class_docstr: MrSQM = Multiple Representations Sequence Miner.
  
  Direct Interface to MrSQMClassifier from mrsqm.
  Note: mrsqm itself is copyleft (GPL3). This interface is permissive license (BSD3).
  
  MrSQM is an efficient time series classifier utilizing symbolic representations of
  time series. MrSQM implements four different feature selection strategies =
  (R,S,RS,SR) that can quickly select subsequences from multiple symbolic
  representations of time series data.
  
  Parameters
  ----------
  strat               : str, one of 'R','S','SR', or 'RS', default="RS"
      feature selection strategy. By default set to 'RS'.
      R and S are single-stage filters while RS and SR are two-stage filters.
  features_per_rep    : int, default=500
      (maximum) number of features selected per representation.
  selection_per_rep   : int, default=2000
      (maximum) number of candidate features selected per representation.
      Only applied in two stages strategies (RS and SR), otherwise ignored.
  nsax                : int, default=1
      number of representations produced by sax transformation.
  nsfa                : int, default=0
      number of representations produced by sfa transformation.
  custom_config       : dict, default=None
      customized parameters for the symbolic transformation.
  random_state        : int, default=None.
      random seed for the classifier.
  sfa_norm            : bool, default=True.
      whether to apply time series normalisation (standardisation).
  
  References
  ----------
  .. [1] Thach Le Nguyen and Georgiana Ifrim.
     "MrSQM: Fast Time Series Classification with Symbolic Representations
     and Efficient Sequence Mining" arXiv preprint arXiv:2109.01036 (2021).
  .. [2] Thach Le Nguyen and Georgiana Ifrim.
     "Fast Time Series Classification with Random Symbolic Subsequences".
     AALTD 2022.
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docstr: Get fitted parameters.
  
  private _get_fitted_params, called from get_fitted_params
  
  State required:
      Requires state to be "fitted".
  
  Returns
  -------
  fitted_params : dict with str keys
      fitted parameters, keyed by names of fitted parameter
docstr_tok: ["Get","fitted","parameters",".","private","_get_fitted_params",",","called","from","get_fitted_params","State","required",":","Requires","state","to","be","``","fitted","''",".","Returns","--","--","--","-","fitted_params",":","dict","with","str","keys","fitted","parameters",",","keyed","by","names","of","fitted","parameter"]
code: def _get_fitted_params(self):
      """Get fitted parameters.
  
      private _get_fitted_params, called from get_fitted_params
  
      State required:
          Requires state to be "fitted".
  
      Returns
      -------
      fitted_params : dict with str keys
          fitted parameters, keyed by names of fitted parameter
      """
      return {}
  
imports: from sktime.classification._delegate import _DelegatedClassifier
label_desc: Use image node_id 2 for calling the MrSQM obj's underlying member method code with example usage: obj._get_fitted_params() and returns: dict