time-to-botec

README.md (5728B)

---

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 # dnanasumors
 
 > Calculate the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation.
 
 <section class="intro">
 
 The [_L1_ norm][l1norm] is defined as
 
 <!-- <equation class="equation" label="eq:l1norm" align="center" raw="\|\mathbf{x}\|_1 = \sum_{i=0}^{n-1} \vert x_i \vert" alt="L1 norm definition."> -->
 
 <div class="equation" align="center" data-raw-text="\|\mathbf{x}\|_1 = \sum_{i=0}^{n-1} \vert x_i \vert" data-equation="eq:l1norm">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@77bf4832b5dc49c733700e76e58555d7e8ccda69/lib/node_modules/@stdlib/blas/ext/base/dnanasumors/docs/img/equation_l1norm.svg" alt="L1 norm definition.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 </section>
 
 <!-- /.intro -->
 
 <section class="usage">
 
 ## Usage
 
 ```javascript
 var dnanasumors = require( '@stdlib/blas/ext/base/dnanasumors' );
 ```
 
 #### dnanasumors( N, x, stride )
 
 Computes the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
 var N = x.length;
 
 var v = dnanasumors( N, x, 1 );
 // returns 5.0
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **x**: input [`Float64Array`][@stdlib/array/float64].
 -   **stride**: index increment for `x`.
 
 The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the sum of absolute values ([_L1_ norm][l1norm]) every other element in `x`,
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
 var N = floor( x.length / 2 );
 
 var v = dnanasumors( N, x, 2 );
 // returns 5.0
 ```
 
 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, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
 var N = floor( x0.length / 2 );
 
 var v = dnanasumors( N, x1, 2 );
 // returns 9.0
 ```
 
 #### dnanasumors.ndarray( N, x, stride, offset )
 
 Computes the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation and alternative indexing semantics.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
 var N = x.length;
 
 var v = dnanasumors.ndarray( N, x, 1, 0 );
 // returns 5.0
 ```
 
 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 sum of absolute values ([_L1_ norm][l1norm]) every other value in `x` starting from the second value
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var N = floor( x.length / 2 );
 
 var v = dnanasumors.ndarray( N, x, 2, 1 );
 // returns 9.0
 ```
 
 </section>
 
 <!-- /.usage -->
 
 <section class="notes">
 
 ## Notes
 
 -   If `N <= 0`, both functions return `0.0`.
 -   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 is acceptable; in all other cases, exercise due caution.
 
 </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 dnanasumors = require( '@stdlib/blas/ext/base/dnanasumors' );
 
 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 );
     }
 }
 console.log( x );
 
 var v = dnanasumors( x.length, x, 1 );
 console.log( v );
 ```
 
 </section>
 
 <!-- /.examples -->
 
 <section class="references">
 
 </section>
 
 <!-- /.references -->
 
 <section class="links">
 
 [@stdlib/array/float64]: https://www.npmjs.com/package/@stdlib/array-float64
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
 [l1norm]: http://en.wikipedia.org/wiki/Norm_%28mathematics%29
 
 </section>
 
 <!-- /.links -->