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compute-cosine-similarity
Advanced tools
Computes the cosine similarity between two arrays.
Cosine similarity defines vector similarity in terms of the angle separating two vectors.
$ npm install compute-cosine-similarity
For use in the browser, use browserify.
var similarity = require( 'compute-cosine-similarity' );
Computes the cosine similarity between two arrays
.
var x = [ 5, 23, 2, 5, 9 ],
y = [ 3, 21, 2, 5, 14 ];
var s = similarity( x, y );
// returns ~0.975
For object arrays
, provide an accessor function
for accessing numeric
values.
var x = [
{'x':2},
{'x':4},
{'x':5}
];
var y = [
[1,3],
[2,1],
[3,5]
];
function getValue( d, i, j ) {
if ( j === 0 ) {
return d.x;
}
return d[ 1 ];
}
var s = similarity( x, y, getValue );
// returns ~0.882
The accessor function
is provided three arguments:
x
has index 0
, and array y
has index 1
.If provided empty arrays
, the function returns null
.
var similarity = require( 'compute-cosine-similarity' );
var x = new Array( 100 ),
y = new Array( 100 ),
s;
for ( var i = 0; i < x.length; i++ ) {
x[ i ] = Math.round( Math.random()*100 );
y[ i ] = Math.round( Math.random()*100 );
}
s = similarity( x, y );
console.log( s );
To run the example code from the top-level application directory,
$ node ./examples/index.js
Unit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:
$ 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:
$ make test-cov
Istanbul creates a ./reports/coverage
directory. To access an HTML version of the report,
$ make view-cov
Copyright © 2015. The Compute.io Authors. All rights reserved.
FAQs
Computes the cosine similarity between two arrays.
The npm package compute-cosine-similarity receives a total of 36,419 weekly downloads. As such, compute-cosine-similarity popularity was classified as popular.
We found that compute-cosine-similarity demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.
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