time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (3931B)

      1 /**
      2 * @license Apache-2.0
      3 *
      4 * Copyright (c) 2020 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 float64ToFloat32 = require( '@stdlib/number/float64/base/to-float32' );
     24 
     25 
     26 // MAIN //
     27 
     28 /**
     29 * Computes the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.
     30 *
     31 * ## Method
     32 *
     33 * -   This implementation uses a one-pass trial mean approach, as suggested by Chan et al (1983).
     34 *
     35 * ## References
     36 *
     37 * -   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958](https://doi.org/10.1145/365719.365958).
     38 * -   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154](https://doi.org/10.2307/2286154).
     39 * -   Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. 1983. "Algorithms for Computing the Sample Variance: Analysis and Recommendations." _The American Statistician_ 37 (3). American Statistical Association, Taylor & Francis, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115](https://doi.org/10.1080/00031305.1983.10483115).
     40 * -   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036).
     41 *
     42 * @param {PositiveInteger} N - number of indexed elements
     43 * @param {number} correction - degrees of freedom adjustment
     44 * @param {Float32Array} x - input array
     45 * @param {integer} stride - stride length
     46 * @param {NonNegativeInteger} offset - starting index
     47 * @returns {number} variance
     48 *
     49 * @example
     50 * var Float32Array = require( '@stdlib/array/float32' );
     51 * var floor = require( '@stdlib/math/base/special/floor' );
     52 *
     53 * var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );
     54 * var N = floor( x.length / 2 );
     55 *
     56 * var v = snanvariancech( N, 1, x, 2, 1 );
     57 * // returns 6.25
     58 */
     59 function snanvariancech( N, correction, x, stride, offset ) {
     60 	var mu;
     61 	var ix;
     62 	var M2;
     63 	var nc;
     64 	var M;
     65 	var d;
     66 	var v;
     67 	var n;
     68 	var i;
     69 
     70 	if ( N <= 0 ) {
     71 		return NaN;
     72 	}
     73 	if ( N === 1 || stride === 0 ) {
     74 		v = x[ offset ];
     75 		if ( v === v && N-correction > 0.0 ) {
     76 			return 0.0;
     77 		}
     78 		return NaN;
     79 	}
     80 	ix = offset;
     81 
     82 	// Find an estimate for the mean...
     83 	for ( i = 0; i < N; i++ ) {
     84 		v = x[ ix ];
     85 		if ( v === v ) {
     86 			mu = v;
     87 			break;
     88 		}
     89 		ix += stride;
     90 	}
     91 	if ( i === N ) {
     92 		return NaN;
     93 	}
     94 	ix += stride;
     95 	i += 1;
     96 
     97 	// Compute the variance...
     98 	M2 = 0.0;
     99 	M = 0.0;
    100 	n = 1;
    101 	for ( i; i < N; i++ ) {
    102 		v = x[ ix ];
    103 		if ( v === v ) {
    104 			d = float64ToFloat32( v - mu );
    105 			M2 = float64ToFloat32( M2 + float64ToFloat32( d*d ) );
    106 			M = float64ToFloat32( M + d );
    107 			n += 1;
    108 		}
    109 		ix += stride;
    110 	}
    111 	nc = n - correction;
    112 	if ( nc <= 0.0 ) {
    113 		return NaN;
    114 	}
    115 	return float64ToFloat32( float64ToFloat32(M2/nc) - float64ToFloat32(float64ToFloat32(M/n)*float64ToFloat32(M/nc)) ); // eslint-disable-line max-len
    116 }
    117 
    118 
    119 // EXPORTS //
    120 
    121 module.exports = snanvariancech;