题解 | 计算相关矩阵

import numpy as np

def cal_cov_matrix(X,Y):
    X_centered = X - X.mean(axis = 0)
    Y_centered = Y - Y.mean(axis = 0)
    return (X_centered.T @ Y_centered) / np.shape(X_centered)[0]

def get_sd(X):
    return np.sqrt(np.mean((X-X.mean(axis = 0))**2,axis = 0))

def calculate_correlation_matrix(X, Y=None):
    if Y is None:
        Y = X
    # 将标准差转换为列向量
    std_X = get_sd(X).reshape(-1, 1)  # 形状 (p, 1)
    std_Y = get_sd(Y).reshape(-1, 1)  # 形状 (q, 1)
    return cal_cov_matrix(X,Y)/(std_X @ std_Y.T)

if __name__ == "__main__":
    X = np.array(eval(input()))
    print(calculate_correlation_matrix(X))