Metadata-Version: 2.1
Name: studenttmixture
Version: 0.0.2.6
Summary: Mixture modeling algorithms using the Student's t-distribution
Home-page: https://github.com/jlparki/mix_T
Author: Jonathan Parkinson
Author-email: jlparkinson1@gmail.com
License: MIT
Description: # studenttmixture
        
        Mixtures of multivariate Student's t distributions are widely used for clustering
        data that may contain outliers, but scipy and scikit-learn do not at present
        offer classes for fitting Student's t mixture models. This package provides classes
        for:
        
        1) Modeling / clustering a dataset using a finite mixture of multivariate Student's
        t distributions fit via the EM algorithm. This is analogous to scikit-learn's 
        GaussianMixture.
        2) Modeling / clustering a dataset using a mixture of multivariate Student's 
        t distributions fit via the variational mean-field approximation. This is analogous to
        scikit-learn's BayesianGaussianMixture.
        
        Unittests for the package are in the tests folder.
        
        ### Installation
        
            pip install studenttmixture
        
        Note that starting in version 0.0.2.3, this package contains C extensions and is therefore
        distributed as a source distribution which is automatically compiled on install. 
        
        It is unusual but problems with source distribution pip packages that contain C extensions are occasionally
        observed on Windows, e.g. an error similar to this:
        
            error: Microsoft Visual C++ 14.0 is required.
        
        in the unlikely event you encounter this, I recommend the solution described under this 
        [StackOverflow and links](https://stackoverflow.com/questions/44951456/pip-error-microsoft-visual-c-14-0-is-required).
        
        ### Usage
        
        - [EMStudentMixture](https://github.com/jlparkI/mix_T/blob/main/Documentation/Finite_Mixture_Docs.md)<br>
        - [VariationalStudentMixture](https://github.com/jlparkI/mix_T/blob/main/Documentation/Variational_Mixture_Docs.md)<br>
        - [Tutorial: Modeling with mixtures](https://github.com/jlparkI/mix_T/blob/main/Documentation/Tutorial.md)<br>
        
        ### Background
        
        - [Deriving the mean-field formula](https://jlparki.github.io/mean_field.pdf)<br>
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
