time-to-botec

Benchmark sampling in different programming languages
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logpmf.js (2715B)

      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 isNonNegativeInteger = require( '@stdlib/math/base/assert/is-nonnegative-integer' );
     24 var binomcoefln = require( '@stdlib/math/base/special/binomcoefln' );
     25 var isnan = require( '@stdlib/math/base/assert/is-nan' );
     26 var log1p = require( '@stdlib/math/base/special/log1p' );
     27 var ln = require( '@stdlib/math/base/special/ln' );
     28 var NINF = require( '@stdlib/constants/float64/ninf' );
     29 var PINF = require( '@stdlib/constants/float64/pinf' );
     30 
     31 
     32 // MAIN //
     33 
     34 /**
     35 * Evaluates the natural logarithm of the probability mass function (PMF) for a binomial distribution with number of trials `n` and success probability `p` at a value `x`.
     36 *
     37 * @param {number} x - input value
     38 * @param {NonNegativeInteger} n - number of trials
     39 * @param {Probability} p - success probability
     40 * @returns {number} evaluated logPMF
     41 *
     42 * @example
     43 * var y = logpmf( 3.0, 20, 0.2 );
     44 * // returns ~-1.583
     45 *
     46 * @example
     47 * var y = logpmf( 21.0, 20, 0.2 );
     48 * // returns -Infinity
     49 *
     50 * @example
     51 * var y = logpmf( 5.0, 10, 0.4 );
     52 * // returns ~-1.606
     53 *
     54 * @example
     55 * var y = logpmf( 0.0, 10, 0.4 );
     56 * // returns ~-5.108
     57 *
     58 * @example
     59 * var y = logpmf( NaN, 20, 0.5 );
     60 * // returns NaN
     61 *
     62 * @example
     63 * var y = logpmf( 0.0, NaN, 0.5 );
     64 * // returns NaN
     65 *
     66 * @example
     67 * var y = logpmf( 0.0, 20, NaN );
     68 * // returns NaN
     69 *
     70 * @example
     71 * var y = logpmf( 2.0, 1.5, 0.5 );
     72 * // returns NaN
     73 *
     74 * @example
     75 * var y = logpmf( 2.0, -2.0, 0.5 );
     76 * // returns NaN
     77 *
     78 * @example
     79 * var y = logpmf( 2.0, 20, -1.0 );
     80 * // returns NaN
     81 *
     82 * @example
     83 * var y = logpmf( 2.0, 20, 1.5 );
     84 * // returns NaN
     85 */
     86 function logpmf( x, n, p ) {
     87 	var out;
     88 	if (
     89 		isnan( x ) ||
     90 		isnan( n ) ||
     91 		isnan( p ) ||
     92 		p < 0.0 ||
     93 		p > 1.0 ||
     94 		!isNonNegativeInteger( n ) ||
     95 		n === PINF
     96 	) {
     97 		return NaN;
     98 	}
     99 	if ( isNonNegativeInteger( x ) ) {
    100 		if ( x > n ) {
    101 			return NINF;
    102 		}
    103 		if ( p === 0.0 ) {
    104 			return ( x === 0 ) ? 0.0 : NINF;
    105 		}
    106 		if ( p === 1.0 ) {
    107 			return ( x === n ) ? 0.0 : NINF;
    108 		}
    109 		out = binomcoefln( n, x );
    110 		out += (x * ln( p )) + (( n - x ) * log1p( -p ));
    111 		return out;
    112 	}
    113 	return NINF;
    114 }
    115 
    116 
    117 // EXPORTS //
    118 
    119 module.exports = logpmf;