class_docstr: Hidden Markov Model with Gaussian mixture emissions.
  
  Parameters
  ----------
  n_components : int
      Number of states in the model.
  n_mix : int
      Number of states in the GMM.
  covariance_type : {"sperical", "diag", "full", "tied"}, optional
      The type of covariance parameters to use:
      * "spherical" --- each state uses a single variance value that
          applies to all features.
      * "diag" --- each state uses a diagonal covariance matrix
          (default).
      * "full" --- each state uses a full (i.e. unrestricted)
          covariance matrix.
      * "tied" --- all mixture components of each state use **the same**
          full covariance matrix (note that this is not the same as for
          `GaussianHMM`).
  min_covar : float, optional
      Floor on the diagonal of the covariance matrix to prevent
      overfitting. Defaults to 1e-3.
  startprob_prior : array, shape (n_components, ), optional
      Parameters of the Dirichlet prior distribution for
      :attr:`startprob_`.
  transmat_prior : array, shape (n_components, n_components), optional
      Parameters of the Dirichlet prior distribution for each row
      of the transition probabilities :attr:`transmat_`.
  weights_prior : array, shape (n_mix, ), optional
      Parameters of the Dirichlet prior distribution for
      :attr:`weights_`.
  means_prior, means_weight : array, shape (n_mix, ), optional
      Mean and precision of the Normal prior distribution for
      :attr:`means_`.
  covars_prior, covars_weight : array, shape (n_mix, ), optional
      Parameters of the prior distribution for the covariance matrix
      :attr:`covars_`.
      If :attr:`covariance_type` is "spherical" or "diag" the prior is
      the inverse gamma distribution, otherwise --- the inverse Wishart
      distribution.
  algorithm : {"viterbi", "map"}, optional
      Decoder algorithm.
  random_state: RandomState or an int seed, optional
      A random number generator instance.
  n_iter : int, optional
      Maximum number of iterations to perform.
  tol : float, optional
      Convergence threshold. EM will stop if the gain in log-likelihood
      is below this value.
  verbose : bool, optional
      Whether per-iteration convergence reports are printed to
      :data:`sys.stderr`.  Convergence can also be diagnosed using the
      :attr:`monitor_` attribute.
  params, init_params : string, optional
      The parameters that get updated during (``params``) or initialized
      before (``init_params``) the training.  Can contain any combination
      of 's' for startprob, 't' for transmat, 'm' for means, 'c'
      for covars, and 'w' for GMM mixing weights.  Defaults to all
      parameters.
  implementation: string, optional
      Determines if the forward-backward algorithm is implemented with
      logarithms ("log"), or using scaling ("scaling").  The default is
      to use logarithms for backwards compatibility.
  
  Attributes
  ----------
  monitor_ : ConvergenceMonitor
      Monitor object used to check the convergence of EM.
  startprob_ : array, shape (n_components, )
      Initial state occupation distribution.
  transmat_ : array, shape (n_components, n_components)
      Matrix of transition probabilities between states.
  weights_ : array, shape (n_components, n_mix)
      Mixture weights for each state.
  means_ : array, shape (n_components, n_mix, n_features)
      Mean parameters for each mixture component in each state.
  covars_ : array
      Covariance parameters for each mixture components in each state.
      The shape depends on :attr:`covariance_type`:
      * (n_components, n_mix)                          if "spherical",
      * (n_components, n_mix, n_features)              if "diag",
      * (n_components, n_mix, n_features, n_features)  if "full"
      * (n_components, n_features, n_features)         if "tied".
  
  Examples
  --------
  >>> from sktime.annotation.hmm_learn import GMMHMM # doctest: +SKIP
  >>> from sktime.annotation.datagen import piecewise_normal # doctest: +SKIP
  >>> data = piecewise_normal( # doctest: +SKIP
  ...    means=[2, 4, 1], lengths=[10, 35, 40], random_state=7
  ...    ).reshape((-1, 1))
  >>> model = GMMHMM(n_components=3) # doctest: +SKIP
  >>> model = model.fit(data) # doctest: +SKIP
  >>> labeled_data = model.predict(data) # doctest: +SKIP
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  Returns
  -------
  params : dict or list of dict
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
      Returns
      -------
      params : dict or list of dict
      """
      params = {
          "n_components": 3,
          "covariance_type": "diag",
          "min_covar": 1e-3,
          "random_state": 7,
      }
  
      return params
  
imports: from sktime.annotation.hmm_learn import BaseHMMLearn
label_desc: Use image node_id 3 for calling the GMMHMM obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params