random/dist/index.cjs.map

Seedable random number generator supporting many common distributions.

{"version":3,"sources":["../src/index.ts","../src/rng.ts","../src/generators/function.ts","../src/utils.ts","../src/generators/arc4.ts","../src/generators/math-random.ts","../src/generators/xor128.ts","../src/validation.ts","../src/distributions/bates.ts","../src/distributions/bernoulli.ts","../src/distributions/binomial.ts","../src/distributions/exponential.ts","../src/distributions/geometric.ts","../src/distributions/irwin-hall.ts","../src/distributions/log-normal.ts","../src/distributions/normal.ts","../src/distributions/pareto.ts","../src/distributions/poisson.ts","../src/distributions/uniform.ts","../src/distributions/uniform-boolean.ts","../src/distributions/uniform-int.ts","../src/distributions/weibull.ts","../src/random.ts"],"sourcesContent":["export * from './generators'\nexport * from './random'\nexport { default } from './random'\nexport * from './rng'\nexport type * from './types'\nexport * from './utils'\n","export abstract class RNG {\n  abstract get name(): string\n\n  abstract next(): number\n\n  abstract clone(): RNG\n}\n","import type { RNGFn } from '../types'\nimport { RNG } from '../rng'\n\nexport class FunctionRNG extends RNG {\n  _name: string\n  _rngFn: RNGFn\n\n  constructor(rngFn: RNGFn) {\n    super()\n\n    this._name = rngFn.name ?? 'function'\n    this._rngFn = rngFn\n  }\n\n  override get name() {\n    return this._name\n  }\n\n  override next() {\n    return this._rngFn()\n  }\n\n  override clone() {\n    return new FunctionRNG(this._rngFn)\n  }\n}\n","import type { Seed, SeedOrRNG } from './types'\nimport { ARC4RNG } from './generators/arc4'\nimport { FunctionRNG } from './generators/function'\nimport { RNG } from './rng'\n\nexport function createRNG(seedOrRNG?: SeedOrRNG) {\n  switch (typeof seedOrRNG) {\n    case 'object':\n      if (seedOrRNG instanceof RNG) {\n        return seedOrRNG\n      }\n      break\n\n    case 'function':\n      return new FunctionRNG(seedOrRNG)\n\n    default:\n      return new ARC4RNG(seedOrRNG)\n  }\n\n  throw new Error(`invalid RNG seed or instance \"${seedOrRNG}\"`)\n}\n\n/**\n * Mixes a string seed into a key that is an array of integers, and returns a\n * shortened string seed that is equivalent to the result key.\n */\nexport function mixKey(seed: Seed, key: number[]): number[] {\n  const seedStr = `${seed}`\n  let smear = 0\n  let j = 0\n\n  while (j < seedStr.length) {\n    key[0xff & j] =\n      0xff & ((smear ^= (key[0xff & j] ?? 0) * 19) + seedStr.charCodeAt(j++))\n  }\n\n  if (!key.length) {\n    return [0]\n  }\n\n  return key\n}\n\nexport function shuffleInPlace<T>(gen: RNG, array: Array<T>) {\n  for (let i = array.length - 1; i > 0; i -= 1) {\n    const j = Math.floor(gen.next() * (i + 1))\n    const tmp = array[i]\n    array[i] = array[j] as T\n    array[j] = tmp as T\n  }\n}\n","import type { Seed } from '../types'\nimport { RNG } from '../rng'\nimport { mixKey } from '../utils'\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of an array\n// of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates the\n// next (count) outputs from ARC4. Its return value is a number x that is in\n// the range 0 <= x < (width ^ count).\n\n// The following constants are related to IEEE 754 limits.\n// const width = 256 // each RC4 output is 0 <= x < 256\n// const chunks = 6 // at least six RC4 outputs for each double\nconst _arc4_startdenom = 281_474_976_710_656 // 256 ** 6 == width ** chunks\nconst _arc4_significance = 4_503_599_627_370_496 // 2 ** 52 significant digits in a double\nconst _arc4_overflow = 9_007_199_254_740_992 // 2 ** 53 == significance * 2\n\nexport class ARC4RNG extends RNG {\n  protected readonly _seed: Seed\n\n  i: number\n  j: number\n  S: number[]\n\n  constructor(seed: Seed = crypto.randomUUID()) {\n    super()\n\n    this._seed = seed\n    const key = mixKey(seed, [])\n\n    const S: number[] = []\n    const keylen = key.length\n    this.i = 0\n    this.j = 0\n    this.S = S\n\n    // Set up S using the standard key scheduling algorithm.\n    let i = 0\n    while (i <= 0xff) {\n      S[i] = i++\n    }\n\n    for (let i = 0, j = 0; i <= 0xff; i++) {\n      const t = S[i]!\n      j = 0xff & (j + key[i % keylen]! + t)\n      S[i] = S[j]!\n      S[j] = t\n    }\n\n    // For more robust unpredictability, the function call below discards an\n    // initial batch of values. This is called RC4-drop.\n    this.g(256)\n  }\n\n  override get name() {\n    return 'arc4'\n  }\n\n  override next() {\n    // This function returns a random double in [0, 1) that contains\n    // randomness in every bit of the mantissa of the IEEE 754 value.\n\n    let n = this.g(6) // Start with a numerator n < 2 ^ 48\n    let d = _arc4_startdenom // and denominator d = 2 ^ 48.\n    let x = 0 // and no 'extra last byte'.\n\n    while (n < _arc4_significance) {\n      // Fill up all significant digits (2 ** 52)\n      n = (n + x) * 256 // by shifting numerator and\n      d *= 256 // denominator and generating a\n      x = this.g(1) // new least-significant-byte.\n    }\n\n    while (n >= _arc4_overflow) {\n      // To avoid rounding past overflow, before adding\n      n /= 2 // last byte, shift everything\n      d /= 2 // right using integer math until\n      x >>>= 1 // we have exactly the desired bits.\n    }\n\n    return (n + x) / d // Form the number within [0, 1).\n  }\n\n  g(count: number) {\n    const { S } = this\n    let { i, j } = this\n    let r = 0\n\n    while (count--) {\n      i = 0xff & (i + 1)\n      const t = S[i]!\n      S[j] = t\n      j = 0xff & (j + t)\n      S[i] = S[j]!\n      r = r * 256 + S[0xff & (S[i]! + t)]!\n    }\n\n    this.i = i\n    this.j = j\n\n    return r\n  }\n\n  override clone() {\n    return new ARC4RNG(this._seed)\n  }\n}\n","import { RNG } from '../rng'\n\nexport class MathRandomRNG extends RNG {\n  override get name() {\n    return 'Math.random'\n  }\n\n  override next() {\n    return Math.random()\n  }\n\n  override clone() {\n    return new MathRandomRNG()\n  }\n}\n","import type { Seed } from '../types'\nimport { RNG } from '../rng'\n\nexport class XOR128RNG extends RNG {\n  protected readonly _seed: Seed\n\n  x: number\n  y: number\n  z: number\n  w: number\n\n  constructor(seed: Seed = crypto.randomUUID()) {\n    super()\n\n    this._seed = seed\n    this.x = 0\n    this.y = 0\n    this.z = 0\n    this.w = 0\n\n    let strSeed: string = ''\n\n    if (typeof seed === 'number') {\n      this.x = seed\n    } else {\n      strSeed += `${seed}`\n    }\n\n    // Mix in string seed, then discard an initial batch of 64 values.\n    for (let i = 0; i < strSeed.length + 64; ++i) {\n      this.x ^= strSeed.charCodeAt(i) | 0\n      this.next()\n    }\n  }\n\n  override get name() {\n    return 'xor128'\n  }\n\n  override next() {\n    const t = this.x ^ (this.x << 11)\n    this.x = this.y\n    this.y = this.z\n    this.z = this.w\n    this.w = this.w ^ ((this.w >>> 19) ^ t ^ (t >>> 8))\n    return (this.w >>> 0) / 0x1_00_00_00_00\n  }\n\n  override clone() {\n    return new XOR128RNG(this._seed)\n  }\n}\n","export function numberValidator(num: number) {\n  return new NumberValidator(num)\n}\n\nexport class NumberValidator {\n  private n: number\n  constructor(num: number) {\n    this.n = num\n  }\n\n  public isInt = (): this => {\n    if (Number.isInteger(this.n)) {\n      return this\n    }\n    throw new Error(`Expected number to be an integer, got ${this.n}`)\n  }\n\n  public isPositive = (): this => {\n    if (this.n > 0) {\n      return this\n    }\n    throw new Error(`Expected number to be positive, got ${this.n}`)\n  }\n\n  public lessThan = (v: number): this => {\n    if (this.n < v) {\n      return this\n    }\n    throw new Error(`Expected number to be less than ${v}, got ${this.n}`)\n  }\n\n  public greaterThanOrEqual = (v: number): this => {\n    if (this.n >= v) {\n      return this\n    }\n    throw new Error(\n      `Expected number to be greater than or equal to ${v}, got ${this.n}`\n    )\n  }\n\n  public greaterThan = (v: number): this => {\n    if (this.n > v) {\n      return this\n    }\n    throw new Error(`Expected number to be greater than ${v}, got ${this.n}`)\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function bates(random: Random, n = 1) {\n  numberValidator(n).isInt().isPositive()\n  const irwinHall = random.irwinHall(n)\n\n  return () => {\n    return irwinHall() / n\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function bernoulli(random: Random, p = 0.5) {\n  numberValidator(p).greaterThanOrEqual(0).lessThan(1)\n\n  return () => {\n    return Math.floor(random.next() + p)\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function binomial(random: Random, n = 1, p = 0.5) {\n  numberValidator(n).isInt().isPositive()\n  numberValidator(p).greaterThanOrEqual(0).lessThan(1)\n\n  return () => {\n    let i = 0\n    let x = 0\n\n    while (i++ < n) {\n      if (random.next() < p) {\n        x++\n      }\n    }\n    return x\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function exponential(random: Random, lambda = 1) {\n  numberValidator(lambda).isPositive()\n\n  return () => {\n    return -Math.log(1 - random.next()) / lambda\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function geometric(random: Random, p = 0.5) {\n  numberValidator(p).greaterThan(0).lessThan(1)\n  const invLogP = 1.0 / Math.log(1.0 - p)\n\n  return () => {\n    return Math.floor(1 + Math.log(random.next()) * invLogP)\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function irwinHall(random: Random, n = 1) {\n  numberValidator(n).isInt().greaterThanOrEqual(0)\n\n  return () => {\n    let sum = 0\n    for (let i = 0; i < n; ++i) {\n      sum += random.next()\n    }\n\n    return sum\n  }\n}\n","import type { Random } from '../random'\n\nexport function logNormal(random: Random, mu = 0, sigma = 1) {\n  const normal = random.normal(mu, sigma)\n  return () => {\n    return Math.exp(normal())\n  }\n}\n","import type { Random } from '../random'\n\nexport function normal(random: Random, mu = 0, sigma = 1) {\n  return () => {\n    let x: number, y: number, r: number\n\n    do {\n      x = random.next() * 2 - 1\n      y = random.next() * 2 - 1\n      r = x * x + y * y\n    } while (!r || r > 1)\n\n    return mu + sigma * y * Math.sqrt((-2 * Math.log(r)) / r)\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function pareto(random: Random, alpha = 1) {\n  numberValidator(alpha).greaterThanOrEqual(0)\n  const invAlpha = 1.0 / alpha\n\n  return () => {\n    return 1.0 / Math.pow(1.0 - random.next(), invAlpha)\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nconst logFactorialTable = [\n  0.0, 0.0, 0.693_147_180_559_945_29, 1.791_759_469_228_055,\n  3.178_053_830_347_945_8, 4.787_491_742_782_045_8, 6.579_251_212_010_101_2,\n  8.525_161_361_065_414_7, 10.604_602_902_745_251, 12.801_827_480_081_469\n]\n\nconst logFactorial = (k: number) => {\n  return logFactorialTable[k]\n}\n\nconst logSqrt2PI = 0.918_938_533_204_672_67\n\nexport function poisson(random: Random, lambda = 1) {\n  numberValidator(lambda).isPositive()\n\n  if (lambda < 10) {\n    // inversion method\n    const expMean = Math.exp(-lambda)\n\n    return () => {\n      let p = expMean\n      let x = 0\n      let u = random.next()\n\n      while (u > p) {\n        u = u - p\n        p = (lambda * p) / ++x\n      }\n\n      return x\n    }\n  } else {\n    // generative method\n    const smu = Math.sqrt(lambda)\n    const b = 0.931 + 2.53 * smu\n    const a = -0.059 + 0.024_83 * b\n    const invAlpha = 1.1239 + 1.1328 / (b - 3.4)\n    const vR = 0.9277 - 3.6224 / (b - 2)\n\n    return () => {\n      while (true) {\n        let u\n        let v = random.next()\n\n        if (v <= 0.86 * vR) {\n          u = v / vR - 0.43\n          return Math.floor(\n            ((2 * a) / (0.5 - Math.abs(u)) + b) * u + lambda + 0.445\n          )\n        }\n\n        if (v >= vR) {\n          u = random.next() - 0.5\n        } else {\n          u = v / vR - 0.93\n          u = (u < 0 ? -0.5 : 0.5) - u\n          v = random.next() * vR\n        }\n\n        const us = 0.5 - Math.abs(u)\n        if (us < 0.013 && v > us) {\n          continue\n        }\n\n        const k = Math.floor(((2 * a) / us + b) * u + lambda + 0.445)\n        v = (v * invAlpha) / (a / (us * us) + b)\n\n        if (k >= 10) {\n          const t =\n            (k + 0.5) * Math.log(lambda / k) -\n            lambda -\n            logSqrt2PI +\n            k -\n            (1 / 12.0 - (1 / 360.0 - 1 / (1260.0 * k * k)) / (k * k)) / k\n\n          if (Math.log(v * smu) <= t) {\n            return k\n          }\n        } else if (k >= 0) {\n          const f = logFactorial(k) ?? 0\n\n          if (Math.log(v) <= k * Math.log(lambda) - lambda - f) {\n            return k\n          }\n        }\n      }\n    }\n  }\n}\n","import type { Random } from '../random'\n\nexport function uniform(random: Random, min = 0, max = 1) {\n  return () => {\n    return random.next() * (max - min) + min\n  }\n}\n","import type { Random } from '../random'\n\nexport function uniformBoolean(random: Random) {\n  return () => {\n    return random.next() >= 0.5\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function uniformInt(random: Random, min = 0, max = 1) {\n  if (max === undefined) {\n    max = min === undefined ? 1 : min\n    min = 0\n  }\n\n  numberValidator(min).isInt()\n  numberValidator(max).isInt()\n\n  return () => {\n    return Math.floor(random.next() * (max - min + 1) + min)\n  }\n}\n","import type { Random } from '../random'\nimport { numberValidator } from '../validation'\n\nexport function weibull(random: Random, lambda: number, k: number) {\n  numberValidator(lambda).greaterThan(0.0)\n  numberValidator(k).greaterThan(0.0)\n  return () => {\n    const u = 1.0 - random.next()\n    return lambda * Math.pow(-Math.log(u), 1.0 / k)\n  }\n}\n","import type { RNG } from './rng'\nimport type { SeedOrRNG } from './types'\nimport { bates } from './distributions/bates'\nimport { bernoulli } from './distributions/bernoulli'\nimport { binomial } from './distributions/binomial'\nimport { exponential } from './distributions/exponential'\nimport { geometric } from './distributions/geometric'\nimport { irwinHall } from './distributions/irwin-hall'\nimport { logNormal } from './distributions/log-normal'\nimport { normal } from './distributions/normal'\nimport { pareto } from './distributions/pareto'\nimport { poisson } from './distributions/poisson'\nimport { uniform } from './distributions/uniform'\nimport { uniformBoolean } from './distributions/uniform-boolean'\nimport { uniformInt } from './distributions/uniform-int'\nimport { weibull } from './distributions/weibull'\nimport { MathRandomRNG } from './generators/math-random'\nimport { createRNG, shuffleInPlace } from './utils'\n\n/**\n * Distribution function\n */\ntype IDistFn<R> = (random: Random, ...argv: any) => R\n\n/**\n * Distribution\n */\ntype IDist<R> = () => R\n\n/**\n * Keyed cache entry\n */\ninterface ICacheEntry<T> {\n  key: string\n  distribution: () => T\n}\n\n/**\n * Seedable random number generator supporting many common distributions.\n *\n * @name Random\n * @class\n *\n * @param {RNG|function|string|number} [rng=Math.random] - Underlying random number generator or a seed for the default PRNG. Defaults to `Math.random`.\n */\nexport class Random {\n  protected _rng!: RNG\n  protected _cache: {\n    [k: string]: ICacheEntry<any>\n  } = {}\n\n  constructor(seedOrRNG: SeedOrRNG = new MathRandomRNG()) {\n    this._rng = createRNG(seedOrRNG)\n  }\n\n  /**\n   * @member {RNG} rng - Underlying pseudo-random number generator.\n   */\n  get rng() {\n    return this._rng\n  }\n\n  /**\n   * Creates a new `Random` instance, optionally specifying parameters to\n   * set a new seed.\n   */\n  clone(seedOrRNG: SeedOrRNG = this.rng.clone()): Random {\n    return new Random(seedOrRNG)\n  }\n\n  /**\n   * Sets the underlying pseudorandom number generator.\n   *\n   * @example\n   * ```ts\n   * import random from 'random'\n   *\n   * random.use('example-seed')\n   * // or\n   * random.use(Math.random)\n   * ```\n   */\n  use(seedOrRNG: SeedOrRNG) {\n    this._rng = createRNG(seedOrRNG)\n    this._cache = {}\n  }\n\n  // --------------------------------------------------------------------------\n  // Uniform utility functions\n  // --------------------------------------------------------------------------\n\n  /**\n   * Convenience wrapper around `this.rng.next()`\n   *\n   * Returns a floating point number in [0, 1).\n   *\n   * @return {number}\n   */\n  next(): number {\n    return this._rng.next()\n  }\n\n  /**\n   * Samples a uniform random floating point number, optionally specifying\n   * lower and upper bounds.\n   *\n   * Convenience wrapper around `random.uniform()`\n   *\n   * @param {number} [min=0] - Lower bound (float, inclusive)\n   * @param {number} [max=1] - Upper bound (float, exclusive)\n   */\n  float(min?: number, max?: number): number {\n    return this.uniform(min, max)()\n  }\n\n  /**\n   * Samples a uniform random integer, optionally specifying lower and upper\n   * bounds.\n   *\n   * Convenience wrapper around `random.uniformInt()`\n   *\n   * @param {number} [min=0] - Lower bound (integer, inclusive)\n   * @param {number} [max=1] - Upper bound (integer, inclusive)\n   */\n  int(min?: number, max?: number): number {\n    return this.uniformInt(min, max)()\n  }\n\n  /**\n   * Samples a uniform random integer, optionally specifying lower and upper\n   * bounds.\n   *\n   * Convenience wrapper around `random.uniformInt()`\n   *\n   * @alias `random.int`\n   *\n   * @param {number} [min=0] - Lower bound (integer, inclusive)\n   * @param {number} [max=1] - Upper bound (integer, inclusive)\n   */\n  integer(min?: number, max?: number): number {\n    return this.uniformInt(min, max)()\n  }\n\n  /**\n   * Samples a uniform random boolean value.\n   *\n   * Convenience wrapper around `random.uniformBoolean()`\n   *\n   * @alias `random.boolean`\n   */\n  bool(): boolean {\n    return this.uniformBoolean()()\n  }\n\n  /**\n   * Samples a uniform random boolean value.\n   *\n   * Convenience wrapper around `random.uniformBoolean()`\n   */\n  boolean(): boolean {\n    return this.uniformBoolean()()\n  }\n\n  /**\n   * Returns an item chosen uniformly at random from the given array.