SURANKCO-TRAINING(1)

✨ SURANKCO-TRAINING ✨

okay bestie so this is the man page for surankco-training β€” a User Command, section 1, the whole vibe πŸ’…


🌸 NAME 🌸

surankco-training β€” training of random forest using contig features and contig scores 🧚✨


πŸŽ€ SYNOPSIS πŸŽ€

surankco-training [options]

that's it, that's the whole synopsis, no positional args, we love a low-maintenance icon πŸ‘‘


πŸ’– DESCRIPTION πŸ’–

So basically πŸ’β€β™€οΈ this program does the training of a random forest using contig features from surankco-feature(1) and contig scores from surankco-score(1). That's the entire job description, and it does it flawlessly, no cap. ✨


πŸ¦‹ OPTIONS πŸ¦‹

  • -f FEATURES, --features=FEATURES β€” point it at a list of surankco feature files, comma separated bestie, and the correct suffixes are mandatory β€” they gotta end in "*.features.txt" or it's not the vibe πŸ™ˆ
  • -d DIRECTORY, --directory=DIRECTORY β€” instead of listing files one by one, just hand over a whole directory that contains surankco feature files ("*.features.txt"). lazy girl math, we love efficiency πŸ’…
  • -o OUTPUT.FILENAME, --output.filename=OUTPUT.FILENAME β€” the name you want the random forest classifiers exported under. default is "surankco_rfs.RData" πŸŽ€
  • -e EXPONENTIAL.QUANTILE, --exponential.quantile=EXPONENTIAL.QUANTILE β€” an exponential distribution quantile used to divide scores. default: 0.25 ⭐
  • -m MANUAL.THRESHOLDS, --manual.thresholds=MANUAL.THRESHOLDS β€” if you don't wanna do quantiles, you can set manual thresholds instead β€” one per score each, comma separated, and they must be in the same order as in the score file or everything gets messy fr fr 😭
  • -h, --help β€” shows this help message and exits. the classic 🫢

πŸ‘‘ AUTHOR πŸ‘‘

This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program. πŸ’œ


and that's the whole page bestie, no notes, she ate πŸ’…βœ¨

signing off with the receipts: this is surankco-training 0.0.r5, dated March 2016 β€” iconic, timeless, still relevant, periodt πŸŽ€πŸŒˆ