#Importation des éléments pour la régression logistique
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import numpy as np

X=join[['temp','press','weekday','month','humidity','wind-avg','nebul','hour','dew-point']]
y=join[['puissance']]

# Initialisation des différents composants
linreg = LinearRegression()

list_ts = [a/20.0 for a in list(range(1,20,1))][1:]
score = []
for ts in list_ts:
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=ts, random_state=0)
        res=linreg.fit(X_train,y_train)
        y_pred=linreg.predict(X_test)
        score+=[res.score(X_test, y_test)]
        # Affichage des différents score
    
max(score)