Random number generator
- 11 differents probability distributions
- Normal (+ Gauss faster implementation), Log-normal, Exponential, Uniform, Beta, Gamma, Pareto, Von Mises, Weibull, Triangular and Restricted set.
- Seeds support (both numeric and textual)
- Utilitaries to use with Arrays
- Choice, Shuffle and Sample
First of all, you'll need to include the library :
You may include the minified library instead :
Then you may create alias for convenience :
let Distribution = Lowlight.Random.Distribution
Create a new generator
To create a new generator, just choose one of the 11 availables distributions. For example, to instantiate a new Normal distribution, type the following code.
let generator = new Distribution.Normal(0, 1)
By default generator will be seeded with current timestamp.
However you can seed it manually by using
Here are some possible ways to seed a generator :
generator.seed(null) //Initialize seed with current timestamp (default behaviour) generator.seed(0xCAFE) //Initialize seed with a number generator.seed("Café") //Initialize seed with a string (will be hashed into a number)
Generator.seed(seed) also return current instance so you can instantiate your generators this way :
generator = Distribution.Normal(0, 1).seed(0xCAFE)
You can read a generator's seed by calling
Generator.seed() without arguments.
Note that once you've started generating numbers with
Generator.next(), you won't be able to seed it again.
You can call
Generator.reset(), which will create a new internal generator function but you'll lose iterations count.
generator.seed("Tea") //No effect generator.reset().seed("Tea") //Will keep previous parameters and reseed generator
Edit generator's parameters
To setup or update a generator's parameter, just call their respective methods to do so. Note that they all return the instance so can just chain them if you need to edit multiples parameters.
For example, with our previous normal distribution generator, we can type the following :
You can also read their values by calling them with no arguments.
generator.mean() //Output 1
In the end, you can instantiate a generator with a single line :
generator = Distribution.Normal().mean(0).deviation(1).seed(0xCAFE)
Generator.next() method to get a new pseudo-random number.
generator.next() //Output a random number
This library also provide some miscelleanous functions to manage sets of values.
You may want create an alias for convenience :
let Utilities = Lowlight.Random.Utilities
Shuffle array or strings
Utilities.shuffle(array, seed) method shuffles an array (it doesn't modify the original one) or a string.
Utilities.shuffle([67, 65, 70, 69], 0) //Output [70, 69, 65, 67] Utilities.shuffle("cafe", 0) //Output "feac"
Utilities.sample(array, length, replace, seed) method sample values from an array or a string. You may specify which length you want for your sampled array and also if values should be replaced in set of possible drawable values.
Utilities.sample([67, 65, 70, 69], 3, false, 0) //Output [67, 69, 65] Utilities.sample([67, 65, 70, 69], 6, true, 0) //Output [67, 69, 67, 69, 67, 67]
Utilities.choice(array, seed) method is equivalent to
Utilities.choice([67, 65, 70, 69], 0) //Output 67
|/bin||Live and dev scrripts files|
|/demo||Demo and codes examples|
Rebuild project and expanding the library
You'll need to run the following command the first time to install dependencies.
Then to rebuild project, just run the following command :
npm run build
This will update
/bin files with included
package.json (which contains
"source" and "output" paths) are preconfigured, you may need to reconfigure them if you're planning to expand this library.
To include a file just use the following syntax in the
"source" file :
/* #include <path/to/file.js> */
package.json (which contains
"jsdoc_source", "jsdoc_output", "jsdoc_config" and "jsdoc_readme") and
docs/categories.json are preconfigured, you may need to reconfigure them if you're planning to expand this library.
This project is licensed under the MIT License.
See LICENSE.md file for details.