Skip to content
This repository has been archived by the owner on Apr 6, 2023. It is now read-only.
/ tsne Public archive

A python wrapper for Barnes-Hut tsne

License

Notifications You must be signed in to change notification settings

danielfrg/tsne

Folders and files

NameName
Last commit message
Last commit date

Latest commit

47e5783 · Apr 6, 2023
May 4, 2021
May 7, 2020
Mar 31, 2021
May 8, 2020
Mar 31, 2021
Mar 31, 2021
Apr 22, 2020
May 7, 2020
Mar 31, 2021
Apr 6, 2023
Mar 31, 2021
Mar 31, 2021
Mar 31, 2021
Mar 31, 2021
Mar 31, 2021
Mar 31, 2021
Mar 31, 2021

Repository files navigation

NOT MAINTAINED

  • I have not used this project myself on a long time
  • No issues or PRs can be created
  • At the time of writting this April 6, 2023 - The lastest release on PyPI (0.3.1) works as intented
  • I recommend moving to a native solution to other frameworks such as Tensorflow or PyTorch

Python-TSNE

pypi build coverage license

Python library containing T-SNE algorithms.

Note: Scikit-learn v0.17 includes TSNE algorithms and you should probably be using that instead.

Installation

Requirements

  • cblas or openblas. Tested version is v0.2.5 and v0.2.6 (not necessary for OSX).

From PyPI:

pip install tsne

From conda:

conda install -c maxibor tsne

Usage

Basic usage:

from tsne import bh_sne
X_2d = bh_sne(X)

Examples

Algorithms

Barnes-Hut-SNE

A python (cython) wrapper for Barnes-Hut-SNE aka fast-tsne.

I basically took osdf's code and made it pip compliant.

Additional resources

  • See Barnes-Hut-SNE (2013), L.J.P. van der Maaten. It is available on arxiv.