https://github.com/chen0040/java-naive-bayes-classifier

Package provides java implementation of naive bayes classifier
https://github.com/chen0040/java-naive-bayes-classifier

classification java naive-bayes-classifier

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Package provides java implementation of naive bayes classifier

• Host: GitHub
• URL: https://github.com/chen0040/java-naive-bayes-classifier
• Owner: chen0040
• License: mit
• Created: 2017-05-27T16:30:27.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-06-03T04:07:27.000Z (over 8 years ago)
• Last Synced: 2023-06-30T13:09:07.075Z (over 2 years ago)
• Topics: classification, java, naive-bayes-classifier
• Language: Java
• Size: 60.5 KB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# java-naive-bayes-classifier

Package provides java implementation of naive bayes classifier (NBC)

[![Build Status](https://travis-ci.org/chen0040/java-naive-bayes-classifier.svg?branch=master)](https://travis-ci.org/chen0040/java-naive-bayes-classifier) [![Coverage Status](https://coveralls.io/repos/github/chen0040/java-naive-bayes-classifier/badge.svg?branch=master)](https://coveralls.io/github/chen0040/java-naive-bayes-classifier?branch=master) 

# Features

* Handle both numerical and categorical inputs

# Install

Add the following dependency to your POM file

```xml

  com.github.chen0040

  java-naive-bayes-classifier

  1.0.1

```

# Usage

To train the NBC:

```java

nbc.fit(trainingData);

```

To use NBC for classification:

```java

String predicted = nbc.classify(dataRow);

```

The trainingData object is an instance of data frame consisting of data rows (Please refers to this [link](https://github.com/chen0040/java-data-frame) to find out how to store data into a data frame)

The sample code below shows how to use NBC to solves the classification problem "heart_scale".

```java

InputStream inputStream = new FileInputStream("heart_scale");

DataFrame dataFrame = DataQuery.libsvm().from(inputStream).build();

dataFrame.unlock();

for(int i=0; i < dataFrame.rowCount(); ++i){

 DataRow row = dataFrame.row(i);

 row.setCategoricalTargetCell("category-label", "" + row.target());

}

dataFrame.lock();

NBC svc = new NBC();

svc.fit(dataFrame);

for(int i = 0; i < dataFrame.rowCount(); ++i){

 DataRow row = dataFrame.row(i);

 String predicted_label = svc.classify(row);

 System.out.println("predicted: "+predicted_label+"\texpected: "+row.categoricalTarget());

}

```