new_contents: import numpy as np
  import matplotlib.pyplot as plt
  
  GRAY_SCALE_RANGE = 255
  
  import pickle
  
  data_filename = 'data_deskewed.pkl'
  print('Loading data from file \'' + data_filename + '\' ...')
  with open(data_filename, 'rb') as f:
      train_labels = pickle.load(f)
      train_images = pickle.load(f)
      test_labels = pickle.load(f)
      test_images = pickle.load(f)
      num_pixel = pickle.load(f)
  print('Data loading complete.')
  
  train_images = np.array(train_images)
  train_images.resize(train_images.size // num_pixel, num_pixel)
  test_images = np.array(test_images)
  test_images.resize(test_images.size // num_pixel, num_pixel)
  test_labels = np.array(test_labels)
  train_labels = np.array(train_labels)
  
  train_labels_ten = np.zeros((train_labels.size, 10))
  test_labels_ten = np.zeros((test_labels.size, 10))
  for i in range(10):
      train_labels_ten[:, i] = train_labels == i
      test_labels_ten[:, i] = test_labels == i
  
  ## normalization
  train_images = train_images / GRAY_SCALE_RANGE
  test_images = test_images / GRAY_SCALE_RANGE
  
  
subject: Add mnist_svm.py