time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (2795B)

      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 single-precision floating-point strided array, ignoring `NaN` values, using a two-pass error correction algorithm with extended accumulation, and returning an extended precision result.
     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 {Float32Array} x - input array
     37 * @param {integer} stride - stride length
     38 * @param {NonNegativeInteger} offset - starting index
     39 * @returns {number} arithmetic mean
     40 *
     41 * @example
     42 * var Float32Array = require( '@stdlib/array/float32' );
     43 * var floor = require( '@stdlib/math/base/special/floor' );
     44 *
     45 * var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
     46 * var N = floor( x.length / 2 );
     47 *
     48 * var v = dsnanmeanpn( N, x, 2, 1 );
     49 * // returns 1.25
     50 */
     51 function dsnanmeanpn( N, x, stride, offset ) {
     52 	var ix;
     53 	var v;
     54 	var s;
     55 	var t;
     56 	var n;
     57 	var i;
     58 
     59 	if ( N <= 0 ) {
     60 		return NaN;
     61 	}
     62 	if ( N === 1 || stride === 0 ) {
     63 		return x[ offset ];
     64 	}
     65 	ix = offset;
     66 
     67 	// Compute an estimate for the mean...
     68 	s = 0.0;
     69 	n = 0;
     70 	for ( i = 0; i < N; i++ ) {
     71 		v = x[ ix ];
     72 		if ( v === v ) {
     73 			n += 1;
     74 			s += v;
     75 		}
     76 		ix += stride;
     77 	}
     78 	if ( n === 0 ) {
     79 		return NaN;
     80 	}
     81 	s /= n;
     82 
     83 	// Compute an error term...
     84 	ix = offset;
     85 	t = 0.0;
     86 	for ( i = 0; i < N; i++ ) {
     87 		v = x[ ix ];
     88 		if ( v === v ) {
     89 			t += v - s;
     90 		}
     91 		ix += stride;
     92 	}
     93 	return s + (t/n);
     94 }
     95 
     96 
     97 // EXPORTS //
     98 
     99 module.exports = dsnanmeanpn;