def min_max_scale(values):
    v_min = min(values)
    v_max = max(values)
    return [(v - v_min) / (v_max - v_min) for v in values]

def mean_normalize(values):
    v_min = min(values)
    v_max = max(values)
    v_mean = sum(values) / len(values)
    return [(v - v_mean) / (v_max - v_min) for v in values]

def z_score_scale(values):
    v_mean = sum(values) / len(values)
    variance = sum((v - v_mean) ** 2 for v in values) / len(values)
    v_std = variance ** 0.5
    return [(v - v_mean) / v_std for v in values]

distance_m = [1000, 2000, 3000, 2000, 4000]

print(min_max_scale(distance_m))
print(mean_normalize(distance_m))
print(z_score_scale(distance_m))

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