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Add exponential logpdf
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‎lib/node_modules/@stdlib/math/base/dist/exponential/logpdf/README.md‎

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# Logarithm of Probability Density Function
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> Evaluate the natural logarithm of the probability density function (PDF) for an [exponential][exponential-distribution] distribution.
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<section class="intro">
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The [probability density function][pdf] (PDF) for an [exponential][exponential-distribution] random variable is
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<!-- <equation class="equation" label="eq:exponential_pdf" align="center" raw="f(x;\lambda) = \begin{cases} \lambda e^{-\lambda x} & x \ge 0 \\ 0 & x < 0 \end{cases}" alt="Probability density function (PDF) for a Exponential distribution."> -->
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<div class="equation" align="center" data-raw-text="f(x;\lambda) = \begin{cases} \lambda e^{-\lambda x} &amp; x \ge 0 \\ 0 &amp; x &lt; 0 \end{cases}" data-equation="eq:exponential_pdf">
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<img src="https://cdn.rawgit.com/stdlib-js/stdlib/bb29798906e119fcb2af99e94b60407a270c9b32/lib/node_modules/@stdlib/math/base/dist/exponential/pdf/docs/img/equation_exponential_pdf.svg" alt="Probability density function (PDF) for a Exponential distribution.">
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<br>
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</div>
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<!-- </equation> -->
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where `λ` is the rate parameter.
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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 logpdf = require( '@stdlib/math/base/dist/exponential/logpdf' );
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```
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#### logpdf( x, lambda )
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Evaluates the natural logarithm of the [probability density function][pdf] (PDF) for an [exponential][exponential-distribution] distribution with rate parameter `lambda`.
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```javascript
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var y = logpdf( 2.0, 0.3 );
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// returns ~-1.804
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y = logpdf( 2.0, 1.0 );
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// returns ~-2.0
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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 = logpdf( NaN, 0.0 );
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// returns NaN
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y = logpdf( 0.0, NaN );
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// returns NaN
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```
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If provided `lambda < 0`, the function returns `NaN`.
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```javascript
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var y = logpdf( 2.0, -1.0 );
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// returns NaN
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```
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#### logpdf.factory( lambda )
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Returns a function for evaluating the natural logarithm of the probability density function ([PDF][pdf]) for an exponential distribution with rate parameter `lambda`.
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```javascript
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var mylogpdf = logpdf.factory( 0.1 );
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var y = mylogpdf( 8.0 );
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// returns ~-3.103
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y = mylogpdf( 5.0 );
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// returns ~-2.803
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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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```javascript
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var randu = require( '@stdlib/math/base/random/randu' );
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var logpdf = require( '@stdlib/math/base/dist/exponential/logpdf' );
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var lambda;
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var x;
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var y;
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var i;
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for ( i = 0; i < 10; i++ ) {
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x = randu() * 10.0;
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lambda = randu() * 10.0;
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y = logpdf( x, lambda );
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console.log( 'x: %d, λ: %d, ln(f(x;λ)): %d', x, lambda, y );
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}
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```
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</section>
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<!-- /.examples -->
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<section class="links">
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[pdf]: https://en.wikipedia.org/wiki/Probability_density_function
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[exponential-distribution]: https://en.wikipedia.org/wiki/Exponential_distribution
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/math/base/dist/exponential/logpdf/benchmark/benchmark.js‎

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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 randu = require( '@stdlib/math/base/random/randu' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var EPS = require( '@stdlib/math/constants/float64-eps' );
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var pkg = require( './../package.json' ).name;
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var logpdf = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var lambda;
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var x;
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var y;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*100.0 );
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lambda = ( randu()*100.0 ) + EPS;
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y = logpdf( x, lambda );
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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 lambda;
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var mylogpdf;
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var x;
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var y;
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var i;
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lambda = 10.0;
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mylogpdf = logpdf.factory( lambda );
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*100.0 ) + EPS;
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y = mylogpdf( x );
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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/math/base/dist/exponential/logpdf/docs/img/equation_exponential_pdf.svg‎

