dnanmskmin

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

Usage

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

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

Computes the minimum value of a double-precision floating-point strided array x according to a mask, ignoring NaN values.

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

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

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

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

Computes the minimum value of a double-precision floating-point strided array according to a mask, ignoring NaN values 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, NaN ] );
var mask = new Uint8Array( [ 0, 0, 1, 0, 0 ] );

var v = dnanmskmin.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 = dnanmskmin.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 dnanmskmin = require( '@stdlib/stats/base/dnanmskmin' );

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