def read_data(only_2_features=True):
iris = datasets.load_iris()
X, y = iris.data, iris.target
if only_2_features:
X = X[:, :2]
return X, y
if __name__ == '__main__':
X, y = read_data()
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
svm1 = svm.SVC()
svm1.fit(X, y)
ax1.set_title("SVC linear")
plot_areas(lambda x: svm1.predict(x), 0.1, X, ax1)
plot_2d_classes(X, y, 'ryb', ax1)
svm2 = svm.SVC(kernel='poly', degree=2)
svm2.fit(X, y)
ax2.set_title("SVC polynomial, deg: 2")
plot_areas(lambda x: svm2.predict(x), 0.1, X, ax2)
plot_2d_classes(X, y, 'ryb', ax2)
svm3 = svm.SVC(kernel='poly', degree=3)
svm3.fit(X, y)
ax3.set_title("SVC polynomial, deg: 3")
plot_areas(lambda x: svm3.predict(x), 0.1, X, ax3)
plot_2d_classes(X, y, 'ryb', ax3)
svm4 = svm.SVC(kernel='poly', degree=6)
svm4.fit(X, y)
ax4.set_title("SVC polynomial, deg: 6")
plot_areas(lambda x: svm4.predict(x), 0.1, X, ax4)
cnn_percents = []
for i in range(10):
X_training, y_training, X_test, y_test = split_dataset(X, y, 0.7)
if cnn:
len_before = X_training.shape[0]
X_training, y_training = cnn_transform(X_training, y_training, k, metric)
len_after = X_training.shape[0]
cnn_percents.append(float(len_after) / float(len_before) * 100.0)
predictions = []
for i in range(X_test.shape[0]):
predictions.append(kNN(X_training, X_training, y_training, X_test[i, :], k, metric))
if SHOW_PREDICTIONS_AND_REAL_VALUES:
print('Prediction, actual:')
for i in range(X_test.shape[0]):
print(predictions[i], y_test[i])
correct = 0
for i in range(len(predictions)):
if y_test[i] == predictions[i]:
correct += 1
accuracies.append(float(correct) / float(len(predictions)) * 100.0)
print("Accuracy:", str(np.mean(accuracies)) + '%', 'StdDev:', np.std(accuracies))
if cnn:
print("CNN:", str(np.mean(cnn_percents)) + '%', 'StdDev:', np.std(cnn_percents))
else:
if cnn:
X, y = cnn_transform(X, y, k, metric)
plot_areas(lambda x: kNN(X, X, y, x, k, metric), 0.1, X)
plot_2d_classes(X, y, 'ryb')
cnn_str = '_cnn' if cnn else ''
plt.savefig('plots/k' + str(k) + '_' + metric.__name__ + cnn_str + '.png')
