dnanasumors

Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

The L1 norm is defined as

Installation

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

Usage

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

dnanasumors( N, x, stride )

Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

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.
• stride: index increment for x.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of absolute values (L1 norm) every other element in x,

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

var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );

var v = dnanasumors( 4, x, 2 );
// returns 5.0

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

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

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 v = dnanasumors( 4, x1, 2 );
// returns 9.0

dnanasumors.ndarray( N, x, stride, offset )

Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation and alternative indexing semantics.

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 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) every other value in x starting from the second value

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

var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = dnanasumors.ndarray( 4, x, 2, 1 );
// returns 9.0

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.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var dnanasumors = require( '@stdlib/blas-ext-base-dnanasumors' );

function rand() {
    if ( bernoulli( 0.5 ) < 0.2 ) {
        return NaN;
    }
    return discreteUniform( 0, 100 );
}

var x = filledarrayBy( 10, 'float64', rand );
console.log( x );

var v = dnanasumors( x.length, x, 1 );
console.log( v );

See Also

• @stdlib/blas-ext/base/dnanasum: calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values.

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.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.