time-to-botec

Benchmark sampling in different programming languages
Log | Files | Refs | README

entropy.js (1615B)


      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 isnan = require( '@stdlib/math/base/assert/is-nan' );
     24 var digamma = require( '@stdlib/math/base/special/digamma' );
     25 var sqrt = require( '@stdlib/math/base/special/sqrt' );
     26 var beta = require( '@stdlib/math/base/special/beta' );
     27 var ln = require( '@stdlib/math/base/special/ln' );
     28 
     29 
     30 // MAIN //
     31 
     32 /**
     33 * Returns the differential entropy of a Student's t distribution.
     34 *
     35 * @param {PositiveNumber} v - degrees of freedom
     36 * @returns {number} entropy
     37 *
     38 * @example
     39 * var v = entropy( 9.0 );
     40 * // returns ~1.533
     41 *
     42 * @example
     43 * var v = entropy( 2.0 );
     44 * // returns ~1.96
     45 *
     46 * @example
     47 * var v = entropy( -0.2 );
     48 * // returns NaN
     49 *
     50 * @example
     51 * var v = entropy( NaN );
     52 * // returns NaN
     53 */
     54 function entropy( v ) {
     55 	var out;
     56 	var vh;
     57 	if ( isnan( v ) || v <= 0.0 ) {
     58 		return NaN;
     59 	}
     60 	vh = v / 2.0;
     61 	out = ( v + 1.0 ) / 2.0;
     62 	out *= digamma( ( 1.0+v ) / 2.0 ) - digamma( vh );
     63 	out += ln( sqrt( v ) * beta( vh, 0.5 ) );
     64 	return out;
     65 }
     66 
     67 
     68 // EXPORTS //
     69 
     70 module.exports = entropy;