ndarray.js (2577B)
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 dsumpw = require( '@stdlib/blas/ext/base/dsumpw' ).ndarray; 24 var dapxsumpw = require( '@stdlib/blas/ext/base/dapxsumpw' ).ndarray; 25 26 27 // MAIN // 28 29 /** 30 * Computes the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction 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 {Float64Array} x - input array 43 * @param {integer} stride - stride length 44 * @param {NonNegativeInteger} offset - starting index 45 * @returns {number} arithmetic mean 46 * 47 * @example 48 * var Float64Array = require( '@stdlib/array/float64' ); 49 * var floor = require( '@stdlib/math/base/special/floor' ); 50 * 51 * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); 52 * var N = floor( x.length / 2 ); 53 * 54 * var v = dmeanpn( N, x, 2, 1 ); 55 * // returns 1.25 56 */ 57 function dmeanpn( N, x, stride, offset ) { 58 var mu; 59 var c; 60 61 if ( N <= 0 ) { 62 return NaN; 63 } 64 if ( N === 1 || stride === 0 ) { 65 return x[ offset ]; 66 } 67 // Compute an estimate for the mean: 68 mu = dsumpw( N, x, stride, offset ) / N; 69 70 // Compute an error term: 71 c = dapxsumpw( N, -mu, x, stride, offset ) / N; 72 73 return mu + c; 74 } 75 76 77 // EXPORTS // 78 79 module.exports = dmeanpn;