Metadata-Version: 1.1
Name: gmr
Version: 1.4
Summary: Gaussian Mixture Regression
Home-page: https://github.com/AlexanderFabisch/gmr
Author: Alexander Fabisch
Author-email: afabisch@googlemail.com
License: new BSD
Description: ===
        gmr
        ===
        
        .. image:: https://api.travis-ci.org/AlexanderFabisch/gmr.png?branch=master
           :target: https://travis-ci.org/AlexanderFabisch/gmr
           :alt: Travis
        
        Gaussian Mixture Models (GMMs) for clustering and regression in Python.
        
        Source code repository: https://github.com/AlexanderFabisch/gmr
        
        .. image:: https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/gmr.png
        
        `(Source code of example) <https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_regression.py>`_
        
        
        Example
        -------
        
        Estimate GMM from samples and sample from GMM:
        
        .. code-block:: python
        
            from gmr import GMM
        
            gmm = GMM(n_components=3, random_state=random_state)
            gmm.from_samples(X)
            X_sampled = gmm.sample(100)
        
        
        For more details, see:
        
        .. code-block:: python
        
            help(gmr)
        
        
        Installation
        ------------
        
        Install from `PyPI`_:
        
        .. code-block:: bash
        
            sudo pip install gmr
        
        or from source:
        
        .. code-block:: bash
        
            sudo python setup.py install
        
        .. _PyPi: https://pypi.python.org/pypi
        
        
        How Does It Compare to scikit-learn?
        ------------------------------------
        
        There is an implementation of Gaussian Mixture Models for clustering in
        `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GMM.html>`_
        as well. Regression could not be easily integrated in the interface of
        sklearn. That is the reason why I put the code in a separate repository.
        It is possible to initialize GMR from sklearn though:
        
        .. code-block:: python
        
            from sklearn.mixture import GaussianMixture
            from gmr import GMM
            gmm_sklearn = GaussianMixture(n_components=3, covariance_type="diag")
            gmm_sklearn.fit(X)
            gmm = GMM(
                n_components=3, priors=gmm_sklearn.weights_, means=gmm_sklearn.means_,
                covariances=np.array([np.diag(c) for c in gmm_sklearn.covariances_]))
        
        
        Gallery
        -------
        
        .. image:: https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/doc/sklearn_initialization.png
            :width: 60%
        
        `Diagonal covariances <https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_iris_from_sklearn.py>`_
        
        .. image:: https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/doc/confidence_sampling.png
            :width: 60%
        
        `Sample from confidence interval <https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_sample_mvn_confidence_interval.py>`_
        
        .. image:: https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/doc/trajectories.png
            :width: 60%
        
        `Generate trajectories <https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_trajectories.py>`_
        
        .. image:: https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/doc/time_invariant_trajectories.png
            :width: 60%
        
        `Sample time-invariant trajectories <https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_time_invariant_trajectories.py>`_
        
        You can find all examples `here <https://github.com/AlexanderFabisch/gmr/tree/master/examples>`_.
        
        
        Saving a Model
        --------------
        
        This library does not directly offer a function to store fitted models. Since
        the implementation is pure Python, it is possible, however, to use standard
        Python tools to store Python objects. For example, you can use pickle to
        temporarily store a GMM:
        
        .. code-block:: python
        
            import numpy as np
            import pickle
            import gmr
            gmm = gmr.GMM(n_components=2)
            gmm.from_samples(X=np.random.randn(1000, 3))
        
            # Save object gmm to file 'file'
            pickle.dump(gmm, open("file", "wb"))
            # Load object from file 'file'
            gmm2 = pickle.load(open("file", "rb"))
        
        It might be required to store models more permanently than in a pickle file,
        which might break with a change of the library or with the Python version.
        In this case you can choose a storage format that you like and store the
        attributes `gmm.priors`, `gmm.means`, and `gmm.covariances`. These can be
        used in the constructor of the GMM class to recreate the object and they can
        also be used in other libraries that provide a GMM implementation. The
        MVN class only needs the attributes `mean` and `covariance` to define the
        model.
        
        
        Publications
        ------------
        
        The first publication that presents the GMR algorithm is
        
            [1] Z. Ghahramani, M. I. Jordan, "Supervised learning from incomplete data via an EM approach," Advances in Neural Information Processing Systems 6, 1994, pp. 120-127, http://papers.nips.cc/paper/767-supervised-learning-from-incomplete-data-via-an-em-approach
        
        but it does not use the term Gaussian Mixture Regression, which to my knowledge occurs first in
        
            [2] S. Calinon, F. Guenter and A. Billard, "On Learning, Representing, and Generalizing a Task in a Humanoid Robot," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, 2007, pp. 286-298, doi: `10.1109/TSMCB.2006.886952 <https://doi.org/10.1109/TSMCB.2006.886952>`_.
        
        A recent survey on various regression models including GMR is the following:
        
            [3] F. Stulp, O. Sigaud, "Many regression algorithms, one unified model: A review," in Neural Networks, vol. 69, 2015, pp. 60-79, doi: `10.1016/j.neunet.2015.05.005 <https://doi.org/10.1016/j.neunet.2015.05.005>`_.
        
        Sylvain Calinon has a good introduction in his `slides on nonlinear regression <http://calinon.ch/misc/EE613/EE613-slides-9.pdf>`_ for his `machine learning course <http://calinon.ch/teaching_EPFL.htm>`_.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires: numpy
Requires: scipy
