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 pylab as pl
  
  # 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 3 images, stored in the `images`
  # attribute of the dataset. If we were working from image files, we
  # could load them using pylab.imread. For these images know which
  # digit they represent: it is given in the 'target' of the dataset.
  for index, (image, label) in enumerate(zip(digits.images, digits.target)[:4]):
      pl.subplot(2, 4, index + 1)
      pl.axis('off')
      pl.imshow(image, cmap=pl.cm.gray_r, interpolation='nearest')
      pl.title('Training: %i' % label)
  
  # To apply an 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))
  
  for index, (image, prediction) in enumerate(
          zip(digits.images[n_samples / 2:], predicted)[:4]):
      pl.subplot(2, 4, index + 5)
      pl.axis('off')
      pl.imshow(image, cmap=pl.cm.gray_r, interpolation='nearest')
      pl.title('Prediction: %i' % prediction)
  
  pl.show()
  