51 lines
1.8 KiB
ReStructuredText
51 lines
1.8 KiB
ReStructuredText
.. _mixture:
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===================================================
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Gaussian mixture models
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===================================================
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`scikits.learn.mixture` is a package which enables to create Mixture
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Models (diagonal, spherical, tied and full covariance matrices
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supported), to sample them, and to estimate them from data using
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Expectation Maximization algorithm. It can also draw confidence
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ellipsoides for multivariate models, and compute the Bayesian
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Information Criterion to assess the number of clusters in the data.
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For the moment, only Gaussian Mixture Models (GMM) are
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implemented. These are a class of probabilistic models describing the
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data as drawn from a mixture of Gaussian probability
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distributions. The challenge that is GMM tackles is to learn the
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parameters of these Gaussians from the data.
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GMM classifier
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==============
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.. currentmodule:: scikits.learn.mixture
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The :class:`GMM` object implements a :meth:`GMM.fit` method to learn a
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Gaussian Mixture Models from train data. Given test data, it can assign
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to each sample the class of the Gaussian it mostly probably belong to
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using the :meth:`GMM.predict` method.
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..
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Alternatively, the probability of each
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sample beloning to the various Gaussians may be retrieved using the
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:meth:`GMM.predict_proba` method.
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.. figure:: ../auto_examples/mixture/images/plot_gmm_classifier.png
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:target: ../auto_examples/cluster/plot_gmm_classifier.html
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:align: center
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:scale: 75%
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.. topic:: Examples:
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* See :ref:`example_mixture_plot_gmm_classifier.py` for an example of
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using a GMM as a classifier on the iris dataset.
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* See :ref:`example_mixture_plot_gmm.py` for an example on plotting the
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confidence ellipsoids.
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* See :ref:`example_mixture_plot_gmm_pdf.py` for an example on plotting the
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density estimation.
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