time-to-botec

Benchmark sampling in different programming languages
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factory.js (1982B)


      1 /**
      2 * @license Apache-2.0
      3 *
      4 * Copyright (c) 2018 The Stdlib Authors.
      5 *
      6 * Licensed under the Apache License, Version 2.0 (the "License");
      7 * you may not use this file except in compliance with the License.
      8 * You may obtain a copy of the License at
      9 *
     10 *    http://www.apache.org/licenses/LICENSE-2.0
     11 *
     12 * Unless required by applicable law or agreed to in writing, software
     13 * distributed under the License is distributed on an "AS IS" BASIS,
     14 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     15 * See the License for the specific language governing permissions and
     16 * limitations under the License.
     17 */
     18 
     19 'use strict';
     20 
     21 // MODULES //
     22 
     23 var constantFunction = require( '@stdlib/utils/constant-function' );
     24 var isnan = require( '@stdlib/math/base/assert/is-nan' );
     25 var ln = require( '@stdlib/math/base/special/ln' );
     26 var NINF = require( '@stdlib/constants/float64/ninf' );
     27 var PINF = require( '@stdlib/constants/float64/pinf' );
     28 
     29 
     30 // MAIN //
     31 
     32 /**
     33 * Returns a function for evaluating the natural logarithm of the probability density function (PDF) for an exponential distribution with parameter `lambda`.
     34 *
     35 * @param {PositiveNumber} lambda - rate parameter
     36 * @returns {Function} logarithm of probability density function (logPDF)
     37 *
     38 * @example
     39 * var logpdf = factory( 0.5 );
     40 * var y = logpdf( 3.0 );
     41 * // returns ~-2.193
     42 *
     43 * y = logpdf( 1.0 );
     44 * // returns ~-1.193
     45 */
     46 function factory( lambda ) {
     47 	if ( isnan( lambda ) || lambda < 0.0 || lambda === PINF ) {
     48 		return constantFunction( NaN );
     49 	}
     50 	return logpdf;
     51 
     52 	/**
     53 	* Evaluates the natural logarithm of the probability density function (PDF) for an exponential distribution.
     54 	*
     55 	* @private
     56 	* @param {number} x - input value
     57 	* @returns {number} evaluated logPDF
     58 	*
     59 	* @example
     60 	* var y = logpdf( 2.3 );
     61 	* // returns <number>
     62 	*/
     63 	function logpdf( x ) {
     64 		if ( isnan( x ) ) {
     65 			return NaN;
     66 		}
     67 		if ( x < 0.0 ) {
     68 			return NINF;
     69 		}
     70 		return -( x*lambda ) + ln( lambda );
     71 	}
     72 }
     73 
     74 
     75 // EXPORTS //
     76 
     77 module.exports = factory;