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test(stats/incr): update test cases for nanewvariance2
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‎lib/node_modules/@stdlib/stats/incr/nanewvariance/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2025 The Stdlib Authors.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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# nanewvariance
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> Compute an [exponentially weighted variance][exponentially weighted variance] incrementally, ignoring `NaN` values.
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<section class="intro">
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The [exponentially weighted variance][exponentially weighted variance] is defined as
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<!-- <equation class="equation" label="eq:ew_variance" align="center" raw="s_t^2 = (1-\alpha) \left( s_{t-1}^2 + \alpha (x_t - \mu_{t-1})^2 \right)" alt="Equation for the exponentially weighted variance."> -->
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```math
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S_n = \begin{cases} 0 & \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) & \textrm{if}\ n > 0 \end{cases}
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```
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<!-- <div class="equation" align="center" data-raw-text="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:exponentially weighted variance">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/nanmean/docs/img/equation_exponentially_weighted_variance.svg" alt="exponentially weighted variance">
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<br>
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</div> -->
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<!-- </equation> -->
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var nanewvariance = require( '@stdlib/stats/incr/nanewvariance' );
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```
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#### nanewvariance()
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Returns an accumulator function which incrementally computes an [exponentially weighted variance][exponentially weighted variance], ignoring `NaN` values.
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```javascript
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var accumulator = nanewvariance( 0.5 );
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```
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#### accumulator( \[x] )
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If provided an input value `x`, the accumulator function returns an updated exponentially weighted variance. If not provided an input value `x`, the accumulator function returns the current exponentially weighted variance.
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```javascript
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var accumulator = nanewvariance( 0.5 );
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var v = accumulator();
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// returns null
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v = accumulator( 2.0 );
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// returns 0
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v = accumulator( 4.0 );
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// returns 1
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v = accumulator( NaN );
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// returns 1
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v = accumulator( 5.0 );
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// returns 1.5
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v = accumulator();
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// returns 1.5
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```
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- Input values are **not** type checked. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var randu = require( '@stdlib/random/base/randu' );
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var nanewvariance = require( '@stdlib/stats/incr/nanewvariance' );
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var accumulator;
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var v;
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var i;
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// Initialize an accumulator with smoothing factor α = 0.5:
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accumulator = nanewvariance( 0.5 );
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// For each simulated datum, update the exponentially weighted variance...
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for ( i = 0; i < 100; i++ ) {
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// With a 20% chance, provide NaN to test that invalid inputs are ignored:
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v = ( randu() < 0.2 ) ? NaN : randu() * 100.0;
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accumulator( v );
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}
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console.log( accumulator() );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[exponentially weighted variance]: https://en.wikipedia.org/wiki/EWMA_chart
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/stats/incr/nanewvariance/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 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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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var pkg = require( './../package.json' ).name;
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var nanewvariance = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var f;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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f = nanewvariance( 0.5 );
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if ( typeof f !== 'function' ) {
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b.fail( 'should return a function' );
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}
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}
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b.toc();
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if ( typeof f !== 'function' ) {
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b.fail( 'should return a function' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::accumulator', function benchmark( b ) {
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var acc;
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var v;
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var i;
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acc = nanewvariance( 0.5 );
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = acc( randu() );
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if ( v !== v ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( v !== v ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

‎lib/node_modules/@stdlib/stats/incr/nanewvariance/docs/img/equation_exponentially_weighted_variance.svg‎

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‎lib/node_modules/@stdlib/stats/incr/nanewvariance/docs/repl.txt‎

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{{alias}}( α )
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Returns an accumulator function which incrementally computes an
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exponentially weighted variance, where α is a smoothing factor between 0 and
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1. This accumulator ignores any input values that are `NaN`.
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If provided a value, the accumulator function returns an updated variance.
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If not provided a value, the accumulator function returns the current
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variance.
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If provided `NaN` or a value which, when used in computations, results in
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`NaN`, that input is ignored and the accumulated variance remains unchanged.
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Parameters
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----------
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α: number
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Smoothing factor (value between 0 and 1).
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Returns
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-------
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acc: Function
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Accumulator function.
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Examples
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--------
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> var accumulator = {{alias}}( 0.5 );
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> var v = accumulator()
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null
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> v = accumulator( 2.0 )
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0
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> v = accumulator( 4.0 )
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1
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> v = accumulator( NaN )
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1
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> v = accumulator( 5.0 )
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1.5
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> v = accumulator()
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1.5
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See Also
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--------
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