class_docstr: Hidden Markov Model with Gaussian mixture emissions.
  
  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".
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code: def _init_covar_priors(self):
      if self.covariance_type == "full":
          if self.covars_prior is None:
              self.covars_prior = 0.0
          if self.covars_weight is None:
              self.covars_weight = -(1.0 + self.n_features + 1.0)
      elif self.covariance_type == "tied":
          if self.covars_prior is None:
              self.covars_prior = 0.0
          if self.covars_weight is None:
              self.covars_weight = -(self.n_mix + self.n_features + 1.0)
      elif self.covariance_type == "diag":
          if self.covars_prior is None:
              self.covars_prior = -1.5
          if self.covars_weight is None:
              self.covars_weight = 0.0
      elif self.covariance_type == "spherical":
          if self.covars_prior is None:
              self.covars_prior = -(self.n_mix + 2.0) / 2.0
          if self.covars_weight is None:
              self.covars_weight = 0.0
  
imports: import logging
  import numpy
  from scipy import linalg
  from sklearn import cluster
  from sklearn.utils import check_random_state
  from .None import _emissions, _utils
  from .base import BaseHMM
  from .utils import fill_covars, normalize
label_desc: Use image node_id 3 for calling the GMMHMM obj's underlying member method code with example usage: obj._init_covar_priors() without return types