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feat(number): add distributor functions #3375
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34e62e3
feat(number): add exponentialDistribution function
ST-DDT d3236f4
chore: simplify tables
ST-DDT 294fa79
feat(number): introduce distributor functions
ST-DDT b56abb7
Merge branch 'next' into feat/number/exponentional-distribution
ST-DDT 2d02ceb
docs: fix missing param tags
ST-DDT 9e60210
docs: add distributors api docs
ST-DDT a769fb0
Merge branch 'next' into feat/number/exponentional-distribution
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import type { Randomizer } from '../randomizer'; | ||
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/** | ||
* A function that determines the distribution of generated values. | ||
* Values generated by a randomizer are considered uniformly distributed, distributor functions can be used to change this. | ||
* If many results are collected the results form a limited distribution between `0` and `1`. | ||
* So an exponential distributor will values resemble a limited exponential distribution. | ||
* | ||
* Common examples of distributor functions are: | ||
* | ||
* - Uniform distributor: All values have the same likelihood. | ||
* - Normal distributor: Values are more likely to be close to a specific value. | ||
* - Exponential distributor: Values are more likely to be close to 0. | ||
* | ||
* Distributor functions can be used by some faker functions such as `faker.number.int()` and `faker.number.float()`. | ||
* | ||
* Please note that the result from the distributor function is processed further by the function accepting it. | ||
* E.g. a distributor result of `0.5` within a call to `faker.number.int({ min: 10, max: 20 })` will result in `15`. | ||
* | ||
* @param randomizer The randomizer to use for generating values. | ||
* | ||
* @returns Generates a random float between 0 (inclusive) and 1 (exclusive). | ||
* | ||
* @example | ||
* import { Distributor, Randomizer, faker } from '@faker-js/faker'; | ||
* | ||
* const alwaysMin: Distributor = () => 0; | ||
* const uniform: Distributor = (randomizer: Randomizer) => randomizer.next(); | ||
* | ||
* faker.number.int({ min: 2, max: 10, distributor: alwaysMin }); // 2 | ||
* faker.number.int({ min: 0, max: 10, distributor: uniform }); // 5 | ||
* | ||
* @since 9.6.0 | ||
*/ | ||
export type Distributor = (randomizer: Randomizer) => number; |
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import { FakerError } from '../errors/faker-error'; | ||
import type { Distributor } from './distributor'; | ||
import { uniformDistributor } from './uniform'; | ||
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/** | ||
* Creates a new function that generates power-law/exponentially distributed values. | ||
* This function uses `(base ** next() - 1) / (base - 1)` to spread the values. | ||
* | ||
* The following table shows the rough distribution of values generated using `exponentialDistributor({ base: x })`: | ||
* | ||
* | Result | Base 0.1 | Base 0.5 | Base 1 | Base 2 | Base 10 | | ||
* | :-------: | -------: | -------: | -----: | -----: | ------: | | ||
* | 0.0 - 0.1 | 4.1% | 7.4% | 10.0% | 13.8% | 27.8% | | ||
* | 0.1 - 0.2 | 4.5% | 7.8% | 10.0% | 12.5% | 16.9% | | ||
* | 0.2 - 0.3 | 5.0% | 8.2% | 10.0% | 11.5% | 12.1% | | ||
* | 0.3 - 0.4 | 5.7% | 8.7% | 10.0% | 10.7% | 9.4% | | ||
* | 0.4 - 0.5 | 6.6% | 9.3% | 10.0% | 10.0% | 7.8% | | ||
* | 0.5 - 0.6 | 7.8% | 9.9% | 10.0% | 9.3% | 6.6% | | ||
* | 0.6 - 0.7 | 9.4% | 10.7% | 10.0% | 8.8% | 5.7% | | ||
* | 0.7 - 0.8 | 12.1% | 11.5% | 10.0% | 8.2% | 5.0% | | ||
* | 0.8 - 0.9 | 16.9% | 12.6% | 10.0% | 7.8% | 4.5% | | ||
* | 0.9 - 1.0 | 27.9% | 13.8% | 10.0% | 7.5% | 4.1% | | ||
* | ||
* The following table shows the rough distribution of values generated using `exponentialDistributor({ bias: x })`: | ||
* | ||
* | Result | Bias -9 | Bias -1 | Bias 0 | Bias 1 | Bias 9 | | ||
* | :-------: | ------: | ------: | -----: | -----: | -----: | | ||
* | 0.0 - 0.1 | 27.9% | 13.7% | 10.0% | 7.4% | 4.1% | | ||
* | 0.1 - 0.2 | 16.9% | 12.5% | 10.0% | 7.8% | 4.5% | | ||
* | 0.2 - 0.3 | 12.1% | 11.6% | 10.0% | 8.3% | 5.1% | | ||
* | 0.3 - 0.4 | 9.5% | 10.7% | 10.0% | 8.8% | 5.7% | | ||
* | 0.4 - 0.5 | 7.8% | 10.0% | 10.0% | 9.3% | 6.6% | | ||
* | 0.5 - 0.6 | 6.6% | 9.3% | 10.0% | 9.9% | 7.7% | | ||
* | 0.6 - 0.7 | 5.7% | 8.8% | 10.0% | 10.7% | 9.5% | | ||
* | 0.7 - 0.8 | 5.0% | 8.2% | 10.0% | 11.5% | 12.1% | | ||
* | 0.8 - 0.9 | 4.5% | 7.8% | 10.0% | 12.6% | 16.8% | | ||
* | 0.9 - 1.0 | 4.1% | 7.4% | 10.0% | 13.7% | 27.9% | | ||
* | ||
* @param options The options for generating the distributor. | ||
* @param options.base The base of the exponential distribution. Should be greater than 0. Defaults to `2`. | ||
* The higher/more above `1` the `base`, the more likely the number will be closer to the minimum value. | ||
* The lower/closer to zero the `base`, the more likely the number will be closer to the maximum value. | ||
* Values of `1` will generate a uniform distributor. | ||
* Can alternatively be configured using the `bias` option. | ||
* @param options.bias An alternative way to specify the `base`. Also accepts values below zero. Defaults to `-1`. | ||
* The higher/more positive the `bias`, the more likely the number will be closer to the maximum value. | ||
* The lower/more negative the `bias`, the more likely the number will be closer to the minimum value. | ||
* Values of `0` will generate a uniform distributor. | ||
* Can alternatively be configured using the `base` option. | ||
* | ||
* @example | ||
* import { exponentialDistributor, generateMersenne53Randomizer } from '@faker-js/faker'; | ||
* | ||
* const randomizer = generateMersenne53Randomizer(); | ||
* const distributor = exponentialDistributor(); | ||
* distributor(randomizer) // 0.04643770898904198 | ||
* distributor(randomizer) // 0.13436127925491848 | ||
* distributor(randomizer) // 0.4202905589842396 | ||
* distributor(randomizer) // 0.5164955927828387 | ||
* distributor(randomizer) // 0.3476359433171099 | ||
* | ||
* @since 9.6.0 | ||
*/ | ||
export function exponentialDistributor( | ||
options?: | ||
| { | ||
/** | ||
* The base of the exponential distribution. Should be greater than 0. | ||
* The higher/more above `1` the `base`, the more likely the number will be closer to the minimum value. | ||
* The lower/closer to zero the `base`, the more likely the number will be closer to the maximum value. | ||
* Values of `1` will generate a uniform distribution. | ||
* Can alternatively be configured using the `bias` option. | ||
* | ||
* @default 2 | ||
*/ | ||
base?: number; | ||
} | ||
| { | ||
/** | ||
* An alternative way to specify the `base`. Also accepts values below zero. | ||
* The higher/more positive the `bias`, the more likely the number will be closer to the maximum value. | ||
* The lower/more negative the `bias`, the more likely the number will be closer to the minimum value. | ||
* Values of `0` will generate a uniform distribution. | ||
* Can alternatively be configured using the `base` option. | ||
* | ||
* @default -1 | ||
*/ | ||
bias?: number; | ||
} | ||
): Distributor; | ||
/** | ||
* Creates a new function that generates exponentially distributed values. | ||
* This function uses `(base ** next() - 1) / (base - 1)` to spread the values. | ||
* | ||
* @param options The options for generating the distributor. | ||
* @param options.base The base of the exponential distribution. Should be greater than 0. Defaults to `2`. | ||
* The higher/more above `1` the `base`, the more likely the number will be closer to the minimum value. | ||
* The lower/closer to zero the `base`, the more likely the number will be closer to the maximum value. | ||
* Values of `1` will generate a uniform distributor. | ||
* Can alternatively be configured using the `bias` option. | ||
* @param options.bias An alternative way to specify the `base`. Also accepts values below zero. Defaults to `-1`. | ||
* The higher/more positive the `bias`, the more likely the number will be closer to the maximum value. | ||
* The lower/more negative the `bias`, the more likely the number will be closer to the minimum value. | ||
* Values of `0` will generate a uniform distributor. | ||
* Can alternatively be configured using the `base` option. | ||
*/ | ||
export function exponentialDistributor( | ||
options: { | ||
base?: number; | ||
bias?: number; | ||
} = {} | ||
): Distributor { | ||
const { bias = -1, base = bias <= 0 ? -bias + 1 : 1 / (bias + 1) } = options; | ||
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if (base === 1) { | ||
return uniformDistributor(); | ||
} else if (base <= 0) { | ||
throw new FakerError('Base should be greater than 0.'); | ||
} | ||
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return ({ next }) => (base ** next() - 1) / (base - 1); | ||
} |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,41 @@ | ||
import type { Distributor } from './distributor'; | ||
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/** | ||
* Creates a new function that generates uniformly distributed values. | ||
* The likelihood of each value is the same. | ||
* | ||
* The following table shows the rough distribution of values generated using `uniformDistributor()`: | ||
* | ||
* | Result | Uniform | | ||
* | :-------: | ------: | | ||
* | 0.0 - 0.1 | 10.0% | | ||
* | 0.1 - 0.2 | 10.0% | | ||
* | 0.2 - 0.3 | 10.0% | | ||
* | 0.3 - 0.4 | 10.0% | | ||
* | 0.4 - 0.5 | 10.0% | | ||
* | 0.5 - 0.6 | 10.0% | | ||
* | 0.6 - 0.7 | 10.0% | | ||
* | 0.7 - 0.8 | 10.0% | | ||
* | 0.8 - 0.9 | 10.0% | | ||
* | 0.9 - 1.0 | 10.0% | | ||
* | ||
* @returns A new uniform distributor function. | ||
* | ||
* @example | ||
* import { generateMersenne53Randomizer, uniformDistributor } from '@faker-js/faker'; | ||
* | ||
* const randomizer = generateMersenne53Randomizer(); | ||
* const distributor = uniformDistributor(); | ||
* distributor(randomizer) // 0.9100215692561207 | ||
* distributor(randomizer) // 0.791632947887336 | ||
* distributor(randomizer) // 0.14770035310214324 | ||
* distributor(randomizer) // 0.28282249581185814 | ||
* distributor(randomizer) // 0.017890944117802343 | ||
* | ||
* @since 9.6.0 | ||
*/ | ||
export function uniformDistributor(): Distributor { | ||
return UNIFORM_DISTRIBUTOR; | ||
} | ||
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const UNIFORM_DISTRIBUTOR: Distributor = ({ next }) => next(); | ||
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I thought quite long about it. What exactly was your reasoning to extract the uniform function implementation into it's own function expression? The only benifit I was able to see, would be function reference comparison:
Was that your reasoning? If yes, is this really desired? 🤔
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The value is exposed as fn because the other distributions are as well.
The value is defined as a const because it is used as default for the number functions. This might be related to my usual backend development habits.
The a===b part is unintentional.