偏相关系数矩阵（python实现）

import numpy as np


def partialCorrelationMatrix(data):
    """
    偏相关系数矩阵

    参数
    --------------
    :param data: 输入样本数据
        维度：n*m，n(变量数)，m(样本数)
    :return: 偏相关系数矩阵
        维度：n*n
    """
    data = np.array(data)
    cov = np.cov(data)  # 相关系数矩阵
    inverse_cov = np.linalg.inv(cov)  # 相关系数矩阵的逆
    partial_cov = np.zeros(inverse_cov.shape)  # 偏相关系数矩阵
    for i in range(partial_cov.shape[0]):
        for j in range(i, partial_cov.shape[1]):
            partial_cov[i, j] = -inverse_cov[i, j] / (np.sqrt(inverse_cov[i, i]) * np.sqrt(inverse_cov[j, j]))
            partial_cov[j, i] = partial_cov[i, j]
    return partial_cov


if __name__ == '__main__':
    data = [[2674, 3293, 3516, 507, 1777, 1795, 3140, 3321, 1716],
            [1338, 1776, 7844, 1788, 5185, 1451, 545, 2566, 750]]
    partial_cov = partialCorrelationMatrix(data)
    print(partial_cov)