npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@thomaschampagne/naive-bayes

v1.0.0

Published

TypeScript Naive Bayes Classifier for Node and Browser

Downloads

10

Readme

TypeScript Naive Bayes Classifier for Node and Browser

This "Naive Bayes Classifier" library is based on the bayes package. Library has been re-implemented as synchronous, refactored and cleaned under TypeScript, Jest, ESLint and Prettier.

What can I use this for?

You can use this for categorizing any text content into any arbitrary set of categories. For example:

  • is an email spam, or not spam ?
  • is a news article about technology, politics, or sports ?
  • is a piece of text expressing positive emotions, or negative emotions?

Installing

npm install naive-bayes

Usage

import { NaiveBayes }  from "naive-bayes";

const classifier = new NaiveBayes();

// Teach it positive phrases
classifier.learn('amazing, awesome movie!! Yeah!! Oh boy.', 'positive');
classifier.learn('Sweet, this is incredibly, amazing, perfect, great!!', 'positive');

// Teach it a negative phrase
classifier.learn('terrible, shitty thing. Damn. Sucks!!', 'negative');

// Now ask it to categorize a document it has never seen before
console.log(classifier.categorize('awesome, cool, amazing!! Yay.')); // => 'positive'

// Serialize the classifier's state as a JSON string.
const model = classifier.toJson();

// Load the classifier back from its JSON representation.
const revivedClassifier = NaiveBayes.fromJson(model);

console.log(revivedClassifier.categorize('Damn')); // => 'negative'

API

const classifier = new NaiveBayes([options])

Returns an instance of a Naive-Bayes Classifier.

Pass in an optional options object to configure the instance. If you specify a tokenizer function in options, it will be used as the instance's tokenizer. It receives a (string) text argument - this is the string value that is passed in by you when you call .learn() or .categorize(). It must return an array of tokens. The default tokenizer removes punctuation and splits on spaces.

Eg.

const classifier = new NaiveBayes({
    tokenizer: text => { return text.split(' ') }
})

classifier.learn(text, category)

Teach your classifier what category the text belongs to. The more you teach your classifier, the more reliable it becomes. It will use what it has learned to identify new documents that it hasn't seen before.

classifier.categorize(text)

Returns the category it thinks text belongs to. Its judgement is based on what you have taught it with .learn().

classifier.toJson()

Returns the JSON representation of a classifier.

var classifier = NaiveBayes.fromJson(jsonStr)

Returns a classifier instance from the JSON representation. Use this with the JSON representation obtained from classifier.toJson()