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Compute the squared absolute value for each element in a single-precision floating-point strided array.
npm install @stdlib/math-strided-special-sabs2Alternatively,
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denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (see README).
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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 sabs2 = require( '@stdlib/math-strided-special-sabs2' );Computes the squared absolute value for each element in a single-precision floating-point strided array x and assigns the results to elements in a single-precision floating-point strided array y.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );
// Compute the squared absolute values in-place:
sabs2( x.length, x, 1, x, 1 );
// x => <Float32Array>[ 4.0, 1.0, 9.0, 25.0, 16.0, 0.0, 1.0, 9.0 ]The function accepts the following arguments:
- N: number of indexed elements.
- 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 x and y are accessed at runtime. For example, to index every other value in x and to index the first N elements of y in reverse order,
var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
var N = floor( x.length / 2 );
sabs2( N, x, 2, y, -1 );
// y => <Float32Array>[ 25.0, 9.0, 1.0, 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' );
var floor = require( '@stdlib/math-base-special-floor' );
// Initial arrays...
var x0 = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y0 = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
// 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
var N = floor( x0.length / 2 );
sabs2( N, x1, -2, y1, 1 );
// y0 => <Float32Array>[ 0.0, 0.0, 0.0, 36.0, 16.0, 4.0 ]Computes the squared absolute value for each element in a single-precision floating-point strided array x and assigns the results to elements in a single-precision floating-point strided array y using alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );
sabs2.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => <Float32Array>[ 1.0, 4.0, 9.0, 16.0, 25.0 ]The function accepts the following additional arguments:
- offsetX: starting index for
x. - offsetY: starting index for
y.
While typed array views mandate a view offset based on the underlying buffer, the offsetX and offsetY parameters support indexing semantics based on starting indices. For example, to index every other value in x starting from the second value and to index the last N elements in y,
var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
var N = floor( x.length / 2 );
sabs2.ndarray( N, x, 2, 1, y, -1, y.length-1 );
// y => <Float32Array>[ 0.0, 0.0, 0.0, 36.0, 16.0, 4.0 ]var round = require( '@stdlib/math-base-special-round' );
var randu = require( '@stdlib/random-base-randu' );
var Float32Array = require( '@stdlib/array-float32' );
var sabs2 = require( '@stdlib/math-strided-special-sabs2' );
var x = new Float32Array( 10 );
var y = new Float32Array( 10 );
var i;
for ( i = 0; i < x.length; i ) {
x[ i ] = round( (randu()*200.0) - 100.0 );
}
console.log( x );
console.log( y );
sabs2.ndarray( x.length, x, 1, 0, y, -1, y.length-1 );
console.log( y );#include "stdlib/math/strided/special/sabs2.h"Computes the squared absolute value for each element in a single-precision floating-point strided array X and assigns the results to elements in a single-precision floating-point strided array Y.
#include <stdint.h>
const float X[] = { -1.0, -2.0, -3.0, -4.0, -5.0, -6.0, -7.0, -8.0 };
float Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
const int64_t N = 4;
stdlib_strided_sabs2( N, X, 2, Y, 2 );The function accepts the following arguments:
- N:
[in] int64_tnumber of indexed elements. - X:
[in] float*input array. - strideX:
[in] int64_tindex increment forX. - Y:
[out] float*output array. - strideY:
[in] int64_tindex increment forY.
void stdlib_strided_sabs2( const int64_t N, const float *X, const int64_t strideX, float *Y, const int64_t strideY );#include "stdlib/math/strided/special/sabs2.h"
#include <stdint.h>
#include <stdio.h>
int main( void ) {
// Create an input strided array:
const float X[] = { -1.0, -2.0, -3.0, -4.0, -5.0, -6.0, -7.0, -8.0 };
// Create an output strided array:
float Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
// Specify the number of elements:
const int64_t N = 4;
// Specify the stride lengths:
const int64_t strideX = 2;
const int64_t strideY = 2;
// Compute the squared absolute value element-wise:
stdlib_strided_sabs2( N, X, strideX, Y, strideY );
// Print the result:
for ( int i = 0; i < 8; i ) {
printf( "Y[ %i ] = %lf\n", i, Y[ i ] );
}
}@stdlib/math-strided/special/abs2: compute the squared absolute value for each element in a strided array.@stdlib/math-strided/special/dabs2: compute the squared absolute value for each element in a double-precision floating-point strided array.@stdlib/math-strided/special/sabs: compute the absolute value for each element in a single-precision floating-point strided array.
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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