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dmskmin

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

Installation

npm install @stdlib/stats-base-dmskmin

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

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

Computes the minimum value of a double-precision floating-point strided array x 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 = dmskmin( x.length, x, 1, mask, 1 );
// returns -2.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Float64Array.
• strideX: index increment 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: index increment for mask.

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

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

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 N = floor( x.length / 2 );

var v = dmskmin( N, 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 floor = require( '@stdlib/math-base-special-floor' );

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 N = floor( x0.length / 2 );

var v = dmskmin( N, x1, 2, mask1, 2 );
// returns -2.0

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

Computes the minimum 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 = dmskmin.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 parameter supports indexing semantics based on a starting index. For example, to calculate the minimum value for every other value in x starting from the second value

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

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 N = floor( x.length / 2 );

var v = dmskmin.ndarray( N, x, 2, 1, mask, 2, 1 );
// returns -2.0

Notes

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

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var dmskmin = require( '@stdlib/stats-base-dmskmin' );

var mask;
var x;
var i;

x = new Float64Array( 10 );
mask = new Uint8Array( x.length );
for ( i = 0; i < x.length; i++ ) {
    if ( randu() < 0.2 ) {
        mask[ i ] = 1;
    } else {
        mask[ i ] = 0;
    }
    x[ i ] = round( (randu()*100.0) - 50.0 );
}
console.log( x );
console.log( mask );

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

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See Also

• @stdlib/stats-base/dmin: calculate the minimum value of a double-precision floating-point strided array.
• @stdlib/stats-base/dmskmax: calculate the maximum value of a double-precision floating-point strided array according to a mask.
• @stdlib/stats-base/dnanmin: calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
• @stdlib/stats-base/dnanmskmin: calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• @stdlib/stats-base/mskmin: calculate the minimum value of a strided array according to a mask.
• @stdlib/stats-base/smskmin: calculate the minimum 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 © 2016-2024. The Stdlib Authors.