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args <- commandArgs(TRUE)
nTrain <- read.csv(args[1],row.names= 1, header = T) # example nTrain.csv file of unknown activity
#save(nTrain,file = "nTrain.RData")
#load("nTrain.RData")
load(args[2]) # model generated from  previous programn  
newdata <- nTrain
modelFit <- Fit
###########
# input csv file must contaion the exact same column as used in model building #
# Also do pre-proccessing by means of centering and scaling 

library(caret)
reqcol <- Fit$finalModel$xNames
newdata <- newdata[,reqcol]
newdata1 <- preProcess(newdata, method = c("center", "scale"))
newdata11 <- predict(newdata1,newdata)
###########
library(stats)
testpredict <- predict(modelFit,newdata11)
#table(testpredict)
#save(testpredict, file = "predict.RData")
#load("predict.RData")
names <- as.data.frame(rownames(nTrain))
colnames(names) <- "COMPOUND"
activity <- as.data.frame(testpredict)
colnames(activity) <- "ACTIVITY"
dw <- cbind(names,activity)
#write.table(dw,file=args[3])
write.csv(dw,file=args[3],row.names=FALSE)


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