# distributions-uniform-pdf

> Uniform distribution probability density function (PDF).

Latest version **1.0.1** (published 2015-09-08) · MIT license · 0 weekly downloads

## Install

```sh
npm install distributions-uniform-pdf
pnpm add distributions-uniform-pdf
yarn add distributions-uniform-pdf
bun add distributions-uniform-pdf
```

## Health

**Score 15/100 (F)** — status: abandoned.

Positive: no vulnerabilities.

Warnings: low downloads; no types; no esm support.

Negative: abandoned; low maintenance score.

## Facts

| | |
|---|---|
| Version | 1.0.1 |
| Published | 2015-09-08 |
| First published | 2015-08-03 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | CommonJS |
| Dependencies | 12 |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 3 |
| Author | Philipp Burckhardt |
| Maintainers | planeshifter, kgryte |
| Keywords | distributions.io, distributions, probability, statistics, stats, pdf, uniform, continuous, symmetric, univariate |

## Links

- npm: https://www.npmjs.com/package/distributions-uniform-pdf
- Repository: https://github.com/distributions-io/uniform-pdf
- Homepage: https://github.com/distributions-io/uniform-pdf#readme
- Issues: https://github.com/distributions-io/uniform-pdf/issues
- npm.io page: https://npm.io/package/distributions-uniform-pdf

## Dependencies (12)

- [utils-deep-get](https://npm.io/package/utils-deep-get.md) ^1.0.0
- [utils-deep-set](https://npm.io/package/utils-deep-set.md) ^1.0.1
- [dstructs-matrix](https://npm.io/package/dstructs-matrix.md) ^2.0.0
- [validate.io-object](https://npm.io/package/validate.io-object.md) ^1.0.4
- [validate.io-function](https://npm.io/package/validate.io-function.md) ^1.0.2
- [validate.io-array-like](https://npm.io/package/validate.io-array-like.md) ^1.0.1
- [validate.io-matrix-like](https://npm.io/package/validate.io-matrix-like.md) ^1.0.2
- [compute-array-constructors](https://npm.io/package/compute-array-constructors.md) ^1.0.0
- [validate.io-number-primitive](https://npm.io/package/validate.io-number-primitive.md) ^1.0.0
- [validate.io-string-primitive](https://npm.io/package/validate.io-string-primitive.md) ^1.0.0
- [validate.io-typed-array-like](https://npm.io/package/validate.io-typed-array-like.md) ^1.0.0
- [validate.io-boolean-primitive](https://npm.io/package/validate.io-boolean-primitive.md) ^1.0.0

## Alternatives

- [@cantoo/pdf-lib](https://npm.io/package/@cantoo/pdf-lib.md) — 297.9K weekly downloads
- [datatables.net-buttons](https://npm.io/package/datatables.net-buttons.md) — 200.1K weekly downloads
- [@ckeditor/ckeditor5-export-pdf](https://npm.io/package/@ckeditor/ckeditor5-export-pdf.md) — 167.0K weekly downloads
- [scanbot-web-sdk](https://npm.io/package/scanbot-web-sdk.md) — 15.0K weekly downloads
- [@syncfusion/ej2-angular-pdfviewer](https://npm.io/package/@syncfusion/ej2-angular-pdfviewer.md) — 8.8K weekly downloads

## Recent versions

- 1.0.1 (latest) — 2015-09-08
- 1.0.0 — 2015-08-03

## README

Probability Density Function
===
[![NPM version][npm-image]][npm-url] [![Build Status][travis-image]][travis-url] [![Coverage Status][codecov-image]][codecov-url] [![Dependencies][dependencies-image]][dependencies-url]

> [Continuous uniform](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)) distribution probability density function (PDF).

The [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) (PDF) for a [continuous uniform](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)) random variable is

<div class="equation" align="center" data-raw-text="f(x;a,b)=\begin{cases} \frac{1}{b - a} &amp; \text{for } x \in [a,b] \\ 0 &amp; \text{otherwise} \end{cases}" data-equation="eq:pdf_function">
	<img src="https://cdn.rawgit.com/distributions-io/uniform-pdf/3f431ac7f7c1eef0bdc89e33caf772164ae8c0a2/docs/img/eqn.svg" alt="Probability density function (PDF) for a continuous uniform distribution.">
	<br>
</div>

where `a` is the minimum support and `b` is the maximum support of the distribution.

## Installation

``` bash
$ npm install distributions-uniform-pdf
```

For use in the browser, use [browserify](https://github.com/substack/node-browserify).


