original_docstring: 
              This function prints and plots the confusion matrix.
              Normalization can be applied by setting `normalize=True`.
      
docstring: This function prints and plots the confusion matrix.
  Normalization can be applied by setting `normalize=True`.
docstring_tokens: This function prints and plots the confusion matrix . Normalization can be applied by setting ` normalize = True ` .
code: def plot_confusion_matrix(cm, classes,
                            normalize=False,
                            title='Confusion matrix',
                            cmap=plt.cm.Blues):
      """
              This function prints and plots the confusion matrix.
              Normalization can be applied by setting `normalize=True`.
      """
      if normalize:
          cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
          print("Normalized confusion matrix")
      else:
          print('Confusion matrix, without normalization')
  
      # print(cm)
  
      plt.imshow(cm, interpolation='nearest', cmap=cmap)
      plt.title(title)
      plt.colorbar()
      tick_marks = np.arange(len(classes))
      plt.xticks(tick_marks, classes, rotation=45)
      plt.yticks(tick_marks, classes)
  
      fmt = '.2f' if normalize 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.tight_layout()
      plt.ylabel('True label')
      plt.xlabel('Predicted label')
code_tokens: def plot_confusion_matrix ( cm , classes , normalize = False , title = 'Confusion matrix' , cmap = plt . cm . Blues ) : if normalize : cm = cm . astype ( 'float' ) / cm . sum ( axis = 1 ) [ : , np . newaxis ] print ( "Normalized confusion matrix" ) else : print ( 'Confusion matrix, without normalization' ) plt . imshow ( cm , interpolation = 'nearest' , cmap = cmap ) plt . title ( title ) plt . colorbar ( ) tick_marks = np . arange ( len ( classes ) ) plt . xticks ( tick_marks , classes , rotation = 45 ) plt . yticks ( tick_marks , classes ) fmt = '.2f' if normalize 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 . tight_layout ( ) plt . ylabel ( 'True label' ) plt . xlabel ( 'Predicted label' )
short_docstring: This function prints and plots the confusion matrix.
short_docstring_tokens: This function prints and plots the confusion matrix .
comment: """
              This function prints and plots the confusion matrix.
              Normalization can be applied by setting `normalize=True`.
      """$SEP$# print(cm)