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 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 branched 10XDev's implementation and openmp enabled the code.
Installation
This library has been added to pypi as tsne-mp
pip install tsne-mp
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.
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Source Distributions
Built Distributions
Hashes for tsne_mp-0.1.11-cp37-cp37m-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | acd8a9024cfecd90750426d957078c8dd401717052586e5c5e253cc25cc2bfb6 |
|
| MD5 | 30a11dae52b3f22d9cdccbfaa95f1bb5 |
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| BLAKE2b-256 | aae903e202362601d2f8c7cebe0dc6a6c9a140639664f170d53f7db168592a2d |
Hashes for tsne_mp-0.1.11-cp37-cp37m-macosx_10_9_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 10bf1ae6b7f44e6230c3bfba5475aa9ac695ad4989e74b5835247997d2ccbad9 |
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| MD5 | f119530bf7e3d4d2019f567fa8b94467 |
|
| BLAKE2b-256 | 0bdaa5ec26adc0a3cdcd5ffac00371fe72c892fc1154fd6ebdfaa243d8391a42 |
Hashes for tsne_mp-0.1.11-cp36-cp36m-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 701ec870e259089673628f91f3cf3cd42bfeceb3a5d6050a3eacb426ff80f2dc |
|
| MD5 | c3c41b1794abf8a0d460306049525d2b |
|
| BLAKE2b-256 | 600efeb5ae09c065e39321a3ac7a9bee412c1e549509f303395ebfc656cde125 |
Hashes for tsne_mp-0.1.11-cp36-cp36m-macosx_10_9_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 44db6c93a91debd81c23ac08add86cdd5080a85873e1923e3baa5db048b8f549 |
|
| MD5 | 5f64b010ccc17497e625980b8a3511ee |
|
| BLAKE2b-256 | 5bcd9c7a9af34bb8986d4b8ba527b9e06d96ff621d1f11431e261f385464564f |
Hashes for tsne_mp-0.1.11-cp35-cp35m-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f35b1cdc875e1a008f1e4203e02be14fd86c501aaf785b53bcc324372fd7d548 |
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| MD5 | 249e8735f5f6083a534d4aabdd2efdab |
|
| BLAKE2b-256 | 7e42ed3b11897ad5facd771c059f4e99cb20bdb8e559d4230759ba601d44c6aa |
Hashes for tsne_mp-0.1.11-cp35-cp35m-macosx_10_6_intel.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 740d5eeb5a705b3551547b1f9b26f1bc1ab8822def86958993750ca5ab6517bc |
|
| MD5 | 1f241f1952e69ddc2dc5e12028f99dd0 |
|
| BLAKE2b-256 | b643a945ffc0d3db628f2f9640bc27735adfd91010871599f2f411504ab4595f |
