.. currentmodule:: microscopes .. _index: .. datamicroscopes documentation master file, created by sphinx-quickstart on Tue Aug 12 20:17:28 2014. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. datamicroscopes: Bayesian nonparametric models in Python ==================== datamicroscopes is a library for discovering structure in your data. It implements several Bayesian nonparametric models for clustering such as the :ref:`Dirichlet Process Mixture Model (DPMM) ` , :ref:`the Infinite Relational Model (IRM) ` , and the :ref:`Hierarchichal Dirichlet Process (HDP) ` . These models rely on the :ref:`Dirichlet Process `, which allow for the automatic learning of the number of clusters in a datset. Additionally, our :ref:`API ` provides users with a flexible set of likelihood models for various types of data, such as binary, ordinal, categorical, and real-valued variables( :ref:`datatypes `) . Please read our :ref:`introduction ` for an overview of clustering and structure discovery. .. raw:: html

Tutorials

.. toctree:: :maxdepth: 2 intro ncluster enron_blog topic .. raw:: html

Datatypes and Likelihood Models

.. toctree:: :maxdepth: 2 datatypes bb dd niw nic gamma_poisson .. raw:: html

Examples

.. toctree:: :maxdepth: 2 gauss2d mnist_predictions enron_email hdp .. raw:: html
Installation ================= First, install `Anaconda `_. Then in the terminal type: .. code-block:: bash $ conda config --add channels distributions $ conda config --add channels datamicroscopes $ conda install microscopes-common $ conda install microscopes-{mixturemodel, irm, lda} .. toctree:: :hidden: docs api