import numpy as np
import matplotlib.pyplot as plt
from math import sqrt
from sklearn import linear_model
import random
def create_data():
    X=np.array(
        [
        [1,3,3],
        [1,4,3],
        [1,1,1],
        [1,0,2]
            ])
    Y= np.array(
            [
            [1],
            [1],
            [-1],
            [-1]
                ] )
    return X,Y

def update_ws(X,Y,W,lr=0.01):
    V= np.dot(X,W)#np.dot(X,W) array矩阵相乘 4*3乘3*1等于4*1
    W_ =lr*(X.T.dot(Y-V))/int(X.shape[0])#np.dot(X,W) array矩阵相乘 3*4乘4*1等于3*1
    W = W+W_
    return W
    
def main():
    x_,y_=create_data()
    W= (np.random.random([3,1])-0.5)*2
    for i in range(300):
        W= update_ws(x_,y_,W,0.05)

    x1 = [3,4]
    y1 = [3,3]

    x2 = [1,0]
    y2 = [1,2]

    k= -W[1]/W[2]
    b= -W[0]/W[2]
    print ("k = {},b= {}".format(k,b));
    x_axis =(0,4)
    #plt.plot(x1[0],x1[1],'b.')
    #plt.plot(y1[0],y1[1],'b.')
    #plt.plot(x2[0],x2[1],'g.')
    #plt.plot(y2[0],y2[1],'g.')
    plt.plot(x_axis,x_axis*k+b,'r')
    plt.scatter(x1,y1,c='b')
    plt.scatter(x2,y2,c='g')
    plt.show()
    
main()

import numpy as np
import matplotlib.pyplot as plt
from math import sqrt
from sklearn import linear_model
import random


def create_data():
    X=np.array(
        [
        [1,0,0,0,0,0],
        [1,0,1,0,0,1],
        [1,1,0,1,0,0],
        [1,1,1,1,1,1]
            ])
    Y= np.array(
            [
            [-1],
            [1],
            [1],
            [-1]
                ] )
    return X,Y

def update_ws(X,Y,W,lr=0.01):
    V= (np.dot(X,W))#np.dot(X,W) array矩阵相乘 4*3乘3*1等于4*1
    W_ =lr*(X.T.dot(Y-V))/int(X.shape[0])#np.dot(X,W) array矩阵相乘 3*4乘4*1等于3*1
    W = W+W_
    return W

def calc_(x,w,r):
    a=w[5]
    b=w[2]+x*w[4]
    c=w[0]+x*w[1]+x*x*w[3]
    if r == 1:
        return (-b+np.sqrt(b*b-4*a*c))/(2*a)
    elif r == 2:
        return (-b-np.sqrt(b*b-4*a*c))/(2*a)
    
def main():
    x_,y_=create_data()
    W= (np.random.random([6,1])-0.5)*2
    for i in range(10000):
        W= update_ws(x_,y_,W,0.11)


    x1 = [0,1]
    y1 = [1,0]

    x2 = [0,1]
    y2 = [0,1]
    
    #W =np.concatenate((W),axis=0)#水平组合
    print ("w = {}".format(W))

    #print ("root1 = {}".format(calc_(x_,W,1)))
    #print ("root2 = {}".format(calc_(x_,W,2)))
    
    x_data =np.linspace(-2,5)
    plt.plot(x_data,calc_(x_data,W,1),'r')
    plt.plot(x_data,calc_(x_data,W,2),'r')
    plt.scatter(x1,y1,c='b')
    plt.scatter(x2,y2,c='g')
    plt.show()

main()

W0*X0+X1*W1+X2*W2+X1^2*W3 +X1*X2*W4 + X2^2*W5 = 0
令X1=X ,X2=Y;
W0 + X*W1 +Y*W2 + X^2*W3 + X*Y*W4+ Y^2*W5 = 0
根据a*x^2+b*x+c=0求解知；
a*Y^2+b*Y+c=0可推出；
c = W0+X*W1+ X^2*W3
b=X*W4+W2
a=W5