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Compute the principal square root for each element in a double-precision floating-point strided array.
npm install @stdlib/math-strided-special-dsqrt
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 dsqrt = require( '@stdlib/math-strided-special-dsqrt' );
Computes the principal square root for each element in a double-precision floating-point strided array x
and assigns the results to elements in a double-precision floating-point strided array y
.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
// Perform operation in-place:
dsqrt( x.length, x, 1, x, 1 );
// x => <Float64Array>[ 0.0, 2.0, 3.0, ~3.464, ~4.899 ]
The function accepts the following arguments:
- N: number of indexed elements.
- x: input
Float64Array
. - strideX: index increment for
x
. - y: output
Float64Array
. - 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 Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
dsqrt( 3, x, 2, y, -1 );
// y => <Float64Array>[ ~4.899, 3.0, 0.0, 0.0, 0.0, 0.0 ]
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float64Array = require( '@stdlib/array-float64' );
// Initial arrays...
var x0 = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var y0 = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
dsqrt( 3, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 8.0, ~3.464, 2.0 ]
Computes the principal square root for each element in a double-precision floating-point strided array x
and assigns the results to elements in a double-precision floating-point strided array y
using alternative indexing semantics.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );
dsqrt.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 0.0, 2.0, 3.0, ~3.464, ~4.899 ]
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 Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
dsqrt.ndarray( 3, x, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 8.0, ~3.464, 2.0 ]
var uniform = require( '@stdlib/random-base-uniform' );
var Float64Array = require( '@stdlib/array-float64' );
var dsqrt = require( '@stdlib/math-strided-special-dsqrt' );
var x = new Float64Array( 10 );
var y = new Float64Array( 10 );
var i;
for ( i = 0; i < x.length; i++ ) {
x[ i ] = uniform( 0.0, 200.0 );
}
console.log( x );
console.log( y );
dsqrt.ndarray( x.length, x, 1, 0, y, -1, y.length-1 );
console.log( y );
#include "stdlib/math/strided/special/dsqrt.h"
Computes the principal square root for each element in a double-precision floating-point strided array X
and assigns the results to elements in a double-precision floating-point strided array Y
.
#include <stdint.h>
const double X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };
double Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
const int64_t N = 4;
stdlib_strided_dsqrt( N, X, 2, Y, 2 );
The function accepts the following arguments:
- N:
[in] int64_t
number of indexed elements. - X:
[in] double*
input array. - strideX:
[in] int64_t
index increment forX
. - Y:
[out] double*
output array. - strideY:
[in] int64_t
index increment forY
.
void stdlib_strided_dsqrt( const int64_t N, const double *X, const int64_t strideX, double *Y, const int64_t strideY );
#include "stdlib/math/strided/special/dsqrt.h"
#include <stdint.h>
#include <stdio.h>
int main( void ) {
// Create an input strided array:
const double X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };
// Create an output strided array:
double 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 results:
stdlib_strided_dsqrt( N, X, strideX, Y, strideY );
// Print the results:
for ( int i = 0; i < 8; i++ ) {
printf( "Y[ %i ] = %lf\n", i, Y[ i ] );
}
}
@stdlib/math-strided/special/dcbrt
: compute the cube root of each element in a double-precision floating-point strided array.@stdlib/math-strided/special/drsqrt
: compute the reciprocal square root for each element in a double-precision floating-point strided array.@stdlib/math-strided/special/sqrt
: compute the principal square root for each element in a strided array.@stdlib/math-strided/special/ssqrt
: compute the principal square root 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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