mu, sigma1 = 0, 10 x_tr, y_tr = load_svmlight_file('Data/Classification/australian.txt') x_tr = x_tr.T x_tr = x_tr.toarray() # y_g = y_g.toarray() data_name = 'Australian' x_tr = (x_tr + 1) / 2 y_tr = y_tr.reshape((y_tr.size, 1)) x_test = x_tr[:, int(x_tr.shape[1] * 0.8):] y_test = y_tr[int(x_tr.shape[1] * 0.8):, :] y_tr = y_tr[:int(x_tr.shape[1] * 0.8), :] x_tr = x_tr[:, :int(x_tr.shape[1] * 0.8)] print("Number of data points: ", x_tr.shape[1]) print("Number of test points: ", x_test.shape[1]) print("Number of features: ", x_tr.shape[0]) # #Generating the starting point # np.random.seed(random_seed_w0) # w0 = np.random.rand(3) model_params = np.array([10., 0.7, 3.]) model_covariance_obj = SquaredExponential(model_params) new_gp = GaussianProcess(model_covariance_obj, lambda x: 0, 'class') new_gp.find_hyper_parameters(x_tr, y_tr, max_iter=100) print(new_gp.covariance_obj.get_params()) predicted_y_test = new_gp.predict(x_test, x_tr, y_tr) print("Mistakes: ", np.sum(predicted_y_test != y_test))
mu, sigma1 = 0, 10 x_tr, y_tr = load_svmlight_file('Data/Classification/australian.txt') x_tr = x_tr.T x_tr = x_tr.toarray() # y_g = y_g.toarray() data_name = 'Australian' x_tr = (x_tr + 1) / 2 y_tr = y_tr.reshape((y_tr.size, 1)) x_test = x_tr[:, int(x_tr.shape[1] * 0.8):] y_test = y_tr[int(x_tr.shape[1] * 0.8):, :] y_tr = y_tr[:int(x_tr.shape[1] * 0.8), :] x_tr = x_tr[:, : int(x_tr.shape[1] * 0.8)] print("Number of data points: ", x_tr.shape[1]) print("Number of test points: ", x_test.shape[1]) print("Number of features: ", x_tr.shape[0]) # #Generating the starting point # np.random.seed(random_seed_w0) # w0 = np.random.rand(3) model_params = np.array([10., 0.7, 3.]) model_covariance_obj = SquaredExponential(model_params) new_gp = GaussianProcess(model_covariance_obj, lambda x: 0, 'class') new_gp.find_hyper_parameters(x_tr, y_tr, max_iter=100) print(new_gp.covariance_obj.get_params()) predicted_y_test = new_gp.predict(x_test, x_tr, y_tr) print("Mistakes: ", np.sum(predicted_y_test != y_test))