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Calculate the sum of single-precision floating-point strided array elements, ignoring
NaN
values, using extended accumulation, and returning an extended precision result.
npm install @stdlib/blas-ext-base-dsnansum
Alternatively,
- To load the package in a website via a
script
tag without installation and bundlers, use the ES Module available on theesm
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.
var dsnansum = require( '@stdlib/blas-ext-base-dsnansum' );
Computes the sum of single-precision floating-point strided array elements, ignoring NaN
values, using extended accumulation, and returning an extended precision result.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var v = dsnansum( x.length, x, 1 );
// returns 1.0
The function has the following parameters:
- N: number of indexed elements.
- x: input
Float32Array
. - strideX: stride length 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 every other element:
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var v = dsnansum( 4, x, 2 );
// returns 5.0
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float32Array = require( '@stdlib/array-float32' );
var x0 = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var v = dsnansum( 4, x1, 2 );
// returns 5.0
Computes the sum of single-precision floating-point strided array elements, ignoring NaN
values, using extended accumulation and alternative indexing semantics, and returning an extended precision result.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var v = dsnansum.ndarray( x.length, x, 1, 0 );
// returns 1.0
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 every other element starting from the second element:
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var v = dsnansum.ndarray( 4, x, 2, 1 );
// returns 5.0
- If
N <= 0
, both functions return0.0
. - Accumulated intermediate values are stored as double-precision floating-point numbers.
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var dsnansum = require( '@stdlib/blas-ext-base-dsnansum' );
function rand() {
if ( bernoulli( 0.7 ) > 0 ) {
return discreteUniform( -10, 10 );
}
return NaN;
}
var x = filledarrayBy( 10, 'float32', rand );
console.log( x );
var v = dsnansum( x.length, x, 1 );
console.log( v );
#include "stdlib/blas/ext/base/dsnansum.h"
Computes the sum of single-precision floating-point strided array elements, ignoring NaN
values, using extended accumulation, and returning an extended precision result.
const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };
double v = stdlib_strided_dsnansum( 4, x, 1 );
// returns 1.0
The function accepts the following arguments:
- N:
[in] CBLAS_INT
number of indexed elements. - X:
[in] float*
input array. - strideX:
[in] CBLAS_INT
stride length forX
.
double stdlib_strided_dsnansum( const CBLAS_INT N, const float *X, const CBLAS_INT strideX );
Computes the sum of single-precision floating-point strided array elements, ignoring NaN
values, using extended accumulation and alternative indexing semantics, and returning an extended precision result.
const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };
double v = stdlib_strided_dsnansum_ndarray( 4, x, 1, 0 );
// returns 1.0
The function accepts the following arguments:
- N:
[in] CBLAS_INT
number of indexed elements. - X:
[in] float*
input array. - strideX:
[in] CBLAS_INT
stride length forX
. - offsetX:
[in] CBLAS_INT
starting index forX
.
double stdlib_strided_dsnansum_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
#include "stdlib/blas/ext/base/dsnansum.h"
#include <stdio.h>
int main( void ) {
// Create a strided array:
const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 0.0f/0.0f, 0.0f/0.0f };
// Specify the number of elements:
const int N = 5;
// Specify the stride length:
const int strideX = 2;
// Compute the sum:
double v = stdlib_strided_dsnansum( N, x, strideX );
// Print the result:
printf( "sum: %lf\n", v );
}
@stdlib/stats-base/dsnanmean
: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using extended accumulation, and returning an extended precision result.@stdlib/blas-ext/base/dssum
: calculate the sum of single-precision floating-point strided array elements using extended accumulation and returning an extended precision result.@stdlib/blas-ext/base/sdsnansum
: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using extended accumulation.@stdlib/blas-ext/base/snansum
: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values.
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.
See LICENSE.
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