def main_people():
""" Main function """
X_img_train, x_train, y_train, X_img_test, x_test, y_test = people_dataset('data')
people_visualization(X_img_train, y_train)
svm_alg = SVM(n_epochs=100, lambDa=0.001, use_bias=True)
# train
svm_alg.fit_gd(x_train, y_train, verbose=True)
# test
predictions = svm_alg.predict(x_test)
accuracy = float(np.sum(predictions == y_test)) / y_test.shape[0]
print('Test accuracy: {}'.format(accuracy))
people_visualize_prediction(X_img_test, y_test, predictions)
def main_people_classification():
"""
Main function to perform people vs non people classification with SVM.
"""
X_img_train, X_feat_train, Y_train, X_img_test, X_feat_test, Y_test = people_dataset('data')
C = 100
kernel = 'rbf'
model = svm.SVC(C=C, kernel=kernel)
model.fit(X_feat_train, Y_train)
Y_pred = model.predict(X_feat_test)
print('Error: {}'.format(float(np.sum(Y_pred != Y_test))/len(Y_test)))
visualize_predictions(X_img_test, Y_test, Y_pred)
def main_people():
""" Main function """
#x_train, y_train, x_test, y_test = gaussians_dataset(2, [100, 150], [[1, 3], [-4, 8]], [[2, 3], [4, 1]])
X_img_train, x_train, y_train, X_img_test, x_test, y_test = people_dataset(
'data')
people_visualization(X_img_train, y_train)
svm_alg = AdaBoost(n_classifiers=6, use_bias=True, Base_Classifier=SVM)
# train
svm_alg.fit_gd(x_train, y_train, verbose=True)
# test
predictions = svm_alg.predict(x_test)
accuracy = float(np.sum(predictions == y_test)) / y_test.shape[0]
print('Test accuracy: {}'.format(accuracy))
people_visualize_prediction(X_img_test, y_test, predictions)