# T-SNE-Java

**Repository Path**: bit2atom/T-SNE-Java

## Basic Information

- **Project Name**: T-SNE-Java
- **Description**: Pure Java implementation of Van Der Maaten and Hinton's t-sne clustering algorithm
- **Primary Language**: Java
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-25
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![Build Status](https://travis-ci.org/lejon/T-SNE-Java.svg?branch=master)](https://travis-ci.org/lejon/T-SNE-Java)

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T-SNE-Java
==========

NEWS 2016-11-02!
----------------
*T-SNE-Java now have support for __Barnes Hut__ which makes it possible to run the amazing t-SNE on much larger data sets (or much faster on small data sets:) )!*

The Barnes Hut version can also be run in parallel! We have seen from 40 % performance improvements on moderate datasets (ca 10 000 samples) to 100 % improvements on larger datasets (MNIST 60000 samples) compared to standard Barnes Hut.

The t-SNE part of running the parallel Barnes Hut t-SNE on MNIST 60000 takes 18.3 minutes on a 2013 Macbook Pro (theta=0.5, perplexity = 50, 1000 iterations)

Both standard and parallel Barnes Hut is of course magnitudes faster than vanilla t-SNE. 

Great research by Dr. Maaten!!

About
=====

Pure Java implementation of Van Der Maaten and Hinton's t-SNE clustering algorithm.

This project is divided into two separate Maven projects, one for the core t-SNE and one for the demos (stand-alone executables that can be run from command line).


Basic command line usage
------------------------

If you just want to use TSne as a command line tool, you should use BarnesHutTSneCsv for the 
Barnes Hut version or TSneCsv for the classic version. 

You must then first build and install 'tsne' and  'tsne-demos' (mvn install).
Then use the tsne-demos JAR you just build according to the examples below. 

You can also download the pre-build binary JAR from the release page.

Examples:

Run TSne on file without headers and no labels.
```shell
java -jar target/tsne-demos-2.4.0.jar -nohdr -nolbls src/main/resources/datasets/iris_X.txt 
```
Run TSne on CSV file with headers and label column nr. 5.
```shell
java -jar target/tsne-demos-2.4.0.jar --lblcolno 5 src/main/resources/datasets/iris.csv
```
Run TSne on file without headers and no labels but supply a separate label file (with the same ordering as the data file).
```shell
java -jar target/tsne-demos-2.4.0.jar --nohdr --nolbls --label_file=src/main/resources/datasets/iris_X_labels.txt src/main/resources/datasets/iris_X.txt
```

Same as above but using parallelization.
```shell
java -jar target/tsne-demos-2.4.0.jar --parallel --nohdr --nolbls --label_file=src/main/resources/datasets/iris_X_labels.txt src/main/resources/datasets/iris_X.txt
```
Example graph of the MNIST data set (60000 samples) generated with Barnes Hut implementation of t-SNE:

![image of MNIST clusters](https://raw.githubusercontent.com/lejon/T-SNE-Java/master/images/mnist-full.png "MNIST (60000 samples)")

For some tips working with t-sne [Klick here] (http://lejon.github.io) or [here] (https://lvdmaaten.github.io/tsne/#faq) (observe that the last link discusses some implementation details of Laurens implementation of t-SNE and not this Java version, but also some general tips and tricks which applies to t-SNE in general) .

To use the Barnes Hut version (recommended):
--------------------------------------------

```java
import java.io.File;

import com.jujutsu.tsne.barneshut.BHTSne;
import com.jujutsu.tsne.barneshut.BarnesHutTSne;
import com.jujutsu.tsne.barneshut.ParallelBHTsne;
import com.jujutsu.utils.MatrixOps;
import com.jujutsu.utils.MatrixUtils;

public class TSneTest {
  public static void main(String [] args) {
    int initial_dims = 55;
    double perplexity = 20.0;
    double [][] X = MatrixUtils.simpleRead2DMatrix(new File("src/main/resources/datasets/mnist2500_X.txt"), "   ");
    System.out.println(MatrixOps.doubleArrayToPrintString(X, ", ", 50,10));
    BarnesHutTSne tsne;
    boolean parallel = false;
	if(parallel) {			
		tsne = new ParallelBHTsne();
	} else {
		tsne = new BHTSne();
	}
    double [][] Y = tsne.tsne(X, 2, initial_dims, perplexity);   
    
    // Plot Y or save Y to file and plot with some other tool such as for instance R
  }
}

```

Basic code usage (not recommended, slow standard t-SNE (yes, well it was fast at the time. :)): 
-----------------------------------------------------------------------------------------------

```java
import java.io.File;

import com.jujutsu.tsne.FastTSne;
import com.jujutsu.tsne.TSne;
import com.jujutsu.utils.MatrixOps;
import com.jujutsu.utils.MatrixUtils;

public class TSneTest {
  public static void main(String [] args) {
    int initial_dims = 55;
    double perplexity = 20.0;
    double [][] X = MatrixUtils.simpleRead2DMatrix(new File("src/main/resources/datasets/mnist2500_X.txt"), "   ");
    System.out.println(MatrixOps.doubleArrayToPrintString(X, ", ", 50,10));
    TSne tsne = new FastTSne();
    double [][] Y = tsne.tsne(X, 2, initial_dims, perplexity);   

    // Plot Y or save Y to file and plot with some other tool such as for instance R

  }
}
```

Version
-------
Demo: 2.4.0
Core: 2.3.0

Acknowledgements
----------------
I'm a very satisfied user of the YourKit profiler. A Great product with great support. It has been sucessfully used for profiling in this project.

![YourKit](https://www.yourkit.com/images/yklogo.png)

YourKit supports open source projects with its full-featured Java Profiler.
YourKit, LLC is the creator of [YourKit Java Profiler](https://www.yourkit.com/java/profiler/)
and [YourKit .NET Profiler](https://www.yourkit.com/.net/profiler/),
innovative and intelligent tools for profiling Java and .NET applications.

Enjoy!
-Leif