%matplotlib inline
import matplotlib.pyplot as plt
from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split

digits = datasets.load_digits()

def Explore_A():
    _, axes = plt.subplots(2, 4)
    images_and_labels = list(zip(digits.images, digits.target))
    for ax, (image, label) in zip(axes[0, :], images_and_labels[:4]):
        ax.set_axis_off()
        ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
        ax.set_title('Training: %i' % label)

def Explore_B():
    plt.figure(1, figsize=(3,3))
    plt.imshow(digits.images[3], cmap=plt.cm.gray_r, interpolation='nearest')

# Explore_A()
Explore_B()

n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))

def Create_method_A():
    classifier = svm.SVC(gamma=0.001)
    return classifier

def Create_method_B():
    classifier = svm.SVC(gamma=0.001, kernel = 'linear')
    return classifier

# classifier = Create_method_A()
classifier = Create_method_B()

X_train, X_test, y_train, y_test = train_test_split(
    data, digits.target, test_size=0.5, shuffle=False)

classifier.fit(X_train, y_train)

predicted = classifier.predict(X_test)

images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
for ax, (image, prediction) in zip(axes[1, :], images_and_predictions[:4]):
    ax.set_axis_off()
    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    ax.set_title('Prediction: %i' % prediction)

print("Classification report for classifier %s:\n%s\n"
      % (classifier, metrics.classification_report(y_test, predicted)))
disp = metrics.plot_confusion_matrix(classifier, X_test, y_test)
disp.figure_.suptitle("Confusion Matrix")
print("Confusion matrix:\n%s" % disp.confusion_matrix)