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(8)

feature<-matrix(rep(0,length(names)*25),length(names),25)

for(i in 1:length(names)){

feature[i,1]<-sum(get(data)[,16])

feature[i,2]<-sum(get(data)[,8])

feature[i,3]<-sum(get(data)[,24])

feature[i,4]<-sum(get(data)[16,])

feature[i,5]<-sum(get(data)[11,])

feature[i,6]<-sum(get(data)[21,])

feature[i,7]<-sum(diag(get(data)))

feature[i,8]<-sum(diag(get(data)[,32:1]))

feature[i,9]<-sum((get(data)[17:32,17:32]))

feature[i,10]<-sum((get(data)[1:8,1:8]))

feature[i,11]<-sum((get(data)[9:16,1:8]))

feature[i,12]<-sum((get(data)[17:24,1:8]))

feature[i,13]<-sum((get(data)[25:32,1:8]))

feature[i,14]<-sum((get(data)[1:8,9:16]))

feature[i,15]<-sum((get(data)[9:16,9:16]))

feature[i,16]<-sum((get(data)[17:24,9:16]))

feature[i,17]<-sum((get(data)[25:32,9:16]))

feature[i,18]<-sum((get(data)[1:8,17:24]))

feature[i,19]<-sum((get(data)[9:16,17:24]))

feature[i,20]<-sum((get(data)[17:24,17:24]))

feature[i,21]<-sum((get(data)[25:32,17:24]))

feature[i,22]<-sum((get(data)[1:8,25:32]))

feature[i,23]<-sum((get(data)[9:16,25:32]))

feature[i,24]<-sum((get(data)[17:24,25:32]))

feature[i,25]<-sum((get(data)[25:32,25:32]))

}

data1 <- data.frame(feature,label)

m1<-nnet(label~.,data=data1,size=25,maxit = 2000,decay = 5e-6, rang = 0.1)

pred<-predict(m1,data1,type="class")

table(pred,label)

sum(diag(table(pred,label)))/length(names)

library("e1071")

m <- svm(feature,label,cross=10,type="C-classification")

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