r语言 支持向量机分类_支持向量机原理_envi支持向量机分类(5)

Delta（w）(n)=-alpha*(1-mc)*Delta(w)(n) mc*Delta(w)(n-1)

(7)测试，输出分类正确率。

完整的R代码：

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iris1<-as.matrix(iris[,3:4])

iris1<-cbind(iris1,c(rep(1,100),rep(0,50)))

set.seed(5)

n<-length(iris1[,1])

samp<-sample(1:n,n/5)

traind<-iris1[-samp,c(1,2)]

train1<-iris1[-samp,3]

testd<-iris1[samp,c(1,2)]

test1<-iris1[samp,3]

set.seed(1)

ntrainnum<-120

nsampdim<-2

net.nin<-2

net.nhidden<-3

net.nout<-1

w<-2*matrix(runif(net.nhidden*net.nin)-0.5,net.nhidden,net.nin)

b<-2*(runif(net.nhidden)-0.5)

net.w1<-cbind(w,b)

W<-2*matrix(runif(net.nhidden*net.nout)-0.5,net.nout,net.nhidden)

B<-2*(runif(net.nout)-0.5)

net.w2<-cbind(W,B)

traind_s<-traind

traind_s[,1]<-traind[,1]-mean(traind[,1])

traind_s[,2]<-traind[,2]-mean(traind[,2])

traind_s[,1]<-traind_s[,1]/sd(traind_s[,1])

traind_s[,2]<-traind_s[,2]/sd(traind_s[,2])

sampinex<-rbind(t(traind_s),rep(1,ntrainnum))

expectedout<-train1

eps<-0.01

a<-0.3

mc<-0.8

maxiter<-2000

iter<-0

errrec<-rep(0,maxiter)

outrec<-matrix(rep(0,ntrainnum*maxiter),ntrainnum,maxiter)

sigmoid<-function(x){

y<-1/(1 exp(-x))

return(y)

}

for(i in 1:maxiter){

hid_input<-net.w1%*%sampinex;

hid_out<-sigmoid(hid_input);

out_input1<-rbind(hid_out,rep(1,ntrainnum));

out_input2<-net.w2%*%out_input1;

out_out<-sigmoid(out_input2);

outrec[,i]<-t(out_out);

err<-expectedout-out_out;

sse<-sum(err^2);

errrec<-sse;

iter<-iter 1;

if(sse<=eps)

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