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Calculate the cumulative sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
npm install @stdlib/blas-ext-base-scusumkbnAlternatively,
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var scusumkbn = require( '@stdlib/blas-ext-base-scusumkbn' );Computes the cumulative sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float32Array( x.length );
scusumkbn( x.length, 0.0, x, 1, y, 1 );
// y => <Float32Array>[ 1.0, -1.0, 1.0 ]
x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
y = new Float32Array( x.length );
scusumkbn( x.length, 10.0, x, 1, y, 1 );
// y => <Float32Array>[ 11.0, 9.0, 11.0 ]The function has the following parameters:
- N: number of indexed elements.
- sum: initial sum.
- x: input
Float32Array. - strideX: index increment for
x. - y: output
Float32Array. - strideY: index increment for
y.
The N and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to compute the cumulative sum of every other element in x,
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float32Array( x.length );
var v = scusumkbn( 4, 0.0, x, 2, y, 1 );
// y => <Float32Array>[ 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 Float32Array = require( '@stdlib/array-float32' );
// Initial arrays...
var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var y0 = new Float32Array( x0.length );
// Create offset views...
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
scusumkbn( 4, 0.0, x1, -2, y1, 1 );
// y0 => <Float32Array>[ 0.0, 0.0, 0.0, 4.0, 6.0, 4.0, 5.0, 0.0 ]Computes the cumulative sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm and alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float32Array( 3 );
scusumkbn.ndarray( 3, 0.0, x, 1, 0, y, 1, 0 );
// y => <Float32Array>[ 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, offset 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 Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var y = new Float32Array( x.length );
scusumkbn.ndarray( 4, 0.0, x, 2, 1, y, -1, y.length-1 );
// y => <Float32Array>[ 0.0, 0.0, 0.0, 0.0, 5.0, 1.0, -1.0, 1.0 ]- If
N <= 0, both functions returnyunchanged.
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var filledarrayBy = require( '@stdlib/array-filled-by' );
var scusumkbn = require( '@stdlib/blas-ext-base-scusumkbn' );
var x = filledarrayBy( 10, 'float32', discreteUniform( 0, 100 ) );
console.log( x );
var y = filledarrayBy( x.length, 'float32', discreteUniform( 0, 10 ) );
console.log( y );
scusumkbn( x.length, 0.0, x, 1, y, -1 );
console.log( y );- Neumaier, Arnold. 1974. "Rounding Error Analysis of Some Methods for Summing Finite Sums." Zeitschrift Für Angewandte Mathematik Und Mechanik 54 (1): 39–51. doi:10.1002/zamm.19740540106.
@stdlib/blas-ext/base/dcusumkbn: calculate the cumulative sum of double-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.@stdlib/blas-ext/base/gcusumkbn: calculate the cumulative sum of strided array elements using an improved Kahan–Babuška algorithm.@stdlib/blas-ext/base/scusum: calculate the cumulative sum of single-precision floating-point strided array elements.@stdlib/blas-ext/base/scusumkbn2: calculate the cumulative sum of single-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.
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