About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

dcusumkbn2

Calculate the cumulative sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.

Installation

npm install @stdlib/blas-ext-base-dcusumkbn2

Alternatively,

To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
If you are using Deno, visit the deno branch (see README for usage intructions).
For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var dcusumkbn2 = require( '@stdlib/blas-ext-base-dcusumkbn2' );

dcusumkbn2( N, sum, x, strideX, y, strideY )

Computes the cumulative sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.

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

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float64Array( x.length );

dcusumkbn2( x.length, 0.0, x, 1, y, 1 );
// y => <Float64Array>[ 1.0, -1.0, 1.0 ]

x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
y = new Float64Array( x.length );

dcusumkbn2( x.length, 10.0, x, 1, y, 1 );
// y => <Float64Array>[ 11.0, 9.0, 11.0 ]

The function has the following parameters:

N: number of indexed elements.
sum: initial sum.
x: input Float64Array.
strideX: index increment for x.
y: output Float64Array.
strideY: index increment for y.

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

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

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

var N = 4;

var v = dcusumkbn2( N, 0.0, x, 2, y, 1 );
// y => <Float64Array>[ 1.0, 3.0, 1.0, 5.0, 0.0, 0.0, 0.0, 0.0 ]

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

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

// Initial arrays...
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var y0 = new Float64Array( x0.length );

// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

var N = 4;

dcusumkbn2( N, 0.0, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 4.0, 6.0, 4.0, 5.0, 0.0 ]

dcusumkbn2.ndarray( N, sum, x, strideX, offsetX, y, strideY, offsetY )

Computes the cumulative sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.

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

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float64Array( x.length );

dcusumkbn2.ndarray( x.length, 0.0, x, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 1.0, -1.0, 1.0 ]

The function has the following additional parameters:

offsetX: starting index for x.
offsetY: starting index for y.

While typed array views mandate a view offset based on the underlying buffer, offsetX and offsetY parameters support indexing semantics based on a starting indices. For example, to calculate the cumulative sum of every other value in x starting from the second value and to store in the last N elements of y starting from the last element

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

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

var N = 4;

dcusumkbn2.ndarray( N, 0.0, x, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, 5.0, 1.0, -1.0, 1.0 ]

Notes

If N <= 0, both functions return y unchanged.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var Float64Array = require( '@stdlib/array-float64' );
var dcusumkbn2 = require( '@stdlib/blas-ext-base-dcusumkbn2' );

var x = discreteUniform( 10, -100, 100, {
    'dtype': 'float64'
});
var y = new Float64Array( x.length );

console.log( x );
console.log( y );

dcusumkbn2( x.length, 0.0, x, 1, y, -1 );
console.log( y );

C APIs

Usage

#include "stdlib/blas/ext/base/dcusumkbn2.h"

stdlib_strided_dcusumkbn2( N, sum, X, strideX, Y, strideY )

Computes the cumulative sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.

const double x[] = { 1.0, 2.0, 3.0, 4.0 }
double y[] = { 0.0, 0.0, 0.0, 0.0 }

stdlib_strided_dcusumkbn2( 4, 0.0, x, 1, y, 1 );

The function accepts the following arguments:

N: [in] CBLAS_INT number of indexed elements.
sum: [in] CBLAS_INT initial sum.
X: [in] double* input array.
strideX: [in] CBLAS_INT index increment for X.
Y: [out] double* output array.
strideY: [in] CBLAS_INT index increment for Y.

void stdlib_strided_dcusumkbn2( const CBLAS_INT N, const CBLAS_INT sum, const double *X, const CBLAS_INT strideX, double *Y, const CBLAS_INT strideY );

stdlib_strided_dcusumkbn2_ndarray( N, sum, X, strideX, offsetX, Y, strideY, offsetY )

Computes the cumulative sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.

const double x[] = { 1.0, 2.0, 3.0, 4.0 }
double y[] = { 0.0, 0.0, 0.0, 0.0 }

stdlib_strided_dcusumkbn2_ndarray( 4, 0.0, x, 1, 0, y, 1, 0 );

The function accepts the following arguments:

N: [in] CBLAS_INT number of indexed elements.
sum: [in] CBLAS_INT initial sum.
X: [in] double* input array.
strideX: [in] CBLAS_INT index increment for X.
offsetX: [in] CBLAS_INT starting index for X.
Y: [out] double* output array.
strideY: [in] CBLAS_INT index increment for Y.
offsetY: [in] CBLAS_INT starting index for Y.

void stdlib_strided_dcusumkbn2_ndarray( const CBLAS_INT N, const CBLAS_INT sum, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, double *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );

Examples

#include "stdlib/blas/ext/base/dcusumkbn2.h"

int main( void ) {
    // Create strided arrays:
    const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
    double y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

    // Specify the number of elements:
    const int N = 4;

    // Specify stride lengths:
    const int strideX = 2;
    const int strideY = -2;

    // Compute the cumulative sum:
    stdlib_strided_dcusumkbn2( N, 0.0, x, strideX, y, strideY );

    // Print the result:
    for ( int i = 0; i < 8; i++ ) {
        printf( "y[ %d ] = %lf\n", i, y[ i ] );
    }
}

References

Klein, Andreas. 2005. "A Generalized Kahan-Babuška-Summation-Algorithm." Computing 76 (3): 279–93. doi:10.1007/s00607-005-0139-x.

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.

stdlib-js/blas-ext-base-dcusumkbn2

dcusumkbn2

Installation

Usage

dcusumkbn2( N, sum, x, strideX, y, strideY )

dcusumkbn2.ndarray( N, sum, x, strideX, offsetX, y, strideY, offsetY )

Notes

Examples

C APIs

Usage

stdlib_strided_dcusumkbn2( N, sum, X, strideX, Y, strideY )

stdlib_strided_dcusumkbn2_ndarray( N, sum, X, strideX, offsetX, Y, strideY, offsetY )

Examples

References

See Also

Notice

Community

License

Copyright

stdlib-js/blas-ext-base-dcusumkbn2

dcusumkbn2

Installation

Usage

dcusumkbn2( N, sum, x, strideX, y, strideY )

dcusumkbn2.ndarray( N, sum, x, strideX, offsetX, y, strideY, offsetY )

Notes

Examples

C APIs

Usage

stdlib_strided_dcusumkbn2( N, sum, *X, strideX, *Y, strideY )

stdlib_strided_dcusumkbn2_ndarray( N, sum, *X, strideX, offsetX, *Y, strideY, offsetY )

Examples

References

See Also

Notice

Community

License

Copyright

stdlib_strided_dcusumkbn2( N, sum, X, strideX, Y, strideY )

stdlib_strided_dcusumkbn2_ndarray( N, sum, X, strideX, offsetX, Y, strideY, offsetY )