class_docstr: Hidden Markov Model with Poisson emissions.
  
  Parameters
  ----------
  n_components : int
      Number of states.
  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_`.
  lambdas_prior, lambdas_weight : array, shape (n_components,), optional
      The gamma prior on the lambda values using alpha-beta notation,
      respectively. If None, will be set based on the method of
      moments.
  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, and 'l' for
      lambdas.  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.
  lambdas_ : array, shape (n_components, n_features)
      The expectation value of the waiting time parameters for each
      feature in a given state.
  
  Examples
  --------
  >>> from sktime.annotation.hmm_learn import PoissonHMM # doctest: +SKIP
  >>> from sktime.annotation.datagen import piecewise_poisson # doctest: +SKIP
  >>> data = piecewise_poisson( # doctest: +SKIP
  ...    lambdas=[1, 2, 3], lengths=[2, 4, 8], random_state=7
  ...    ).reshape((-1, 1))
  >>> model = PoissonHMM(n_components=3) # doctest: +SKIP
  >>> model = model.fit(data) # doctest: +SKIP
  >>> labeled_data = model.predict(data) # doctest: +SKIP
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code: def __init__(
      self,
      n_components: int = 1,
      startprob_prior: float = 1.0,
      transmat_prior: float = 1.0,
      lambdas_prior: float = 0.0,
      lambdas_weight: float = 0.0,
      algorithm: str = "viterbi",
      random_state: int = None,
      n_iter: int = 10,
      tol: float = 1e-2,
      verbose: bool = False,
      params: str = "stl",
      init_params: str = "stl",
      implementation: str = "log",
  ):
      self.n_components = n_components
      self.startprob_prior = startprob_prior
      self.transmat_prior = transmat_prior
      self.lambdas_prior = lambdas_prior
      self.lambdas_weight = lambdas_weight
      self.algorithm = algorithm
      self.random_state = random_state
      self.n_iter = n_iter
      self.tol = tol
      self.verbose = verbose
      self.params = params
      self.init_params = init_params
      self.implementation = implementation
      super().__init__()
  
imports: from sktime.annotation.hmm_learn import BaseHMMLearn
label_desc: Use image node_id 1 to create a new PoissonHMM object from inherited base classes: BaseHMMLearn with example: obj = PoissonHMM(n_components, startprob_prior, transmat_prior, lambdas_prior, lambdas_weight, algorithm, random_state, n_iter, tol, verbose, params, init_params, implementation)