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‎lib/node_modules/@stdlib/math/base/dist/exponential/logpdf/docs/repl.txt‎

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{{alias}}( x, λ )
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Evaluates the natural logarithm of the probability density function (PDF)
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for an exponential distribution with rate parameter `λ` at a value `x`.
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If provided `NaN` as any argument, the function returns `NaN`.
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If provided a negative value for `λ`, the function returns `NaN`.
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Parameters
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----------
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x: number
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Input value.
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λ: number
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Rate parameter.
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Returns
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-------
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out: number
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Evaluated logPDF.
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Examples
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--------
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> var y = {{alias}}( 0.3, 4.0 )
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~0.186
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> y = {{alias}}( 2.0, 0.7 )
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~1.757
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> y = {{alias}}( -1.0, 0.5 )
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-Infinity
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> y = {{alias}}( 0, NaN )
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NaN
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> y = {{alias}}( NaN, 2.0 )
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NaN
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// Negative rate:
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> y = {{alias}}( 2.0, -1.0 )
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NaN
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{{alias}}.factory( λ )
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Returns a function for evaluating the natural logarithm of the probability
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density function (PDF) for an exponential distribution with rate parameter
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`λ`.
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Parameters
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----------
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λ: number
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Rate parameter.
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Returns
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-------
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logpdf: Function
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Logarithm of probability density function (PDF).
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Examples
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--------
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> var mylogpdf = {{alias}}.factory( 0.5 );
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> var y = mylogpdf( 3.0 )
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~-2.193
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See Also
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--------
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‎lib/node_modules/@stdlib/math/base/dist/exponential/logpdf/examples/index.js‎

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'use strict';
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var randu = require( '@stdlib/math/base/random/randu' );
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var logpdf = require( './../lib' );
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var lambda;
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var x;
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var y;
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var i;
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for ( i = 0; i < 10; i++ ) {
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x = randu() * 10.0;
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lambda = randu() * 10.0;
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y = logpdf( x, lambda );
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console.log( 'x: %d, λ: %d, ln(f(x;λ)): %d', x, lambda, y );
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}

‎lib/node_modules/@stdlib/math/base/dist/exponential/logpdf/lib/factory.js‎

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'use strict';
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// MODULES //
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var constantFunction = require( '@stdlib/utils/constant-function' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var ln = require( '@stdlib/math/base/special/ln' );
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var NINF = require( '@stdlib/math/constants/float64-ninf' );
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var PINF = require( '@stdlib/math/constants/float64-pinf' );
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// MAIN //
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/**
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* Returns a function for evaluating the natural logarithm of the probability density function (PDF) for an exponential distribution with parameter `lambda`.
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*
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* @param {PositiveNumber} lambda - rate parameter
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* @returns {Function} logarithm of probability density function (logPDF)
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*
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* @example
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* var logpdf = factory( 0.5 );
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* var y = logpdf( 3.0 );
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* // returns ~-2.913
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*
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* y = logpdf( 1.0 );
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* // returns ~-1.193
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*/
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function factory( lambda ) {
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if ( isnan( lambda ) || lambda < 0.0 || lambda === PINF ) {
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return constantFunction( NaN );
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}
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return logpdf;
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/**
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* Evaluates the natural logarithm of the probability density function (PDF) for an exponential distribution.
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*
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* @private
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* @param {number} x - input value
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* @returns {number} evaluated logPDF
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*
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* @example
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* var y = logpdf( 2.3 );
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* // returns <number>
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*/
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function logpdf( x ) {
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if ( isnan( x ) ) {
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return NaN;
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}
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if ( x < 0.0 ) {
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return NINF;
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}
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return -( x*lambda ) + ln( lambda );
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} // end FUNCTION logpdf()
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} // end FUNCTION factory()
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// EXPORTS //
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module.exports = factory;

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