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dnanminabs

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

Installation

npm install @stdlib/stats-base-dnanminabs

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

dnanminabs( N, x, strideX )

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

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

var v = dnanminabs( x.length, x, 1 );
// returns 1.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Float64Array.
• strideX: stride length for x.

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

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

var x = new Float64Array( [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, NaN, NaN ] );

var v = dnanminabs( 4, x, 2 );
// returns 1.0

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

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

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

var v = dnanminabs( 4, x1, 2 );
// returns 1.0

dnanminabs.ndarray( N, x, strideX, offsetX )

Computes the minimum absolute value of a double-precision floating-point strided array, ignoring NaN values and using alternative indexing semantics.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

var v = dnanminabs.ndarray( x.length, x, 1, 0 );
// returns 1.0

The function has the following additional parameters:

• offsetX: starting index for x.

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 absolute value for every other element in x starting from the second element

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

var x = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, NaN, NaN ] );

var v = dnanminabs.ndarray( 4, x, 2, 1 );
// returns 1.0

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

var x;
var i;

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

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

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C APIs

Usage

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

stdlib_strided_dnanminabs( N, *X, strideX )

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

const double x[] = { 1.0, -2.0, 0.0 / 0.0, -4.0 };

double v = stdlib_strided_dnanminabs( 4, x, 1 );
// returns 1.0

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.

double stdlib_strided_dnanminabs( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );

stdlib_strided_dnanminabs_ndarray( N, *X, strideX, offsetX )

Computes the minimum absolute value of a double-precision floating-point strided array, ignoring NaN values and using alternative indexing semantics.

const double x[] = { 1.0, -2.0, 0.0 / 0.0, -4.0 };

double v = stdlib_strided_dnanminabs_ndarray( 4, x, 1, 0 );
// returns 1.0

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.

double stdlib_strided_dnanminabs_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

#include "stdlib/stats/base/dnanminabs.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, 0.0/0.0, 0.0/0.0 };

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

    // Specify the stride length:
    const int strideX = 2;

    // Compute the minimum absolute value:
    double v = stdlib_strided_dnanminabs( N, x, strideX );

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

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See Also

• @stdlib/stats-base/dminabs: calculate the minimum absolute value of a double-precision floating-point strided array.
• @stdlib/stats-base/dnanmaxabs: calculate the maximum absolute value of a double-precision floating-point strided array, ignoring NaN values.
• @stdlib/stats-base/dnanmin: calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
• @stdlib/stats-base/nanminabs: calculate the minimum absolute value of a strided array, ignoring NaN values.
• @stdlib/stats-base/snanminabs: calculate the minimum absolute value of a single-precision floating-point strided array, ignoring NaN values.

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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.