Computes the Euclidean distance between two arrays.
npm install compute-euclidean-distanceEuclidean Distance
===
[![NPM version][npm-image]][npm-url] [![Build Status][travis-image]][travis-url] [![Coverage Status][coveralls-image]][coveralls-url] [![Dependencies][dependencies-image]][dependencies-url]
> Computes the Euclidean distance between two arrays.
The Euclidean distance is the straight line distance between two points in Euclidean space.
`` bash`
$ npm install compute-euclidean-distance
For use in the browser, use browserify.
` javascript`
var euclidean = require( 'compute-euclidean-distance' );
#### euclidean( x, y[, accessor] )
Computes the Euclidean distance between two arrays.
` javascript
var x = [ 2, 4, 5, 3, 8, 2 ],
y = [ 3, 1, 5, -3, 7, 2 ];
var d = euclidean( x, y );
// returns ~6.86
`
For object arrays, provide an accessor function for accessing numeric values.
` javascript
var x, y, d;
x = [
[1,2],
[2,4],
[3,5],
[4,3],
[5,8],
[6,2]
];
y = [
{'y':3},
{'y':1},
{'y':5},
{'y':-3},
{'y':7},
{'y':2}
];
function getValue( d, i, j ) {
if ( j === 0 ) {
return d[ 1 ];
}
return d.y;
}
d = euclidean( x, y, getValue );
// returns ~6.86
`
The accessor function is provided three arguments:
- __d__: current datum.
- __i__: current datum index.
- __j__: array index; e.g., array x has index 0, and array y has index 1.
If provided empty arrays, the function returns null.
` javascript
var euclidean = require( 'compute-euclidean-distance' );
var x = new Array( 100 ),
y = new Array( 100 );
for ( var i = 0; i < x.length; i++ ) {
x[ i ] = Math.round( Math.random()*10 );
y[ i ] = Math.round( Math.random()*10 );
}
console.log( euclidean( x, y ) );
`
To run the example code from the top-level application directory,
` bash`
$ node ./examples/index.js
- Dahlquist, Germund and Bjorck, Ake. _Numerical Methods in Scientific Computing_.
- Blue, James (1978) "A Portable Fortran Program To Find the Euclidean Norm of a Vector". _ACM Transactions on Mathematical Software_.
- Higham, Nicholas J. _Accuracy and Stability of Numerical Algorithms, Second Edition_.
This module implements a one-pass algorithm proposed by S.J. Hammarling.
Unit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:
` bash`
$ make test
All new feature development should have corresponding unit tests to validate correct functionality.
This repository uses Istanbul as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:
` bash`
$ make test-cov
Istanbul creates a ./reports/coverage directory. To access an HTML version of the report,
` bash``
$ make view-cov
---
Copyright © 2015. Athan Reines.
[npm-image]: http://img.shields.io/npm/v/compute-euclidean-distance.svg
[npm-url]: https://npmjs.org/package/compute-euclidean-distance
[travis-image]: http://img.shields.io/travis/compute-io/euclidean-distance/master.svg
[travis-url]: https://travis-ci.org/compute-io/euclidean-distance
[coveralls-image]: https://img.shields.io/coveralls/compute-io/euclidean-distance/master.svg
[coveralls-url]: https://coveralls.io/r/compute-io/euclidean-distance?branch=master
[dependencies-image]: http://img.shields.io/david/compute-io/euclidean-distance.svg
[dependencies-url]: https://david-dm.org/compute-io/euclidean-distance
[dev-dependencies-image]: http://img.shields.io/david/dev/compute-io/euclidean-distance.svg
[dev-dependencies-url]: https://david-dm.org/dev/compute-io/euclidean-distance
[github-issues-image]: http://img.shields.io/github/issues/compute-io/euclidean-distance.svg
[github-issues-url]: https://github.com/compute-io/euclidean-distance/issues