题解 | 计算协方差矩阵

import numpy as np

def calculate_covariance_matrix(vectors):
    # 补全代码
    feature_num = len(vectors)
    sample_num = len(vectors[0])

    cov_matrix = np.zeros((feature_num,feature_num))

    feature_mean = [sum(feature)/sample_num for feature in vectors]

    for i in range(feature_num):
        for j in range(feature_num):
            f1_mean = feature_mean[i]
            f2_mean = feature_mean[j]
            tot = 0
            for k in range(sample_num):
                tot += (vectors[i][k] - f1_mean)*(vectors[j][k] - f2_mean)
            cov_matrix[i][j] = tot/(sample_num - 1)
    
    return cov_matrix.tolist()


        






# 主程序
if __name__ == "__main__":
    # 输入
    ndarrayA = input()

    # 处理输入
    import ast
    A = ast.literal_eval(ndarrayA)

    # 调用函数计算
    output = calculate_covariance_matrix(A)
    
    # 输出结果
    print(output)