[2]:.4f} | {res[3]:.4f} | {res[4]:.6f}")