学步园

C.M Bishop的《Pattern Recognition and Machine Learning》12章介绍PCA，式（12.3）计算样本的协方差矩阵，写了个简单的python代码计算下，并与numpy中的cov函数对比下，结果一致

python代码如下：

import numpy as np

def cov(data):
    mean_ = np.mean(data, axis = 0)
    data = data - mean_
    cov_mat = data.T.dot(data) / (data.shape[0] - 1)
    return cov_mat
if __name__ == '__main__':
    a = np.random.random((10,2))
    
    cov_mat = np.cov(a, rowvar = 0)
    print 'covariance matrix using Numpy'
    print cov_mat
    my_cov_mar = cov(a)
    print 'covariance matrix from Bishop book (eq12.3).<<Pattern Recognition and Machine Learning>>'
    print my_cov_mar

计算结果为：
covariance matrix using Numpy
[[ 0.1063101  -0.00794282]
 [-0.00794282  0.07562934]]
covariance matrix from Bishop book (eq12.3).<<Pattern Recognition and Machine Learning>>
[[ 0.1063101  -0.00794282]
 [-0.00794282  0.07562934]]