dpmmpython 0.1.3.dev0

Python wrapper for DPMMSubClusters julia package

DPMMSubClusters

This package is a wrapper for DPMMSubClusters.jl Julia package.

Installation

pip install dpmmpython

If you already have Julia installed, install PyJulia and add the package DPMMSubClusters to your julia installation.

The next part only works with Ubuntu distributions at the moment!
If you do not have Julia installed, or wish to create a clean installation for the purpose of using this package. after installing (with pip), do the following:

import dpmmpython
dpmmpython.install()

Optional arguments are install(julia_download_path = 'https://julialang-s3.julialang.org/bin/linux/x64/1.4/julia-1.4.0-linux-x86_64.tar.gz', julia_target_path = None), where the former specify the julia download file, and the latter the installation path, if the installation path is not specified, $HOME$/julia will be used.

Usage Example:

from dpmmpython.dpmmwrapper import DPMMPython
from dpmmpython.priors import niw
import numpy as np

data,gt = DPMMPython.generate_gaussian_data(10000, 2, 10, 100.0)
prior = niw(1,np.zeros(2),4,np.eye(2))
labels,_,sub_labels= DPMMPython.fit(data,100,prior = prior,verbose = True, gt = gt)

Iteration: 1 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.004499912261962891 || Total time:0.004499912261962891
Iteration: 2 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.0038819313049316406 || Total time:0.008381843566894531
...
Iteration: 98 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.015907764434814453 || Total time:0.5749104022979736
Iteration: 99 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.01072382926940918 || Total time:0.5856342315673828
Iteration: 100 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.010260820388793945 || Total time:0.5958950519561768