def manual_regression(trainInputs, trainOutputs): regressor = Regression() regressor.fit(trainInputs, trainOutputs) w0, w1, w2 = regressor.intercept_, regressor.coef_[0], regressor.coef_[1] print('The learnt model is: f(x) = ', w0, ' + ', w1, ' * x1', ' + ', w2, ' * x2') return regressor
def main(): X, y = make_regression(n_samples=100, n_features=1, noise=20) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) n_samples, n_features = np.shape(X) model = Regression(n_iterations=100, learning_rate=0.01) model.fit(X_train, y_train) # Training error plot n = len(model.training_errors) training, = plt.plot(range(n), model.training_errors, label="Training Error") plt.legend(handles=[training]) plt.title("Error Plot") plt.ylabel('Mean Squared Error') plt.xlabel('Iterations') plt.show() y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print ("Mean squared error: %s" % (mse)) y_pred_line = model.predict(X) # Color map cmap = plt.get_cmap('viridis') # Plot the results m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10) m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10) plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction") plt.suptitle("Base Regression") plt.title("MSE: %.2f" % mse, fontsize=10) plt.xlabel('Day') plt.ylabel('Temperature in Celcius') plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right') plt.show()
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server.setblocking(0) server_address = ('192.168.0.52', 10001) server.bind(server_address) server.listen(5) inputs = [server] outputs = [] pools = {} message_queues = {} args = parse_args() # Start connection # Build model model_ufmg = Regression(args.bufmg, args.algorithm, args.features) model_ufrgs = Regression(args.bufrgs, args.algorithm, args.features) model_ufmg.fit() model_ufrgs.fit() # Train model # Predict output #server = Server() #print(server.handleConnection()) while inputs: # Wait for at least one of the sockets to be ready for processing #print >>sys.stderr, '\nwaiting for the next event' readable, writable, exceptional = select.select( inputs, outputs, inputs) # Handle inputs
# print('rms: ', rms) # print('') # if rms < best_rms: # best_rms = rms # best_x = x # best_y = y # best_state = state #print('Best RMS: %f' % (best_rms)) #print('hidden_layer_sizes=(%d, %d)' % (best_x, best_y)) #print('random_state=%d' % (best_state)) #print('gama: ', best_gama) #print('C: ', best_C) #print('epsilon: ', best_epsilon) best_est = 0 best_rms = 9999999.0 for est in range(1, 2000): regression = Regression(database, 'rfr', 10) rms = regression.fit(est) if rms < best_rms: best_rms = rms best_est = est print('estimators: ', est) print('RMS: ', rms) print('') print('Best estimator: ', best_est) print('Best RMS: ', best_rms)
def main(): reg = Regression() google = GoogleQuery(ticker='AAPL', dataset_id='my_dataset', table_id='live_AAPL') reg.fit()
from regression import REG_ALGORITHMS if __name__ == '__main__': parser = argparse.ArgumentParser( description='Regression Module Test Suite') parser.add_argument('-i', '--input', action='store', type=str, required=True, help='CSV input database path') parser.add_argument('-a', '--algorithm', action='store', type=str, choices=REG_ALGORITHMS, default='nnet', help='Regression algorithm to be used for prediction') parser.add_argument('-f', '--features', action='store', type=int, default=10, help='Number of features in the database') args = parser.parse_args() # Build model regression = Regression(args.input, args.algorithm, args.features) rms = regression.fit() print(rms)
from preProcessor import PreProcessor import argparse from regression import Regression from picDrawer import PicDrawer if __name__ == '__main__': ''' python run -f [filepath] -s [filepath] -c [stock code] output: stock error : implement by regression.score() picture : implement by drawer ''' # create pre processor data_cleaner = PreProcessor() train_feature, train_label, test_feature, test_label = data_cleaner.run() reg = Regression() reg.fit(train_feature, train_label) pred_result = reg.predict(test_feature) score = reg.score(test_label, pred_result) drawer = PicDrawer() drawer.run()