content: """
  ================================
  Recognizing hand-written digits
  ================================
  
  An example showing how the scikit-learn can be used to recognize images of
  hand-written digits.
  
  This example is commented in the
  :ref:`tutorial section of the user manual <introduction>`.
  
  """
  print(__doc__)
  
  # Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
  # License: BSD 3 clause
  
  # Standard scientific Python imports
  import matplotlib.pyplot as plt
  
  # Import datasets, classifiers and performance metrics
  from sklearn import datasets, svm, metrics
  
  # The digits dataset
  digits = datasets.load_digits()
  
  # The data that we are interested in is made of 8x8 images of digits, let's
  # have a look at the first 4 images, stored in the `images` attribute of the
  # dataset.  If we were working from image files, we could load them using
  # matplotlib.pyplot.imread.  Note that each image must have the same size. For these
  # images, we know which digit they represent: it is given in the 'target' of
  # the dataset.
  images_and_labels = list(zip(digits.images, digits.target))
  for index, (image, label) in enumerate(images_and_labels[:4]):
      plt.subplot(2, 4, index + 1)
      plt.axis('off')
      plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
      plt.title('Training: %i' % label)
  
  # To apply a classifier on this data, we need to flatten the image, to
  # turn the data in a (samples, feature) matrix:
  n_samples = len(digits.images)
  data = digits.images.reshape((n_samples, -1))
  
  # Create a classifier: a support vector classifier
  classifier = svm.SVC(gamma=0.001)
  
  # We learn the digits on the first half of the digits
  classifier.fit(data[:n_samples / 2], digits.target[:n_samples / 2])
  
  # Now predict the value of the digit on the second half:
  expected = digits.target[n_samples / 2:]
  predicted = classifier.predict(data[n_samples / 2:])
  
  print("Classification report for classifier %s:\n%s\n"
        % (classifier, metrics.classification_report(expected, predicted)))
  print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
  
  images_and_predictions = list(zip(digits.images[n_samples / 2:], predicted))
  for index, (image, prediction) in enumerate(images_and_predictions[:4]):
      plt.subplot(2, 4, index + 5)
      plt.axis('off')
      plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
      plt.title('Prediction: %i' % prediction)
  
  plt.show()
  