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245 changes: 245 additions & 0 deletions lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/README.md
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<!--
@license Apache-2.0
Copyright (c) 2026 The Stdlib Authors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Entropy

> [Anglit][anglit-distribution] distribution [differential entropy][entropy].
<section class="intro">

The [differential entropy][entropy] for an [anglit][anglit-distribution] random variable is

<!-- <equation class="equation" label="eq:anglit_entropy" align="center" raw="h\left( X \right) = 1 - \ln(2) + \ln(\sigma)" alt="Differential entropy for an anglit distribution."> -->

```math
h\left( X \right) = 1 - \ln(2) + \ln(\sigma)
```

<!-- </equation> -->

where `σ > 0` is the scale parameter.

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var entropy = require( '@stdlib/stats/base/dists/anglit/entropy' );
```

#### entropy( mu, sigma )

Returns the [differential entropy][entropy] of an [Anglit][anglit-distribution] distribution with location parameter mu and scale parameter sigma

```javascript
var v = entropy( 0.0, 1.0 );
// returns ~0.307

v = entropy( 0.0, 2.0 );
// returns ~1.000

v = entropy( 1.0, 0.5 );
// returns ~-0.386
```

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

```javascript
var v = entropy( NaN, 1.0 );
// returns NaN

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

If provided `sigma <= 0`, the function returns `NaN`.

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

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

</section>

<!-- /.usage -->

<!-- Package usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- Package usage examples. -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var uniform = require( '@stdlib/random/array/uniform' );
var logEachMap = require( '@stdlib/console/log-each-map' );
var entropy = require( '@stdlib/stats/base/dists/anglit/entropy' );

var opts = {
'dtype': 'float64'
};

var mu = uniform( 25, -5.0, 5.0, opts );
var sigma = uniform( mu.length, 0.5, 5.0, opts );

logEachMap( 'mu: %0.4f, sigma: %0.4f, h(X;mu,sigma): %0.4f', mu, sigma, entropy );
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Do update the documented version with the changes in the corresponding JS file.

```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/stats/base/dists/anglit/entropy.h"
```

#### stdlib_base_dists_anglit_entropy( mu, sigma )

Returns the differential entropy of an Anglit distribution with location parameter `mu` and scale parameter `sigma`.

```c
double out = stdlib_base_dists_anglit_entropy( 0.0, 1.0 );
// returns ~0.307
```

The function accepts the following arguments:

- **mu**: `[in] double` location parameter.
- **sigma**: `[in] double` scale parameter.

```c
The function accepts the following arguments:

- **x**: `[in] double` input value.
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Suggested change
- **x**: `[in] double` input value.

Anglit Entropy utilizes only 2 parameters mu and sigma. I think describing x here causes discrepancies in the documentation.

- **mu**: `[in] double` location parameter.
- **sigma**: `[in] double` scale parameter.

```c
double stdlib_base_dists_anglit_entropy( const double mu, const double sigma );
```
</section>
<!-- /.usage -->
<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
<section class="notes">
</section>
<!-- /.notes -->
<!-- C API usage examples. -->
<section class="examples">
### Examples
```c
#include "stdlib/stats/base/dists/anglit/entropy.h"
#include <stdlib.h>
#include <stdio.h>
static double random_uniform( const double min, const double max ) {
double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
return min + ( v*(max-min) );
}
int main( void ) {
double mu;
double sigma;
double y;
int i;
for ( i = 0; i < 25; i++ ) {
mu = random_uniform( -5.0, 5.0 );
sigma = random_uniform( 0.5, 10.0 );
y = stdlib_base_dists_anglit_entropy( mu, sigma );
printf( "mu: %lf, sigma: %lf, h(X;mu,sigma): %lf\n", mu, sigma, y );
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Same Comment but w.r.t the C examples.

}
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="links">

[entropy]: https://en.wikipedia.org/wiki/Entropy_%28information_theory%29

[anglit-distribution]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anglit.html

</section>

<!-- /.links -->
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/**
* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

'use strict';

// MODULES //

var bench = require( '@stdlib/bench' );
var uniform = require( '@stdlib/random/array/uniform' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var pkg = require( './../package.json' ).name;
var entropy = require( './../lib' );


// MAIN //

bench( pkg, function benchmark( b ) {
var sigma;
var opts;
var mu;
var y;
var i;

opts = {
'dtype': 'float64'
};
mu = uniform( 100, -5.0, 5.0, opts );
sigma = uniform( 100, 0.1, 10.0, opts );

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
y = entropy( mu[ i % mu.length ], sigma[ i % sigma.length ] );
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
}
b.toc();

if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
b.pass( 'benchmark finished' );
b.end();
});
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/**
* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

'use strict';

// MODULES //

var resolve = require( 'path' ).resolve;
var bench = require( '@stdlib/bench' );
var tryRequire = require( '@stdlib/utils/try-require' );
var uniform = require( '@stdlib/random/array/uniform' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var format = require( '@stdlib/string/format' );
var pkg = require( './../package.json' ).name;


// VARIABLES //

var entropy = tryRequire( resolve( __dirname, './../lib/native.js' ) );
var opts = {
'skip': ( entropy instanceof Error )
};


// MAIN //

bench( format( '%s::native', pkg ), opts, function benchmark( b ) {
var sigma;
var mu;
var y;
var i;

mu = uniform( 100, -5.0, 5.0, {
'dtype': 'float64'
});
sigma = uniform( 100, 0.1, 10.0, {
'dtype': 'float64'
});
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Suggested change
});
arrayOpts = {
'dtype': 'float64'
});
mu = uniform( 100, -5.0, 5.0, arrayOpts );
sigma = uniform( 100, 0.1, 10.0, arrayOpts );

you can represent this a bit better this way.


b.tic();
for ( i = 0; i < b.iterations; i++ ) {
y = entropy( mu[ i % mu.length ], sigma[ i % sigma.length ] );
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
}
b.toc();

if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
b.pass( 'benchmark finished' );
b.end();
});
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