JSPM

@stdlib/stats-strided-dnanmeanwd

0.1.1
  • ESM via JSPM
  • ES Module Entrypoint
  • Export Map
  • Keywords
  • License
  • Repository URL
  • TypeScript Types
  • README
  • Created
  • Published
  • Downloads 45
  • Score
    100M100P100Q71952F
  • License Apache-2.0

Calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring NaN values.

Package Exports

  • @stdlib/stats-strided-dnanmeanwd
  • @stdlib/stats-strided-dnanmeanwd/lib/index.js
  • @stdlib/stats-strided-dnanmeanwd/lib/main.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@stdlib/stats-strided-dnanmeanwd) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

dnanmeanwd

NPM version Build Status Coverage Status

Calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring NaN values.

The arithmetic mean is defined as

Equation for the arithmetic mean.

Installation

npm install @stdlib/stats-strided-dnanmeanwd

Usage

var dnanmeanwd = require( '@stdlib/stats-strided-dnanmeanwd' );

dnanmeanwd( N, x, strideX )

Computes the arithmetic mean of a double-precision floating-point strided array x, using Welford's algorithm and ignoring NaN values.

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

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

var v = dnanmeanwd( x.length, x, 1 );
// returns ~0.3333

The function has the following parameters:

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

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

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

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

var v = dnanmeanwd( 5, x, 2 );
// returns 1.25

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, -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 = dnanmeanwd( 5, x1, 2 );
// returns 1.25

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

Computes the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using Welford's algorithm and alternative indexing semantics.

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

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

var v = dnanmeanwd.ndarray( x.length, x, 1, 0 );
// returns ~0.33333

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 arithmetic mean 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, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );

var v = dnanmeanwd.ndarray( 5, x, 2, 1 );
// returns 1.25

Notes

  • If N <= 0, both functions return NaN.
  • If every indexed element is NaN, 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 dnanmeanwd = require( '@stdlib/stats-strided-dnanmeanwd' );

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() * 10.0 );
    }
}
console.log( x );

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

C APIs

Usage

#include "stdlib/stats/strided/dnanmeanwd.h"

stdlib_strided_dnanmeanwd( N, *X, strideX )

Computes the arithmetic mean of a double-precision floating-point strided array x, using Welford's algorithm and ignoring NaN values.

const double x[] = { 1.0, 2.0, 0.0/0.0, 3.0, 0.0/0.0, 4.0, 5.0, 6.0, 0.0/0.0, 7.0, 8.0, 0.0/0.0 };

double v = stdlib_strided_dnanmeanwd( 6, x, 2 );
// returns ~4.67

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

stdlib_strided_dnanmeanwd_ndarray( N, *X, strideX, offsetX )

Computes the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using Welford's algorithm and alternative indexing semantics.

const double x[] = { 1.0, 2.0, 0.0/0.0, 3.0, 0.0/0.0, 4.0, 5.0, 6.0, 0.0/0.0, 7.0, 8.0, 0.0/0.0 };

double v = stdlib_strided_dnanmeanwd_ndarray( 6, x, 2, 0 );
// returns ~4.67

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

Examples

#include "stdlib/stats/strided/dnanmeanwd.h"
#include <stdio.h>

int main( void ) {
    // Create a strided array:
    const double x[] = { 1.0, 2.0, 0.0/0.0, 3.0, 0.0/0.0, 4.0, 5.0, 6.0, 0.0/0.0, 7.0, 8.0, 0.0/0.0 };

    // Specify the number of elements:
    const int N = 6;

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

    // Compute the arithmetic mean:
    double v = stdlib_strided_dnanmeanwd( N, x, strideX );

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

References

  • Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." Technometrics 4 (3). Taylor & Francis: 419–20. doi:10.1080/00401706.1962.10490022.
  • van Reeken, A. J. 1968. "Letters to the Editor: Dealing with Neely's Algorithms." Communications of the ACM 11 (3): 149–50. doi:10.1145/362929.362961.

See Also

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

Chat

License

See LICENSE.

Copyright © 2016-2026. The Stdlib Authors.