compute-cosine-similarity

What is compute-cosine-similarity?

The compute-cosine-similarity npm package is used to calculate the cosine similarity between two vectors. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. This is commonly used in various applications such as text analysis, recommendation systems, and clustering.

What are compute-cosine-similarity's main functionalities?

Calculate Cosine Similarity

This feature allows you to calculate the cosine similarity between two vectors. The code sample demonstrates how to use the package to compute the similarity between two example vectors, vectorA and vectorB.

const cosineSimilarity = require('compute-cosine-similarity');

const vectorA = [1, 2, 3];
const vectorB = [4, 5, 6];

const similarity = cosineSimilarity(vectorA, vectorB);
console.log(similarity); // Output: 0.9746318461970762

Other packages similar to compute-cosine-similarity

cosine-similarity

The cosine-similarity package provides a simple way to calculate the cosine similarity between two vectors. It is similar to compute-cosine-similarity in functionality but may have different performance characteristics or additional features.

ml-distance

The ml-distance package offers a variety of distance and similarity measures, including cosine similarity. It is more comprehensive than compute-cosine-similarity as it provides multiple distance metrics, making it suitable for broader applications in machine learning.

similarity

The similarity package is a general-purpose library for calculating various similarity metrics, including cosine similarity. It is more versatile than compute-cosine-similarity, as it supports multiple similarity measures, which can be useful for different types of data analysis.

README

Cosine Similarity

Computes the cosine similarity between two arrays.

Cosine similarity defines vector similarity in terms of the angle separating two vectors.

Installation

$ npm install compute-cosine-similarity

For use in the browser, use browserify.

Usage

var similarity = require( 'compute-cosine-similarity' );

similarity( x, y[, accessor] )

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:

• 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.

Examples

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

Tests

Unit

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.

Test Coverage

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

License

MIT license.

Copyright

Copyright © 2015. The Compute.io Authors. All rights reserved.