title: Error - Calculating Euclidean distance for PCA in python
body: <p>I am trying to implement face recognition by Principal Component Analysis (PCA) using python. I am following the steps in this tutorial: <a href="http://onionesquereality.wordpress.com/2009/02/11/face-recognition-using-eigenfaces-and-distance-classifiers-a-tutorial/" rel="nofollow">http://onionesquereality.wordpress.com/2009/02/11/face-recognition-using-eigenfaces-and-distance-classifiers-a-tutorial/</a></p>
  
  <p>Here is my code:</p>
  
  <pre><code>import os
  from PIL import Image
  import numpy as np
  import glob
  import numpy.linalg as linalg
  
  
  #Step1: put database images into a 2D array
  filenames = glob.glob('C:\\Users\\Karim\\Downloads\\att_faces\\New folder/*.pgm')
  filenames.sort()
  img = [Image.open(fn).convert('L').resize((90, 90)) for fn in filenames]
  images = np.asarray([np.array(im).flatten() for im in img])
  
  
  #Step 2: find the mean image and the mean-shifted input images
  mean_image = images.mean(axis=0)
  shifted_images = images - mean_image
  
  
  #Step 3: Covariance
  c = np.cov(shifted_images)
  
  
  #Step 4: Sorted eigenvalues and eigenvectors
  eigenvalues,eigenvectors = linalg.eig(c)
  idx = np.argsort(-eigenvalues)
  eigenvalues = eigenvalues[idx]
  eigenvectors = eigenvectors[:, idx]
  
  
  #Step 5: Only keep the top 'num_eigenfaces' eigenvectors
  num_components = 20
  eigenvalues = eigenvalues[0:num_components].copy()
  eigenvectors = eigenvectors[:, 0:num_components].copy()
  
  
  #Step 6: Finding weights
  w = eigenvectors.T * np.asmatrix(shifted_images)
  
  
  #Step 7: Input image
  input_image = Image.open('C:\\Users\\Karim\\Downloads\\att_faces\\1.pgm').convert('L').resize((90, 90))
  input_image = np.asarray(input_image).flatten()
  
  
  #Step 8: get the normalized image, covariance, eigenvalues and eigenvectors for input image
  shifted_in = input_image - mean_image
  c = np.cov(input_image)
  cmat = c.reshape(1,1)
  eigenvalues_in, eigenvectors_in = linalg.eig(cmat)
  
  
  #Step 9: Fing weights of input image
  w_in = eigenvectors_in.T * np.asmatrix(shifted_in)
  print w_in
  print w_in.shape
  
  #Step 10: Euclidean distance
  d = np.sqrt(np.sum((w - w_in)**2))
  idx = np.argmin(d)
  match = images[idx]
  </code></pre>
  
  <p>I am havin a problem in Step 10 as I am getting this error:
  <code>Traceback (most recent call last):
    File "C:/Users/Karim/Desktop/Bachelor 2/New folder/new3.py", line 59, in &lt;module&gt;
      d = np.sqrt(np.sum((w - w_in)**2))
    File "C:\Python27\lib\site-packages\numpy\matrixlib\defmatrix.py", line 343, in __pow__
      return matrix_power(self, other)
    File "C:\Python27\lib\site-packages\numpy\matrixlib\defmatrix.py", line 160, in matrix_power
      raise ValueError("input must be a square array")
  ValueError: input must be a square array</code></p>
  
  <p>Anyone can help??</p>