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#!/usr/bin/python3.6
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy import stats
from sklearn.datasets.samples_generator import make_regression
# fonction gradient_descent
def gradient_descent(x,y,theta_1,theta_2,learning_rate,n_epochs):
m = len(x)
#construire le vecteur theta
theta = np.array([theta_1,theta_2])
#ajouter une colone de 1 à x
for i in range(m):
x[i] = np.array([1,x[i]])
#construire le tableau des pertes
J_thetas = np.zeros(n_epochs)
#construire le tableau des gradients
gradient_J_thetas = np.zeros(n_epochs,2)
#implementer le gradient descent
for i in range(n_epochs):
#calculer(x.theta - y)
x_dot_theta_minus_y = np.dot(x,theta) - y
#calculer la fonction loss de la i-eme iteration
J_thetas[i] = 0.5*np.dot(x_dot_theta_minus_y,x_dot_theta_minus_y)
print("la fonction loss de l'itteration i est egale a ", J_thetas[i])
#calculer le gradient de la i-eme iteration
gradient_J_thetas[i] = np.dot(x,x_dot_theta_minus_y)
print("le gradient de l'itteration i est egale a ", gradient_J_thetas[i])
#faire evoluer theta
theta = theta - learning_rate*gradient_J_thetas[i]
print ("le nouveau theta est : ", theta)
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