https://github.com/ladjs/naivebayes

A ladjs naivebayes package forked from `https://github.com/surmon-china/naivebayes`
https://github.com/ladjs/naivebayes

Last synced: 6 months ago
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A ladjs naivebayes package forked from `https://github.com/surmon-china/naivebayes`

• Host: GitHub
• URL: https://github.com/ladjs/naivebayes
• Owner: ladjs
• License: mit
• Created: 2020-06-05T23:32:30.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-12-07T01:49:26.000Z (about 2 years ago)
• Last Synced: 2025-06-03T23:51:31.327Z (7 months ago)
• Language: JavaScript
• Size: 490 KB
• Stars: 12
• Watchers: 4
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# [**@ladjs/naivebayes**](https://github.com/ladjs/naivebayes)

[![build status](https://img.shields.io/travis/com/shaunwarman/naivebayes.svg)](https://travis-ci.com/shaunwarman/naivebayes)

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> A ladjs naivebayes package forked from surmon-china/naivebayes

## Table of Contents

* [What can I use this for](#what-can-i-use-this-for)

* [Install](#install)

  * [npm](#npm)

  * [yarn](#yarn)

* [Usage](#usage)

* [API](#api)

  * [Class](#class)

  * [Learn](#learn)

  * [Probabilities](#probabilities)

  * [Categorize](#categorize)

  * [ToJson](#tojson)

  * [ToJsonObject](#tojsonobject)

  * [FromJson](#fromjson)

  * [Debug](#debug)

* [Contributors](#contributors)

## What can I use this for

Naive-Bayes classifier for JavaScript.

`naivebayes` takes a document (piece of text), and tells you what category that document belongs to.

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?

## Install

### npm

```sh

npm install @ladjs/naivebayes

```

### yarn

```sh

yarn add @ladjs/naivebayes

```

## Usage

```javascript

const NaiveBayes = require('naivebayes')

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, cruddy thing. Damn. Sucks!!', 'negative')

// now ask it to categorize a document it has never seen before

classifier.categorize('awesome, cool, amazing!! Yay.')

// => 'positive'

// serialize the classifier's state as a JSON string.

const stateJson = classifier.toJson()

// load the classifier back from its JSON representation.

const revivedClassifier = NaiveBayes.fromJson(stateJson)

```

```javascript

const NaiveBayes = require('naivebayes')

const Segment = require('segment')

const segment = new Segment()

segment.useDefault()

const classifier = new NaiveBayes({

    tokenizer(sentence) {

        const sanitized = sentence.replace(/[^(a-zA-Z\u4e00-\u9fa50-9_)+\s]/g, ' ')

        return segment.doSegment(sanitized, { simple: true })

    }

})

```

## API

### Class

```javascript

const classifier = new NaiveBayes([options])

```

Returns an instance of a Naive-Bayes Classifier.

#### Options

* `tokenizer(text)` - (type: `function`) -  Configure your own tokenizer.

* `vocabularyLimit` - (type: `number` default: 0) - Reference a max word count where `0` is the default, meaning no limit.

* `stopwords` - (type: `boolean` default: false) - To remove [stopwords](https://en.wikipedia.org/wiki/Stop_words) from text

Eg.

```javascript

const classifier = new NaiveBayes({

    tokenizer(text) {

        return text.split(' ')

    }

})

```

### Learn

```javascript

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.

### Probabilities

```javascript

classifier.probabilities(text)

```

Returns an array of `{ category, probability }` objects with probability calculated for each category. Its judgement is based on what you have taught it with `.learn()`.

### Categorize

```javascript

classifier.categorize(text ,[probability])

```

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

### ToJson

```javascript

classifier.toJson()

```

Returns the JSON representation of a classifier. This is the same as `JSON.stringify(classifier.toJsonObject())`.

### ToJsonObject

```javascript

classifier.toJsonObject()

```

Returns a JSON-friendly representation of the classifier as an `object`.

### FromJson

```javascript

const classifier = NaiveBayes.fromJson(jsonObject)

```

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

### Debug

To run `naivebayes` in debug mode simply set `DEBUG=naivebayes` when running your script.

## Contributors

| Name             | Website                    |

| ---------------- | -------------------------- |

| **Surmon**       |         |

| **Shaun Warman** |  |