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feat : add C implementation for stats/base/dists/truncated-normal/pdf
--- type: pre_push_report description: Results of running various checks prior to pushing changes. report: - task: run_javascript_examples status: na - task: run_c_examples status: na - task: run_cpp_examples status: na - task: run_javascript_readme_examples status: na - task: run_c_benchmarks status: na - task: run_cpp_benchmarks status: na - task: run_fortran_benchmarks status: na - task: run_javascript_benchmarks status: na - task: run_julia_benchmarks status: na - task: run_python_benchmarks status: na - task: run_r_benchmarks status: na - task: run_javascript_tests status: na ---
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‎lib/node_modules/@stdlib/stats/base/dists/truncated-normal/pdf/README.md‎

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@@ -40,7 +40,7 @@ f(x;\mu,\sigma,a,b) = \begin{cases} \frac{\frac{1}{\sigma}\phi(\frac{x - \mu}{\
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<!-- </equation> -->
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where `Phi` and `phi` denote the [cumulative distribution function][cdf] and [density function][pdf] of the [normal][normal-distribution] distribution, respectively, `mu` is the location and `sigma > 0` is the scale parameter of the distribution. `a` and `b` are the minimum and maximum support.
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where `Phi` and `phi` denote the [cumulative distribution function][pdf] and [density function][pdf] of the [normal][normal-distribution] distribution, respectively, `mu` is the location and `sigma > 0` is the scale parameter of the distribution. `a` and `b` are the minimum and maximum support.
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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/truncated-normal/pdf.h"
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```
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#### stdlib_base_dists_truncated_normal_pdf( x, a, b , mu , sigma )
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Evaluates the r distribution function (pdf) for an truncated-normal distribution.
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```c
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double out = stdlib_base_dists_truncated_normal_pdf( 0.9, 0.0, 1.0, 0.0, 1.0 );
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// returns ~0.7795
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```
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The function accepts the following arguments:
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- **x**: `[in] double` input value.
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- **a**: `[in] double` lower limit.
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- **b**: `[in] double` upper limit.
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- **mu**: `[in] double` location parameter.
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- **sigma**: `[in] double` scale parameter.
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```c
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double stdlib_base_dists_truncated-normal_pdf( const double x, const double a, const double b , const double mu , 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/truncated-normal/pdf.h"
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.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 x;
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double a;
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double b;
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double mu;
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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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x = random_uniform( -10.0, 10.0 );
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a = random_uniform( -20.0, 0.0 );
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b = random_uniform( a, a + 40.0 );
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mu = random_uniform( a, b );
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sigma = random_uniform( 0.1, 5.0 );
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y = stdlib_base_dists_truncated_normal_pdf( x, a, b, mu, sigma );
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printf( "x: %lf, a: %lf, b: %lf, mu: %lf, sigma: %lf, f(x;a,b,mu,sigma): %lf\n", x, a, b, mu, 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 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 class="links">
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[cdf]: https://en.wikipedia.org/wiki/Cumulative_distribution_function
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[pdf]: https://en.wikipedia.org/wiki/Cumulative_distribution_function
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[pdf]: https://en.wikipedia.org/wiki/Probability_density_function
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‎lib/node_modules/@stdlib/stats/base/dists/truncated-normal/pdf/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) 2025 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 uniform = require( '@stdlib/random/base/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pkg = require( './../package.json' ).name;
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var pdf = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var len;
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var x;
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var a;
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var bmin;
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var bmax;
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var mu;
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var sigma;
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var y;
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var i;
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len = 100;
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x = new Float64Array( len );
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a = new Float64Array( len );
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bmin = new Float64Array( len );
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bmax = new Float64Array( len );
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mu = new Float64Array( len );
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sigma = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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x[ i ] = uniform( -10.0, 10.0 );
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a[ i ] = uniform( -20.0, 0.0 );
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bmin[ i ] = uniform( a[ i ], a[ i ] + 40.0 );
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bmax[ i ] = uniform( 0.1, 5.0 );
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mu[ i ] = uniform( -5.0, 5.0 );
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sigma[ i ] = uniform( 0.1, 5.0 );
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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 = pdf( x[ i % len ], a[ i % len ], bmin[ i % len ], mu[ i % len ], 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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bench( pkg+':factory', function benchmark( b ) {
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var mypdf;
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var sigma;
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var bmin;
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var len;
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var mu;
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var a;
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var x;
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var y;
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var i;
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sigma = 1.0;
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bmin = 1.0;
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len = 100;
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mu = 0.5;
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a = 0.0;
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x = new Float64Array( len );
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mypdf = pdf.factory( a, bmin, mu, sigma );
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for ( i = 0; i < len; i++ ) {
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x[ i ] = uniform( -2.0, 2.0 );
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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 = mypdf( x[ 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/truncated-normal/pdf/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) 2025 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 uniform = require( '@stdlib/random/base/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var pkg = require( './../package.json' ).name;
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// VARIABLES //
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var pdf = tryRequire( resolve( __dirname, './../lib/native.js' ) );
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var opts = {
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'skip': ( pdf instanceof Error )
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};
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// MAIN //
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bench( pkg+'::native', opts, function benchmark( b ) {
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var sigma;
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var bmin;
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var len;
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var mu;
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var x;
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var a;
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var y;
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var i;
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len = 100;
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x = new Float64Array( len );
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a = new Float64Array( len );
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bmin = new Float64Array( len );
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mu = new Float64Array( len );
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sigma = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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x[ i ] = uniform( -10.0, 10.0 );
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a[ i ] = uniform( -20.0, 0.0 );
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bmin[ i ] = uniform( a[ i ], a[ i ] + 40.0 );
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mu[ i ] = uniform( -5.0, 5.0 );
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sigma[ i ] = uniform( 0.1, 5.0 );
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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 = pdf( x[ i % len ], a[ i % len ], bmin[ i % len ], mu[ i % len ], 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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