import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn import svm

digits = datasets.load_digits()

clf = svm.SVC(gamma=0.0001, C=100)

training_set = digits.data[:-10]
labels = digits.target[:-10]

x, y = training_set, labels
clf.fit(x, y)

for i in range(10):
    print("Prediction: {}".format(clf.predict([digits.data[-i]])))
    print("Digit:      [{}]".format(digits.target[-i]))

# print('Prediction: ',  clf.predict([digits.data[-1]]))
# plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')
# plt.show()

from sklearn import datasets
from sklearn import svm

digits = datasets.load_digits()

clf = svm.SVC(gamma=0.0001, C=100)

training_set = digits.data[:-10]
training_labels = digits.target[:-10]

testing_set = digits.data[-10:]
testing_labels = digits.target[-10:]

x, y = training_set, training_labels
clf.fit(x, y)

for i in range(10):
    print("Test set: {}. Predicted: {}".format(testing_labels[i], clf.predict([testing_set[i]])[0]))

Make variables self descriptive and create a testing set.
Make variables self descriptive and create a testing set.
