# -*- coding: utf-8 -*-
"""
Created on Wed Sep 30 12:19:56 2020

@author: hallgato
"""

import numpy as np;
import matplotlib.pyplot as plt;
from sklearn import datasets, utils, linear_model, neural_network;

diabetes = datasets.load_diabetes();
n = diabetes.data.shape[0];
p = diabetes.data.shape[1];

sample_size = 200;
rep = 100;
intercept = [];
coef = [];
score = [];
reg = linear_model.LinearRegression();
for i in range(rep):
    index = utils.random.sample_without_replacement(n_population=n,n_samples=sample_size);
    X = diabetes.data[index];
    y = diabetes.target[index];
    reg.fit(X,y);
    intercept.append(reg.intercept_);
    coef.append(reg.coef_);
    score.append(reg.score(X,y));
    
X,y = utils.resample(diabetes.data,diabetes.target,n_samples=1000000);
reg.fit(X,y);
intercept = reg.intercept_;
coef = reg.coef_;
score = reg.score(X,y);

neural = neural_network.MLPRegressor(hidden_layer_sizes=(),activation='identity');
neural.fit(diabetes.data,diabetes.target);
neural_intercept = neural.intercepts_;
neural_coef = neural.coefs_;
neural_score = neural.score(diabetes.data,diabetes.target);
y_nnpred = neural.predict(diabetes.data);

my_reg_data = datasets.make_regression(n_samples=10000, 
        n_features=100, n_informative=10, n_targets=1,coef='True');
reg.fit(my_reg_data[0],my_reg_data[1]);
coef = reg.coef_;