High Performance TSNE implementations for python
Project description
Python TSNE implementation utilizing openmp for performance
This is based on the 10XDev/tsne fork of L.J.P. van der Maaten BH-tSNE implementation.
It has fixes to allow this to run in Python 3 and performance has been significantly increased with OpenMP parallelism. (see: tsne-perf-test)
Note: While Scikit-learn v0.17 has a tsne implementation, this implementation performs significantly faster than scikit-learn's. If you need speed, use this.
Algorithms
Barnes-Hut-SNE
A python (cython) wrapper for Barnes-Hut-SNE aka fast-tsne.
We forked 10XDev's implementation and openmp enabled the code.
Installation
This library has been added to pypi as tsne-mp
pip install tsne-mp
It requires openmp support.
- OSX -
brew install libomp
- linux - 'sudo apt-get install libgomp1'
- Windows - Included with Visual Studio C++
Usage
Basic usage:
from tsne import bh_sne
X_2d = bh_sne(X)
Or, the wheels also contain an executable that can be used from the command-line as described in the original project.
Examples
More Information
See Barnes-Hut-SNE (2013), L.J.P. van der Maaten. It is available on arxiv.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for tsne_mp-0.1.13-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b303d61b0d761f7b3a07fdf3d5519cce4601b48769f7a51c9736dfb6c8521791 |
|
MD5 | 9130eb8ff263dd15afa7e7f1504ce589 |
|
BLAKE2b-256 | d56605f7cf3b260af22e901fdb8bf3c0d6144259f41cdf05cf2150e500417489 |
Hashes for tsne_mp-0.1.13-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 89f13e23baaaa9b7665152079dfaf1285db041af8883f93235497ecfef976dc7 |
|
MD5 | de0f46da7edb3623af4bcacd243332c0 |
|
BLAKE2b-256 | 68827c45c7d7ec3d02c518a1acce5d694e1ccd4ecff666b923036a82136ac477 |
Hashes for tsne_mp-0.1.13-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e66113589e0d32cad761f59900ef84c76fb21c5b0681368d889f613c50bf996c |
|
MD5 | a718769849e51d0af998d175ca95eedb |
|
BLAKE2b-256 | 91d46ad415128c0c31f57484f7019bdd5b594ffcc667bb43cde708c9eb48b898 |
Hashes for tsne_mp-0.1.13-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f2fdea39424eb5c314e36809c2788f24e63c1650db2416dcf2ee63efaea14d6c |
|
MD5 | 6c99eeb57bfc8e80b725fb8e12367a5c |
|
BLAKE2b-256 | b98d4f788d45e468ce6771b9bde9b880f369aee876f4e7823c1cf98a317173f7 |
Hashes for tsne_mp-0.1.13-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 93398182d5997fd414c0c79ff516fad59ea2014c684f6dd78361cd032049edb2 |
|
MD5 | df934a2f07e2fda0c23ca44e3e21aac6 |
|
BLAKE2b-256 | 2bbda01f08bb844987e80296059ce6a399f8abecc362753f6abe75fccb5adcf8 |
Hashes for tsne_mp-0.1.13-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3f9f203dc49bf2b4149c45e2043c21809d42623f484beacf31273f0f8d05775a |
|
MD5 | 3fa00564c22b09a629e129f33e740a4c |
|
BLAKE2b-256 | a089e22e4504b29aaa9018922e0eec1067bce33f94c8b8d1b6c6aa0c918418d0 |
Hashes for tsne_mp-0.1.13-cp35-cp35m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3ac951045f5a7c6db6932136045ab3135cdc8e7bcece3f2be37cd7754efa72e3 |
|
MD5 | 145c8e24125a73e445ab04ea5e4618d2 |
|
BLAKE2b-256 | 4be1c5833419ae690fd15a7d434d5d4778e49a3bd8e5f743de0a35f4a1d49ea8 |
Hashes for tsne_mp-0.1.13-cp35-cp35m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 82b43fd3b60f553502fc8979f5f1d84eedd56c99fbf49b4795661b0fc32259b7 |
|
MD5 | 82a97c03353fcd2a7d7d79c5b6a4e247 |
|
BLAKE2b-256 | c4ad373fdd7c437ddf2fbe2f2de79abe19e97079c62ad16fc8e41bf5d1b2aa6a |
Hashes for tsne_mp-0.1.13-cp35-cp35m-macosx_10_6_intel.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 90bfde9aa987641f00c5572300928fd51d603cef45f5c66365fb385abc42239a |
|
MD5 | f329a2224e839c67b9dcdc4c72801d5e |
|
BLAKE2b-256 | fb38a82ff23115513c71a34aa733b10a8631d9ab0f80574c847b5c6c13f25462 |