time-to-botec

dsmeanpn.js (2446B)

---

 /**
 * @license Apache-2.0
 *
 * Copyright (c) 2020 The Stdlib Authors.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
 
 'use strict';
 
 // MODULES //
 
 var dssum = require( '@stdlib/blas/ext/base/dssum' );
 var dsapxsum = require( '@stdlib/blas/ext/base/dsapxsum' );
 
 
 // MAIN //
 
 /**
 * Computes the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.
 *
 * ## Method
 *
 * -   This implementation uses a two-pass approach, as suggested by Neely (1966).
 *
 * ## References
 *
 * -   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).
 * -   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).
 *
 * @param {PositiveInteger} N - number of indexed elements
 * @param {Float32Array} x - input array
 * @param {integer} stride - stride length
 * @returns {number} arithmetic mean
 *
 * @example
 * var Float32Array = require( '@stdlib/array/float32' );
 *
 * var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
 * var N = x.length;
 *
 * var v = dsmeanpn( N, x, 1 );
 * // returns ~0.3333
 */
 function dsmeanpn( N, x, stride ) {
 	var mu;
 	var c;
 
 	if ( N <= 0 ) {
 		return NaN;
 	}
 	if ( N === 1 || stride === 0 ) {
 		return x[ 0 ];
 	}
 	// Compute an estimate for the mean:
 	mu = dssum( N, x, stride ) / N;
 
 	// Compute an error term:
 	c = dsapxsum( N, -mu, x, stride ) / N;
 
 	return mu + c;
 }
 
 
 // EXPORTS //
 
 module.exports = dsmeanpn;