@stdlib/stats-base-mskmax v0.2.2
mskmax
Calculate the maximum value of a strided array according to a mask.
Installation
npm install @stdlib/stats-base-mskmaxUsage
var mskmax = require( '@stdlib/stats-base-mskmax' );mskmax( N, x, strideX, mask, strideMask )
Computes the maximum value of a strided array x according to a mask.
var x = [ 1.0, -2.0, 4.0, 2.0 ];
var mask = [ 0, 0, 1, 0 ];
var v = mskmax( x.length, x, 1, mask, 1 );
// returns 2.0The function has the following parameters:
- N: number of indexed elements.
- x: input
Arrayortyped array. - strideX: index increment for
x. - mask: mask
Arrayortyped array. If amaskarray element is0, the corresponding element inxis considered valid and included in computation. If amaskarray element is1, the corresponding element inxis 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 maximum value of every other element in x,
var floor = require( '@stdlib/math-base-special-floor' );
var x = [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, 5.0, 6.0 ];
var mask = [ 0, 0, 0, 0, 0, 0, 1, 1 ];
var N = floor( x.length / 2 );
var v = mskmax( N, x, 2, mask, 2 );
// returns 4.0Note 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 = mskmax( N, x1, 2, mask1, 2 );
// returns 4.0mskmax.ndarray( N, x, strideX, offsetX, mask, strideMask, offsetMask )
Computes the maximum value of a strided array according to a mask and using alternative indexing semantics.
var x = [ 1.0, -2.0, 4.0, 2.0 ];
var mask = [ 0, 0, 1, 0 ];
var v = mskmax.ndarray( x.length, x, 1, 0, mask, 1, 0 );
// returns 2.0The 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 maximum value for every other value in x starting from the second value
var floor = require( '@stdlib/math-base-special-floor' );
var x = [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, 5.0, 6.0 ];
var mask = [ 0, 0, 0, 0, 0, 0, 1, 1 ];
var N = floor( x.length / 2 );
var v = mskmax.ndarray( N, x, 2, 1, mask, 2, 1 );
// returns 4.0Notes
- If
N <= 0, both functions returnNaN. - Depending on the environment, the typed versions (
dmskmax,smskmax, etc.) are likely to be significantly more performant.
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 mskmax = require( '@stdlib/stats-base-mskmax' );
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 = mskmax( x.length, x, 1, mask, 1 );
console.log( v );See Also
@stdlib/stats-base/dmskmax: calculate the maximum value of a double-precision floating-point strided array according to a mask.@stdlib/stats-base/max: calculate the maximum value of a strided array.@stdlib/stats-base/mskmin: calculate the minimum value of a strided array according to a mask.@stdlib/stats-base/nanmax: calculate the maximum value of a strided array, ignoring NaN values.@stdlib/stats-base/smskmax: calculate the maximum value of a single-precision floating-point strided array according to a mask.
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 © 2016-2024. The Stdlib Authors.