## Usage

``` javascript
var pdf = require( 'distributions-uniform-pdf' );
```

#### pdf( x[, options] )

Evaluates the [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) (PDF) for the [continuous uniform](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)) distribution. `x` may be either a [`number`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Number), an [`array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array), a [`typed array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Typed_arrays), or a [`matrix`](https://github.com/dstructs/matrix).

``` javascript
var matrix = require( 'dstructs-matrix' ),
	mat,
	out,
	x,
	i;

// Standard uniform: [a,b] = [0,1]
out = pdf( 1 );
// returns 1

out = pdf( -1 );
// returns 0

// Non-standard uniform:
out = pdf( 3, {
	'a': 0,
	'b': 10
}
// returns 1 / 10

out = pdf( -1 );
// returns 0

// Arrays:
x = [ 0, 0.5, 1, 1.5, 2, 2.5 ];
out = pdf( x );
// returns [ 0, 1, 1, 0, 0, 0 ]

x = new Int8Array( x );
out = pdf( x );
// returns Float64Array( [1,1,1,0,0,0] )

x = new Float64Array( 6 );
for ( i = 0; i < 6; i++ ) {
	x[ i ] = i * 0.5;
}
mat = matrix( x, [3,2], 'float64' );
/*
	[ 0  0.5
	  1  1.5
	  2  2.5 ]
*/

out = pdf( mat );
/*
	[ 1 1
	  1 0
	  0 0 ]
*/
```

The function accepts the following `options`:

*	__a__: minimum support. Default: `0`.
*	__b__: maximum support. Default: `1`.
* 	__accessor__: accessor `function` for accessing `array` values.
* 	__dtype__: output [`typed array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Typed_arrays) or [`matrix`](https://github.com/dstructs/matrix) data type. Default: `float64`.
*	__copy__: `boolean` indicating if the `function` should return a new data structure. Default: `true`.
*	__path__: [deepget](https://github.com/kgryte/utils-deep-get)/[deepset](https://github.com/kgryte/utils-deep-set) key path.
*	__sep__: [deepget](https://github.com/kgryte/utils-deep-get)/[deepset](https://github.com/kgryte/utils-deep-set) key path separator. Default: `'.'`.

A [continuous uniform](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)) distribution is a function of two parameters: `a` (minimum support) and `b` (maximum support). By default, `a` is equal to `0` and `b` is equal to `1`. The parameters must satisfy `a < b`. To adjust either parameter, set the corresponding option.

``` javascript
var x = [ 0, 0.5, 1, 1.5, 2, 2.5 ];

var out = pdf( x, {
	'a': 2,
	'b': 4,
});
// returns [ 0, 0, 0, 0, 1/2, 1/2 ]
```

For non-numeric `arrays`, provide an accessor `function` for accessing `array` values.

``` javascript
var data = [
	[0,0],
	[1,0.5],
	[2,1],
	[3,1.5],
	[4,2],
	[5,2.5]
];

function getValue( d, i ) {
	return d[ 1 ];
}

var out = pdf( data, {
	'accessor': getValue
});
// returns [ 1, 1, 1, 0, 0, 0 ]
```


To [deepset](https://github.com/kgryte/utils-deep-set) an object `array`, provide a key path and, optionally, a key path separator.

``` javascript
var data = [
	{'x':[0,0]},
	{'x':[1,0.5]},
	{'x':[2,1]},
	{'x':[3,1.5]},
	{'x':[4,2]},
	{'x':[5,2.5]}
];

var out = pdf( data, {
	'path': 'x|1',
	'sep': '|'
});
/*
	[
		{'x':[0,1]},
		{'x':[1,1]},
		{'x':[2,1]},
		{'x':[3,0]},
		{'x':[4,0]},
		{'x':[5,0]}
	]
*/

var bool = ( data === out );
// returns true
```

By default, when provided a [`typed array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Typed_arrays) or [`matrix`](https://github.com/dstructs/matrix), the output data structure is `float64` in order to preserve precision. To specify a different data type, set the `dtype` option (see [`matrix`](https://github.com/dstructs/matrix) for a list of acceptable data types).

``` javascript
var x, out;

x = new Int8Array( [0,1,2,3,4] );

out = pdf( x, {
	'a': 2,
	'b' : 3,
	'dtype': 'int32'
});
// returns Int32Array( [0,0,1,1,0] )

// Works for plain arrays, as well...
out = pdf( [0,1,2,3,4], {
	'a': 2,
	'b' : 3,
	'dtype': 'uint8'
});
// returns Uint8Array( [0,0,1,1,0] )
```

