time-to-botec

Benchmark sampling in different programming languages
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dnanmeanpn.js (2623B)

      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 arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm.
     25 *
     26 * ## Method
     27 *
     28 * -   This implementation uses a two-pass approach, as suggested by Neely (1966).
     29 *
     30 * ## References
     31 *
     32 * -   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).
     33 * -   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).
     34 *
     35 * @param {PositiveInteger} N - number of indexed elements
     36 * @param {Float64Array} x - input array
     37 * @param {integer} stride - stride length
     38 * @returns {number} arithmetic mean
     39 *
     40 * @example
     41 * var Float64Array = require( '@stdlib/array/float64' );
     42 *
     43 * var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
     44 * var N = x.length;
     45 *
     46 * var v = dnanmeanpn( N, x, 1 );
     47 * // returns ~0.3333
     48 */
     49 function dnanmeanpn( N, x, stride ) {
     50 	var ix;
     51 	var v;
     52 	var s;
     53 	var t;
     54 	var n;
     55 	var i;
     56 	var o;
     57 
     58 	if ( N <= 0 ) {
     59 		return NaN;
     60 	}
     61 	if ( N === 1 || stride === 0 ) {
     62 		return x[ 0 ];
     63 	}
     64 	if ( stride < 0 ) {
     65 		ix = (1-N) * stride;
     66 	} else {
     67 		ix = 0;
     68 	}
     69 	o = ix;
     70 
     71 	// Compute an estimate for the mean...
     72 	s = 0.0;
     73 	n = 0;
     74 	for ( i = 0; i < N; i++ ) {
     75 		v = x[ ix ];
     76 		if ( v === v ) {
     77 			n += 1;
     78 			s += v;
     79 		}
     80 		ix += stride;
     81 	}
     82 	if ( n === 0 ) {
     83 		return NaN;
     84 	}
     85 	s /= n;
     86 
     87 	// Compute an error term...
     88 	t = 0.0;
     89 	ix = o;
     90 	for ( i = 0; i < N; i++ ) {
     91 		v = x[ ix ];
     92 		if ( v === v ) {
     93 			t += v - s;
     94 		}
     95 		ix += stride;
     96 	}
     97 	return s + (t/n);
     98 }
     99 
    100 
    101 // EXPORTS //
    102 
    103 module.exports = dnanmeanpn;