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About stdlib...

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dinv

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Compute the multiplicative inverse for each element in a double-precision floating-point strided array.

Usage

import dinv from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-strided-special-dinv@esm/index.mjs';

dinv( N, x, strideX, y, strideY )

Computes the multiplicative inverse 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.

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var x = new Float64Array( [ -20.0, -1.0, 2.0, 4.0, 10.0 ] );

// Perform operation in-place:
dinv( x.length, x, 1, x, 1 );
// x => <Float64Array>[ -0.05, -1.0, 0.5, 0.25, 0.1 ]

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,

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var x = new Float64Array( [ -20.0, -1.0, 2.0, 4.0, 10.0, 100.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dinv( 3, x, 2, y, -1 );
// y => <Float64Array>[ 0.1, 0.5, -0.05, 0.0, 0.0, 0.0 ]

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

// Initial arrays...
var x0 = new Float64Array( [ -20.0, -1.0, 2.0, 4.0, 10.0, 100.0 ] );
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

dinv( 3, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 0.01, 0.25, -1.0 ]

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

Computes the multiplicative inverse 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.

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var x = new Float64Array( [ -20.0, -1.0, 2.0, 4.0, 10.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dinv.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => <Float64Array>[ -0.05, -1.0, 0.5, 0.25, 0.1 ]

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,

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var x = new Float64Array( [ -20.0, -1.0, 2.0, 4.0, 10.0, 100.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dinv.ndarray( 3, x, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 0.01, 0.25, -1.0 ]

Examples

<!DOCTYPE html>
<html lang="en">
<body>
<script type="module">

import uniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-uniform@esm/index.mjs';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
import dinv from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-strided-special-dinv@esm/index.mjs';

var x = new Float64Array( 10 );
var y = new Float64Array( 10 );

var i;
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = uniform( -50.0, 50.0 );
}
console.log( x );
console.log( y );

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

</script>
</body>
</html>

See Also


Notice

This package is part of stdlib, a standard library 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.

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.