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feat: add stats/base/dists/halfnormal/entropy
PR-URL: #9753 Ref: #9416 Co-authored-by: Philipp Burckhardt <pburckhardt@outlook.com> Reviewed-by: Philipp Burckhardt <pburckhardt@outlook.com> Signed-off-by: Philipp Burckhardt <pburckhardt@outlook.com>
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‎lib/node_modules/@stdlib/stats/base/dists/halfnormal/entropy/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2026 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# Entropy
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> [Half-normal][half-normal-distribution] distribution [differential entropy][entropy].
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<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
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<section class="intro">
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The [differential entropy][entropy] (in [nats][nats]) for a [half-normal][half-normal-distribution] random variable is
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<!-- <equation class="equation" label="eq:halfnormal_entropy" align="center" raw="h\left( X \right) = \frac{1}{2}+\ln(\sigma)+\ln\left(\sqrt{\frac{\pi}{2}}\right)+\frac{\gamma}{2}" alt="Differential entropy for a half-normal distribution."> -->
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```math
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h\left( X \right) = \frac{1}{2}+\ln(\sigma)+\ln\left(\sqrt{\frac{\pi}{2}}\right)+\frac{\gamma}{2}
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```
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<!-- </equation> -->
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where `σ > 0` is the scale parameter.
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</section>
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<!-- /.intro -->
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<!-- Package usage documentation. -->
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<section class="usage">
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## Usage
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```javascript
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var entropy = require( '@stdlib/stats/base/dists/halfnormal/entropy' );
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```
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#### entropy( sigma )
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Returns the [differential entropy][entropy] of a [half-normal][half-normal-distribution] distribution with scale `sigma` (in [nats][nats]).
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```javascript
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var y = entropy( 1.0 );
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// returns ~1.014
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y = entropy( 5.0 );
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// returns ~2.624
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```
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If provided `sigma ≤ 0`, the function returns `NaN`.
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```javascript
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var y = entropy( -1.0 );
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// returns NaN
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```
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</section>
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<!-- /.usage -->
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<!-- Package usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- Package usage examples. -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var uniform = require( '@stdlib/random/array/uniform' );
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var logEachMap = require( '@stdlib/console/log-each-map' );
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var entropy = require( '@stdlib/stats/base/dists/halfnormal/entropy' );
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var opts = {
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'dtype': 'float64'
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};
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var sigma = uniform( 10, 0.1, 20.0, opts );
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logEachMap( 'σ: %0.4f, h(X;σ): %0.4f', sigma, entropy );
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```
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</section>
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<!-- /.examples -->
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<!-- C interface documentation. -->
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* * *
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<section class="c">
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## C APIs
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<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
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<section class="intro">
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</section>
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<!-- /.intro -->
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<!-- C usage documentation. -->
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<section class="usage">
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### Usage
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```c
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#include "stdlib/stats/base/dists/halfnormal/entropy.h"
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```
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#### stdlib_base_dists_halfnormal_entropy( sigma )
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Returns the differential entropy of a half-normal distribution.
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```c
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double out = stdlib_base_dists_halfnormal_entropy( 1.0 );
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// returns ~1.014
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```
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The function accepts the following arguments:
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- **sigma**: `[in] double` scale parameter.
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```c
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double stdlib_base_dists_halfnormal_entropy( const double sigma );
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```
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</section>
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<!-- /.usage -->
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<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- C API usage examples. -->
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<section class="examples">
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### Examples
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```c
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#include "stdlib/stats/base/dists/halfnormal/entropy.h"
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#include <stdlib.h>
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#include <stdio.h>
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static double random_uniform( const double min, const double max ) {
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double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
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return min + ( v*(max-min) );
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}
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int main( void ) {
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double sigma;
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double y;
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int i;
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for ( i = 0; i < 25; i++ ) {
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sigma = random_uniform( 0.1, 20.0 );
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y = stdlib_base_dists_halfnormal_entropy( sigma );
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printf( "σ: %lf, h(σ): %lf\n", sigma, y );
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}
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}
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.c -->
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<!-- Section to include cited references. If references are included, add a horizontal rule *before* the section. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="references">
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</section>
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<!-- /.references -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[half-normal-distribution]: https://en.wikipedia.org/wiki/Half-normal_distribution
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[entropy]: https://en.wikipedia.org/wiki/Entropy_%28information_theory%29
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[nats]: https://en.wikipedia.org/wiki/Nat_%28unit%29
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/stats/base/dists/halfnormal/entropy/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var Float64Array = require( '@stdlib/array/float64' );
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var randu = require( '@stdlib/random/base/randu' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var EPS = require( '@stdlib/constants/float64/eps' );
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var pkg = require( './../package.json' ).name;
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var entropy = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var sigma;
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var len;
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var y;
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var i;
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len = 100;
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sigma = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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sigma[ i ] = ( randu()*20.0 ) + EPS;
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}
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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y = entropy( sigma[ i % len ] );
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

‎lib/node_modules/@stdlib/stats/base/dists/halfnormal/entropy/benchmark/benchmark.native.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench' );
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var Float64Array = require( '@stdlib/array/float64' );
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var randu = require( '@stdlib/random/base/randu' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var EPS = require( '@stdlib/constants/float64/eps' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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// VARIABLES //
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var entropy = tryRequire( resolve( __dirname, './../lib/native.js' ) );
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var opts = {
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'skip': ( entropy instanceof Error )
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};
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// MAIN //
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bench( format( '%s::native', pkg ), opts, function benchmark( b ) {
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var sigma;
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var len;
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var y;
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var i;
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len = 100;
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sigma = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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sigma[ i ] = ( randu() * 20.0 ) + EPS;
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}
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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y = entropy( sigma[ i % len ] );
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

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