data=read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/00194/sensor_readings_2.data",header=FALSE)

pairs(
	data[1:2],
	bg=c("red", "green", "blue", "yellow")[data[[3]]],
	pch=21,
	cex=0.75
)

selected <- sample(1:nrow(data), 0.5 * nrow(data))

summary(data)
library(nnet)
net=nnet(V3 ~ ., data, subset=selected, size=10,maxit=25)
result=predict(net, data[selected, 1:2], type="class")
tab=table(data[["V3"]][selected], result)
print(tab)
cat("accuracy on training set: ",100*sum(diag(tab))/sum(tab),"%\n",sep="")

result2=predict(net, data[-selected, 1:2], type="class")
tab2=table(data[["V3"]][-selected], result2)
print(tab2)
cat("accuracy on test set: ",100*sum(diag(tab2))/sum(tab2),"%\n",sep="")