104 lines
2.6 KiB
JavaScript
104 lines
2.6 KiB
JavaScript
|
/**
|
|||
|
|
* @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';
|
|||
|
|
|
|||
|
|
// MAIN //
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm.
|
|||
|
|
*
|
|||
|
|
* ## 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 {Float64Array} x - input array
|
|||
|
|
* @param {integer} stride - stride length
|
|||
|
|
* @returns {number} arithmetic mean
|
|||
|
|
*
|
|||
|
|
* @example
|
|||
|
|
* var Float64Array = require( '@stdlib/array/float64' );
|
|||
|
|
*
|
|||
|
|
* var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
|
|||
|
|
* var N = x.length;
|
|||
|
|
*
|
|||
|
|
* var v = dnanmeanpn( N, x, 1 );
|
|||
|
|
* // returns ~0.3333
|
|||
|
|
*/
|
|||
|
|
function dnanmeanpn( N, x, stride ) {
|
|||
|
|
var ix;
|
|||
|
|
var v;
|
|||
|
|
var s;
|
|||
|
|
var t;
|
|||
|
|
var n;
|
|||
|
|
var i;
|
|||
|
|
var o;
|
|||
|
|
|
|||
|
|
if ( N <= 0 ) {
|
|||
|
|
return NaN;
|
|||
|
|
}
|
|||
|
|
if ( N === 1 || stride === 0 ) {
|
|||
|
|
return x[ 0 ];
|
|||
|
|
}
|
|||
|
|
if ( stride < 0 ) {
|
|||
|
|
ix = (1-N) * stride;
|
|||
|
|
} else {
|
|||
|
|
ix = 0;
|
|||
|
|
}
|
|||
|
|
o = ix;
|
|||
|
|
|
|||
|
|
// Compute an estimate for the mean...
|
|||
|
|
s = 0.0;
|
|||
|
|
n = 0;
|
|||
|
|
for ( i = 0; i < N; i++ ) {
|
|||
|
|
v = x[ ix ];
|
|||
|
|
if ( v === v ) {
|
|||
|
|
n += 1;
|
|||
|
|
s += v;
|
|||
|
|
}
|
|||
|
|
ix += stride;
|
|||
|
|
}
|
|||
|
|
if ( n === 0 ) {
|
|||
|
|
return NaN;
|
|||
|
|
}
|
|||
|
|
s /= n;
|
|||
|
|
|
|||
|
|
// Compute an error term...
|
|||
|
|
t = 0.0;
|
|||
|
|
ix = o;
|
|||
|
|
for ( i = 0; i < N; i++ ) {
|
|||
|
|
v = x[ ix ];
|
|||
|
|
if ( v === v ) {
|
|||
|
|
t += v - s;
|
|||
|
|
}
|
|||
|
|
ix += stride;
|
|||
|
|
}
|
|||
|
|
return s + (t/n);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
// EXPORTS //
|
|||
|
|
|
|||
|
|
module.exports = dnanmeanpn;
|