72 lines
2.8 KiB
TypeScript
72 lines
2.8 KiB
TypeScript
import { Numberish } from '@0x/contracts-test-utils';
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import { BigNumber } from '@0x/utils';
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import * as seedrandom from 'seedrandom';
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class PRNGWrapper {
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public readonly seed = process.env.SEED || Math.random().toString();
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public readonly rng = seedrandom(this.seed);
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/*
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* Pseudorandom version of _.sample. Picks an element of the given array with uniform probability.
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* Return undefined if the array is empty.
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*/
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public sample<T>(arr: T[]): T | undefined {
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if (arr.length === 0) {
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return undefined;
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}
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const index = Math.abs(this.rng.int32()) % arr.length;
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return arr[index];
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}
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/*
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* Pseudorandom version of _.sampleSize. Returns an array of `n` samples from the given array
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* (with replacement), chosen with uniform probability. Return undefined if the array is empty.
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*/
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public sampleSize<T>(arr: T[], n: number): T[] | undefined {
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if (arr.length === 0) {
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return undefined;
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}
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const samples = [];
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for (let i = 0; i < n; i++) {
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samples.push(this.sample(arr) as T);
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}
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return samples;
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}
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/*
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* Pseudorandom version of getRandomPortion/getRandomInteger. If no distribution is provided,
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* samples an integer between the min and max uniformly at random. If a distribution is
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* provided, samples an integer from the given distribution (assumed to be defined on the
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* interval [0, 1]) scaled to [min, max].
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*/
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public integer(min: Numberish, max: Numberish, distribution: () => Numberish = this.rng): BigNumber {
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const range = new BigNumber(max).minus(min);
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return new BigNumber(distribution())
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.times(range)
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.integerValue(BigNumber.ROUND_HALF_UP)
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.plus(min);
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}
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/*
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* Returns a function that produces samples from the Kumaraswamy distribution parameterized by
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* the given alpha and beta. The Kumaraswamy distribution is like the beta distribution, but
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* with a nice closed form.
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* https://en.wikipedia.org/wiki/Kumaraswamy_distribution
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* https://www.johndcook.com/blog/2009/11/24/kumaraswamy-distribution/
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*/
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public kumaraswamy(this: PRNGWrapper, alpha: Numberish, beta: Numberish): () => BigNumber {
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const ONE = new BigNumber(1);
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return () => {
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const u = new BigNumber(this.rng()).modulo(ONE); // u ~ Uniform(0, 1)
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// Evaluate the inverse CDF at `u` to obtain a sample from Kumaraswamy(alpha, beta)
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return ONE.minus(ONE.minus(u).exponentiatedBy(ONE.dividedBy(beta))).exponentiatedBy(ONE.dividedBy(alpha));
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};
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}
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}
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export const Pseudorandom = new PRNGWrapper();
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export const Distributions = {
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Uniform: Pseudorandom.rng,
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Kumaraswamy: Pseudorandom.kumaraswamy.bind(Pseudorandom),
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};
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