Fix PCGRandom.integer() bias: use Lemire rejection sampling#6201
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Zelys-DFKH wants to merge 1 commit intoEffect-TS:mainfrom
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Fix PCGRandom.integer() bias: use Lemire rejection sampling#6201Zelys-DFKH wants to merge 1 commit intoEffect-TS:mainfrom
Zelys-DFKH wants to merge 1 commit intoEffect-TS:mainfrom
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…th Lemire rejection sampling
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Summary
PCGRandom.integer()usedMath.round(this.number() * Number.MAX_SAFE_INTEGER) % max, which baked two sources of statistical bias into one expression and consumed twice the PRNG state per call.Closes #6184
ryanleecode's comment in the thread spotted and quantified the bias clearly. I wanted to make sure a fix landed.
Problem
Math.round boundary bias:
Math.roundmaps[0, 1)to integers non-uniformly. Buckets at each end of the range receive half the probability mass of interior values.Modulo bias:
Number.MAX_SAFE_INTEGERis not evenly divisible by most values ofmax, so some output values have one extra preimage in the modulo cycle and are overrepresented.Double state consumption:
number()calls_next()twice to build a 53-bit float. Integer generation only needs one 32-bit draw.The practical bias magnitude is tiny (ryanleecode's analysis put it at ~10^-16), but a PRNG that is correct-by-construction is worth having.
Fix
For
max <= 2^32: Lemire (2018) rejection sampling on raw 32-bit output (arXiv:1805.10941). Same approach as OpenBSD'sarc4random_uniformand the PCG reference implementation. Every value in[0, max)gets exactlyfloor((2^32 - threshold) / max)accepting preimages, provably uniform.For
max > 2^32(e.g.nextIntpassingNumber.MAX_SAFE_INTEGER): falls back toMath.floor(this.number() * max).Math.floorremoves theMath.roundboundary bias; residual floating-point discretization bias at that scale is negligible.Tests
nextIntBetweenstays in[min, max)across several small ranges, where rejection sampling is most aggressively exercised.