kstats-sampling

Ranking, normalization, binning, and weighted random sampling utilities.

Getting started

val data = doubleArrayOf(3.0, 1.0, 4.0, 1.0, 5.0)

data.rank()              // [3.0, 1.5, 4.0, 1.5, 5.0] (average ties)
data.zScore()            // standardized to mean=0, sd=1
data.minMaxNormalize()   // scaled to [0.0, 1.0]

val die = WeightedDice(mapOf("A" to 0.7, "B" to 0.2, "C" to 0.1), Random(42))
die.roll()               // "A" (most likely)

Ranking

• rank(tieMethod) — assign ranks to values with configurable tie-breaking. TieMethod: AVERAGE, MIN, MAX, DENSE, ORDINAL.

• percentileRank() — percentile rank of each element.

Normalization

• zScore() — standardize to mean = 0, standard deviation = 1.

• minMaxNormalize() — scale to [0, 1].

• minMaxNormalize(newMin, newMax) — scale to a custom range.

Binning

• bin(binCount), bin(binSize) — partition data into Bin<Double> entries.

• frequencyTable(binCount), frequencyTable(binSize) — partition into FrequencyBin entries with counts.

Sampling

• randomSample(n, random) — sample without replacement.

• bootstrapSample(n, random) — sample with replacement.

Weighted sampling

• WeightedDice<T>(weights) — categorical sampler from a map of outcomes to probabilities.

• WeightedCoin(probability) — Bernoulli sampler with roll().