pca-svd-vriance-covirance

PCA

def compact_pca(ndarr, fea_num):

    #covariance = np.dot(ndarr.transpose(), ndarr)

    ndarr = ndarr - ndarr.mean(axis=0)

    cov = np.cov(ndarr.transpose())

    evalues, evectors = np.linalg.eigh(cov)
    index = np.argsort(evalues)[::-1]    

    evalues = evalues[index]

    evectors = evectors[:,index]

    evec = evectors[:,:fea_num]

    return  np.dot(evec.T, ndarr.transpose()).transpose()

covariance matrix:  Covariance indicates the level to which two variables vary together

c = a - a.mean(axis=0)

print np.dot(c.transpose(), c)

a = np.random.rand(3,6)*3+10

cov = np.cov(a.T)