@thi.ng/k-means/mean-cut.js

k-means & k-medians with customizable distance functions and centroid initializations for n-D vectors

import { mean, vmean } from "@thi.ng/vectors/mean";
import { vmedian } from "@thi.ng/vectors/median";
const meanCut = (k, samples) => computeCutWith(vmean, samples, samples[0].length, k).map(
  (x) => mean([], x)
);
const medianCut = (k, samples) => computeCutWith(vmedian, samples, samples[0].length, k).map(
  (x) => mean([], x)
);
const computeCutWith = (cut, samples, dim, depth) => {
  if (!samples.length) return [];
  if (depth <= 1) return [samples];
  const channels = new Array(dim);
  let maxRange = 0, maxRangeID = 0, i = 0, j, n = samples.length, min, max, value, range, channel;
  for (; i < dim; i++) {
    channel = channels[i] = new Array(n);
    min = Infinity;
    max = -Infinity;
    for (j = 0; j < n; j++) {
      value = channel[j] = samples[j][i];
      if (value < min) min = value;
      if (value > max) max = value;
    }
    range = max - min;
    if (range > maxRange) {
      maxRange = range;
      maxRangeID = i;
    }
  }
  channel = channels[maxRangeID];
  const split = cut(channel);
  const lo = [];
  const hi = [];
  for (j = 0; j < n; j++) {
    (channel[j] <= split ? lo : hi).push(samples[j]);
  }
  return computeCutWith(cut, lo, dim, depth >> 1).concat(
    computeCutWith(cut, hi, dim, depth + 1 >> 1)
  );
};
export {
  computeCutWith,
  meanCut,
  medianCut
};