pietyta · February 18, 2019 16:20
diff --git a/plot_digits_classification.py b/plot_digits_classification.py
 # 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()
	# 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()