mainpseudo

import RelevantLibraries

User input:
  Args:
    + input file--
    + DistProx cutoff
    + k size
    + output file (default to console)
    + xfold validation (default #1)

main:
  +pass input to parser/(i/o), return a list, specify if prox or dist
  DistProx {} = iofile.parse(fp, cutoff?, prox)

  for trainset, testset in iofile.xfold(DistProx, userarg.xfold)
    frequencies are done in the math file, calc frequencies
    proximalfreq
    distalfreq

    imeter = calculate.score(proximalfreq, distalfreq, k, don, acc)
    meanscores = statistics.mean(pscores or dscores)
    avgmeans.append(mean(meanscores))

  io.display(avgmeans)

#scoring relies on if xfold has been implemented, otherwise its just scoring the same dataset, isn't it
 scores are going to be done similarly to demo...xfold is probably where I want to start.


-include a way to train on the entire data set, perhaps as a final output?