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dmskmax

Calculate the maximum value of a double-precision floating-point strided array according to a mask.

Installation

npm install @stdlib/stats-base-dmskmax

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (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.

Usage

var dmskmax = require( '@stdlib/stats-base-dmskmax' );

dmskmax( N, x, strideX, mask, strideMask )

Computes the maximum value of a double-precision floating-point strided array according to a mask.

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 1.0, -2.0, 4.0, 2.0 ] );
var mask = new Uint8Array( [ 0, 0, 1, 0 ] );

var v = dmskmax( x.length, x, 1, mask, 1 );
// returns 2.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Float64Array.
• strideX: stride length for x.
• mask: mask Uint8Array. If a mask array element is 0, the corresponding element in x is considered valid and included in computation. If a mask array element is 1, the corresponding element in x is considered invalid/missing and excluded from computation.
• strideMask: stride length for mask.

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

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, 5.0, 6.0 ] );
var mask = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );

var v = dmskmax( 4, x, 2, mask, 2 );
// returns 4.0

Note that indexing is relative to the first index. To introduce offsets, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x0 = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, 5.0, 6.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var mask0 = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );
var mask1 = new Uint8Array( mask0.buffer, mask0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var v = dmskmax( 4, x1, 2, mask1, 2 );
// returns 4.0

dmskmax.ndarray( N, x, strideX, offsetX, mask, strideMask, offsetMask )

Computes the maximum value of a double-precision floating-point strided array according to a mask and using alternative indexing semantics.

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 1.0, -2.0, 4.0, 2.0 ] );
var mask = new Uint8Array( [ 0, 0, 1, 0 ] );

var v = dmskmax.ndarray( x.length, x, 1, 0, mask, 1, 0 );
// returns 2.0

The function has the following additional parameters:

• offsetX: starting index for x.
• offsetMask: starting index for mask.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on a starting indices. For example, to calculate the maximum value for every other element in x starting from the second element

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, 5.0, 6.0 ] );
var mask = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );

var v = dmskmax.ndarray( 4, x, 2, 1, mask, 2, 1 );
// returns 4.0

Notes

• If N <= 0, both functions return NaN.

Examples

var uniform = require( '@stdlib/random-array-uniform' );
var bernoulli = require( '@stdlib/random-array-bernoulli' );
var dmskmax = require( '@stdlib/stats-base-dmskmax' );

var uniformOptions = {
    'dtype': 'float64'
};
var bernoulliOptions = {
    'dtype': 'uint8'
};

var x = uniform( 10, -50.0, 50.0, uniformOptions );
var mask = bernoulli( x.length, 0.2, bernoulliOptions );
console.log( x );
console.log( mask );

var v = dmskmax( x.length, x, 1, mask, 1 );
console.log( v );

Usage

#include "stdlib/stats/base/dmskmax.h"

stdlib_strided_dmskmax( N, *X, strideX, *Mask, strideMask )

Computes the maximum value of a double-precision floating-point strided array according to a mask.

#include <stdint.h>

const double x[] = { 1.0, -2.0, 2.0 };
const uint8_t mask[] = { 0, 1, 0 };

double v = stdlib_strided_dmskmax( 3, x, 1, mask, 1 );
// returns 2.0

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] double* input array.
• strideX: [in] CBLAS_INT stride length for X.
• Mask: [in] uint8_t* mask array. If a Mask array element is 0, the corresponding element in X is considered valid and included in computation. If a Mask array element is 1, the corresponding element in X is considered invalid/missing and excluded from computation.
• strideMask: [in] CBLAS_INT stride length for Mask.

double stdlib_strided_dmskmax( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const uint8_t *Mask, const CBLAS_INT strideMask );

stdlib_strided_dmskmax_ndarray( N, *X, strideX, offsetX, *Mask, strideMask, offsetMask )

Computes the maximum value of a double-precision floating-point strided array according to a mask and using alternative indexing semantics.

#include <stdint.h>

const double x[] = { 1.0, -2.0, 2.0 };
const uint8_t mask[] = { 0, 1, 0 };

double v = stdlib_strided_dmskmax_ndarray( 3, x, 1, 0, mask, 1, 0 );
// returns 2.0

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] double* input array.
• strideX: [in] CBLAS_INT stride length for X.
• offsetX: [in] CBLAS_INT starting index for X.
• Mask: [in] uint8_t* mask array. If a Mask array element is 0, the corresponding element in X is considered valid and included in computation. If a Mask array element is 1, the corresponding element in X is considered invalid/missing and excluded from computation.
• strideMask: [in] CBLAS_INT stride length for Mask.
• offsetMask: [in] CBLAS_INT starting index for Mask.

double stdlib_strided_dmskmax_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const uint8_t *Mask, const CBLAS_INT strideMask, const CBLAS_INT offsetMask );

Examples

#include "stdlib/stats/base/dmskmax.h"
#include <stdint.h>
#include <stdio.h>

int main( void ) {
    // Create a strided array:
    const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };

    // Create a mask array:
    const uint8_t mask[] = { 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 };

    // Specify the number of elements:
    const int N = 5;

    // Specify the stride lengths:
    const int strideX = 2;
    const int strideMask = 2;

    // Compute the maximum value:
    double v = stdlib_strided_dmskmax( N, x, strideX, mask, strideMask );

    // Print the result:
    printf( "max: %lf\n", v );
}

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See Also

• @stdlib/stats-base/dmax: calculate the maximum value of a double-precision floating-point strided array.
• @stdlib/stats-base/dmskmin: calculate the minimum value of a double-precision floating-point strided array according to a mask.
• @stdlib/stats-base/dnanmax: calculate the maximum value of a double-precision floating-point strided array, ignoring NaN values.
• @stdlib/stats-base/dnanmskmax: calculate the maximum value of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• @stdlib/stats-base/mskmax: calculate the maximum value of a strided array according to a mask.
• @stdlib/stats-base/smskmax: calculate the maximum value of a single-precision floating-point strided array according to a mask.

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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

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License

See LICENSE.

Copyright

Copyright © 2016-2025. The Stdlib Authors.