#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 21 14:42:10 2019
Regresija
@author: dsp
"""

#%%

import numpy as np
from matplotlib import pyplot as plt
from sklearn import neural_network
    #%% data regression
np.random.seed(42)
xs = np.random.rand(10) * 2*np.pi
xs=xs[:,np.newaxis]
noise = np.random.normal(size = xs.shape) * .5

ys = np.sin(xs)
ys += noise
plt.figure()
plt.scatter(xs,ys)




#%% train neuron
reg = neural_network.MLPRegressor(
        hidden_layer_sizes=(100,10),
        activation='tanh',
        solver='adam',
        alpha=0,
        learning_rate= 'adaptive',
        tol = 1e-15,
        max_iter=10000,
        random_state=42,
        early_stopping=False,
        validation_fraction=.2,
        verbose=1)

reg.fit(xs,ys)

#%% predict

xs_axis = np.linspace(0,2*np.pi,100)
xs_axis = xs_axis[:,np.newaxis]
ys_pred = reg.predict(xs_axis)

plt.figure()
plt.scatter(xs,ys)
plt.plot(xs_axis,ys_pred,lw=2,alpha=0.7)