About stdlib...
We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Evaluate the ramp function for each element in a double-precision floating-point strided array according to a strided mask array.
import dmskramp from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-strided-special-dmskramp@esm/index.mjs';Evaluates the ramp function for each element in a double-precision floating-point strided array x according to a strided mask array 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';
import Uint8Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-uint8@esm/index.mjs';
var x = new Float64Array( [ 1.1, 2.5, -3.5, 4.0, -5.9 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
var y = new Float64Array( x.length );
dmskramp( x.length, x, 1, m, 1, y, 1 );
// y => <Float64Array>[ 1.1, 2.5, 0.0, 4.0, 0.0 ]The function accepts the following arguments:
- N: number of indexed elements.
- x: input
Float64Array. - sx: index increment for
x. - m: mask
Uint8Array. - sm: index increment for
m. - y: output
Float64Array. - 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,
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
import Uint8Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-uint8@esm/index.mjs';
var x = new Float64Array( [ 1.1, 2.5, -3.5, 4.0, -5.9, 6.4 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
dmskramp( 3, x, 2, m, 2, y, -1 );
// y => <Float64Array>[ 0.0, 0.0, 1.1, 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';
import Uint8Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-uint8@esm/index.mjs';
// Initial arrays...
var x0 = new Float64Array( [ 1.1, 2.5, -3.5, 4.0, -5.9, 6.4 ] );
var m0 = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
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 m1 = new Uint8Array( m0.buffer, m0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
dmskramp( 3, x1, -2, m1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, 4.0, 2.5 ]Evaluates the ramp function for each element in a double-precision floating-point strided array x according to a strided mask array 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';
import Uint8Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-uint8@esm/index.mjs';
var x = new Float64Array( [ 1.1, 2.5, -3.5, 4.0, -5.9 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );
dmskramp.ndarray( x.length, x, 1, 0, m, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 1.1, 2.5, 0.0, 4.0, 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,
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
import Uint8Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-uint8@esm/index.mjs';
var x = new Float64Array( [ 1.1, 2.5, -3.5, 4.0, -5.9, 6.4 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
dmskramp.ndarray( 3, x, 2, 1, m, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, 4.0, 2.5 ]<!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 Uint8Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-uint8@esm/index.mjs';
import dmskramp from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-strided-special-dmskramp@esm/index.mjs';
var x = new Float64Array( 10 );
var m = new Uint8Array( 10 );
var y = new Float64Array( 10 );
var i;
for ( i = 0; i < x.length; i++ ) {
x[ i ] = uniform( -10.0, 10.0 );
if ( uniform( 0.0, 1.0 ) < 0.5 ) {
m[ i ] = 1;
}
}
console.log( x );
console.log( m );
console.log( y );
dmskramp.ndarray( x.length, x, 1, 0, m, 1, 0, y, -1, y.length-1 );
console.log( y );
</script>
</body>
</html>@stdlib/math-strided/special/dramp: evaluate the ramp function for each element in a double-precision floating-point strided array.@stdlib/math-strided/special/smskramp: evaluate the ramp function for each element in a single-precision floating-point strided array according to a strided mask array.
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.
