dnannsumkbn2
Calculate the sum of double-precision floating-point strided array elements, ignoring
NaNvalues and using a second-order iterative Kahan–Babuška algorithm.
Usage
var dnannsumkbn2 = require( '@stdlib/blas/ext/base/dnannsumkbn2' );
dnannsumkbn2( N, x, strideX, out, strideOut )
Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.
var Float64Array = require( '@stdlib/array/float64' );
var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );
var v = dnannsumkbn2( x.length, x, 1, out, 1 );
// returns <Float64Array>[ 1.0, 3 ]
The function has the following parameters:
- N: number of indexed elements.
- x: input
Float64Array. - strideX: stride length for
x. - out: output
Float64Arraywhose first element is the sum and whose second element is the number of non-NaN elements. - strideOut: stride length 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 Float64Array = require( '@stdlib/array/float64' );
var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );
var v = dnannsumkbn2( 4, 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 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 out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element
var v = dnannsumkbn2( 4, x1, 2, out1, 1 );
// returns <Float64Array>[ 5.0, 4 ]
dnannsumkbn2.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )
Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and 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, NaN, 2.0 ] );
var out = new Float64Array( 2 );
var v = dnannsumkbn2.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, offset parameters support indexing semantics based on starting indices. For example, to calculate the sum of every other element starting from the second element:
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 out = new Float64Array( 4 );
var v = dnannsumkbn2.ndarray( 4, 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 to0.0.
Examples
var bernoulli = require( '@stdlib/random/base/bernoulli' );
var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
var filledarrayBy = require( '@stdlib/array/filled-by' );
var Float64Array = require( '@stdlib/array/float64' );
var dnannsumkbn2 = require( '@stdlib/blas/ext/base/dnannsumkbn2' );
function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return discreteUniform( 0, 100 );
}
return NaN;
}
var x = filledarrayBy( 10, 'float64', rand );
console.log( x );
var out = new Float64Array( 2 );
dnannsumkbn2( x.length, x, 1, out, 1 );
console.log( out );
C APIs
Usage
#include "stdlib/blas/ext/base/dnannsumkbn2.h"
stdlib_strided_dnannsumkbn2( N, *X, strideX, *n )
Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.
const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };
CBLAS_INT n = 0;
double v = stdlib_strided_dnannsumkbn2( 4, x, 1, &n );
// returns 7.0
The function accepts the following arguments:
- N:
[in] CBLAS_INTnumber of indexed elements. - X:
[in] double*input array. - strideX:
[in] CBLAS_INTstride length forX. - n:
[out] CBLAS_INT*pointer for storing the number of non-NaN elements.
double stdlib_strided_dnannsumkbn2( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, CBLAS_INT *n );
stdlib_strided_dnannsumkbn2_ndarray( N, *X, strideX, offsetX, *n )
Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.
const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };
CBLAS_INT n = 0;
double v = stdlib_strided_dnannsumkbn2_ndarray( 4, x, 1, 0, &n );
// returns 7.0
The function accepts the following arguments:
- N:
[in] CBLAS_INTnumber of indexed elements. - X:
[in] double*input array. - strideX:
[in] CBLAS_INTstride length forX. - offsetX:
[in] CBLAS_INTstarting index forX. - n:
[out] CBLAS_INT*pointer for storing the number of non-NaN elements.
double stdlib_strided_dnannsumkbn2_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, CBLAS_INT *n );
Examples
#include "stdlib/blas/ext/base/dnannsumkbn2.h"
#include "stdlib/blase/base/shared.h"
#include <stdio.h>
int main( void ) {
// Create a strided array:
const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };
// Specify the number of elements:
const int N = 5;
// Specify the stride length:
const int strideX = 2;
// Initialize a variable for storing the number of non-NaN elements:
CBLAS_INT n = 0;
// Compute the sum:
double v = stdlib_strided_dnannsumkbn2( N, x, strideX, &n );
// Print the result:
printf( "sum: %lf\n", v );
printf( "n: %"CBLAS_IFMT"\n", n );
}
References
- Klein, Andreas. 2005. "A Generalized Kahan-Babuška-Summation-Algorithm." Computing 76 (3): 279–93. doi:10.1007/s00607-005-0139-x.