@stdlib/blas-ext-base-dsnannsumors

Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

Usage no npm install needed!

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README

dsnannsumors

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Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

Installation

npm install @stdlib/blas-ext-base-dsnannsumors

Usage

var dsnannsumors = require( '@stdlib/blas-ext-base-dsnannsumors' );

dsnannsumors( N, x, strideX, out, strideOut )

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors( x.length, x, 1, out, 1 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following parameters:

  • N: number of indexed elements.
  • x: input Float32Array.
  • strideX: index increment for x.
  • out: output Float64Array whose first element is the sum and whose second element is the number of non-NaN elements.
  • strideOut: index increment for out.

The N and stride parameters determine which elements are accessed at runtime. For example, to compute the sum of every other element in x,

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );
var N = floor( x.length / 2 );

var v = dsnannsumors( N, x, 2, out, 1 );
// returns <Float64Array>[ 5.0, 2 ]

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x0 = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element

var N = floor( x0.length / 2 );

var v = dsnannsumors( N, x1, 2, out1, 1 );
// returns <Float64Array>[ 5.0, 4 ]

dsnannsumors.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation with extended accumulation and alternative indexing semantics.

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors.ndarray( x.length, x, 1, 0, out, 1, 0 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following additional parameters:

  • offsetX: starting index for x.
  • offsetOut: starting index for out.

While 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 every other value in x starting from the second value

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var out = new Float64Array( 4 );
var N = floor( x.length / 2 );

var v = dsnannsumors.ndarray( N, x, 2, 1, out, 2, 1 );
// returns <Float64Array>[ 0.0, 5.0, 0.0, 4 ]

Notes

  • If N <= 0, both functions return a sum equal to 0.0.
  • Accumulated intermediate values are stored as double-precision floating-point numbers.

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var dsnannsumors = require( '@stdlib/blas-ext-base-dsnannsumors' );

var x;
var i;

x = new Float32Array( 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 out = new Float64Array( 2 );
dsnannsumors( x.length, x, 1, out, 1 );
console.log( out );

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

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License

See LICENSE.

Copyright

Copyright © 2016-2021. The Stdlib Authors.