感知机算法的python实现+可视化

最入门的机器学习算法 简单的实现+可视化

上代码：

from numpy import *
import matplotlib.pyplot as plt
def getLinaerSeparatableData(weight,numLines):
    a=len(weight)
    dataSet=zeros((numLines,a+1))
    for i in range(numLines):
        b=random.rand(1,a)*20-10

        if sum(b*weight)<=0:
            dataSet[i]=append(b,-1)
        else:
            dataSet[i]=append(b,+1)
    return dataSet

dataset=getLinaerSeparatableData([3,4],100)

def train(dataSet):
    separated=False
    numLines=dataSet.shape[0]
    numFeatures=(dataSet.shape[1]-1)
    w=zeros((1,numFeatures))
    i=0
    while separated==False and i<numLines:
        if(dataSet[i][-1]*sum(w*dataSet[i,0:-1]))<=0:
            w=w+dataSet[i][-1]*dataSet[i,0:-1]
            separated=False
            i=0
        else:
            i=i+1
    return w
w=train(dataset)
a=w[0][0]
b=w[0][1]
x=[-10,10]
y=[(-a/b)*-10,(-a/b)*10]
plt.plot(x,y,color="black")
idx1=where(dataset[:,2]==1)
a1=dataset[idx1,0]
a2=dataset[idx1,1]
idx2=where(dataset[:,2]==-1)
b1=dataset[idx2,0]
b2=dataset[idx2,1]
plt.scatter(a1,a2,color="red")
plt.scatter(b1,b2,color="green")
plt.show()