FLUJOS/VISUALIZACION/node_modules/ngraph.random/README.md
2025-11-07 00:06:12 +01:00

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ngraph.random
=============
Operation with seeded random numbers for ngraph.*.
[![build status](https://secure.travis-ci.org/anvaka/ngraph.random.png)](http://travis-ci.org/anvaka/ngraph.random)
Install
=======
You can use CDN:
``` html
<script src='https://cdn.jsdelivr.net/npm/ngraph.random/dist/ngraph.random.js'></script>
```
or via [npm](http://npmjs.org):
```
npm install ngraph.random
```
and then:
``` js
var ngraphRandom = require('ngraph.random);
```
Usage
=====
API provides random number generation, and array shuffling.
Let's start with random number generation:
``` js
// create generator, seeded with 42
var randomGenerator = ngraphRandom(42);
// prints double number from [0..1)
console.log(randomGenerator.nextDouble());
// Get next non-negative random number, less than 100.
console.log(randomGenerator.next(100)); // prints 20, we are seeded
// Note: next() always expect maxValue. If you don't pass it it will return NaN.
// This is done for performance reasons, we don't want to check input arguments
// on each call.
```
Second part of the API is array shuffling:
``` js
var ngraphRandom = require('ngraph.random');
// create "shuffling" iterator:
var originalArray = [0, 1, 2, 3, 4, 5];
var randomIterator = ngraphRandom.randomIterator(originalArray);
// iterate over array in random order:
randomIterator.forEach(function(x) {
console.log(x); // prints originalArray's items in random order
});
// Note: using random iterator does modify original array.
// This is done to save memory.
// If you want to re-shuffle array in-place, you can use:
randomIterator.shuffle();
// Finally if you want to have seeded shuffling you can pass optional seeded
// random number generator:
var seededGenerator = ngraphRandom.random(42);
ngraphRandom.randomIterator(originalArray, seededGenerator);
```
## distributions
The library supports random number generation that follow Gaussian distribution:
``` js
var generator = ngraphRandom(42);
// returns a random number from a gaussian distribution with mean 0 and
// standard deviation 1
generator.gaussian();
```
License
=======
BSD 3-clause