digit_recongnition

# Standard scientific Python imports%matplotlib inlineimport matplotlib.pyplot as plt# Import datasets, classifiers and performance metricsfrom sklearn import datasets, svm, metrics# The digits datasetdigits = 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.  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))

Automatically created module for IPython interactive environment

n_samples

1797

data.shape

(1797, 64)

# Create a classifier: a support vector classifierclassifier = svm.SVC(gamma=0.001)#train the modleclassifier.fit(data[:n_samples *2/3], digits.target[:n_samples*2/3])#predictexpected = digits.target[n_samples *2/3:]predicted = classifier.predict(data[n_samples*2/3:])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 *2/3:], 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()

/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:4: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:7: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:8: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the futureClassification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',  max_iter=-1, probability=False, random_state=None, shrinking=True,  tol=0.001, verbose=False):             precision    recall  f1-score   support          0       1.00      0.98      0.99        59          1       0.97      1.00      0.98        62          2       1.00      0.98      0.99        60          3       0.96      0.81      0.88        62          4       0.98      0.95      0.97        62          5       0.95      0.98      0.97        59          6       0.98      0.98      0.98        61          7       0.95      1.00      0.98        61          8       0.89      0.98      0.93        55          9       0.95      0.97      0.96        58avg / total       0.96      0.96      0.96       599Confusion matrix:[[58  0  0  0  1  0  0  0  0  0] [ 0 62  0  0  0  0  0  0  0  0] [ 0  0 59  1  0  0  0  0  0  0] [ 0  0  0 50  0  2  0  3  7  0] [ 0  0  0  0 59  0  0  0  0  3] [ 0  0  0  0  0 58  1  0  0  0] [ 0  1  0  0  0  0 60  0  0  0] [ 0  0  0  0  0  0  0 61  0  0] [ 0  1  0  0  0  0  0  0 54  0] [ 0  0  0  1  0  1  0  0  0 56]]/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:13: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future