67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
"""
|
|
================================
|
|
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()
|