demo/cvskda.R

In skda: Sparse (Multicategory) Kernel Discriminant Analysis

        # a binary example
          rm(list=ls())
          n=200
          p=20
          r=0.8
          x=matrix(rnorm(n*p,mean=0,sd=1),n,p)
          y=rbinom(n,1,0.5)
          x[,3]=x[,3]+(2*y-1)*r
          x[,11]=x[,11]+(2*y-1)*r

          y=y+1

          ind1=which(y>1.5)
          ind0=which(y<1.5)
          plot(-4:4, -4:4, type = "n")
          points(x[ind1, 3], x[ind1,11],col="blue")
          points(x[ind0, 3], x[ind0,11],col="red")

          taus=seq(1,15,2)
          lam=cvskda(x,y, taus,nfolds=10)


          xtest=matrix(rnorm(10*n*p,mean=0,sd=1),10*n,p)
          ytest=rbinom(10*n,1,0.5)
          xtest[,3]=xtest[,3]+(2*ytest-1)*r
          xtest[,11]=xtest[,11]+(2*ytest-1)*r
          ytest=ytest+1

          ptesthat=predprob(x,y,lam, xtest)
          ytesthat=apply(ptesthat, 1,which.max)
          error=sum(abs(ytesthat-ytest)>0.5)
          error/length(ytest)





        # a three-class example
          rm(list=ls())
          n=200
          p=20
          r=2
          x=matrix(rnorm(n*p,mean=0,sd=1),n,p)
          y=ceiling(runif(n,0,3))
          thetas=c(0, 2*pi/3, 4*pi/3)
          x[,3]=x[,3]+r*cos(thetas[y])
          x[,11]=x[,11]+r*sin(thetas[y])

          ind1=which(y==1)
          ind2=which(y==2)
          ind3=which(y==3)

          plot(-6:6, -6:6, type = "n")
          points(x[ind1, 3], x[ind1,11],col="blue")
          points(x[ind2, 3], x[ind2,11],col="red")
          points(x[ind3, 3], x[ind3,11],col="black")



          taus=seq(1,15,2)
          lam=cvskda(x,y, taus,nfolds=10)


          xtest=matrix(rnorm(10*n*p,mean=0,sd=1),10*n,p)
          ytest=ceiling(runif(10*n,0,3))
          xtest[,3]=xtest[,3]+r*cos(thetas[ytest])
          xtest[,11]=xtest[,11]+r*sin(thetas[ytest])


          ptesthat=predprob(x,y,lam, xtest)
          ytesthat=apply(ptesthat, 1,which.max)
          error=sum(abs(ytesthat-ytest)>0.5)
          error/length(ytest)