This Git repository contains the code that accompanies a research publication so-called "t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections". The details are described in this paper.

Note: This repository is a version of t-SNE modified to support ongoing research. It may be slightly slower than the original. If you're just trying to run t-SNE, check the original repository that we forked from.

Installation

On Linux or OS X, compile the source using the following command:

g++ sptree.cpp tsne.cpp tsne_main.cpp -o bh_tsne -O2

The executable will be called bh_tsne.

On Windows using Visual C++, do the following in your command line:

• Find the vcvars64.bat file in your Visual C++ installation directory. This file may be named vcvars64.bat or something similar. For example:

  // Visual Studio 12
  "C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64\vcvars64.bat"

  // Visual Studio 2013 Express:
  C:\VisualStudioExp2013\VC\bin\x86_amd64\vcvarsx86_amd64.bat

• From cmd.exe, go to the directory containing that .bat file and run it.

• Go to bhtsne directory and run:

  nmake -f Makefile.win all

The executable will be called windows\bh_tsne.exe.

Usage

The code comes with wrappers for Matlab and Python. These wrappers write your data to a file called data.dat, run the bh_tsne binary, and read the result file result.dat that the binary produces. There are also external wrappers available for Torch, R, and Julia. Writing your own wrapper should be straightforward; please refer to one of the existing wrappers for the format of the data and result files.

Demonstration of usage in Python:

# first terminal: hosting the visualization side (client)
python3 -m http.server # for Python3
#or 
python -m SimpleHTTPServer 8000 # for Python2

# second terminal: hosting the computational side (server)
FLASK_APP=tsneGrid.py flask run

t-viSNE