By default, the function returns a new data structure. To mutate the input data structure (e.g., when input values can be discarded or when optimizing memory usage), set the `copy` option to `false`.

``` javascript
var bool,
	mat,
	out,
	x,
	i;

x = [ 0, 0.5, 1, 1.5, 2 ];

out = pdf( x, {
	'copy': false
});
// returns [ 1, 1, 1, 0, 0 ]

bool = ( x === out );
// returns true

x = new Float32Array( 6 );
for ( i = 0; i < 6; i++ ) {
	x[ i ] = i * 0.5;
}
mat = matrix( x, [3,2], 'float32' );
/*
	[ 0  0.5
	  1  1.5
	  2  2.5 ]
*/

out = pdf( mat, {
	'copy': false
});
/*
	[ 1 1
	  1 0
	  0 0 ]
*/

bool = ( mat === out );
// returns true
```


## Notes

*	If an element is __not__ a numeric value, the evaluated [PDF](https://en.wikipedia.org/wiki/Uniform_distribution) is `NaN`.

	``` javascript
	var data, out;

	out = pdf( null );
	// returns NaN

	out = pdf( true );
	// returns NaN

	out = pdf( {'a':'b'} );
	// returns NaN

	out = pdf( [ true, null, [] ] );
	// returns [ NaN, NaN, NaN ]

	function getValue( d, i ) {
		return d.x;
	}
	data = [
		{'x':true},
		{'x':[]},
		{'x':{}},
		{'x':null}
	];

	out = pdf( data, {
		'accessor': getValue
	});
	// returns [ NaN, NaN, NaN, NaN ]

	out = pdf( data, {
		'path': 'x'
	});
	/*
		[
			{'x':NaN},
			{'x':NaN},
			{'x':NaN,
			{'x':NaN}
		]
	*/
	```

*	Be careful when providing a data structure which contains non-numeric elements and specifying an `integer` output data type, as `NaN` values are cast to `0`.

	``` javascript
	var out = pdf( [ true, null, [] ], {
		'dtype': 'int8'
	});
	// returns Int8Array( [0,0,0] );
	```


## Examples

``` javascript
var pdf = require( 'distributions-uniform-pdf' ),
	matrix = require( 'dstructs-matrix' );

var data,
	mat,
	out,
	tmp,
	i;

// Plain arrays...
data = new Array( 10 );
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = Math.random() * 2;
}
out = pdf( data );

// Object arrays (accessors)...
function getValue( d ) {
	return d.x;
}
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = {
		'x': data[ i ]
	};
}
out = pdf( data, {
	'accessor': getValue
});

// Deep set arrays...
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = {
		'x': [ i, data[ i ].x ]
	};
}
out = pdf( data, {
	'path': 'x/1',
	'sep': '/'
});

// Typed arrays...
data = new Float32Array( 10 );
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = Math.random() * 2;
}
out = pdf( data );

// Matrices...
mat = matrix( data, [5,2], 'float32' );
out = pdf( mat );

// Matrices (custom output data type)...
out = pdf( mat, {
	'dtype': 'uint8'
});
```

To run the example code from the top-level application directory,

``` bash
$ node ./examples/index.js
```


## Tests

### Unit

Unit tests use the [Mocha](http://mochajs.org/) test framework with [Chai](http://chaijs.com) assertions. To run the tests, execute the following command in the top-level application directory:

``` bash
$ make test
```

All new feature development should have corresponding unit tests to validate correct functionality.


### Test Coverage

This repository uses [Istanbul](https://github.com/gotwarlost/istanbul) as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:

``` bash
$ make test-cov
```

Istanbul creates a `./reports/coverage` directory. To access an HTML version of the report,

``` bash
$ make view-cov
```


---
## License

[MIT license](http://opensource.org/licenses/MIT).


## Copyright

Copyright &copy; 2015. The [Compute.io](https://github.com/compute-io) Authors.


[npm-image]: http://img.shields.io/npm/v/distributions-uniform-pdf.svg
[npm-url]: https://npmjs.org/package/distributions-uniform-pdf

[travis-image]: http://img.shields.io/travis/distributions-io/uniform-pdf/master.svg
[travis-url]: https://travis-ci.org/distributions-io/uniform-pdf

[codecov-image]: https://img.shields.io/codecov/c/github/distributions-io/uniform-pdf/master.svg
[codecov-url]: https://codecov.io/github/distributions-io/uniform-pdf?branch=master

[dependencies-image]: http://img.shields.io/david/distributions-io/uniform-pdf.svg
[dependencies-url]: https://david-dm.org/distributions-io/uniform-pdf

[dev-dependencies-image]: http://img.shields.io/david/dev/distributions-io/uniform-pdf.svg
[dev-dependencies-url]: https://david-dm.org/dev/distributions-io/uniform-pdf

[github-issues-image]: http://img.shields.io/github/issues/distributions-io/uniform-pdf.svg
[github-issues-url]: https://github.com/distributions-io/uniform-pdf/issues

---
_Source: https://npm.io/package/distributions-uniform-pdf · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._