Metadata-Version: 1.1
Name: ard-em
Version: 0.1.4
Summary: ARD EM algorithm with automatic determination of components/clusters number
Home-page: https://github.com/Leensman/ard-em
Author: Artem Ryzhikov
Author-email: artemryzhikoff@yandex.ru
License: Apache-2.0 License
Description: # ARD EM
        ARD (Automatic Relevance Determination) EM implementation on Python.
        The classical EM-algorithm for reconstructing a mixture of normal distributions does not allow to determine the amount of components of the mixture. The ARD EM implementation suggests algorithm for automatically determining the number of components ARD EM, based on the method of relevant vectors. The idea of the algorithm is to use at the initial stage of a knowingly excessive amount of the components of the mixture with further determination of the relevant components by maximizing
        validity. Experiments on model problems show that the number of found clusters either coincides with the true one, or slightly
        excels him. In addition, clustering with ARD EM is closer to the true than the analogs based on sliding control and
        character of the minimum description length. It's EM algorithm with automatic determination of number of components. It's powerful and fast algorithm for gaussian mixture learning and clustering with unknown number of components.
        
        # Implementation
        The implemented [GaussianMixtureARD](ard_em.py) class has the same interface as SkLearn's [GaussianMixture](http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture) one, but with 3 additional parameters:
        ```python
        init_components="sqrt" # Initial number of components. sqrt(N) if "sqrt"
        alpha_bound=1e3 # Drop all components with weight_reg (alpha) > alpha_bound
        weight_bound=1e-3 # Drop all components with weight < weight_bound
        ```
        and without **n_components** one.
        
        # Installation
        ```
        pip install git+https://github.com/Leensman/ard-em.git
        ```
        
        ## Example
        ```python
        from ard_em import GaussianMixtureARD
        gmm = GaussianMixtureARD()
        gmm = gmm.fit(X)
        print('Bayesian information criterion: ', gmm.bic(X))
        best_n_components = gmm.n_components
        print('Best number of components: ', best_n_components)
        gmm.predict(X)
        ```
        For more examples go to [GaussianMixture.ipynb](https://github.com/Leensman/ard-em/blob/master/ard-em/examples/Gaussian%20mixture.ipynb)
        
        ## Links
        [Original paper](http://www.machinelearning.ru/wiki/images/d/dc/Vetrov-ArdEm-JVMMF-2009.pdf)
        
        ## Author
        Artem Ryzhikov, LAMBDA laboratory, Higher School of Economics, Yandex School of Data Analysis
        
        **E-mail:** artemryzhikoff@yandex.ru
        
        **Linkedin:** https://www.linkedin.com/in/artem-ryzhikov-2b6308103/
        
        **HSE profile:** https://www.hse.ru/org/persons/190912317
        
Keywords: pattern recognition,density reconstruction,cluster analysis,clusters number determination,EM algorithm,Bayes Learning,automatic relevance determination
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.7 
Classifier: Programming Language :: Python :: 3.4 
