@stdlib/math-strided-special-smskramp
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    smskramp

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    Evaluate the ramp function for each element in a single-precision floating-point strided array according to a strided mask array.

    Installation

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

    Usage

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

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

    Evaluates the ramp function 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( [ 1.0, 2.0, -3.0, 4.0, -5.0 ] );
    var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
    var y = new Float32Array( x.length );
    
    smskramp( x.length, x, 1, m, 1, y, 1 );
    // y => <Float32Array>[ 1.0, 2.0, 0.0, 4.0, 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( [ 1.0, 2.0, -3.0, 4.0, -5.0, 6.0 ] );
    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 ] );
    
    smskramp( 3, x, 2, m, 2, y, -1 );
    // y => <Float32Array>[ 0.0, 0.0, 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 Uint8Array = require( '@stdlib/array-uint8' );
    
    // Initial arrays...
    var x0 = new Float32Array( [ 1.0, 2.0, -3.0, 4.0, -5.0, 6.0 ] );
    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
    
    smskramp( 3, x1, -2, m1, -2, y1, 1 );
    // y0 => <Float32Array>[ 0.0, 0.0, 0.0, 0.0, 4.0, 2.0 ]

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

    Evaluates the ramp function 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( [ 1.0, 2.0, -3.0, 4.0, -5.0 ] );
    var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
    var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );
    
    smskramp.ndarray( x.length, x, 1, 0, m, 1, 0, y, 1, 0 );
    // y => <Float32Array>[ 1.0, 2.0, 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,

    var Float32Array = require( '@stdlib/array-float32' );
    var Uint8Array = require( '@stdlib/array-uint8' );
    
    var x = new Float32Array( [ 1.0, 2.0, -3.0, 4.0, -5.0, 6.0 ] );
    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 ] );
    
    smskramp.ndarray( 3, x, 2, 1, m, 2, 1, y, -1, y.length-1 );
    // y => <Float32Array>[ 0.0, 0.0, 0.0, 0.0, 4.0, 2.0 ]

    Examples

    var uniform = require( '@stdlib/random-base-uniform' );
    var Float32Array = require( '@stdlib/array-float32' );
    var Uint8Array = require( '@stdlib/array-uint8' );
    var smskramp = require( '@stdlib/math-strided-special-smskramp' );
    
    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( -10.0, 10.0 );
        if ( uniform( 0.0, 1.0 ) < 0.5 ) {
            m[ i ] = 1;
        }
    }
    console.log( x );
    console.log( m );
    console.log( y );
    
    smskramp.ndarray( x.length, x, 1, 0, m, 1, 0, y, -1, y.length-1 );
    console.log( y );

    C APIs

    Installation

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

    Usage

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

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

    Evaluates the ramp function 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>
    
    float X[] = { 1.1, 2.5, -3.5, 4.0, -5.9, 6.4, -7.0, 8.2 };
    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 };
    
    int64_t N = 4;
    
    stdlib_strided_smskramp( 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_smskramp( 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/smskramp.h"
    #include <stdint.h>
    #include <stdio.h>
    
    int main() {
        // Create an input strided array:
        float X[] = { 1.1, 2.5, -3.5, 4.0, -5.9, 6.4, -7.0, 8.2 };
    
        // Create a mask strided array:
        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:
        int64_t N = 4;
    
        // Specify the stride lengths:
        int64_t strideX = 2;
        int64_t strideM = 2;
        int64_t strideY = 2;
    
        // Compute the results:
        stdlib_strided_smskramp( 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 ] );
        }
    }

    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 © 2016-2021. The Stdlib Authors.

    Install

    npm i @stdlib/math-strided-special-smskramp

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