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感知器学习算法步骤如下： |
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图1-10 感知器的分类例子 |
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2．输入一样本X＝(X1 ，X2 ，…，Xn+1 )以及它的期望输出d。 期望输出值d在样本的类属不同时取值不同。如果x是A类，则取d＝1,如果x是B类，则取-1。期望输出d也即是教师信号。 3．计算实际输出值Y 4．根据实际输出求误差e e＝d-Y(t) (1-21) 5．用误差e去修改权系数 i=1,2,…,n,n+1 (1-22) 其中，η称为权重变化率，0<η≤1 |
# -*- coding: cp936 -*- import numpy import pylab import sys class neuralNetwork: b = 1 learnRaito = 0.5 trainData = numpy.array([[b,1,3],[b,2,3],[b,1,8],[b,2,15],[b,3,7],[b,4,29],[b,4,8],[b,4,20]]) #训练数据 可以训练不同的方程 模型 trainResult = numpy.array([1,1,-1,-1,1,-1,1,-1]) weight = numpy.array([b,0,0]) error = 0.001 def Out(self,v): """求值的取向""" if v>=0: return 1 else: return -1 def exceptSignal(self,oldw,inx): #a bug here #print '-'*20 #print oldw #print inx #print numpy.dot(oldw.T,inx) #print '+'*20 #return 1 ans = numpy.dot(oldw.T,inx) return self.Out(ans) def trainOnce(self,oldw,inx,correctResult): """one training""" error = correctResult - self.exceptSignal(oldw,inx) newWeight = oldw + self.learnRaito*error*inx self.weight = newWeight return error def getAbs(self,x): if x<0: return -x else: return x def trainWeight(self): """traing the weight of data""" error = 1 while error > self.error: i = 0 error = 0 for inx in self.trainData: error += self.getAbs(self.trainOnce(self.weight,inx,self.trainResult[i])) i = i+1 def drawTrainResult(self): """ draw graph of Result""" xor = self.trainData[:,1]#切片，获取第一列，x坐标 yor = self.trainData[:,2]#切片，获取第二列，y坐标 pylab.subplot(111) xMax = numpy.max(xor)+15 xMin = numpy.min(xor)-5 yMax = numpy.max(yor)+50 yMin = numpy.min(yor)-5 pylab.xlabel(u'xor') pylab.ylabel(u'yor') pylab.xlim(xMin,xMax) pylab.ylim(yMin,yMax) #draw point for i in range(0,len(self.trainResult)): if self.trainResult[i] == 1: pylab.plot(xor[i],yor[i],'r*') else: pylab.plot(xor[i],yor[i],'ro') def drawTestResult(self,data): test = data#numpy.array(data) if self.exceptSignal(self.weight,test)>0: pylab.plot(test[1],test[2],'b*') else: pylab.plot(test[1],test[2],'bo') def drawTrueLine(self): """真实函数分界线""" xtest = numpy.array(range(0,20)) ytest = xtest*2+1.68 pylab.plot(xtest,ytest,'g--') def showGraph(self): pylab.show() testData = [[1,5,11],[1,5,12],[1,4,16],[1,6,7],[1,3,12],[1,2,22]] neural = neuralNetwork() print neural.Out(124.32423) neural.trainWeight() neural.drawTrainResult() neural.drawTrueLine() #neural.showGraph() for test in testData: neural.drawTestResult(test) print neural.weight neural.showGraph()
红色是训练数据，蓝色是测试数据，圆点代表是在线上方，*代表在线下方，由图可知这个算法还不错