title: Eigendecomposition makes me wonder in numpy
body: <p>I test the theorem that A = Q * Lambda * Q_inverse where Q the Matrix with the Eigenvectors and Lambda the Diagonal matrix having the Eigenvalues in the Diagonal.</p>
  
  <p>My code is the following:</p>
  
  <pre><code>import numpy as np
  from numpy import linalg as lg
  
  Eigenvalues, Eigenvectors = lg.eigh(np.array([
  
      [1, 3],
  
      [2, 5]
  
  
  ]))
  
  Lambda = np.diag(Eigenvalues)
  
  
  Eigenvectors @ Lambda @ lg.inv(Eigenvectors)
  </code></pre>
  
  <p>Which returns :</p>
  
  <pre><code>array([[ 1.,  2.],
         [ 2.,  5.]])
  </code></pre>
  
  <p>Shouldn't the returned Matrix be the same as the Original one that was decomposed?</p>