'std recall': [], 'best_cf': [] } results = { 'realization': [], 'ACCURACY': [], 'AUC': [], 'MCC': [], 'f1_score': [], 'precision': [], 'recall': [], 'cf': [] } base = load_mock(type='LOGICAL_AND') pos = base[:, :2][where(base[:, 2] == 1)[0]] neg = base[:, :2][where(base[:, 2] == 0)[0]] plt.plot(pos[:, 0], pos[:, 1], 'bo') plt.plot(neg[:, 0], neg[:, 1], 'ro') plt.show() C = [0, 1] for realization in range(20): train, test = split_random(base, train_percentage=.8) x_train = train[:, :2] y_train = train[:, 2]
'best_cf': [], 'alphas': [] } results = { 'realization': [], 'ACCURACY': [], # 'MCC': [], 'f1_score': [], 'precision': [], 'recall': [], 'cf': [], 'alphas': [] } base = load_mock(type='TRIANGLE_CLASSES') # normalizar a base base[['x1', 'x2']] = normalization(base[['x1', 'x2']], type='min-max') x = array(base[['x1', 'x2']]) y = array(base[['y']]) classe0 = x[np.where(y == 0)[0]] classe1 = x[np.where(y == 1)[0]] classe2 = x[np.where(y == 2)[0]] plt.plot(classe0[:, 0], classe0[:, 1], 'b^') plt.plot(classe1[:, 0], classe1[:, 1], 'go') plt.plot(classe2[:, 0], classe2[:, 1], 'm*') plt.xlabel("X1") plt.ylabel("X2")
'best_cf': [], 'alphas': [] } results = { 'realization': [], 'ACCURACY': [], # 'MCC': [], 'f1_score': [], 'precision': [], 'recall': [], 'cf': [], 'alphas': [] } base = pd.DataFrame(load_mock(type='LOGICAL_XOR'), columns=['x1', 'x2', 'y']) base[['x1', 'x2']] = normalization(base[['x1', 'x2']], type='min-max') x = array(base[['x1', 'x2']]) y = array(base[['y']]) classe0 = x[np.where(y == 0)[0]] classe1 = x[np.where(y == 1)[0]] plt.plot(classe0[:, 0], classe0[:, 1], 'b^') plt.plot(classe1[:, 0], classe1[:, 1], 'go') plt.xlabel("X1") plt.ylabel("X2") plt.savefig(get_project_root() + '/run/TR-05/XOR/results/' + 'dataset_xor_artificial.png')
from mlfwk.visualization import generate_space, coloring if __name__ == '__main__': print("run artificial seno") final_result = { 'MSE': [], 'std MSE': [], 'RMSE': [], 'std RMSE': [], 'R2': [], 'std R2': [] } results = {'realization': [], 'MSE': [], 'RMSE': [], 'R2': []} base = load_mock(type='MOCK_SENO') # normalizar a base base[['x1']] = normalization(base[['x1']], type='min-max') sn.set_style('whitegrid') sn.scatterplot(data=base, x="x1", y="y", color='c') plt.xlabel("X1") plt.ylabel("Y") plt.savefig(get_project_root() + '/run/TR-05/ARTIFICIAL_REGRESSAO/results/' + 'dataset_seno_artificial.png') plt.show() x = array(base[['x1']]) y = array(base[['y']])
'MSE': [], 'std MSE': [], 'RMSE': [], 'std RMSE': [], 'alphas': [] } results = { 'realization': [], 'MSE': [], 'RMSE': [], 'erros': [], 'alphas': [] } F, x1, x2 = load_mock(type='2D_REGRESSOR') fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(x1, x2, F, color='k') ax.set_title("y = ax1 + bx2 + c") ax.set_xlabel("x1") ax.set_ylabel("x2") ax.set_zlabel("y") plt.savefig(get_project_root() + '/run/TR-02/ARTIFICIAL/results/' + 'adaline_fig_2.jpg') plt.show() base = concatenate( [array(x1, ndmin=2), array(x2, ndmin=2),
'MSE': [], 'std MSE': [], 'RMSE': [], 'std RMSE': [], 'alphas': [] } results = { 'realization': [], 'MSE': [], 'RMSE': [], 'erros': [], 'alphas': [] } F, x = load_mock(type='LINEAR_REGRESSOR') # plt.plot(x, F, 'bo', color='k') # # plt.plot(array(x, ndmin=2).T, regressor_adaline.predict(array(x, ndmin=2).T)) # plt.xlabel('x') # plt.ylabel('y') # plt.savefig(get_project_root() + '/run/TR-02/ARTIFICIAL/results/' + 'adaline_fig_1.jpg') # plt.show() plt.style.use('default') plt.style.use('ggplot') fig, ax = plt.subplots(figsize=(8, 4)) # ax.plot(array(x, ndmin=2).T, regressor_adaline.predict(array(x, ndmin=2).T), color='k', label='g(x)') ax.scatter(x,