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dlt.RDForestII.R
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library(randomForest)
trains_1 <-tail(dlt,302)[1:300,]
trains_2 <-tail(dlt,302)[2:301,]
results<-tail(dlt,300)
tests_1<-tail(dlt,2)[1,]
tests_2<-tail(dlt,2)[2,]
#A:
trn1<-trains_1$n
trn2<-trains_2$n
a1.1<-trains_1$a1
a2.1<-trains_1$a2
a3.1<-trains_1$a3
a4.1<-trains_1$a4
a5.1<-trains_1$a5
a1.2<-trains_2$a1
a2.2<-trains_2$a2
a3.2<-trains_2$a3
a4.2<-trains_2$a4
a5.2<-trains_2$a5
resa1<-results$a1
resa2<-results$a2
resa3<-results$a3
resa4<-results$a4
resa5<-results$a5
trains.a1<-data.frame(trn1,trn2,a1.2,a1.1,resa1)
trains.a2<-data.frame(trn1,trn2,a2.2,a2.1,resa2)
trains.a3<-data.frame(trn1,trn2,a3.2,a3.1,resa3)
trains.a4<-data.frame(trn1,trn2,a4.2,a4.1,resa4)
trains.a5<-data.frame(trn1,trn2,a5.2,a5.1,resa5)
#B:
b1.1<-trains_1$b1
b2.1<-trains_1$b2
b1.2<-trains_2$b1
b2.2<-trains_2$b2
resb1<-results$b1
resb2<-results$b2
trains.b1<-data.frame(trn1,trn2,b1.2,b1.1,resb1)
trains.b2<-data.frame(trn1,trn2,b2.2,b2.1,resb2)
#build 建DRForest model
fit_drf.a11 = randomForest(resa1 ~ a1.2+a1.1,data = trains.a1,importance = T)
fit_drf.a22 = randomForest(resa2 ~ a2.2+a2.1,data = trains.a2,importance = T)
fit_drf.a33 = randomForest(resa3 ~ a3.2+a3.1,data = trains.a3,importance = T)
fit_drf.a44 = randomForest(resa4 ~ a4.2+a4.1,data = trains.a4,importance = T)
fit_drf.a55 = randomForest(resa5 ~ a5.2+a5.1,data = trains.a5,importance = T)
fit_drf.b11 = randomForest(resb1 ~ b1.2+b1.1,data = trains.b1,importance = T)
fit_drf.b22 = randomForest(resb2 ~ b2.2+b1.1,data = trains.b1,importance = T)
#Buil test data
#A:
tsn1<-tests_1$n
tsn2<-tests_2$n
a1.1<-tests_1$a1
a2.1<-tests_1$a2
a3.1<-tests_1$a3
a4.1<-tests_1$a4
a5.1<-tests_1$a5
a1.2<-tests_2$a1
a2.2<-tests_2$a2
a3.2<-tests_2$a3
a4.2<-tests_2$a4
a5.2<-tests_2$a5
tests.a11<-data.frame(tsn1,tsn2,a1.2,a1.1)
tests.a22<-data.frame(tsn1,tsn2,a2.2,a2.1)
tests.a33<-data.frame(tsn1,tsn2,a3.2,a3.1)
tests.a44<-data.frame(tsn1,tsn2,a4.2,a4.1)
tests.a55<-data.frame(tsn1,tsn2,a5.2,a5.1)
#B:
b1.1<-tests_1$b1
b2.1<-tests_1$b2
b1.2<-tests_2$b1
b2.2<-tests_2$b2
tests.b11<-data.frame(tsn1,tsn2,b1.2,b1.1)
tests.b22<-data.frame(tsn1,tsn2,b2.2,b2.1)
#SVM predict test model
p.a11 = predict(fit_drf.a11,tests.a11)
p.a22 = predict(fit_drf.a22,tests.a22)
p.a33 = predict(fit_drf.a33,tests.a33)
p.a44 = predict(fit_drf.a44,tests.a44)
p.a55 = predict(fit_drf.a55,tests.a55)
p.b11 = predict(fit_drf.b11,tests.b11)
p.b22 = predict(fit_drf.b22,tests.b22)
result<-c(p.a11,p.a22,p.a33,p.a44,p.a55,p.b11,p.b22)
result
plot(result)