@stdlib/stats-base-nanmskmin

v0.2.2

Published

4 months ago

Calculate the minimum value of a strided array according to a mask, ignoring NaN values.

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201

nanmskmin

Calculate the minimum value of a strided array according to a mask, ignoring NaN values.

Installation

npm install @stdlib/stats-base-nanmskmin

Usage

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

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

Computes the minimum value of a strided array x according to a mask, ignoring NaN values.

var x = [ 1.0, -2.0, -4.0, 2.0, NaN ];
var mask = [ 0, 0, 1, 0, 0 ];

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

The function has the following parameters:

N: number of indexed elements.
x: input Array or typed array.
strideX: index increment for x.
mask: mask Array or typed 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: 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 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 = nanmskmin( N, x, 2, mask, 2 );
// returns -7.0

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 = nanmskmin( N, x1, 2, mask1, 2 );
// returns -2.0

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

Computes the minimum value of a strided array according to a mask, ignoring NaN values and using alternative indexing semantics.

var x = [ 1.0, -2.0, -4.0, 2.0, NaN ];
var mask = [ 0, 0, 1, 0, 0 ];

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

Notes

If N <= 0, both functions return NaN.
Depending on the environment, the typed versions (dnanmskmin, snanmskmin, 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 nanmskmin = require( '@stdlib/stats-base-nanmskmin' );

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;
    }
    if ( randu() < 0.1 ) {
        x[ i ] = NaN;
    } else {
        x[ i ] = round( (randu()*100.0) - 50.0 );
    }
}
console.log( x );
console.log( mask );

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

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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See LICENSE.

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