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https://github.com/chainrand/chainrand-js
Javascript library for verifiable hybrid-chain RNG.
https://github.com/chainrand/chainrand-js
chainlink chainlink-hackathon-2021 chainlink-vrf cryptographic-random-generator es5-javascript javascript nodejs random random-number-generators
Last synced: about 19 hours ago
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Javascript library for verifiable hybrid-chain RNG.
- Host: GitHub
- URL: https://github.com/chainrand/chainrand-js
- Owner: chainrand
- Created: 2021-11-01T15:40:36.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-04T06:50:40.000Z (about 3 years ago)
- Last Synced: 2024-11-15T22:18:10.642Z (2 months ago)
- Topics: chainlink, chainlink-hackathon-2021, chainlink-vrf, cryptographic-random-generator, es5-javascript, javascript, nodejs, random, random-number-generators
- Language: JavaScript
- Homepage: https://chainrand.io
- Size: 14.6 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Chainrand-js — Verifiable hybrid-chain RNG.
Many applications require off-chain generation of random numbers for efficiency, security, etc.
This class allows you to generate a stream of deterministic, high-quality,
cryptographically secure random numbers.
By seeding it with a Chainlink VRF result that is requested **only once for the project**,
it can be used to demonstrate that the random numbers are **not cherry-picked**.
# Requirements
A Javascript ES5 envrionment: NodeJS, most web browsers, even older ones.
# Installation
**Browser / CDN:**
```html
```
**NPM:**
```bash
npm i chainrand
```
Or you can clone/download this GitHub repository.
## Usage
```jsx
var rng = chainrand.CRNG("base10()" + "")
// prints 10 determinstic random numbers between [0, 1)
for (var i = 0; i < 10; ++i) {
console.log(rng())
}
```
# Reproducibility
Current and future versions of this library will generate the same stream of random numbers from the same seed.
# Functions
## Constructor
```jsx
chainrand.CRNG(seed)
```
Creates an instance of the crng initialized with the `seed`.
**Parameters:**
- `seed: String` If empty, defaults to the empty string `""`.
**Example:**
```jsx
var crng = chainrand.CRNG("base10()" + "")
```
## random
```jsx
crng.random(): Number
```
Alias for `crng()`.
Returns a random number uniformly distributed in [0, 1).
The numbers are in multiples of `2**-53`.
**Parameters:**
none
**Returns:**
A random number uniformly distributed in [0, 1).
## randrange
```jsx
crng.randrange(start, stop[, step]): Integer
crng.randrange(stop): Integer
```
Returns a random integer uniformly distributed in [start, stop).
The integers are spaced with intervals of |step|.
**Parameters:**
- `start: Integer` The start of the range. (optional, default=`0`)
- `stop: Integer` The end of the range.
- `step: Integer` The interval step. (optional, default=`1`)
**Returns:**
A random integer uniformly distributed in [start, stop).
**Examples:**
```jsx
r = crng.randrange(3) // returns a random number in {0,1,2}
r = crng.randrange(-3) // returns a random number in {0,-1,-2}
r = crng.randrange(0, 6, 2) // returns a random number in {0,2,4}
r = crng.randrange(5, 0, 1) // returns a random number in {5,4,3,2,1}
r = crng.randrange(5, -5, -2) // returns a random number in {5,3,1,-1,-3}
```
## randint
```jsx
crng.randint(start, stop): Integer
crng.randint(stop): Integer
```
Returns a random integer uniformly distributed in [start, stop].
The integers are spaced with intervals of |step|.
**Parameters:**
- `start: Integer` The start of the range. (optional, default=`0`)
- `stop: Integer` The end of the range.
**Returns:**
A random integer uniformly distributed in [start, stop].
**Examples:**
```jsx
r = crng.randint(3) // returns a random number in {0,1,2,3}
r = crng.randint(-3) // returns a random number in {0,-1,-2,-3}
r = crng.randint(-3, 1) // returns a random number in {-3,-2,-1,0,1}
r = crng.randint(3, -1) // returns a random number in {3,2,1,0,-1}
```
## choice
```jsx
crng.choice(population[, weights]): Array
```
Returns a random element from the population.
If weights is not provided, every element of population will be equally weighted.
If weights is a non-empty array and is of different length to population,
only the first `Math.min(population.length, weights.length)` elements of population are sampled.
If the sum of the weights is less than or equal to zero,
every element of population will be equally weighted.
**Parameters:**
- `population: Array` The population.
- `weights: Array` The weights of the population. (optional)
**Returns:**
A random element in the population.
**Examples:**
```jsx
/* returns a random number in {1,2,3} */
r = crng.choice([1,2,3])
/* returns a random number in {1,2,3}
with the weights {1:10, 2:1, 3:0.1} */
r = crng.choice([1,2,3], [10,1,0.1])
```
## sample
```jsx
crng.sample(population, k=1[, weights]): Array
```
Returns `k` random elements from the population, sampling **without** replacement.
If `k` is more than the length of the population, only `k` elements will be returned.
If weights is not provided, every element of population will be equally weighted.
If weights is a non-empty array and is of different length to population,
only the first `Math.min(population.length, weights.length)` elements of population are sampled.
If the sum of the weights is less than or equal to zero,
every element of population will be equally weighted.
**Parameters:**
- `population: Array` The population.
- `k: Integer` The number of elements to choose.
- `weights: Array` The weights of the population. (optional)
**Returns:**
An array of `k` random elements from the population.
**Examples:**
```jsx
/* returns an array of 1 random element from {1,2,3} */
r = crng.sample([1,2,3])
/* returns an array of 2 random elements from {1,2,3} */
r = crng.sample([1,2,3], 2)
/* returns an array of 2 random elements from {1,2,3}
with the weights {1:10, 2:1, 3:0.1} */
r = crng.sample([1,2,3], 2, [10,1,0.1])
```
## shuffle
```jsx
crng.shuffle(population)
```
Shuffles the array in-place.
**Parameters:**
- `population: Array` The population.
**Returns:**
The shuffled array.
## gauss
```jsx
crng.gauss(mu=0.0, sigma=1.0): Number
```
Normal distribution, also called the Gaussian distribution.
**Parameters:**
- `mu: Number` The mean. (optional, default=`0.0`)
- `sigma: Number` The standard deviation. (optional, default=`1.0`)
**Returns:**
A random number from the Gaussian distribution.
# License
MIT