time-to-botec

README.md (5887B)

---

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 @license Apache-2.0
 
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 # nanmeanors
 
 > Calculate the [arithmetic mean][arithmetic-mean] of a strided array, ignoring `NaN` values and using ordinary recursive summation.
 
 <section class="intro">
 
 The [arithmetic mean][arithmetic-mean] is defined as
 
 <!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> -->
 
 <div class="equation" align="center" data-raw-text="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@9175c13dbc0434906c9644e01a0c6d2f700fc087/lib/node_modules/@stdlib/stats/base/nanmeanors/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 </section>
 
 <!-- /.intro -->
 
 <section class="usage">
 
 ## Usage
 
 ```javascript
 var nanmeanors = require( '@stdlib/stats/base/nanmeanors' );
 ```
 
 #### nanmeanors( N, x, stride )
 
 Computes the [arithmetic mean][arithmetic-mean] of a strided array `x`, ignoring `NaN` values and using ordinary recursive summation.
 
 ```javascript
 var x = [ 1.0, -2.0, NaN, 2.0 ];
 var N = x.length;
 
 var v = nanmeanors( N, x, 1 );
 // returns ~0.3333
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
 -   **stride**: index increment for `x`.
 
 The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`,
 
 ```javascript
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];
 var N = floor( x.length / 2 );
 
 var v = nanmeanors( N, x, 2 );
 // returns 1.25
 ```
 
 Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
 
 <!-- eslint-disable stdlib/capitalized-comments -->
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
 var N = floor( x0.length / 2 );
 
 var v = nanmeanors( N, x1, 2 );
 // returns 1.25
 ```
 
 #### nanmeanors.ndarray( N, x, stride, offset )
 
 Computes the [arithmetic mean][arithmetic-mean] of a strided array, ignoring `NaN` values and using ordinary recursive summation and alternative indexing semantics.
 
 ```javascript
 var x = [ 1.0, -2.0, NaN, 2.0 ];
 var N = x.length;
 
 var v = nanmeanors.ndarray( N, x, 1, 0 );
 // returns ~0.33333
 ```
 
 The function has the following additional parameters:
 
 -   **offset**: starting index for `x`.
 
 While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other value in `x` starting from the second value
 
 ```javascript
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ];
 var N = floor( x.length / 2 );
 
 var v = nanmeanors.ndarray( N, x, 2, 1 );
 // returns 1.25
 ```
 
 </section>
 
 <!-- /.usage -->
 
 <section class="notes">
 
 ## Notes
 
 -   If `N <= 0`, both functions return `NaN`.
 -   If every indexed element is `NaN`, both functions return `NaN`.
 -   Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation to compute an arithmetic mean is acceptable; in all other cases, exercise due caution.
 -   Depending on the environment, the typed versions ([`dnanmeanors`][@stdlib/stats/base/dnanmeanors], [`snanmeanors`][@stdlib/stats/base/snanmeanors], etc.) are likely to be significantly more performant.
 
 </section>
 
 <!-- /.notes -->
 
 <section class="examples">
 
 ## Examples
 
 <!-- eslint no-undef: "error" -->
 
 ```javascript
 var randu = require( '@stdlib/random/base/randu' );
 var round = require( '@stdlib/math/base/special/round' );
 var Float64Array = require( '@stdlib/array/float64' );
 var nanmeanors = require( '@stdlib/stats/base/nanmeanors' );
 
 var x;
 var i;
 
 x = new Float64Array( 10 );
 for ( i = 0; i < x.length; i++ ) {
     if ( randu() < 0.2 ) {
         x[ i ] = NaN;
     } else {
         x[ i ] = round( (randu()*100.0) - 50.0 );
     }
 }
 console.log( x );
 
 var v = nanmeanors( x.length, x, 1 );
 console.log( v );
 ```
 
 </section>
 
 <!-- /.examples -->
 
 <section class="references">
 
 </section>
 
 <!-- /.references -->
 
 <section class="links">
 
 [arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean
 
 [mdn-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
 [@stdlib/stats/base/dnanmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmeanors
 
 [@stdlib/stats/base/snanmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/snanmeanors
 
 </section>
 
 <!-- /.links -->