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!
Apply a binary function to double-precision floating-point strided input arrays according to a strided mask array and assign results to a double-precision floating-point strided output array.
npm install @stdlib/strided-base-dmskmap2Alternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (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 dmskmap2 = require( '@stdlib/strided-base-dmskmap2' );Applies a binary function to double-precision floating-point strided input arrays according to a strided mask array and assigns results to a double-precision floating-point strided output array.
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var add = require( '@stdlib/math-base-ops-add' );
var x = new Float64Array( [ -2.0, 1.0, -3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );
var y = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
var z = new Float64Array( x.length );
var m = new Uint8Array( [ 0, 0, 1, 0, 0, 1, 1, 0 ] );
dmskmap2( x.length, x, 1, y, 1, m, 1, z, 1, add );
// z => <Float64Array>[ -1.0, 3.0, 0.0, -1.0, 9.0, 0.0, 0.0, 5.0 ]The function accepts the following arguments:
- N: number of indexed elements.
- x: input
Float64Array. - strideX: index increment for
x. - y: input
Float64Array. - strideY: index increment for
y. - mask: mask
Uint8Array. - strideMask: index increment for
mask. - z: output
Float64Array. - strideZ: index increment for
z. - fcn: function to apply.
The N and stride parameters determine which elements in the strided arrays 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 Uint8Array = require( '@stdlib/array-uint8' );
var add = require( '@stdlib/math-base-ops-add' );
var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 2.0, 2.0, 3.0, 3.0 ] );
var z = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0, 1 ] );
dmskmap2( 3, x, 2, y, -1, m, 2, z, 1, add );
// z => <Float64Array>[ 1.0, 0.0, -4.0, 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' );
var Uint8Array = require( '@stdlib/array-uint8' );
var add = require( '@stdlib/math-base-ops-add' );
// Initial arrays...
var x0 = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y0 = new Float64Array( [ 1.0, 1.0, 2.0, 2.0, 3.0, 3.0 ] );
var z0 = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
var m0 = new Uint8Array( [ 0, 0, 1, 0, 0, 1 ] );
// 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
var z1 = new Float64Array( z0.buffer, z0.BYTES_PER_ELEMENT*2 ); // start at 3rd element
var m1 = new Uint8Array( m0.buffer, m0.BYTES_PER_ELEMENT*3 ); // start at 4th element
dmskmap2( 3, x1, -2, y1, 1, m1, 1, z1, 1, add );
// z0 => <Float64Array>[ 0.0, 0.0, -4.0, -1.0, 0.0, 0.0 ]dmskmap2.ndarray( N, x, strideX, offsetX, y, strideY, offsetY, mask, strideMask, offsetMask, z, strideZ, offsetZ, fcn )
Applies a binary function to double-precision floating-point strided input arrays according to a strided mask array and assigns results to a double-precision floating-point strided output array using alternative indexing semantics.
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var add = require( '@stdlib/math-base-ops-add' );
var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 2.0, 2.0, 3.0 ] );
var z = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0 ] );
dmskmap2.ndarray( x.length, x, 1, 0, y, 1, 0, m, 1, 0, z, 1, 0, add );
// z => <Float64Array>[ 0.0, -1.0, 0.0, -2.0, -2.0 ]The function accepts the following additional arguments:
- offsetX: starting index for
x. - offsetY: starting index for
y. - offsetMask: starting index for
mask. - offsetZ: starting index for
z.
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 in reverse order,
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var add = require( '@stdlib/math-base-ops-add' );
var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 2.0, 2.0, 3.0, 3.0 ] );
var z = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0, 1 ] );
dmskmap2.ndarray( 3, x, 2, 1, y, -1, y.length-1, m, 2, 1, z, 1, 0, add );
// z => <Float64Array>[ 1.0, -1.0, 0.0, 0.0, 0.0, 0.0 ]var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var bernoulli = require( '@stdlib/random-base-bernoulli' ).factory;
var Float64Array = require( '@stdlib/array-float64' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var add = require( '@stdlib/math-base-ops-add' );
var dmskmap2 = require( '@stdlib/strided-base-dmskmap2' );
var x = filledarrayBy( 10, 'float64', discreteUniform( -100, 100 ) );
console.log( x );
var y = filledarrayBy( x.length, 'float64', discreteUniform( -100, 100 ) );
console.log( y );
var m = filledarrayBy( x.length, 'uint8', bernoulli( 0.2 ) );
console.log( m );
var z = new Float64Array( x.length );
console.log( z );
dmskmap2.ndarray( x.length, x, 1, 0, y, -1, y.length-1, m, 1, 0, z, 1, 0, add );
console.log( z );#include "stdlib/strided/base/dmskmap2.h"Applies a binary function to double-precision floating-point strided input arrays according to a strided mask array and assigns results to a double-precision floating-point strided output array.
#include <stdint.h>
static double add( const double x, const double y ) {
return x + y;
}
double X[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
double Y[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
double Z[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
uint8_t M[] = { 0, 0, 1, 0, 0, 1 };
int64_t N = 6;
stdlib_strided_dmskmap2( N, X, 1, Y, 1, M, 1, Z, 1, add );The function accepts the following arguments:
- N:
[in] int64_tnumber of indexed elements. - X:
[in] double*input array. - strideX
[in] int64_tindex increment forX. - Y:
[int] double*input array. - strideY:
[in] int64_tindex increment forY. - Mask:
[in] uint8_t*mask array. - strideMask:
[in] int64_tindex increment forMask. - Z:
[out] double*output array. - strideZ:
[in] int64_tindex increment forZ. - fcn:
[in] double (*fcn)( double, double )binary function to apply.
void stdlib_strided_dmskmap2( const int64_t N, const double *X, const int64_t strideX, const double *Y, const int64_t strideY, const uint8_t *Mask, const int64_t strideMask, double *Z, const int64_t strideZ, double (*fcn)( double, double ) );#include "stdlib/strided/base/dmskmap2.h"
#include <stdint.h>
#include <stdio.h>
#include <inttypes.h>
// Define a callback:
static double add( const double x, const double y ) {
return x + y;
}
int main( void ) {
// Create input strided arrays:
double X[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
double Y[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
// Create a mask strided array:
uint8_t M[] = { 0, 0, 1, 0, 0, 1 };
// Create an output strided array:
double Z[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
// Specify the number of elements:
int64_t N = 6;
// Define the strides:
int64_t strideX = 1;
int64_t strideY = -1;
int64_t strideZ = 1;
int64_t strideM = 1;
// Apply the callback:
stdlib_strided_dmskmap2( N, X, strideX, Y, strideY, M, strideM, Z, strideZ, add );
// Print the results:
for ( int64_t i = 0; i < N; i++ ) {
printf( "Z[ %"PRId64" ] = %lf\n", i, Z[ i ] );
}
}@stdlib/strided-base/dmap2: apply a binary function to double-precision floating-point strided input arrays and assign results to a double-precision floating-point strided output array.@stdlib/strided-base/dmskmap: apply a unary function to a double-precision floating-point strided input array according to a strided mask array and assign results to a double-precision floating-point strided output array.@stdlib/strided-base/smskmap2: apply a binary function to single-precision floating-point strided input arrays according to a strided mask array and assign results to a single-precision floating-point strided output 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.
Copyright © 2016-2024. The Stdlib Authors.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent