# coding: utf-8
import numpy as np
import matplotlib.pylab as plt
from book.ch04.gradient_2d import numerical_gradient


def gradient_descent(f, init_x, lr=0.01, step_num=100):
    x = init_x
    # x_history = []

    for i in range(step_num):
        # x_history.append( x.copy() )

        grad = numerical_gradient(f, x)
        x -= lr * grad
        print(i, x)

    # return x, np.array(x_history)
    return x


def function_2(x):
    return x[0]**2 + x[1]**2

init_x = np.array([-3.0, 4.0])    

lr = 1e-10
step_num = 100000000
x, x_history = gradient_descent(function_2, init_x, lr=lr, step_num=step_num)

plt.plot( [-5, 5], [0,0], '--b')
plt.plot( [0,0], [-5, 5], '--b')
plt.plot(x_history[:,0], x_history[:,1], 'o')

plt.xlim(-3.5, 3.5)
plt.ylim(-4.5, 4.5)
plt.xlabel("X0")
plt.ylabel("X1")
plt.show()
