pymix
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pymix [2013/09/09 11:27] hkoller [Installing] |
pymix [2013/09/09 13:07] (current) hkoller [Clustering] |
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| + | 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> | ||
| + | |||
| + | |||
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