@stdlib/strided-base-smap

Apply a unary function to a single-precision floating-point strided input array and assign results to a single-precision floating-point strided output array.

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README

smap

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Apply a unary function to a single-precision floating-point strided input array and assign results to a single-precision floating-point strided output array.

Installation

npm install @stdlib/strided-base-smap

Usage

var smap = require( '@stdlib/strided-base-smap' );

smap( N, x, strideX, y, strideY, fcn )

Applies a unary function to a single-precision floating-point strided input array and assigns results to a single-precision floating-point strided output array.

var Float32Array = require( '@stdlib/array-float32' );
var absf = require( '@stdlib/math-base-special-absf' );

var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

// Compute the absolute values in-place:
smap( x.length, x, 1, x, 1, absf );
// x => <Float32Array>[ 2.0, 1.0, 3.0, 5.0, 4.0, 0.0, 1.0, 3.0 ]

The function accepts the following arguments:

  • N: number of indexed elements.
  • x: input Float32Array.
  • strideX: index increment for x.
  • y: output Float32Array.
  • strideY: index increment for y.
  • fcn: function to apply.

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 absf = require( '@stdlib/math-base-special-absf' );

var x = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

smap( 3, x, 2, y, -1, absf );
// y => <Float32Array>[ 5.0, 3.0, 1.0, 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' );
var absf = require( '@stdlib/math-base-special-absf' );

// Initial arrays...
var x0 = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
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

smap( 3, x1, -2, y1, 1, absf );
// y0 => <Float32Array>[ 0.0, 0.0, 0.0, 6.0, 4.0, 2.0 ]

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

Applies a unary function to a single-precision floating-point strided input array and assigns results to a single-precision floating-point strided output array using alternative indexing semantics.

var Float32Array = require( '@stdlib/array-float32' );
var absf = require( '@stdlib/math-base-special-absf' );

var x = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

smap.ndarray( x.length, x, 1, 0, y, 1, 0, absf );
// y => <Float32Array>[ 1.0, 2.0, 3.0, 4.0, 5.0 ]

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 in reverse order,

var Float32Array = require( '@stdlib/array-float32' );
var absf = require( '@stdlib/math-base-special-absf' );

var x = new Float32Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

smap.ndarray( 3, x, 2, 1, y, -1, y.length-1, absf );
// y => <Float32Array>[ 0.0, 0.0, 0.0, 6.0, 4.0, 2.0 ]

Examples

var round = require( '@stdlib/math-base-special-round' );
var randu = require( '@stdlib/random-base-randu' );
var Float32Array = require( '@stdlib/array-float32' );
var smap = require( '@stdlib/strided-base-smap' );

function scale( x ) {
    return x * 10.0;
}

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

var i;
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = round( (randu()*200.0) - 100.0 );
}
console.log( x );
console.log( y );

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

C APIs

Usage

#include "stdlib/strided/base/smap.h"

stdlib_strided_smap( N, *X, strideX, *Y, strideY, fcn )

Applies a unary function to a single-precision floating-point strided input array and assigns results to a single-precision floating-point strided output array.

#include <stdint.h>

static float scale( const float x ) {
    return x * 10.0f;
}

float X[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
float Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

int64_t N = 6;

stdlib_strided_smap( N, X, 1, Y, 1, scale );

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.
  • fcn: [in] float (*fcn)( float ) unary function to apply.
void stdlib_strided_smap( const int64_t N, const float *X, const int64_t strideX, float *Y, const int64_t strideY, float (*fcn)( float ) );

Examples

#include "stdlib/strided/base/smap.h"
#include <stdint.h>
#include <stdio.h>
#include <inttypes.h>

// Define a callback:
static float scale( const float x ) {
    return x * 10.0;
}

int main() {
    // Create an input strided array:
    float X[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };

    // Create an output strided array:
    float Y[] = { 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;

    // Apply the callback:
    stdlib_strided_smap( N, X, strideX, Y, strideY, scale );

    // Print the results:
    for ( int64_t i = 0; i < N; i++ ) {
        printf( "Y[ %"PRId64" ] = %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.

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License

See LICENSE.

Copyright

Copyright © 2016-2022. The Stdlib Authors.