神经网络 感知机 Perceptron python实现

import numpy as np
import matplotlib.pyplot as plt
import math

def create_data(w1=3,w2=-7,b=4,seed=1,size=30):
    np.random.seed(seed)
    w = np.array([w1,w2])
    x1 = np.arange(0,size)
    v = np.random.normal(loc=0,scale=5,size=size)
    x2 = v - (b+w[0]*x1)/(w[1]*1.0)
    y_train=[]
    x_train = np.array(zip(x1,x2))
    for item in v:
        if item >=0:
            y_train.append(1)
        else:
            y_train.append(-1)
    y_train = np.array(y_train)
    return x_train,y_train

def SGD(x_train,y_train):
    alpha=0.01
    w,b=np.array([0,0]),0
    c,i=0,0
    while i<len(x_train):
        if (x_train[i].dot(w)+b)*y_train[i] <=0:
            c +=1
            w=w+alpha*y_train[i]*x_train[i]
            b=b+alpha*y_train[i]
            print("count:%s index:%s w:%s:b:%s" %(c,i,w,b))
            i=0
        else:
            i=i+1
    return w,b

def test_and_show(w1,w2,b,size,w_estimate,b_estimate,x_train,y_train):
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    plt.xlabel('x1')
    plt.ylabel('x2')
    x1 = np.arange(0,size+1,size)
    x2 = -(b+w1*x1)/(w2*1.0)
    ax1.plot(x1,x2,c="black")
    x2 = -(b_estimate+w_estimate[0]*x1)/w_estimate[1]*1.0
    ax1.plot(x1,x2,c="red")
    for i in range(0,len(x_train)):
        if y_train[i]>0:
            ax1.scatter(x_train[i,0],x_train[i,1],c="r",marker='o')
        else:
            ax1.scatter(x_train[i,0],x_train[i,1],c="b",marker="^")
    plt.show()

if __name__ == '__main__':
    w1,w2,b=3,-7,4
    size=50
    x_train,y_train=create_data(w1,w2,b,1,size)
    w_estimate,b_estimate=SGD(x_train,y_train)
    test_and_show(w1,w2,b,size,w_estimate,b_estimate,x_train,y_train)

count:1 index:0 w:[0.         0.08693155]:b:0.01
count:2 index:9 w:[-0.09        0.05511436]:b:0.0
count:3 index:8 w:[-0.01        0.11106631]:b:0.01
count:4 index:9 w:[-0.1         0.07924912]:b:0.0
count:5 index:8 w:[-0.02        0.13520107]:b:0.01
count:6 index:9 w:[-0.11        0.10338388]:b:0.0
count:7 index:8 w:[-0.03        0.15933583]:b:0.01
count:8 index:9 w:[-0.12        0.12751864]:b:0.0
count:9 index:8 w:[-0.04        0.18347059]:b:0.01
count:10 index:9 w:[-0.13       0.1516534]:b:0.0
count:11 index:8 w:[-0.05        0.20760535]:b:0.01
count:12 index:9 w:[-0.14        0.17578815]:b:0.0
count:13 index:8 w:[-0.06        0.23174011]:b:0.01
count:14 index:9 w:[-0.15        0.19992291]:b:0.0
count:15 index:8 w:[-0.07        0.25587487]:b:0.01
count:16 index:9 w:[-0.16        0.22405767]:b:0.0
count:17 index:8 w:[-0.08        0.28000963]:b:0.01
count:18 index:9 w:[-0.17        0.24819243]:b:0.0
count:19 index:18 w:[0.01       0.33316026]:b:0.01
count:20 index:7 w:[-0.06        0.33550632]:b:0.0
count:21 index:9 w:[-0.15        0.30368913]:b:-0.01
count:22 index:18 w:[0.03       0.38865696]:b:0.0
count:23 index:7 w:[-0.04        0.39100302]:b:-0.01
count:24 index:9 w:[-0.13        0.35918582]:b:-0.02
count:25 index:16 w:[-0.29        0.29352152]:b:-0.03
count:26 index:8 w:[-0.21        0.34947347]:b:-0.02
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count:42 index:44 w:[0.23       0.65296677]:b:-0.06
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count:63 index:8 w:[-0.58       0.6397624]:b:-0.15
count:64 index:8 w:[-0.5         0.69571435]:b:-0.14
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count:76 index:18 w:[-0.3         0.89748019]:b:-0.12
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