#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov  6 11:57:16 2017

@author: valerie
"""
import os
import numpy as np
import matplotlib.pyplot as plt
# Régression linéaire, skin cancer

skin_cancer = open('https://perso.univ-rennes1.fr/valerie.monbet/PYTHON/skin_cancer.dat', 'r')

os.chdir('/users/valerie/Dropbox/ENSEIGNEMENT/PYTHON')
skin_cancer = open('skin_cancer.dat', 'r')
lignes = skin_cancer.readlines()
skin_cancer.close()

tab = []
for chn in lignes:
    tab.append(chn.split(' ,'))
state = []
latitude = np.zeros(len(tab)-1)
mortalite = np.zeros(len(tab)-1)
for k in range(len(tab)-1):
    state.append(tab[k+1][0])
    latitude[k] = float(tab[k+1][1])
    mortalite[k] = float(tab[k+1][2])

x = latitude
y = mortalite

from sklearn import linear_model

lr = linear_model.LinearRegression()
lr.fit(np.reshape(x,[len(y),1]),np.reshape(y,[len(y),1]))
lr.coef_
lr.intercept_



class RegLin:
    def __init__(self):
        self.intercept = 0
        self.pente = 0
    
    def fit(self,x,y):
        xc = x-np.mean(x)
        yc = y-np.mean(y)
        a = np.sum(xc*yc)/np.sum(xc**2)
        b = np.mean(y)-a*np.mean(x)
        self.intercept = b
        self.pente = a
        
    def getParams(self):
        print("Pente = ", self.pente)
        print("Intercept = ", self.intercept)
        return([self.intercept,self.pente])
        
    def plotReg(self,x,y):
        plt.plot(x,y,"+")
        t = np.arange(np.min(x),np.max(x),0.1)
        plt.plot(t,self.intercept+self.pente*t,"r")
        plt.show()
        
mod = RegLin()
mod.fit(x,y)
mod.getParams()
mod.plotReg(x,y)    

plt.plot(x,y,'+')
plt.plot(t,self.intercept+self.pente*t,"r")
plt.show()
t = np.arange(np.min(x),np.max(x),0.1)
plt.plot(t,self.intercept+self.pente*t,"r")
plt.show()

from sklearn import linear_model

lr = linear_model.LinearRegression()
lr.fit(np.reshape(x,[len(y),1]),np.reshape(y,[len(y),1]))
lr.coef_
lr.intercept_