1 | filterOutIterations(wall, hall, cnt_errors, cnt_reconstructed, params)
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wall |
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hall |
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cnt_errors |
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cnt_reconstructed |
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params |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (wall, hall, cnt_errors, cnt_reconstructed, params)
{
num_processesToExtract <- params$num_processesToExtract
thresh_removeLastPercent <- params$thresh_removeLastPercent
num_totIterations <- ncol(wall)/num_processesToExtract
tot.rm.iterations <- round(thresh_removeLastPercent * num_totIterations)
if (tot.rm.iterations > 0) {
closeness.mutCounts <- matrix(0, nrow = num_totIterations,
ncol = 1)
for (i in 1:num_totIterations) {
closeness.mutCounts[i, ] <- base::norm(cnt_errors[[i]],
"F")
}
indexClosenessGenomes <- order(closeness.mutCounts, decreasing = TRUE)
removeIterations <- indexClosenessGenomes[1:tot.rm.iterations]
removeIterationSets <- matrix(0, nrow = (num_processesToExtract *
tot.rm.iterations), ncol = 1)
for (i in 1:tot.rm.iterations) {
iStart <- num_processesToExtract * (removeIterations[i] -
1) + 1
iEnd <- num_processesToExtract * removeIterations[i]
tmpRowRange <- (num_processesToExtract * (i - 1) +
1):(num_processesToExtract * i)
removeIterationSets[tmpRowRange, ] <- iStart:iEnd
}
wall <- wall[, -removeIterationSets]
hall <- hall[-removeIterationSets, ]
cnt_errors <- cnt_errors[-removeIterations]
cnt_reconstructed <- cnt_reconstructed[-removeIterations]
}
res.list <- list()
res.list$Wall <- wall
res.list$Hall <- hall
res.list$mutCounts.errors <- cnt_errors
res.list$mutCounts.reconstructed <- cnt_reconstructed
return(res.list)
}
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