scumax

Calculate the cumulative maximum of single-precision floating-point strided array elements.

Installation

npm install @stdlib/stats-strided-scumax

Usage

var scumax = require( '@stdlib/stats-strided-scumax' );

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

Computes the cumulative maximum of single-precision floating-point strided array elements.

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float32Array( x.length );

scumax( x.length, x, 1, y, 1 );
// y => <Float32Array>[ 1.0, 1.0, 2.0 ]

The function has the following parameters:

• N: number of indexed elements.
• x: input Float32Array.
• strideX: stride length for x.
• y: output Float32Array.
• strideY: stride length for y.

The N and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to compute the cumulative maximum of every other element in x,

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float32Array( x.length );

var v = scumax( 4, x, 2, y, 1 );
// y => <Float32Array>[ 1.0, 2.0, 2.0, 4.0, 0.0, 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' );

// Initial arrays...
var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var y0 = new Float32Array( x0.length );

// 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

scumax( 4, x1, -2, y1, 1 );
// y0 => <Float32Array>[ 0.0, 0.0, 0.0, 4.0, 4.0, 4.0, 4.0, 0.0 ]

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

Computes the cumulative maximum of single-precision floating-point strided array elements using alternative indexing semantics.

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float32Array( x.length );

scumax.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => <Float32Array>[ 1.0, 1.0, 2.0 ]

The function has the following additional parameters:

• offsetX: starting index for x.
• offsetY: 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 calculate the cumulative maximum of every other element in x starting from the second element and to store in the last N elements of y starting from the last element

var Float32Array = require( '@stdlib/array-float32' );

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

scumax.ndarray( 4, x, 2, 1, y, -1, y.length-1 );
// y => <Float32Array>[ 0.0, 0.0, 0.0, 0.0, 4.0, 2.0, 1.0, 1.0 ]

Notes

• If N <= 0, both functions return y unchanged.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var Float32Array = require( '@stdlib/array-float32' );
var scumax = require( '@stdlib/stats-strided-scumax' );

var x = discreteUniform( 10, -50, 50, {
    'dtype': 'float32'
});
console.log( x );

var y = new Float32Array( x.length );
console.log( y );

scumax( x.length, x, 1, y, -1 );
console.log( y );

C APIs

Usage

#include "stdlib/stats/strided/scumax.h"

stdlib_strided_scumax( N, *X, strideX, *Y, strideY )

Computes the cumulative maximum of single-precision floating-point strided array elements.

const float x[] = { 1.0f, 2.0f, -3.0f, 4.0f, -5.0f, 6.0f, 7.0f, 8.0f };
float y[] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };

stdlib_strided_scumax( 4, x, 2, y, -2 );

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] float* input array.
• strideX: [in] CBLAS_INT stride length for X.
• Y: [out] float* output array.
• strideY: [in] CBLAS_INT stride length for Y.

void stdlib_strided_scumax( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, float *Y, const CBLAS_INT strideY );

stdlib_strided_scumax_ndarray( N, *X, strideX, offsetX, *Y, strideY, offsetY )

Computes the cumulative maximum of single-precision floating-point strided array elements using alternative indexing semantics.

const float x[] = { 1.0f, 2.0f, -3.0f, 4.0f, -5.0f, 6.0f, 7.0f, 8.0f };
float y[] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };

stdlib_strided_scumax_ndarray( 4, x, 2, 0, y, -2, 0 );

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] float* input array.
• strideX: [in] CBLAS_INT stride length for X.
• offsetX: [in] CBLAS_INT starting index for X.
• Y: [out] float* output array.
• strideY: [in] CBLAS_INT stride length for Y.
• offsetY: [in] CBLAS_INT starting index for Y.

void stdlib_strided_scumax_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, float *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );

Examples

#include "stdlib/stats/strided/scumax.h"
#include <stdio.h>

int main( void ) {
    // Create strided arrays:
    const float x[] = { 1.0f, 2.0f, -3.0f, 4.0f, -5.0f, 6.0f, 7.0f, 8.0f };
    float y[] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };

    // Specify the number of elements:
    const int N = 4;

    // Specify stride lengths:
    const int strideX = 2;
    const int strideY = -2;

    // Compute the cumulative maximum:
    stdlib_strided_scumax( N, x, strideX, y, strideY );

   // Print the result:
    for ( int i = 0; i < 8; i++ ) {
        printf( "y[ %d ] = %f\n", i, y[ i ] );
    }
}

See Also

• @stdlib/stats-base/cumax: calculate the cumulative maximum of a strided array.
• @stdlib/stats-strided/dcumax: calculate the cumulative maximum of double-precision floating-point strided array elements.
• @stdlib/stats-strided/scumin: calculate the cumulative minimum of single-precision floating-point strided array elements.

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.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.