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)