time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (1985B)

      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 double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.
     25 *
     26 * @param {PositiveInteger} N - number of indexed elements
     27 * @param {number} correction - degrees of freedom adjustment
     28 * @param {Float64Array} x - input array
     29 * @param {integer} stride - stride length
     30 * @param {NonNegativeInteger} offset - starting index
     31 * @returns {number} variance
     32 *
     33 * @example
     34 * var Float64Array = require( '@stdlib/array/float64' );
     35 * var floor = require( '@stdlib/math/base/special/floor' );
     36 *
     37 * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );
     38 * var N = floor( x.length / 2 );
     39 *
     40 * var v = dnanvariancetk( N, 1, x, 2, 1 );
     41 * // returns 6.25
     42 */
     43 function dnanvariancetk( N, correction, x, stride, offset ) {
     44 	var S2;
     45 	var ix;
     46 	var nc;
     47 	var S;
     48 	var v;
     49 	var n;
     50 	var i;
     51 
     52 	if ( N <= 0 ) {
     53 		return NaN;
     54 	}
     55 	if ( N === 1 || stride === 0 ) {
     56 		v = x[ offset ];
     57 		if ( v === v && N-correction > 0.0 ) {
     58 			return 0.0;
     59 		}
     60 		return NaN;
     61 	}
     62 	ix = offset;
     63 	S2 = 0.0;
     64 	S = 0.0;
     65 	n = 0;
     66 	for ( i = 0; i < N; i++ ) {
     67 		v = x[ ix ];
     68 		if ( v === v ) {
     69 			S2 += v * v;
     70 			S += v;
     71 			n += 1;
     72 		}
     73 		ix += stride;
     74 	}
     75 	nc = n - correction;
     76 	if ( nc <= 0.0 ) {
     77 		return NaN;
     78 	}
     79 	return (S2 - ((S/n)*S)) / nc;
     80 }
     81 
     82 
     83 // EXPORTS //
     84 
     85 module.exports = dnanvariancetk;