Python knn_regressor 예제들

예제 #1

파일: main_window_passangers.py 프로젝트: davis-9fv/Project

y_hat_predicted = y_test
rmse = compare(y_test, y_hat_predicted)
print('RMSE NoPredic  %.3f' % (rmse))

# Dummy
y_predicted_dummy = x_train[:, 0]
rmse = compare(y_train, y_predicted_dummy)
print('RMSE Dummy   %.3f' % (rmse))

# ElasticNet
y_predicted_en = algorithm.elastic_net2(x_train, y_train, x_train)
rmse = compare(y_train, y_predicted_en)
print('RMSE Elastic %.3f' % (rmse))

# KNN5
y_predicted_knn5 = algorithm.knn_regressor(x_train, y_train, x_train, 5)
rmse = compare(y_train, y_predicted_knn5)
print('RMSE KNN(5)  %.3f' % (rmse))

# KNN10
y_predicted_knn10 = algorithm.knn_regressor(x_train, y_train, x_train, 10)
rmse = compare(y_train, y_predicted_knn10)
print('RMSE KNN(10) %.3f' % (rmse))

# SGD
y_predicted_sgd = algorithm.sgd_regressor(x_train, y_train, x_train)
rmse = compare(y_train, y_predicted_sgd)
print('RMSE SGD     %.3f' % (rmse))

print('------- Test --------')
# No Prediction

예제 #2

파일: main_window_passangers_stat.py 프로젝트: davis-9fv/Project

y_hat_predicted = y_train
rmse, y_predicted = compare_train(x_test, y_hat_predicted)
print('RMSE NoPredic  %.3f' % (rmse))

# Dummy
y_hat_predicted = x_train[:, 0]
rmse, y_predicted = compare_train(x_test, y_hat_predicted)
print('RMSE Dummy   %.3f' % (rmse))

# ElasticNet
y_hat_predicted = algorithm.elastic_net2(x_train, y_train, x_train, normalize=False)
rmse, y_predicted = compare_train(x_test, y_hat_predicted)
print('RMSE Elastic %.3f' % (rmse))

# KNN5
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_train, 5)
rmse, y_predicted = compare_train(x_test, y_hat_predicted)
print('RMSE KNN(5)  %.3f' % (rmse))

# KNN10
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_train, 10)
rmse, y_predicted = compare_train(x_test, y_hat_predicted)
print('RMSE KNN(10)  %.3f' % (rmse))

# SGD
y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_train)
rmse, y_predicted = compare_train(x_test, y_hat_predicted)
print('RMSE SGD     %.3f' % (rmse))

# LSTM
y_hat_predicted = algorithm.lstm(x_train, y_train, x_train, batch_size=1, nb_epoch=3, neurons=1)