# @stdlib/stats-base-dists-triangular-entropy Release 0.0.5

Triangular distribution differential entropy.

Keywords
stdlib, stdmath, statistics, stats, distribution, dist, entropy, information, shannon, nats, continuous, probability, prob, triangular, univariate, javascript, node, node-js, nodejs
Apache-2.0
Install
``` npm install @stdlib/stats-base-dists-triangular-entropy@0.0.5 ```

# Entropy

Triangular distribution differential entropy.

The differential entropy (in nats) for a triangular random variable with lower limit `a`, upper limit `b`, and mode `c` is

## Installation

`npm install @stdlib/stats-base-dists-triangular-entropy`

## Usage

`var entropy = require( '@stdlib/stats-base-dists-triangular-entropy' );`

#### entropy( a, b, c )

Returns the differential entropy of a triangular distribution with minimum support `a`, maximum support`b`, and mode `c` (in nats).

```var v = entropy( 0.0, 1.0, 0.8 );
// returns ~-0.193

v = entropy( 4.0, 12.0, 5.0 );
// returns ~1.886

v = entropy( 2.0, 8.0, 5.0 );
// returns ~1.599```

If provided `NaN` as any argument, the function returns `NaN`.

```var v = entropy( NaN, 4.0, 2.0 );
// returns NaN

v = entropy( 0.0, NaN, 2.0 );
// returns NaN

v = entropy( 0.0, 4.0, NaN );
// returns NaN```

If provided parameters not satisfying `a <= c <= b`, the function returns `NaN`.

```var y = entropy( 1.0, 0.0, 1.5 );
// returns NaN

y = entropy( 0.0, 1.0, -1.0 );
// returns NaN

y = entropy( 0.0, -1.0, 0.5 );
// returns NaN```

## Examples

```var randu = require( '@stdlib/random-base-randu' );
var entropy = require( '@stdlib/stats-base-dists-triangular-entropy' );

var a;
var b;
var c;
var v;
var i;

for ( i = 0; i < 10; i++ ) {
a = ( randu()*10.0 );
b = ( randu()*10.0 ) + a;
c = ( randu()*( b-a ) ) + a;
v = entropy( a, b, c );
console.log( 'a: %d, b: %d, c: %d, h(X;a,b,c): %d', a.toFixed( 4 ), b.toFixed( 4 ), c.toFixed( 4 ), v.toFixed( 4 ) );
}```

## Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

#### Community 