time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (2491B)

      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 // MAIN //
     22 
     23 /**
     24 * Computes the variance of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer.
     25 *
     26 * ## Method
     27 *
     28 * -   This implementation uses a one-pass algorithm, as proposed by Youngs and Cramer (1971).
     29 *
     30 * ## References
     31 *
     32 * -   Youngs, Edward A., and Elliot M. Cramer. 1971. "Some Results Relevant to Choice of Sum and Sum-of-Product Algorithms." _Technometrics_ 13 (3): 657–65. doi:[10.1080/00401706.1971.10488826](https://doi.org/10.1080/00401706.1971.10488826).
     33 *
     34 * @param {PositiveInteger} N - number of indexed elements
     35 * @param {number} correction - degrees of freedom adjustment
     36 * @param {NumericArray} x - input array
     37 * @param {integer} stride - stride length
     38 * @param {NonNegativeInteger} offset - starting index
     39 * @returns {number} variance
     40 *
     41 * @example
     42 * var floor = require( '@stdlib/math/base/special/floor' );
     43 *
     44 * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
     45 * var N = floor( x.length / 2 );
     46 *
     47 * var v = nanvarianceyc( N, 1, x, 2, 1 );
     48 * // returns 6.25
     49 */
     50 function nanvarianceyc( N, correction, x, stride, offset ) {
     51 	var sum;
     52 	var ix;
     53 	var nc;
     54 	var S;
     55 	var v;
     56 	var d;
     57 	var n;
     58 	var i;
     59 
     60 	if ( N <= 0 ) {
     61 		return NaN;
     62 	}
     63 	if ( N === 1 || stride === 0 ) {
     64 		v = x[ offset ];
     65 		if ( v === v && N-correction > 0.0 ) {
     66 			return 0.0;
     67 		}
     68 		return NaN;
     69 	}
     70 	ix = offset;
     71 
     72 	// Find the first non-NaN element...
     73 	for ( i = 0; i < N; i++ ) {
     74 		v = x[ ix ];
     75 		if ( v === v ) {
     76 			break;
     77 		}
     78 		ix += stride;
     79 	}
     80 	if ( i === N ) {
     81 		return NaN;
     82 	}
     83 	ix += stride;
     84 	sum = v;
     85 	S = 0.0;
     86 	i += 1;
     87 	n = 1;
     88 	for ( i; i < N; i++ ) {
     89 		v = x[ ix ];
     90 		if ( v === v ) {
     91 			n += 1;
     92 			sum += v;
     93 			d = (n*v) - sum;
     94 			S += (1.0/(n*(n-1))) * d * d;
     95 		}
     96 		ix += stride;
     97 	}
     98 	nc = n - correction;
     99 	if ( nc <= 0.0 ) {
    100 		return NaN;
    101 	}
    102 	return S / nc;
    103 }
    104 
    105 
    106 // EXPORTS //
    107 
    108 module.exports = nanvarianceyc;