class_docstr: Heteregeneous Intrinsic Dimensionality Algorithm (Hidalgo) model.
  
  Hidalgo is a robust approach in discriminating regions with
  different local intrinsic dimensionality (topological feature
  measuring complexity). Hidalgo offers unsupervised segmentation
  of high-dimensional data.
  
  Parameters
  ----------
  metric : str, or callable, optional, default="euclidean"
      directly passed to sklearn KNearestNeighbors,
      must be str or callable that can be passed to KNearestNeighbors
      distance used in the nearest neighbors part of the algorithm
  K : int, optional, default=2
      number of manifolds used in algorithm
  zeta : float, optional, default=0.8
      "local homogeneity level" used in the algorithm, see equation (4)
  q : int, optional, default=3
      number of points for local Z interaction, "local homogeneity range"
      see equation (4)
  n_iter : int, optional, default=1000
      number of Gibbs sampling iterations
  n_replicas : int, optional, default=1
      number of random starts to run Gibbs sampling
  burn_in : float, optional, default=0.9
      percentage of Gibbs sampling iterations discarded, "burn-in fraction"
  fixed_Z : bool, optional, default=False
      estimate parameters with fixed z (joint posterior approximation via Gibbs)
      z = (z_1, ..., z_K) is a latent variable introduced, where z_i = k
      indicates point i belongs to manifold K
  use_Potts : bool, optional, default=True
      if using local interaction between z, see equation (4)
  estimate_zeta : bool, optional, default=False
      update zeta in the sampling
  sampling_rate: int, optional, default=10
      rate at which to save samples for each n_iter
  a : np.ArrayLike, optional, default=None
      prior parameters of d, the dimensionality of manifold k
  b : np.ArrayLike, optional, default=None
      prior parameters of d, the dimensionality of manifold k
  c : np.ArrayLike, optional, default=None
      prior parameters of p, the probability that point belongs to manifold k
  f : np.ArrayLike, optional, default=None
      parameters of zeta
  seed : int, optional, default = 1
      generate random numbers with seed
  
  References
  ----------
  Allegra, Michele, et al. "Data segmentation based on the local
  intrinsic dimension." Scientific reports 10.1 (2020): 1-12.
  https://www.nature.com/articles/s41598-020-72222-0
  
  
  Examples
  --------
  >>> from sktime.transformations.series.hidalgo import Hidalgo
  >>> import numpy as np
  >>> np.random.seed(123)
  >>> X = np.random.rand(10,3)
  >>> X[:6, 1:] += 10
  >>> X[6:, 1:] = 0
  >>> model = Hidalgo(K=2, burn_in=0.8, n_iter=100, seed=10)
  >>> fitted_model = model.fit(X)
  >>> Z = fitted_model.transform(X)
  >>> Z.tolist()
  [1, 1, 1, 1, 1, 1, 0, 0, 0, 0]
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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.
      Reserved values for classifiers:
          "results_comparison" - used for identity testing in some classifiers
              should contain parameter settings comparable to "TSC bakeoff"
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
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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.
          Reserved values for classifiers:
              "results_comparison" - used for identity testing in some classifiers
                  should contain parameter settings comparable to "TSC bakeoff"
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      return {
          "metric": "euclidean",
          "K": 1,
          "zeta": 0.8,
          "q": 3,
          "n_iter": 10,
          "n_replicas": 1,
          "burn_in": 0.5,
          "fixed_Z": False,
          "use_Potts": True,
          "estimate_zeta": False,
          "sampling_rate": 2,
          "a": None,
          "b": None,
          "c": None,
          "f": None,
          "seed": 1,
      }
  
file_path: sktime/sktime/transformations/series/hidalgo.py
imports: from functools import reduce
  import numpy
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils.validation import check_random_state
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 8 for calling the Hidalgo obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict