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Fix comments in hypergeometric logpmf
Fixes stdlib-js#5747 Address commit comments in `11e8a56`. * Remove extra space between `logpmf` and `(` in `lib/node_modules/@stdlib/stats/base/dists/hypergeometric/logpmf/README.md`. * Add Oxford comma after `K` and before `and` in `lib/node_modules/@stdlib/stats/base/dists/hypergeometric/logpmf/src/main.c`. * Change test description to "returns expected value" in `lib/node_modules/@stdlib/stats/base/dists/hypergeometric/logpmf/test/test.native.js`. --- For more details, open the [Copilot Workspace session](https://copilot-workspace.githubnext.com/stdlib-js/stdlib/issues/5747?shareId=XXXX-XXXX-XXXX-XXXX).
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<!--
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@license Apache-2.0
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Copyright (c) 2018 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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# Logarithm of Probability Mass Function
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> Evaluate the natural logarithm of the [probability mass function][pmf] (PMF) for a [hypergeometric][hypergeometric-distribution] distribution.
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<section class="intro">
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Imagine a scenario with a population of size `N`, of which a subpopulation of size `K` can be considered successes. We draw `n` observations from the total population. Defining the random variable `X` as the number of successes in the `n` draws, `X` is said to follow a [hypergeometric distribution][hypergeometric-distribution]. The [probability mass function][pmf] (PMF) for a [hypergeometric][hypergeometric-distribution] random variable is given by
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<!-- <equation class="equation" label="eq:hypergeometric_pmf" align="center" raw="f(x;N,K,n)=P(X=x;N,K,n)=\begin{cases} {{{K \choose x} {N-K \choose {n-x}}}\over {{N} \choose n}} & \text{ for } x = 0,1,2,\ldots \\ 0 & \text{ otherwise} \end{cases}" alt="Probability mass function (PMF) for a hypergeometric distribution."> -->
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```math
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f(x;N,K,n)=P(X=x;N,K,n)=\begin{cases} {{{K \choose x} {N-K \choose {n-x}}}\over {{N} \choose n}} & \text{ for } x = 0,1,2,\ldots \\ 0 & \text{ otherwise} \end{cases}
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```
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<!-- <div class="equation" align="center" data-raw-text="f(x;N,K,n)=P(X=x;N,K,n)=\begin{cases} {{{K \choose x} {N-K \choose {n-x}}}\over {{N} \choose n}} &amp; \text{ for } x = 0,1,2,\ldots \\ 0 &amp; \text{ otherwise} \end{cases}" data-equation="eq:hypergeometric_pmf">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/logpmf/docs/img/equation_hypergeometric_pmf.svg" alt="Probability mass function (PMF) for a hypergeometric distribution.">
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<br>
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</div> -->
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<!-- </equation> -->
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var logpmf = require( '@stdlib/stats/base/dists/hypergeometric/logpmf' );
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```
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#### logpmf( x, N, K, n )
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Evaluates the natural logarithm of the [probability mass function][pmf] (PMF) for a [hypergeometric][hypergeometric-distribution] distribution with parameters `N` (population size), `K` (subpopulation size), and `n` (number of draws).
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```javascript
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var y = logpmf( 1.0, 8, 4, 2 );
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// returns ~-0.56
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y = logpmf( 2.0, 8, 4, 2 );
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// returns ~-1.54
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y = logpmf( 0.0, 8, 4, 2 );
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// returns ~-1.54
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y = logpmf( 1.5, 8, 4, 2 );
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// returns -Infinity
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```
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If provided `NaN` as any argument, the function returns `NaN`.
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```javascript
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var y = logpmf( NaN, 10, 5, 2 );
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// returns NaN
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y = logpmf( 0.0, NaN, 5, 2 );
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// returns NaN
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y = logpmf( 0.0, 10, NaN, 2 );
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// returns NaN
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y = logpmf( 0.0, 10, 5, NaN );
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// returns NaN
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```
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If provided a population size `N`, subpopulation size `K`, or draws `n` which is not a nonnegative integer, the function returns `NaN`.
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```javascript
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var y = logpmf( 2.0, 10.5, 5, 2 );
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// returns NaN
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y = logpmf( 2.0, 10, 1.5, 2 );
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// returns NaN
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y = logpmf( 2.0, 10, 5, -2.0 );
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// returns NaN
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```
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If the number of draws `n` or the subpopulation size `K` exceed population size `N`, the function returns `NaN`.
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```javascript
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var y = logpmf( 2.0, 10, 5, 12 );
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// returns NaN
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y = logpmf( 2.0, 8, 3, 9 );
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// returns NaN
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```
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#### logpmf.factory( N, K, n )
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Returns a function for evaluating the natural logarithm of the [probability mass function][pmf] (PMF) of a [hypergeometric ][hypergeometric-distribution] distribution with parameters `N` (population size), `K` (subpopulation size), and `n` (number of draws).
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```javascript
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var mylogpmf = logpmf.factory( 30, 20, 5 );
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var y = mylogpmf( 4.0 );
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// returns ~-1.079
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y = mylogpmf( 1.0 );
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// returns ~-3.524
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```
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</section>
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<!-- /.usage -->
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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 randu = require( '@stdlib/random/base/randu' );
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var round = require( '@stdlib/math/base/special/round' );
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var logpmf = require( '@stdlib/stats/base/dists/hypergeometric/logpmf' );
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var i;
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var N;
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var K;
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var n;
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var x;
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var y;
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for ( i = 0; i < 10; i++ ) {
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x = round( randu() * 5.0 );
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N = round( randu() * 20.0 );
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K = round( randu() * N );
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n = round( randu() * N );
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y = logpmf( x, N, K, n );
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console.log( 'x: %d, N: %d, K: %d, n: %d, ln(P(X=x;N,K,n)): %d', x, N, K, n, y.toFixed( 4 ) );
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}
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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/hypergeometric/logpmf.h"
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```
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#### stdlib_base_dists_hypergeometric_logpmf( x, N, K, n )
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Evaluates the natural logarithm of the [probability mass function][pmf] (PMF) for a [hypergeometric][hypergeometric-distribution] distribution with parameters `N` (population size), `K` (subpopulation size), and `n` (number of draws).
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```c
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double out = stdlib_base_dists_hypergeometric_logpmf( 1.0, 8, 4, 2 );
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// returns ~-0.56
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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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- **n**: `[in] int32_t` number of draws.
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```c
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double stdlib_base_dists_hypergeometric_logpmf ( const double x, const int32_t N, const int32_t K, const int32_t n );
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double stdlib_base_dists_hypergeometric_logpmf( const double x, const int32_t N, const int32_t K, const int32_t n );
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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/hypergeometric/logpmf.h"
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#include "stdlib/math/base/special/round.h"
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#include <stdlib.h>
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#include <stdint.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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int32_t N;
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int32_t K;
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int32_t n;
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double y;
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double x;
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int i;
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for ( i = 0; i < 10; i++ ) {
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x = stdlib_base_round( random_uniform( 0.0, 5.0 ) );
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N = stdlib_base_round( random_uniform( 0.0, 20.0 ) );
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K = stdlib_base_round( random_uniform( 0.0, N ) );
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n = stdlib_base_round( random_uniform( 0.0, N ) );
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y = stdlib_base_dists_hypergeometric_logpmf( x, N, K, n );
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printf( "x: %lf, N: %d, K: %d, n: %d, ln(P(X=x;N,K,n)): %lf\n", x, N, K, n, 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>
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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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[hypergeometric-distribution]: https://en.wikipedia.org/wiki/Hypergeometric_distribution
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[pmf]: https://en.wikipedia.org/wiki/Probability_mass_function
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</section>
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<!-- /.links -->
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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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#include "stdlib/stats/base/dists/hypergeometric/logpmf.h"
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#include "stdlib/math/base/assert/is_nonnegative_integer.h"
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#include "stdlib/math/base/assert/is_nan.h"
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#include "stdlib/math/base/special/factorialln.h"
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#include "stdlib/math/base/special/max.h"
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#include "stdlib/math/base/special/min.h"
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#include "stdlib/constants/float64/ninf.h"
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#include <stdint.h>
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/**
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* Evaluates the natural logarithm of the probability mass function (PMF) for a hypergeometric distribution with population size `N`, subpopulation size `K` and number of draws `n`.
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* Evaluates the natural logarithm of the probability mass function (PMF) for a hypergeometric distribution with population size `N`, subpopulation size `K`, and number of draws `n`.
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*
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* @param x input value
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* @param N population size
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* @return evaluated logPMF
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*
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* @example
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* double y = stdlib_base_dists_hypergeometric_logpmf( 1.0, 8, 4, 2 );
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* // returns ~-0.56
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* double y = stdlib_base_dists_hypergeometric_logpmf( 1.0, 10, 5, 3 );
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* // returns <number>
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*/
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double stdlib_base_dists_hypergeometric_logpmf( const double x, const int32_t N, const int32_t K, const int32_t n ) {
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double ldenom;
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double lnum;
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double maxs;
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double mins;
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if ( stdlib_base_is_nan( x ) || N < 0 || K < 0 || n < 0 || K > N || n > N ) {
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return 0.0/0.0; // NaN
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}
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mins = stdlib_base_max( 0, n+K-N );
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maxs = stdlib_base_min( K, n );
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if ( stdlib_base_is_nonnegative_integer( x ) && mins <= x && x <= maxs ) {
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lnum = stdlib_base_factorialln( n ) + stdlib_base_factorialln( K ) + stdlib_base_factorialln( N-n ) + stdlib_base_factorialln( N-K );
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ldenom = stdlib_base_factorialln( N ) + stdlib_base_factorialln( x ) + stdlib_base_factorialln( n-x );
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ldenom += stdlib_base_factorialln( K-x ) + stdlib_base_factorialln( N-K+x-n );
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return lnum - ldenom;
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}
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return STDLIB_CONSTANT_FLOAT64_NINF;
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if ( stdlib_base_is_nan( x ) || stdlib_base_is_nan( N ) || stdlib_base_is_nan( K ) || stdlib_base_is_nan( n ) ) {
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return STDLIB_CONSTANT_FLOAT64_NINF;
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}
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if ( N <= 0 || K < 0 || K > N || n < 0 || n > N ) {
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return STDLIB_CONSTANT_FLOAT64_NINF;
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}
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if ( x < stdlib_base_max( 0, n - (N-K) ) || x > stdlib_base_min( n, K ) ) {
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return STDLIB_CONSTANT_FLOAT64_NINF;
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}
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return stdlib_base_factorialln( K ) - stdlib_base_factorialln( x ) - stdlib_base_factorialln( K-x ) + stdlib_base_factorialln( N-K ) - stdlib_base_factorialln( n-x ) - stdlib_base_factorialln( (N-K)-(n-x) ) - stdlib_base_factorialln( N ) + stdlib_base_factorialln( n ) + stdlib_base_factorialln( N-n );
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}

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