:py:mod:`rofunc.learning.ml.gmm`
================================

.. py:module:: rofunc.learning.ml.gmm

.. autodoc2-docstring:: rofunc.learning.ml.gmm
   :allowtitles:

Module Contents
---------------

Classes
~~~~~~~

.. list-table::
   :class: autosummary longtable
   :align: left

   * - :py:obj:`GMM <rofunc.learning.ml.gmm.GMM>`
     -

API
~~~

.. py:class:: GMM(nb_states=1, nb_dim=None, init_zeros=False, mu=None, lmbda=None, sigma=None, priors=None, log_priors=None)
   :canonical: rofunc.learning.ml.gmm.GMM

   Bases: :py:obj:`pbdlib.model.Model`

   .. py:method:: get_matching_mvn(max=False, mass=None)
      :canonical: rofunc.learning.ml.gmm.GMM.get_matching_mvn

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.get_matching_mvn

   .. py:method:: moment_matching(h)
      :canonical: rofunc.learning.ml.gmm.GMM.moment_matching

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.moment_matching

   .. py:method:: marginal_model(dims)
      :canonical: rofunc.learning.ml.gmm.GMM.marginal_model

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.marginal_model

   .. py:method:: lintrans(A, b)
      :canonical: rofunc.learning.ml.gmm.GMM.lintrans

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.lintrans

   .. py:method:: lintrans_dyna(A, b)
      :canonical: rofunc.learning.ml.gmm.GMM.lintrans_dyna

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.lintrans_dyna

   .. py:method:: concatenate_gaussian(q, get_mvn=True, reg=None)
      :canonical: rofunc.learning.ml.gmm.GMM.concatenate_gaussian

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.concatenate_gaussian

   .. py:method:: compute_resp(demo=None, dep=None, table=None, marginal=None, norm=True)
      :canonical: rofunc.learning.ml.gmm.GMM.compute_resp

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.compute_resp

   .. py:method:: init_params_scikit(data, cov_type='full')
      :canonical: rofunc.learning.ml.gmm.GMM.init_params_scikit

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.init_params_scikit

   .. py:method:: init_params_kmeans(data)
      :canonical: rofunc.learning.ml.gmm.GMM.init_params_kmeans

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.init_params_kmeans

   .. py:method:: init_params_random(data)
      :canonical: rofunc.learning.ml.gmm.GMM.init_params_random

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.init_params_random

   .. py:method:: em(data, reg=1e-08, maxiter=100, minstepsize=1e-05, diag=False, reg_finish=False, kmeans_init=False, random_init=True, dep_mask=None, verbose=False, only_scikit=False, no_init=False)
      :canonical: rofunc.learning.ml.gmm.GMM.em

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.em

   .. py:method:: init_hmm_kbins(demos, dep=None, reg=1e-08, dep_mask=None)
      :canonical: rofunc.learning.ml.gmm.GMM.init_hmm_kbins

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.init_hmm_kbins

   .. py:method:: add_trash_component(data, scale=2.0)
      :canonical: rofunc.learning.ml.gmm.GMM.add_trash_component

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.add_trash_component

   .. py:method:: mvn_pdf(x, reg=None)
      :canonical: rofunc.learning.ml.gmm.GMM.mvn_pdf

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.mvn_pdf

   .. py:method:: log_prob(x)
      :canonical: rofunc.learning.ml.gmm.GMM.log_prob

      .. autodoc2-docstring:: rofunc.learning.ml.gmm.GMM.log_prob