Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 14x 2x 2x 12x 12x 12x 12x 12x 14x 52x 52x 38x 38x 38x 52x 52x 14x 2x 2x 10x 10x 10x 10x 10x 14x 48x 48x 38x 38x 48x 48x 10x 14x 3x 3x 3x 3x 3x | /**
* @license Apache-2.0
*
* Copyright (c) 2025 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 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 {Object} x - input array object
* @param {Collection} x.data - input array data
* @param {Array<Function>} x.accessors - array element accessors
* @param {integer} strideX - stride length
* @param {NonNegativeInteger} offsetX - starting index
* @returns {number} arithmetic mean
*
* @example
* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
* var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
*
* var x = toAccessorArray( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
*
* var v = nanmeanpn( 4, arraylike2object( x ), 2, 1 );
* // returns 1.25
*/
function nanmeanpn( N, x, strideX, offsetX ) {
var xbuf;
var xget;
var ix;
var v;
var s;
var t;
var n;
var i;
// Cache references to array data:
xbuf = x.data;
// Cache references to element accessors:
xget = x.accessors[ 0 ];
if ( N === 1 || strideX === 0 ) {
return xget( xbuf, offsetX );
}
ix = offsetX;
// Compute an estimate for the mean...
s = 0.0;
n = 0;
for ( i = 0; i < N; i++ ) {
v = xget( xbuf, ix );
if ( v === v ) {
n += 1;
s += v;
}
ix += strideX;
}
if ( n === 0 ) {
return NaN;
}
s /= n;
// Compute an error term...
ix = offsetX;
t = 0.0;
for ( i = 0; i < N; i++ ) {
v = xget( xbuf, ix );
if ( v === v ) {
t += v - s;
}
ix += strideX;
}
return s + (t/n);
}
// EXPORTS //
module.exports = nanmeanpn;
|