import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
import random
import time
'''initialize weight'''
ww = [0, 0]

b = 0

positive_x = [3,4]
positive_y = [3,3]

negative_x = [1]
negative_y = [1]

label = [1,1,-1]
label_select = [0,0,0]
data_size = 3

data_x = positive_x + negative_x
data_y = positive_y + negative_y

c_pos = [positive_x,positive_y]
c_pos = np.asarray(c_pos).T

d_neg = [negative_x,negative_y]
d_neg = np.asarray(d_neg).T

data = [data_x,data_y]
data = np.asarray(data).T


for iteration in range(1000):
    i = random.randint(0,data_size-1)
    print('iteration=', iteration)
    print('i=',i)
    plt.show()
    time.sleep(1)
    st = label[i]*(np.matmul(ww,data[i].T+b)+b)
    if st <= 0:
        ww = ww +label[i] * data[i]
        b = b + label[i]
        w = ww[0]+0.0001/(ww[1]+0.0001)
        fig, ax = plt.subplots()
        ax.scatter(positive_x,positive_y,c='b',marker='o')
        ax.scatter(negative_x,negative_y,c='r',marker='x')
        ax.plot([w*i + b for i in list(range(-5,5,1))])
        fig.suptitle('perception')
        plt.show()

    else:
        label_select[i]=1
    an = 1
    for j in label_select:
        an = an & j
        print(an)
    if an == 1:
        break




'''for i in range(len(data_x)):
    st = label[i]*(np.matmul(ww,data[i].T+b)+b)
    if st <= 0:
        ww = ww +label[i] * data[i]
        b = b + label[i]



    w = ww[0]+0.0001/(ww[1]+0.0001)
    fig, ax = plt.subplots()
    ax.scatter(positive_x,positive_y,c='b',marker='o')
    ax.scatter(negative_x,negative_y,c='r',marker='x')
    ax.plot([w*i + b for i in list(range(-5,5,1))])
    fig.suptitle('perception')
    plt.show()'''