time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (3570B)

      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 isnan = require( '@stdlib/math/base/assert/is-nan' );
     24 var dsumpw = require( '@stdlib/blas/ext/base/dsumpw' ).ndarray;
     25 
     26 
     27 // MAIN //
     28 
     29 /**
     30 * Computes the mean and variance of a double-precision floating-point strided array using a two-pass algorithm.
     31 *
     32 * ## Method
     33 *
     34 * -   This implementation uses a two-pass approach, as suggested by Neely (1966).
     35 *
     36 * ## References
     37 *
     38 * -   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).
     39 * -   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).
     40 *
     41 * @param {PositiveInteger} N - number of indexed elements
     42 * @param {number} correction - degrees of freedom adjustment
     43 * @param {Float64Array} x - input array
     44 * @param {integer} strideX - `x` stride length
     45 * @param {NonNegativeInteger} offsetX - `x` starting index
     46 * @param {Float64Array} out - output array
     47 * @param {integer} strideOut - `out` stride length
     48 * @param {NonNegativeInteger} offsetOut - `out` starting index
     49 * @returns {Float64Array} output array
     50 *
     51 * @example
     52 * var Float64Array = require( '@stdlib/array/float64' );
     53 * var floor = require( '@stdlib/math/base/special/floor' );
     54 *
     55 * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
     56 * var out = new Float64Array( 2 );
     57 *
     58 * var N = floor( x.length / 2 );
     59 *
     60 * var v = dmeanvarpn( N, 1, x, 2, 1, out, 1, 0 );
     61 * // returns <Float64Array>[ 1.25, 6.25 ]
     62 */
     63 function dmeanvarpn( N, correction, x, strideX, offsetX, out, strideOut, offsetOut ) { // eslint-disable-line max-len
     64 	var mu;
     65 	var ix;
     66 	var io;
     67 	var M2;
     68 	var M;
     69 	var d;
     70 	var c;
     71 	var n;
     72 	var i;
     73 
     74 	ix = offsetX;
     75 	io = offsetOut;
     76 	if ( N <= 0 ) {
     77 		out[ io ] = NaN;
     78 		out[ io+strideOut ] = NaN;
     79 		return out;
     80 	}
     81 	n = N - correction;
     82 	if ( N === 1 || strideX === 0 ) {
     83 		out[ io ] = x[ ix ];
     84 		if ( n <= 0.0 ) {
     85 			out[ io+strideOut ] = NaN;
     86 		} else {
     87 			out[ io+strideOut ] = 0.0;
     88 		}
     89 		return out;
     90 	}
     91 	// Compute an estimate for the mean:
     92 	mu = dsumpw( N, x, strideX, offsetX ) / N;
     93 	if ( isnan( mu ) ) {
     94 		out[ io ] = NaN;
     95 		out[ io+strideOut ] = NaN;
     96 		return out;
     97 	}
     98 	// Compute the sum of squared differences from the mean...
     99 	M2 = 0.0;
    100 	M = 0.0;
    101 	for ( i = 0; i < N; i++ ) {
    102 		d = x[ ix ] - mu;
    103 		M2 += d * d;
    104 		M += d;
    105 		ix += strideX;
    106 	}
    107 	// Compute an error term for the mean:
    108 	c = M / N;
    109 
    110 	out[ io ] = mu + c;
    111 	if ( n <= 0.0 ) {
    112 		out[ io+strideOut ] = NaN;
    113 	} else {
    114 		out[ io+strideOut ] = (M2/n) - (c*(M/n));
    115 	}
    116 	return out;
    117 }
    118 
    119 
    120 // EXPORTS //
    121 
    122 module.exports = dmeanvarpn;