Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?


Failed to load latest commit information.
Latest commit message
Commit time

Build Status

This software package contains a Barnes-Hut implementation of the t-SNE algorithm. The implementation is described in this paper.


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

  • Go to bhtsne directory and run:

  nmake -f all

The executable will be called windows\bh_tsne.exe.


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 Matlab:

filename = websave('mnist_train.mat', '');
numDims = 2; pcaDims = 50; perplexity = 50; theta = .5; alg = 'svd';
map = fast_tsne(digits', numDims, pcaDims, perplexity, theta, alg);
gscatter(map(:,1), map(:,2), labels');

Demonstration of usage in Python:

import numpy as np
import bhtsne

data = np.loadtxt("mnist2500_X.txt", skiprows=1)

embedding_array = bhtsne.run_bh_tsne(data, initial_dims=data.shape[1])

Python Wrapper


python [-h] [-d NO_DIMS] [-p PERPLEXITY] [-t THETA]
                  [-r RANDSEED] [-n INITIAL_DIMS] [-v] [-i INPUT]
                  [-o OUTPUT] [--use_pca] [--no_pca] [-m MAX_ITER]

Below are the various options the wrapper program expects:

  • -h, --help show this help message and exit
  • -d NO_DIMS, --no_dims NO_DIMS
  • -p PERPLEXITY, --perplexity PERPLEXITY
  • -t THETA, --theta THETA
  • -r RANDSEED, --randseed RANDSEED
  • -n INITIAL_DIMS, --initial_dims INITIAL_DIMS
  • -v, --verbose
  • -i INPUT, --input INPUT: the input file, expects a TSV with the first row as the header.
  • -o OUTPUT, --output OUTPUT: A TSV file having each row as the d dimensional embedding.
  • --use_pca
  • --no_pca
  • -m MAX_ITER, --max_iter MAX_ITER