Thanks for the help,
Perhaps I should elaborate a bit, I am working on bioinformatics project in
which I am trying to run a forward selection algorithm for machine learning
classification of two biological conditions.
At each iteration I want to find the gene that in addition to those I have
found already does the best classification.
It looks something like this:
for (j in 1:5030)
{
tp <- 0;
for (i in 1:5030)
{
if (!(i %in% idx))
{
classifier<-naiveBayes(trn[,c(i,idx)], trn[,20118])
tbl <-table(predict(classifier, trn[,-20118]), trn[,20118])
success <- (tbl[[1]] +tbl[[4]])/(tbl[[1]] +tbl[[4]]+tbl[[2]]+tbl[[3]])
if (success > tp)
{
tp <- success
ind <- i
gene <- names(trn)[i]
}
}
}
idx <- c(idx,ind)
res <- rbind(res, data.frame(Iteration=j,Success=tp*100,Gene=gene))
}
I am no expert when it comes to programming so I am not sure how can I
optimize my relatively primitive code in the best way...
Thanks,
Moriah
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