dmeanpw

Compute the arithmetic mean of a double-precision floating-point strided array using pairwise summation.

Installation

npm install @stdlib/stats-strided-wasm-dmeanpw

Usage

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

dmeanpw.main( N, x, strideX )

Computes the arithmetic mean of a double-precision floating-point strided array using pairwise summation.

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

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

var y = dmeanpw.main( x.length, x, 1 );
// returns ~0.3333

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 access 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 ] );

var y = dmeanpw.main( 4, 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 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var y = dmeanpw.main( 4, x1, 2 );
// returns 1.25

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

Computes the arithmetic mean of a double-precision floating-point strided array using pairwise summation and alternative indexing semantics.

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

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

var y = dmeanpw.ndarray( x.length, x, 1, 0 );
// returns ~0.3333

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 access every other element 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 ] );

var y = dmeanpw.ndarray( 4, x, 2, 1 );
// returns 1.25

Module

dmeanpw.Module( memory )

Returns a new WebAssembly module wrapper instance which uses the provided WebAssembly memory instance as its underlying memory.

var Memory = require( '@stdlib/wasm-memory' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
    'initial': 10,
    'maximum': 100
});

// Create a new routine:
var mod = new dmeanpw.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

dmeanpw.Module.prototype.main( N, xp, sx )

Computes the arithmetic mean of a double-precision floating-point strided array using pairwise summation.

var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
    'initial': 10,
    'maximum': 100
});

// Create a new routine:
var mod = new dmeanpw.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

// Define a vector data type:
var dtype = 'float64';

// Specify a vector length:
var N = 3;

// Define a pointer (i.e., byte offset) for storing the input vector:
var xptr = 0;

// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );

// Perform computation:
var y = mod.main( N, xptr, 1 );
// returns 2.0

The function has the following parameters:

• N: number of indexed elements.
• xp: input Float64Array pointer (i.e., byte offset).
• sx: stride length for x.

dmeanpw.Module.prototype.ndarray( N, alpha, xp, sx, ox )

Computes the arithmetic mean of a double-precision floating-point strided array using pairwise summation and alternative indexing semantics.

var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
    'initial': 10,
    'maximum': 100
});

// Create a new routine:
var mod = new dmeanpw.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

// Define a vector data type:
var dtype = 'float64';

// Specify a vector length:
var N = 3;

// Define a pointer (i.e., byte offset) for storing the input vector:
var xptr = 0;

// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );

// Perform computation:
var y = mod.ndarray( N, xptr, 1, 0 );
// returns 2.0

The function has the following additional parameters:

• ox: starting index for x.

Notes

• If N <= 0, both main and ndarray methods return 0.0.
• This package implements routines using WebAssembly. When provided arrays which are not allocated on a dmeanpw module memory instance, data must be explicitly copied to module memory prior to computation. Data movement may entail a performance cost, and, thus, if you are using arrays external to module memory, you should prefer using @stdlib/stats-strided/dmeanpw. However, if working with arrays which are allocated and explicitly managed on module memory, you can achieve better performance when compared to the pure JavaScript implementations found in @stdlib/stats/strided/dmeanpw. Beware that such performance gains may come at the cost of additional complexity when having to perform manual memory management. Choosing between implementations depends heavily on the particular needs and constraints of your application, with no one choice universally better than the other.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dmeanpw = require( '@stdlib/stats-strided-wasm-dmeanpw' );

var opts = {
    'dtype': 'float64'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );

var y = dmeanpw.ndarray( x.length, x, 1, 0 );
console.log( y );

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 © 2016-2026. The Stdlib Authors.