library(nnet)
dat<-read.csv("http://www.matsuka.info/data_folder/tdkReg01.csv")
set.seed(5)
x = dat[,1:3]
y = dat[,4]
dat.nnet = nnet(x,y, size = 150, linout= TRUE, maxit = 1000)
nnet.pred <-predict(dat.nnet,dat)
cor(dat.nnet$fitted.values,dat$sales)^2

n.data = nrow(dat);
n.sample = n.data*0.6;
n.rep = 100
trainNN.cor = rep(0,n.rep); trainLM.cor = rep(0,n.rep)
testNN.cor = rep(0,n.rep); testLM.cor = rep(0,n.rep)
for (i.rep in 1:n.rep){
  randperm = sample(1:n.data)
  train.idx = randperm[1:n.sample]
  test.idx = randperm[(n.sample+1):n.data]
  dat.nnet <- nnet(sales~.,size = c(10), linout=T,decay= 0.01, maxit=1000, data = dat[train.idx,])
  dat.lm <-lm(sales~.,data=dat[train.idx, ])
  trainNN.cor[i.rep] <- cor(predict(dat.nnet,dat[train.idx, ]), dat[train.idx,]$sales)
  trainLM.cor[i.rep] <- cor(predict(dat.lm,dat[train.idx, ]), dat[train.idx,]$sales)
  testNN.cor[i.rep] <- cor(predict(dat.nnet,dat[test.idx, ]), dat[test.idx,]$sales)
  testLM.cor[i.rep] <- cor(predict(dat.lm,dat[test.idx, ]), dat[test.idx,]$sales)
}
print(c(mean(trainNN.cor,na.rm=T),mean(testNN.cor,na.rm=T)))
print(c(mean(trainLM.cor,na.rm=T),mean(testLM.cor,na.rm=T)))
print(c(max(trainNN.cor,na.rm=T),max(testNN.cor,na.rm=T)))
print(c(max(trainLM.cor,na.rm=T),max(testLM.cor,na.rm=T)))
print(c(min(trainNN.cor,na.rm=T),min(testNN.cor,na.rm=T)))
print(c(min(trainLM.cor,na.rm=T),min(testLM.cor,na.rm=T)))

dat<-read.csv("http://matsuka.info/data_folder/tdkDA01.csv", header=T)
dat.nnet<-nnet(class~.,dat,size=30,maxit=1000,decay=0.05)
dat.pred<-predict(dat.nnet,dat,type="class")
table(dat.pred,dat$class)

dat.glm<-glm(class~., family="binomial",dat)
glm.pred<-predict(dat.glm, dat, type="response")>0.5
table(glm.pred,dat$class)

dat<-read.csv("http://www.matsuka.info/data_folder/cda7-16.csv")
wts = rep(1,nrow(dat))
wts[which(dat$survival=="no")]=45
dat.nnet<-nnet(survival~., weights=wts, dat, size=100, maxit=1000, decay=0.1)
dat.pred<-predict(dat.nnet,dat,type="class")
table(dat.pred,dat$survival)

dat<-read.csv("http://matsuka.info/data_folder/tdkDA02.csv",header=T)
class.id<-class.ind(dat$class)
x = dat[,1:6]
dat.nnet<-nnet(x, class.id, size=30, maxit=1000, decay=0.01, softmax=TRUE)
max.id = apply(dat.nnet$fitted.values,1,which.max)
table(max.id,dat$class)

dat<-read.table("http://www.matsuka.info/data_folder/tdkPCA01.txt")
dat.nnet<-nnet(dat,dat,size=2, maxit=1000, decay=0.01, linout=TRUE)