The Python Mixture Package (PyMix , http://www.pymix.org/pymix/ ) is a freely available Python library implementing algorithms and data structures for a wide variety of data mining applications with basic and extended mixture models. Features include

   * Finite mixture models of discrete and continuous features
   * Wide range of available distributions (Normal, Exponential, Discrete, Dirichlet, Normal-Gamma, Uniform, HMM)
   * Bayesian mixture models
   * ML and MAP parameter estimation
   * Context-specific independence structure learning
   * Partially supervised parameter learning
   * Parameter estimation for pairwise constrained samples 

====== Installing ======
(tested on Ubuntu 12.10)

   * download newest Version from http://www.pymix.org/pymix/index.php?n=PyMix.Download
   * Adjust setup.py for ubuntu:

<code>
> vi setup.py

##
# 1.
# replace the line 
# from distutils.core import setup, Extension,DistutilsExecError
# with

from distutils.core import setup, Extension
from distutils.errors import DistutilsExecError

##
# 2.
# replace the line
#   numpypath =  prefix + '/lib/python' +pyvs + '/site-packages/numpy/core/include/numpy'  # path to arrayobject.h
# with
    numpypath = '/usr/share/pyshared/numpy/core/include/numpy' # path to arrayobject.h

</code>
    * build and install
<code bash>
python setup.py build
sudo python setup.py install --prefix /usr/local/
</code>

====== Clustering ======
Following the tutorial from http://www.pymix.org/pymix/index.php?n=PyMix.Tutorial 

<code python>
import numpy
import mixture

# create dummy data with speeds from lkw and pkw
raw_data = numpy.array([75 , 80 , 120, 83, 134, 150, 89, 160, 80, 160] )
data = mixture.DataSet()
data.fromArray(raw_data)

# create mixture model
n1 = mixture.NormalDistribution(80,3.0)
n2 = mixture.NormalDistribution(130,10.0)
m = mixture.MixtureModel(2,[0.5,0.5], [n1,n2])

# Perform Expectation Maximization Algorithm
m.EM(data, max_iter=40, delta=0.1)  # finished after 40 iterations or when delta < 0.1

# show cluster assignment of data
clust = m.classify(data)
print clust

</code>