stdlib-js/math-strided-special-smskrsqrt

Compute the reciprocal square root for each element in a single-precision floating-point strided array according to a strided mask array.

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smskrsqrt

Compute the reciprocal square root for each element in a single-precision floating-point strided array according to a strided mask array.

Installation

npm install @stdlib/math-strided-special-smskrsqrt

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm 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.

Usage

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

smskrsqrt( N, x, sx, m, sm, y, sy )

Computes the reciprocal square root for each element in a single-precision floating-point strided array x according to a strided mask array and assigns the results to elements in a single-precision floating-point strided array y.

var Float32Array = require( '@stdlib/array-float32' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
var y = new Float32Array( x.length );

smskrsqrt( x.length, x, 1, m, 1, y, 1 );
// y => <Float32Array>[ Infinity, 0.5, 0.0, ~0.289, 0.0 ]

The function accepts the following arguments:

• N: number of indexed elements.
• x: input Float32Array.
• sx: index increment for x.
• m: mask Uint8Array.
• sm: index increment for m.
• y: output Float32Array.
• sy: index increment for y.

The N and stride parameters determine which strided array elements 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 Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

smskrsqrt( 3, x, 2, m, 2, y, -1 );
// y => <Float32Array>[ 0.0, 0.0, Infinity, 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 Uint8Array = require( '@stdlib/array-uint8' );

// Initial arrays...
var x0 = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var m0 = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
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 m1 = new Uint8Array( m0.buffer, m0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

smskrsqrt( 3, x1, -2, m1, -2, y1, 1 );
// y0 => <Float32Array>[ 0.0, 0.0, 0.0, 0.0, ~0.289, 0.5 ]

smskrsqrt.ndarray( N, x, sx, ox, m, sm, om, y, sy, oy )

Computes the reciprocal square root for each element in a single-precision floating-point strided array x according to a strided mask array 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 Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

smskrsqrt.ndarray( x.length, x, 1, 0, m, 1, 0, y, 1, 0 );
// y => <Float32Array>[ Infinity, 0.5, 0.0, ~0.289, 0.0 ]

The function accepts the following additional arguments:

• ox: starting index for x.
• om: starting index for m.
• oy: starting index for y.

While typed array views mandate a view offset based on the underlying buffer, the offset 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 Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

smskrsqrt.ndarray( 3, x, 2, 1, m, 2, 1, y, -1, y.length-1 );
// y => <Float32Array>[ 0.0, 0.0, 0.0, 0.0, ~0.289, 0.5 ]

Examples

var uniform = require( '@stdlib/random-base-uniform' );
var Float32Array = require( '@stdlib/array-float32' );
var Uint8Array = require( '@stdlib/array-uint8' );
var smskrsqrt = require( '@stdlib/math-strided-special-smskrsqrt' );

var x = new Float32Array( 10 );
var m = new Uint8Array( 10 );
var y = new Float32Array( 10 );

var i;
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = uniform( 0.0, 200.0 );
    if ( uniform( 0.0, 1.0 ) < 0.5 ) {
        m[ i ] = 1;
    }
}
console.log( x );
console.log( m );
console.log( y );

smskrsqrt.ndarray( x.length, x, 1, 0, m, 1, 0, y, -1, y.length-1 );
console.log( y );

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C APIs

Usage

#include "stdlib/math/strided/special/smskrsqrt.h"

stdlib_strided_smskrsqrt( N, *X, strideX, *Mask, strideMask, *Y, strideY )

Computes the reciprocal square root for each element in a single-precision floating-point strided array X according to a strided mask array and assigns the results to elements in a single-precision floating-point strided array Y.

#include <stdint.h>

const float X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };
const uint8_t Mask[] = { 0, 0, 1, 0, 1, 1, 0, 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_smskrsqrt( N, X, 2, Mask, 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.
• Mask: [in] uint8_t* mask array.
• strideMask: [in] int64_t index increment for Mask.
• Y: [out] float* output array.
• strideY: [in] int64_t index increment for Y.

void stdlib_strided_smskrsqrt( const int64_t N, const float *X, const int64_t strideX, const uint8_t *Mask, const int64_t strideMask, float *Y, const int64_t strideY );

Examples

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

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

    // Create a mask strided array:
    const uint8_t M[] = { 0, 0, 1, 0, 1, 1, 0, 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 strideM = 2;
    const int64_t strideY = 2;

    // Compute the results:
    stdlib_strided_smskrsqrt( N, X, strideX, M, strideM, Y, strideY );

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

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See Also

• @stdlib/math-strided/special/dmskrsqrt: compute the reciprocal square root for each element in a double-precision floating-point strided array according to a strided mask array.
• @stdlib/math-strided/special/smsksqrt: compute the principal square root for each element in a single-precision floating-point strided array according to a strided mask array.
• @stdlib/math-strided/special/srsqrt: compute the reciprocal square root for each element in a single-precision floating-point strided array.

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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.

Community

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License

See LICENSE.

Copyright

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