if 'num_mpi' in params.keys(): T_input = np.loadtxt(T_data_path + 'training_input0.dat') T_output = np.loadtxt(T_data_path + 'training_output0.dat') for i in range(params['num_mpi'] - 1): file_name = T_data_path + 'training_input' + str(i + 1) + '.dat' file_name1 = T_data_path + 'training_output' + str(i + 1) + '.dat' T_input = np.append(T_input, np.loadtxt(file_name), axis=0) T_output = np.append(T_output, np.loadtxt(file_name1), axis=0) else: T_input = np.loadtxt(T_data_path + 'training_input.dat') T_output = np.loadtxt(T_data_path + 'training_output.dat') if (training_type == 'Neural_Network'): ml = MLPRegressor() elif (training_type == 'Random_Forest'): ml = RandomForestRegressor() elif (training_type == 'Decision_Tree'): ml = tree.DecisionTreeRegressor() ml.fit(T_input, T_output) ml.type = training_type ml.output_param_path = param_path sk2f(ml) print('-------------------------------\n') print('training_completed\n\n') print('training_type:\n') print(training_type + '\n') print('-------------------------------\n')