rm(list = ls())

# INPUT: RData file from prediction

base <- "database"


library("klaR") 
library("stringr") # for chopping strings (for filenames)


 

#read all files in directory
allfiles <- list.files()

for (i in 1:length(allfiles)) {
	filename <- allfiles[[i]]
	TF <- str_sub(filename, 7,-18)
	dataname <- paste("inputfile.txt", sep="")
	dat <- read.table(dataname, header=T, sep="\t", stringsAsFactors=T)
	naiveTrain <- readRDS(filename)
	fileTest <- predict(naiveTrain,dat[,-6, drop=FALSE], usekernel=T)

 	df <- rbind(data.frame(reality=dat$case, prediction=fileTest$class, result=fileTest$posterior, fileid=dat$fileindex, sourceid=dat$sourceind, tf=dat$tf, score=dat$maxscore, matrix=dat$matrix))
  	outfile <- paste("outputfile.txt", sep="") 
  	write.table(df, file=outfile, append=F, sep="\t", row.names=F, col.names=T)
 }