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
Usage
var dnanasumors = require( '@stdlib/blas/ext/base/dnanasumors' );
dnanasumors( N, x, strideX )
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
. - strideX: 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) for 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, strideX, offsetX )
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:
- offsetX: 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) for 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 return0.0
. - 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 );
C APIs
Usage
#include "stdlib/blas/ext/base/dnanasumors.h"
stdlib_strided_dnanasumors( N, *X, strideX )
Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN
values and using ordinary recursive summation.
const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };
double v = stdlib_strided_dnanasumors( 4, x, 1 );
// returns 7.0
The function accepts the following arguments:
- N:
[in] CBLAS_INT
number of indexed elements. - X:
[in] double*
input array. - strideX:
[in] CBLAS_INT
index increment forX
.
double stdlib_strided_dnanasumors( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );
stdlib_strided_dnanasumors_ndarray( N, *X, strideX, offsetX )
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.
const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };
double v = stdlib_strided_dnanasumors_ndarray( 4, x, 1, 0 );
// returns 7.0
The function accepts the following arguments:
- N:
[in] CBLAS_INT
number of indexed elements. - X:
[in] double*
input array. - strideX:
[in] CBLAS_INT
index increment forX
. - offsetX:
[in] CBLAS_INT
starting index forX
.
double stdlib_strided_dnanasumors_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
Examples
#include "stdlib/blas/ext/base/dnanasumors.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;
// Compute the sum:
double v = stdlib_strided_dnanasumors( N, x, strideX );
// Print the result:
printf( "sumabs: %lf\n", v );
}