ndarray.js (2713B)
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 var ssumpw = require( '@stdlib/blas/ext/base/ssumpw' ).ndarray; 25 var sapxsumpw = require( '@stdlib/blas/ext/base/sapxsumpw' ).ndarray; 26 27 28 // MAIN // 29 30 /** 31 * Computes the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm. 32 * 33 * ## Method 34 * 35 * - This implementation uses a two-pass approach, as suggested by Neely (1966). 36 * 37 * ## References 38 * 39 * - 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). 40 * - 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). 41 * 42 * @param {PositiveInteger} N - number of indexed elements 43 * @param {Float32Array} x - input array 44 * @param {integer} stride - stride length 45 * @param {NonNegativeInteger} offset - starting index 46 * @returns {number} arithmetic mean 47 * 48 * @example 49 * var Float32Array = require( '@stdlib/array/float32' ); 50 * var floor = require( '@stdlib/math/base/special/floor' ); 51 * 52 * var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); 53 * var N = floor( x.length / 2 ); 54 * 55 * var v = smeanpn( N, x, 2, 1 ); 56 * // returns 1.25 57 */ 58 function smeanpn( N, x, stride, offset ) { 59 var mu; 60 var c; 61 62 if ( N <= 0 ) { 63 return NaN; 64 } 65 if ( N === 1 || stride === 0 ) { 66 return x[ offset ]; 67 } 68 // Compute an estimate for the mean: 69 mu = float64ToFloat32( ssumpw( N, x, stride, offset ) / N ); 70 71 // Compute an error term: 72 c = float64ToFloat32( sapxsumpw( N, -mu, x, stride, offset ) / N ); 73 74 return float64ToFloat32( mu + c ); 75 } 76 77 78 // EXPORTS // 79 80 module.exports = smeanpn;