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