topsis熵权法示例代码

以下是 topsis 熵权法的示例代码：

import numpy as np

def topsis(data, weights):
    # normalize the data
    norm_data = data / np.sqrt(np.sum(data**2, axis=0))

    # calculate the weighted normalized decision matrix
    weighted_norm_data = norm_data * weights

    # calculate the ideal and anti-ideal solutions
    ideal = np.max(weighted_norm_data, axis=1)
    anti_ideal = np.min(weighted_norm_data, axis=1)

    # calculate the distance to ideal and anti-ideal solutions
    dist_to_ideal = np.sqrt(np.sum((weighted_norm_data - ideal[:, np.newaxis])**2, axis=1))
    dist_to_anti_ideal = np.sqrt(np.sum((weighted_norm_data - anti_ideal[:, np.newaxis])**2, axis=1))

    # calculate the relative closeness to ideal solution
    relative_closeness = dist_to_anti_ideal / (dist_to_ideal + dist_to_anti_ideal)

    # return the ranking of the alternatives
    return np.argsort(relative_closeness)[::-1]

这是一个 Python 函数，用于实现 topsis 熵权法。它接受两个参数：一个是数据矩阵，另一个是权重向量。它返回一个排名列表，其中排名最高的是最优解。