time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (2938B)

      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 float64ToFloat32 = require( '@stdlib/number/float64/base/to-float32' );
     24 
     25 
     26 // MAIN //
     27 
     28 /**
     29 * Computes the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm.
     30 *
     31 * ## Method
     32 *
     33 * -   This implementation uses a two-pass approach, as suggested by Neely (1966).
     34 *
     35 * ## References
     36 *
     37 * -   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).
     38 * -   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).
     39 *
     40 * @param {PositiveInteger} N - number of indexed elements
     41 * @param {Float32Array} x - input array
     42 * @param {integer} stride - stride length
     43 * @param {NonNegativeInteger} offset - starting index
     44 * @returns {number} arithmetic mean
     45 *
     46 * @example
     47 * var Float32Array = require( '@stdlib/array/float32' );
     48 * var floor = require( '@stdlib/math/base/special/floor' );
     49 *
     50 * var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
     51 * var N = floor( x.length / 2 );
     52 *
     53 * var v = snanmeanpn( N, x, 2, 1 );
     54 * // returns 1.25
     55 */
     56 function snanmeanpn( N, x, stride, offset ) {
     57 	var ix;
     58 	var v;
     59 	var s;
     60 	var t;
     61 	var n;
     62 	var i;
     63 
     64 	if ( N <= 0 ) {
     65 		return NaN;
     66 	}
     67 	if ( N === 1 || stride === 0 ) {
     68 		return x[ offset ];
     69 	}
     70 	ix = offset;
     71 
     72 	// Compute an estimate for the mean...
     73 	s = 0.0;
     74 	n = 0;
     75 	for ( i = 0; i < N; i++ ) {
     76 		v = x[ ix ];
     77 		if ( v === v ) {
     78 			s = float64ToFloat32( s + v );
     79 			n += 1;
     80 		}
     81 		ix += stride;
     82 	}
     83 	if ( n === 0 ) {
     84 		return NaN;
     85 	}
     86 	s = float64ToFloat32( s / n );
     87 
     88 	// Compute an error term...
     89 	ix = offset;
     90 	t = 0.0;
     91 	for ( i = 0; i < N; i++ ) {
     92 		v = x[ ix ];
     93 		if ( v === v ) {
     94 			t = float64ToFloat32( t + float64ToFloat32(v-s) );
     95 		}
     96 		ix += stride;
     97 	}
     98 	return float64ToFloat32( s + float64ToFloat32(t/n) );
     99 }
    100 
    101 
    102 // EXPORTS //
    103 
    104 module.exports = snanmeanpn;