利用R语言 进行SVM练习

set.seed(1)

x=matrix(rnorm(100*2),ncol=2)                   # generate data

y=c(rep(-1,50),rep(1,50))

x[1:50,]=x[1:50,]+1

x[51:100,]=x[51:100,]-0.75

dat=data.frame(x=x,y=as.factor(y))

plot(x,col=y+3)

# radial

tout2=tune(svm,y~., data =dat[train ,], kernel ="radial", ranges =list(cost=c (0.1,1 ,10 ,100 ,1000), gamma =c(0.5 ,1 ,2 ,3 ,4) ))

summary(tout2)

svmfit2=svm(y~., data=dat [train ,], kernel ="radial", gamma =0.5, cost =10)

plot(svmfit2,dat[train,])

table(true=dat[-train,"y"], pred = predict (tout2$best.model,dat[-train ,]) )

# supportvector classifier (linear)

tone3=tune(svm,y~.,data=dat[train,],kernel="linear",ranges=list(cost=c (0.1 ,1 ,10 ,100 ,1000), gamma =c(0.5 ,1 ,2 ,3 ,4) ) )

summary(tone3)

svmfit3=svm(y~., data=dat [train ,], kernel ="linear", gamma =0.5, cost=1,decision.values=TRUE)

plot(svmfit3,dat[train,])

table(true=dat[-train,"y"], pred = predict (tone3$best.model, dat[-train,]) )