time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (1802B)


      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 provided a known mean and using a one-pass textbook algorithm.
     25 *
     26 * @param {PositiveInteger} N - number of indexed elements
     27 * @param {number} mean - mean
     28 * @param {number} correction - degrees of freedom adjustment
     29 * @param {Float64Array} x - input array
     30 * @param {integer} stride - stride length
     31 * @param {NonNegativeInteger} offset - starting index
     32 * @returns {number} variance
     33 *
     34 * @example
     35 * var Float64Array = require( '@stdlib/array/float64' );
     36 * var floor = require( '@stdlib/math/base/special/floor' );
     37 *
     38 * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
     39 * var N = floor( x.length / 2 );
     40 *
     41 * var v = dvarmtk( N, 1.25, 1, x, 2, 1 );
     42 * // returns 6.25
     43 */
     44 function dvarmtk( N, mean, correction, x, stride, offset ) {
     45 	var ix;
     46 	var M2;
     47 	var d;
     48 	var n;
     49 	var i;
     50 
     51 	n = N - correction;
     52 	if ( N <= 0 || n <= 0.0 ) {
     53 		return NaN;
     54 	}
     55 	if ( N === 1 || stride === 0 ) {
     56 		return 0.0;
     57 	}
     58 	ix = offset;
     59 	M2 = 0.0;
     60 	for ( i = 0; i < N; i++ ) {
     61 		d = x[ ix ] - mean;
     62 		M2 += d * d;
     63 		ix += stride;
     64 	}
     65 	return M2 / n;
     66 }
     67 
     68 
     69 // EXPORTS //
     70 
     71 module.exports = dvarmtk;