95 lines
2.3 KiB
JavaScript
95 lines
2.3 KiB
JavaScript
/**
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* @license Apache-2.0
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*
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* Copyright (c) 2020 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MAIN //
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/**
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* Computes the variance of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.
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*
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* ## References
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*
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* - Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." _Technometrics_ 4 (3). Taylor & Francis: 419–20. doi:[10.1080/00401706.1962.10490022](https://doi.org/10.1080/00401706.1962.10490022).
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* - van Reeken, A. J. 1968. "Letters to the Editor: Dealing with Neely's Algorithms." _Communications of the ACM_ 11 (3): 149–50. doi:[10.1145/362929.362961](https://doi.org/10.1145/362929.362961).
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*
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* @param {PositiveInteger} N - number of indexed elements
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* @param {number} correction - degrees of freedom adjustment
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* @param {Float64Array} x - input array
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* @param {integer} stride - stride length
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* @returns {number} variance
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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*
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* var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
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* var N = x.length;
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*
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* var v = dnanvariancewd( N, 1, x, 1 );
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* // returns ~4.3333
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*/
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function dnanvariancewd( N, correction, x, stride ) {
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var delta;
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var mu;
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var M2;
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var ix;
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var nc;
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var v;
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var n;
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var i;
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if ( N <= 0 ) {
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return NaN;
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}
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if ( N === 1 || stride === 0 ) {
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v = x[ 0 ];
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if ( v === v && N-correction > 0.0 ) {
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return 0.0;
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}
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return NaN;
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}
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if ( stride < 0 ) {
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ix = (1-N) * stride;
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} else {
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ix = 0;
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}
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M2 = 0.0;
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mu = 0.0;
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n = 0;
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for ( i = 0; i < N; i++ ) {
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v = x[ ix ];
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if ( v === v ) {
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delta = v - mu;
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n += 1;
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mu += delta / n;
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M2 += delta * ( v - mu );
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}
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ix += stride;
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}
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nc = n - correction;
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if ( nc <= 0.0 ) {
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return NaN;
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}
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return M2 / nc;
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}
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// EXPORTS //
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module.exports = dnanvariancewd;
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