# @stdlib/math-strided-special-ssqrt

0.1.1 • Public • Published

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# ssqrt

Compute the principal square root for each element in a single-precision floating-point strided array.

## Installation

`npm install @stdlib/math-strided-special-ssqrt`

## Usage

`var ssqrt = require( '@stdlib/math-strided-special-ssqrt' );`

#### ssqrt( N, x, strideX, y, strideY )

Computes the principal square root 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( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );

// Perform operation in-place:
ssqrt( x.length, x, 1, x, 1 );
// x => <Float32Array>[ 0.0, 2.0, 3.0, ~3.464, ~4.899 ]```

The function accepts the following arguments:

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 x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

ssqrt( 3, x, 2, y, -1 );
// y => <Float32Array>[ ~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 Float32Array = require( '@stdlib/array-float32' );

// Initial arrays...
var x0 = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
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

ssqrt( 3, x1, -2, y1, 1 );
// y0 => <Float32Array>[ 0.0, 0.0, 0.0, 8.0, ~3.464, 2.0 ]```

#### ssqrt.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )

Computes the principal square root 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( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

ssqrt.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => <Float32Array>[ 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 Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

ssqrt.ndarray( 3, x, 2, 1, y, -1, y.length-1 );
// y => <Float32Array>[ 0.0, 0.0, 0.0, 8.0, ~3.464, 2.0 ]```

## Examples

```var uniform = require( '@stdlib/random-base-uniform' );
var Float32Array = require( '@stdlib/array-float32' );
var ssqrt = require( '@stdlib/math-strided-special-ssqrt' );

var x = new Float32Array( 10 );
var y = new Float32Array( 10 );

var i;
for ( i = 0; i < x.length; i++ ) {
x[ i ] = uniform( 0.0, 200.0 );
}
console.log( x );
console.log( y );

ssqrt.ndarray( x.length, x, 1, 0, y, -1, y.length-1 );
console.log( y );```

## C APIs

### Usage

`#include "stdlib/math/strided/special/ssqrt.h"`

#### stdlib_strided_ssqrt( N, *X, strideX, *Y, strideY )

Computes the principal square root 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>

float X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };
float Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

int64_t N = 4;

stdlib_strided_ssqrt( N, X, 2, Y, 2 );```

The function accepts the following arguments:

• N: `[in] int64_t` number of indexed elements.
• X: `[in] float*` input array.
• strideX: `[in] int64_t` index increment for `X`.
• Y: `[out] float*` output array.
• strideY: `[in] int64_t` index increment for `Y`.
`void stdlib_strided_ssqrt( const int64_t N, const float *X, const int64_t strideX, float *Y, const int64_t strideY );`

### Examples

```#include "stdlib/math/strided/special/ssqrt.h"
#include <stdint.h>
#include <stdio.h>

int main( void ) {
// Create an input strided array:
float X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };

// 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:
int64_t N = 4;

// Specify the stride lengths:
int64_t strideX = 2;
int64_t strideY = 2;

// Compute the results:
stdlib_strided_ssqrt( N, X, strideX, Y, strideY );

// Print the results:
for ( int i = 0; i < 8; i++ ) {
printf( "Y[ %i ] = %f\n", i, Y[ i ] );
}
}```

## Notice

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.

## Package Sidebar

### Install

`npm i @stdlib/math-strided-special-ssqrt`

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