old_contents: import numpy as np
  from sklearn.metrics import confusion_matrix
  import matplotlib.pyplot as plt
  import itertools
  classes = np.array([1,2])
  cm = np.array(([1,2],[0,1]))
  plt.figure()
  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
  plt.colorbar()
  tick_marks = np.arange(len(classes))
  plt.xticks(tick_marks, classes, rotation=45)
  plt.yticks(tick_marks, classes)
  plt.ylabel('True label')
  plt.xlabel('Predicted label')
  fmt = '.2f' if False else 'd'
  thresh = cm.max() / 2.
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
          plt.text(j, i, format(cm[i, j], fmt),
                   horizontalalignment="center",
                   color="white" if cm[i, j] > thresh else "black")
  plt.show()
new_contents: import numpy as np
  from sklearn.metrics import confusion_matrix
  import matplotlib.pyplot as plt
  import itertools
  
  classes = np.array([1, 2, 3, 4, 5, 6, 8, 9, 10, 11])
  cm = np.array((	[1, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
  				[0, 1, 0, 0, 0, 0, 0, 0, 0, 0], 
  				[0, 0, 1, 0, 0, 0, 0, 0, 0, 0], 
  				[0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 
  				[0, 0, 0, 0, 1, 0, 0, 0, 0, 0], 
  				[0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 
  				[0, 0, 0, 0, 0, 0, 1, 0, 0, 0], 
  				[0, 0, 0, 0, 0, 0, 0, 1, 0, 0], 
  				[0, 0, 0, 0, 0, 0, 0, 0, 1, 0], 
  				[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]))
  plt.figure()
  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
  plt.colorbar()
  tick_marks = np.arange(len(classes))
  plt.xticks(tick_marks, classes, rotation=45)
  plt.yticks(tick_marks, classes)
  plt.ylabel('True label')
  plt.xlabel('Predicted label')
  fmt = '.2f' if False else 'd'
  thresh = cm.max() / 2.
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
          plt.text(j, i, format(cm[i, j], fmt),
                   horizontalalignment="center",
                   color="white" if cm[i, j] > thresh else "black")
  plt.show()
subject: update confusion_matrix.py