remove(list=ls())

xnam <- paste("x", 1:25, sep="")

(fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+"))))

set.seed(20)
data<-read.table("officePriceData.txt", header = FALSE, fill = TRUE)
nFeatures<-data[1][1,] #data$V1[1]
nTrain<-data[2][1,] #data$V2[1]
nTest<-data[1][nTrain+2,] #data$V1[nTrain+2]

trimTrain<-c(1,2+nTrain:length(data$V1))

trimTrain<-c(1,2+nTrain:length(data[[1]]))
trainData<-data[-trimTrain,]

trimTest<-c(1:(2+nTrain))
testData<-data[-trimTest,]

paste1<-paste("trainData$V", 1:nFeatures, sep="",collapse= ",")
degree<-4
paste2<-paste("trainData$V",nFeatures+1,"~ poly(",paste1,",degree = ",degree,",raw = TRUE)", collapse = ",",sep = "")
fmla <- as.formula(paste2)

lmModelDegree4<-lm(trainData$V3~poly(trainData$V1,trainData$V2,degree = 4,raw = TRUE))

lm4<-lm(V3~V1+V2,data=trainData)

lmModelDegree3<-lm(trainData$V3poly(trainData$V1,trainData$V2,degree = 3,raw = TRUE))
lmModelDegree2<-lm(trainData$V3poly(trainData$V1,trainData$V2,degree = 2,raw = TRUE))
lmModelDegree1<-lm(trainData$V3~poly(trainData$V1,trainData$V2,degree = 1,raw = TRUE))

predictedValues<-vector(,length(testData[[1]]))

for (i in 1:length(predictedValues)){
#newdata<-data.frame(chargeTime<-newdata1$chargeTime[i])
newdata<-testData[i,]
pre<-predict(lmModelDegree4,newdata=newdata)
pre
}

indata <- read.table(file("stdin"), header=F, fill=T)

cat(outputdata)

n <- as.numeric(readLines(file("stdin")))

laptop <- read.table("trainingdata.txt",header = FALSE,sep = ",")
names(laptop)[1]<-"chargeTime"
names(laptop)[2]<-"batteryLife"
maxBatteryLife<-max(laptop$batteryLife)
chargeTime<-laptop$chargeTime[laptop$batteryLife<maxBatteryLife]
batteryLife<-laptop$batteryLife[laptop$batteryLife<maxBatteryLife]

chargeTime <- as.numeric(readLines(file("stdin")))

lmModel<-lm(batteryLife~chargeTime)

newdata1<-data.frame(chargeTime<-chargeTime)

newdata1<-data.frame(chargeTime=chargeTime)
boundaryValue<-min(laptop$chargeTime[laptop$batteryLife==maxBatteryLife])
predictedValues<-vector(,length(chargeTime))

for (i in 1:length(newdata1$chargeTime) ){

if (newdata1$chargeTime[i]<boundaryValue){
#newdata<-data.frame(chargeTime<-newdata1$chargeTime[i])
newdata<-data.frame(chargeTime=newdata1$chargeTime[i])
pre<-predict(lmModel,newdata=newdata$chargeTime)
predictedValues[i]<-pre
for (value in pre) {
if (pre>8){
pre=8
predictedValues[i]<-pre
}
}
}

else {
pre<-maxBatteryLife
predictedValues[i]<-pre
}

}

cat(predictedValues)