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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
import dnanasumors from 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dnanasumors@esm/index.mjs';
Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN
values and using ordinary recursive summation.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
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
,
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
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.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
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
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.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
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
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
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
- 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.
<!DOCTYPE html>
<html lang="en">
<body>
<script type="module">
import discreteUniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-discrete-uniform@esm/index.mjs';
import bernoulli from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-bernoulli@esm/index.mjs';
import filledarrayBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-filled-by@esm/index.mjs';
import dnanasumors from 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dnanasumors@esm/index.mjs';
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 );
</script>
</body>
</html>
@stdlib/blas-ext/base/dnanasum
: calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values.
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For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
See LICENSE.
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