Python: Why are eigenvectors not the same as first PCA weights?

Question

Let's generate an array:

import numpy as np

data = np.arange(30).reshape(10,3)
data=data*data
array([[  0,   1,   4],
       [  9,  16,  25],
       [ 36,  49,  64],
       [ 81, 100, 121],
       [144, 169, 196],
       [225, 256, 289],
       [324, 361, 400],
       [441, 484, 529],
       [576, 625, 676],
       [729, 784, 841]])

Then find the eigenvalues of the covariance matrix:

mn = np.mean(data, axis=0)
data -= mn
C = np.cov(data.T)
evals, evecs = la.eig(C)
idx = np.argsort(evals)[::-1]
evecs = evecs[:,idx]
print evecs
array([[-0.53926461, -0.73656433,  0.40824829],
       [-0.5765472 , -0.03044111, -0.81649658],
       [-0.61382979,  0.67568211,  0.40824829]])

Now let's run the matplotlib.mlab.PCA function on the data:

import matplotlib.mlab as mlab
mpca=mlab.PCA(data)
print mpca.Wt
[[ 0.57731894  0.57740574  0.57732612]
 [ 0.72184459 -0.03044628 -0.69138514]
 [ 0.38163232 -0.81588947  0.43437443]]

Why are the two matrices different? I thought that in finding the PCA, first one had to find the eigenvectors of the covariance matrix, and that this would be exactly equal to the weights.

Answer 1

You need to normalize your data, not just center it, and the output of np.linalg.eig has to be transposed to match that of mlab.PCA:

>>> n_data = (data - data.mean(axis=0)) / data.std(axis=0)
>>> evals, evecs = np.linalg.eig(np.cov(n_data.T))
>>> evecs = evecs[:, np.argsort(evals)[::-1]].T
>>> mlab.PCA(data).Wt
array([[ 0.57731905,  0.57740556,  0.5773262 ],
       [ 0.72182079, -0.03039546, -0.69141222],
       [ 0.38167716, -0.8158915 ,  0.43433121]])
>>> evecs
array([[-0.57731905, -0.57740556, -0.5773262 ],
       [-0.72182079,  0.03039546,  0.69141222],
       [ 0.38167716, -0.8158915 ,  0.43433121]])