\n   *\n   * Convenience wrapper around `random.uniformInt()`\n   *\n   * @param {Array<T>} [array] - Input array\n   */\n  choice<T>(array: Array<T>): T | undefined {\n    if (!Array.isArray(array)) {\n      throw new TypeError(\n        `Random.choice expected input to be an array, got ${typeof array}`\n      )\n    }\n\n    const length = array.length\n\n    if (length > 0) {\n      const index = this.uniformInt(0, length - 1)()\n      return array[index]\n    } else {\n      return undefined\n    }\n  }\n  /**\n   * Returns a shuffled copy of the given array.\n   *\n   * @param {Array<T>} [array] - Input array\n   */\n  shuffle<T>(array: Array<T>): Array<T> {\n    if (!Array.isArray(array)) {\n      throw new TypeError(\n        `Random.shuffle expected input to be an array, got ${typeof array}`\n      )\n    }\n\n    const copy = [...array]\n    shuffleInPlace(this.rng, copy)\n\n    return copy\n  }\n\n  /**\n   * Generates a thunk which returns shuffled copies of the given array.\n   *\n   * @param {Array<T>} [array] - Input array\n   */\n  shuffler<T>(array: Array<T>): () => Array<T> {\n    if (!Array.isArray(array)) {\n      throw new TypeError(\n        `Random.shuffler expected input to be an array, got ${typeof array}`\n      )\n    }\n\n    const gen = this.rng\n    const copy = [...array]\n\n    return () => {\n      shuffleInPlace(gen, copy)\n      return [...copy]\n    }\n  }\n\n  // --------------------------------------------------------------------------\n  // Uniform distributions\n  // --------------------------------------------------------------------------\n\n  /**\n   * Generates a [Continuous uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)).\n   *\n   * @param {number} [min=0] - Lower bound (float, inclusive)\n   * @param {number} [max=1] - Upper bound (float, exclusive)\n   */\n  uniform(min?: number, max?: number): IDist<number> {\n    return this._memoize<number>('uniform', uniform, min, max)\n  }\n\n  /**\n   * Generates a [Discrete uniform distribution](https://en.wikipedia.org/wiki/Discrete_uniform_distribution).\n   *\n   * @param {number} [min=0] - Lower bound (integer, inclusive)\n   * @param {number} [max=1] - Upper bound (integer, inclusive)\n   */\n  uniformInt(min?: number, max?: number): IDist<number> {\n    return this._memoize<number>('uniformInt', uniformInt, min, max)\n  }\n\n  /**\n   * Generates a [Discrete uniform distribution](https://en.wikipedia.org/wiki/Discrete_uniform_distribution),\n   * with two possible outcomes, `true` or `false.\n   *\n   * This method is analogous to flipping a coin.\n   */\n  uniformBoolean(): IDist<boolean> {\n    return this._memoize<boolean>('uniformBoolean', uniformBoolean)\n  }\n\n  // --------------------------------------------------------------------------\n  // Normal distributions\n  // --------------------------------------------------------------------------\n\n  /**\n   * Generates a [Normal distribution](https://en.wikipedia.org/wiki/Normal_distribution).\n   *\n   * @param {number} [mu=0] - Mean\n   * @param {number} [sigma=1] - Standard deviation\n   */\n  normal(mu?: number, sigma?: number): IDist<number> {\n    return normal(this, mu, sigma)\n  }\n\n  /**\n   * Generates a [Log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution).\n   *\n   * @param {number} [mu=0] - Mean of underlying normal distribution\n   * @param {number} [sigma=1] - Standard deviation of underlying normal distribution\n   */\n  logNormal(mu?: number, sigma?: number): IDist<number> {\n    return logNormal(this, mu, sigma)\n  }\n\n  // --------------------------------------------------------------------------\n  // Bernoulli distributions\n  // --------------------------------------------------------------------------\n\n  /**\n   * Generates a [Bernoulli distribution](https://en.wikipedia.org/wiki/Bernoulli_distribution).\n   *\n   * @param {number} [p=0.5] - Success probability of each trial.