simple_neural_network_mlp_sklearn_regression_sine

import os
import ipdb
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn import datasets
from sklearn.model_selection import train_test_split

X = np.arange(0,1,0.01)
Y = np.sin(2*np.pi*X)

# use the scaled input

scalerX = MinMaxScaler(feature_range=(-1,1))
scalerY = MinMaxScaler(feature_range=(-1,1))

X_scaled = scalerX.fit_transform(X.reshape(-1,1))
Y_scaled = scalerY.fit_transform(Y.reshape(-1,1))#.reshape(-1,1))

X = X_scaled
Y = Y_scaled.reshape(1,-1)[0]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)


clf = MLPRegressor(solver='sgd', hidden_layer_sizes=(5,3),
                    activation='tanh',
                    learning_rate='constant',
                    learning_rate_init=0.1,
                    tol=0.0001,
                    max_iter=1000,
                    batch_size=1,
                    momentum=0,
                    random_state=0)

# clf = MLPRegressor(solver='sgd', hidden_layer_sizes=(30,3),
#                     activation='tanh',
#                     learning_rate='constant',
#                     learning_rate_init=0.1,
#                     tol=0.0001,
#                     max_iter=1000,
#                     batch_size=1,
#                     momentum=0,
#                     random_state=0)


clf.fit(X_train, Y_train)
predictedY_train = clf.predict(X_train)
mean_square_error_train = mean_squared_error(Y_train, predictedY_train)
#similar a clf.score(X,Y)

predictedY_test = clf.predict(X_test)
mean_square_error_test = mean_squared_error(Y_test, predictedY_test)

print("Mean_Square_Error for train set is: %f" %mean_square_error_train)
print("Mean_Square_Error for test set is: %f" %mean_square_error_test)

# visualizar o número de iterações rodadas
print("Number of iterations: %d" %clf.n_iter_)

#for i,x in enumerate(X):
#    print("Input: %s \t Output: %s" %(x,clf.predict([x])[0]))

plt.plot(Y_train,label="Y for train set")
plt.plot(predictedY_train,label="predicted Y for train set")
plt.show()

plt.plot(Y_test,label="Y for test set")
plt.plot(predictedY_test,label="predicted Y for test set")
plt.show()


predictedY_total = clf.predict(X)
plt.plot(Y,label="Y total")
plt.plot(predictedY_total,label="predicted Y for total set")
plt.show()