import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
import random as random
import numpy as np
import csv

x_data = [ 338., 333.,  328., 207. , 226.  , 25. , 179. ,  60. , 208.,  606.]
y_data = [ 640., 633.,  619., 393.  , 428. ,   27.  , 193.  ,  66. ,  226. , 1591.]

x = np.arange(-200, -100, 1) #bias
y = np.arange(-5, 5, 0.1) # weight
Z = np.zeros((len(x), len(y)))
X, Y = np.meshgrid(x,y)

for i in range(len(x)):
    for j in range(len(y)):
        b = x[i]
        w = y[j]
        Z[j][i] = 0
        for n in range(len(x_data)):
            Z[j][i] = Z[j][i] + (y_data[n] - b - w*x_data[n])**2
        Z[j][i] = Z[j][i]/len(x_data)

# ydata = b + w*xdata
b = -120 # initial b
w = -4 # initial w
lr = 1 # learing rate
iteration = 100000

# Store initial values for plotting
b_history = [b]
w_history = [w]

lr_b = 0
lr_w = 0

#Iterations
for i in range(iteration):

    b_grad = 0.0
    w_grad = 0.0
    for n in range(len(x_data)):
        b_grad = b_grad - 2.0*(y_data[n] - b - w*x_data[n])*1.0
        w_grad = w_grad - 2.0*(y_data[n] - b - w*x_data[n])*x_data[n]

    lr_b = lr_b + b_grad**2
    lr_w = lr_w + w_grad**2
    # Update parameters.
    b = b - lr/np.sqrt(lr_b) * b_grad
    w = w - lr/np.sqrt(lr_w) * w_grad

    # Store parameters for plotting
    b_history.append(b)
    w_history.append(w)

# plot the figure
plt.contourf(x,y,Z, 50, alpha=0.5, cmap=plt.get_cmap('jet'))
plt.plot([-188.4], [2.67], 'x', ms=12, markeredgewidth=3, color='orange')
plt.plot(b_history, w_history, 'o-', ms=3, lw=1.5, color='black')
plt.xlim(-200,-100)
plt.ylim(-5,5)
plt.xlabel(r'$b$', fontsize=16)
plt.ylabel(r'$w$', fontsize=16)
plt.show()
