CODEWITHSUNDEEP
numpy
JungleDiff
JungleDiff

Reputation: 3505

Numpy eigenvectors

I am trying to use the numpy's linalg.eig() function to get eigenvectors and eigenvalues.

import numpy as np
M = np.array([[168.04570515, 1.38100609, -48.60662242],
              [1.38100609, 16.49647382, 9.18370965], 
              [-48.60662242, 9.18370965, 38.37842496]])
eigenvalue, eigenvector = np.linalg.eig(M)

>>>eigenvalue
array([184.25812834,  28.91371368,   9.74876191])

>>>eigenvector
array([[-0.94849917, -0.26039343, -0.18040119],
   [ 0.0095255 , -0.59267178,  0.80538775],
   [ 0.31663637, -0.7621912 , -0.56462906]])

However, when I input the same M values into WolframAlpha, I get the following: enter image description here

Why do I get different eigenvectors?

Upvotes: 1

Views: 61

Answers (1)

cmxu
cmxu

Reputation: 972

So you are actually getting the same eigenvectors. It's just that Wolfram Alpha scales the eigenvectors so the last element is 1 (if you recall, eigenvectors are scale invariant). Let's look at the first column of your result. If you run,

np.array([-0.94849917, 0.0095255, 0.31663637])/0.31663637

You will get

array([-2.99554713,  0.0300834 ,  1.        ])

Which if you notice is what Wolfram Alpha reports. You can check the other two as well.

Upvotes: 1

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