\n   */\n  bernoulli(p?: number): IDist<number> {\n    return bernoulli(this, p)\n  }\n\n  /**\n   * Generates a [Binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution).\n   *\n   * @param {number} [n=1] - Number of trials.\n   * @param {number} [p=0.5] - Success probability of each trial.\n   */\n  binomial(n?: number, p?: number): IDist<number> {\n    return binomial(this, n, p)\n  }\n\n  /**\n   * Generates a [Geometric distribution](https://en.wikipedia.org/wiki/Geometric_distribution).\n   *\n   * @param {number} [p=0.5] - Success probability of each trial.\n   */\n  geometric(p?: number): IDist<number> {\n    return geometric(this, p)\n  }\n\n  // --------------------------------------------------------------------------\n  // Poisson distributions\n  // --------------------------------------------------------------------------\n\n  /**\n   * Generates a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution).\n   *\n   * @param {number} [lambda=1] - Mean (lambda > 0)\n   */\n  poisson(lambda?: number): IDist<number> {\n    return poisson(this, lambda)\n  }\n\n  /**\n   * Generates an [Exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution).\n   *\n   * @param {number} [lambda=1] - Inverse mean (lambda > 0)\n   */\n  exponential(lambda?: number): IDist<number> {\n    return exponential(this, lambda)\n  }\n\n  // --------------------------------------------------------------------------\n  // Misc distributions\n  // --------------------------------------------------------------------------\n\n  /**\n   * Generates an [Irwin Hall distribution](https://en.wikipedia.org/wiki/Irwin%E2%80%93Hall_distribution).\n   *\n   * @param {number} [n=1] - Number of uniform samples to sum (n >= 0)\n   */\n  irwinHall(n?: number): IDist<number> {\n    return irwinHall(this, n)\n  }\n\n  /**\n   * Generates a [Bates distribution](https://en.wikipedia.org/wiki/Bates_distribution).\n   *\n   * @param {number} [n=1] - Number of uniform samples to average (n >= 1)\n   */\n  bates(n?: number): IDist<number> {\n    return bates(this, n)\n  }\n\n  /**\n   * Generates a [Pareto distribution](https://en.wikipedia.org/wiki/Pareto_distribution).\n   *\n   * @param {number} [alpha=1] - Alpha\n   */\n  pareto(alpha?: number): IDist<number> {\n    return pareto(this, alpha)\n  }\n\n  /**\n   * Generates a [Weibull distribution](https://en.wikipedia.org/wiki/Weibull_distribution).\n   *\n   * @param {number} [lambda] - Lambda, the scale parameter\n   * @param {number} [k] - k, the shape parameter\n   */\n  weibull(lambda: number, k: number) {\n    return weibull(this, lambda, k)\n  }\n\n  // --------------------------------------------------------------------------\n  // Internal\n  // --------------------------------------------------------------------------\n\n  /**\n   * Memoizes distributions to ensure they're only created when necessary.\n   *\n   * Returns a thunk which that returns independent, identically distributed\n   * samples from the specified distribution.\n   *\n   * @internal\n   *\n   * @param {string} label - Name of distribution\n   * @param {function} getter - Function which generates a new distribution\n   * @param {...*} args - Distribution-specific arguments\n   */\n  protected _memoize<T>(\n    label: string,\n    getter: IDistFn<any>,\n    ...args: any[]\n  ): IDist<T> {\n    // return getter(this, ...args)\n    const key = `${args.join(';')}`\n    let value = this._cache[label]\n\n    if (value === undefined || value.key !== key) {\n      value = {\n        key,\n        distribution: getter(this, ...args)\n      }\n      this._cache[label] = value\n    }\n\n    return value.distribution\n  }\n}\n\nexport default new 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