class_docstr: Time series K-mean implementation.
  
  Parameters
  ----------
  n_clusters: int, defaults = 8
      The number of clusters to form as well as the number of
      centroids to generate.
  init_algorithm: str, np.ndarray (3d array of shape (n_clusters, n_dimensions,
      series_length)), defaults = 'random'
      Method for initializing cluster centers or an array of initial cluster centers.
      If string, any of the following strings are valid:
          ['kmeans++', 'random', 'forgy'].
      If 3D np.ndarray, initializes cluster centers with the provided array. The array
          must have shape (n_clusters, n_dimensions, series_length) and the number of
          clusters in the array must be the same as what is provided to the n_clusters
          argument.
  metric: str or Callable, defaults = 'dtw'
      Distance metric to compute similarity between time series. Any of the following
      are valid: ['dtw', 'euclidean', 'erp', 'edr', 'lcss', 'squared', 'ddtw', 'wdtw',
      'wddtw']
  n_init: int, defaults = 10
      Number of times the k-means algorithm will be run with different
      centroid seeds. The final result will be the best output of n_init
      consecutive runs in terms of inertia.
  max_iter: int, defaults = 30
      Maximum number of iterations of the k-means algorithm for a single
      run.
  tol: float, defaults = 1e-6
      Relative tolerance with regards to Frobenius norm of the difference
      in the cluster centers of two consecutive iterations to declare
      convergence.
  verbose: bool, defaults = False
      Verbosity mode.
  random_state: int or np.random.RandomState instance or None, defaults = None
      Determines random number generation for centroid initialization.
  averaging_method: str or Callable, defaults = 'mean'
      Averaging method to compute the average of a cluster. Any of the following
      strings are valid: ['mean', 'dba']. If a Callable is provided must take the form
      Callable[[np.ndarray], np.ndarray].
  average_params: dict, defaults = None = no parameters
      Dictionary containing kwargs for averaging_method.
  distance_params: dict, defaults = None = no parameters
      Dictionary containing kwargs for the distance metric being used.
  
  Attributes
  ----------
  cluster_centers_: np.ndarray (3d array of shape (n_clusters, n_dimensions,
      series_length))
      Time series that represent each of the cluster centers. If the algorithm stops
      before fully converging these will not be consistent with labels_.
  labels_: np.ndarray (1d array of shape (n_instance,))
      Labels that is the index each time series belongs to.
  inertia_: float
      Sum of squared distances of samples to their closest cluster center, weighted by
      the sample weights if provided.
  n_iter_: int
      Number of iterations run.
  
  Examples
  --------
  >>> from sktime.datasets import load_arrow_head
  >>> from sktime.clustering.k_means import TimeSeriesKMeans
  >>> X_train, y_train = load_arrow_head(split="train")
  >>> X_test, y_test = load_arrow_head(split="test")
  >>> clusterer = TimeSeriesKMeans(n_clusters=3)  # doctest: +SKIP
  >>> clusterer.fit(X_train)  # doctest: +SKIP
  TimeSeriesKMeans(n_clusters=3)
  >>> y_pred = clusterer.predict(X_test)  # doctest: +SKIP
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, 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`
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, 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`
      """
      params1 = {
          "n_clusters": 2,
          "metric": "euclidean",
          "init_algorithm": "random",
          "n_init": 1,
          "max_iter": 10,
          "random_state": 0,
      }
      params2 = {
          "n_clusters": 3,
          "metric": "dtw",
          "init_algorithm": np.random.normal(size=(3, 4, 10)),
          "n_init": 1,
          "max_iter": 15,
          "random_state": 1,
      }
      return [params1, params2]
  
file_path: sktime/sktime/clustering/k_means/_k_means.py
imports: from typing import Callable, Union
  import numpy
  from numpy.random import RandomState
  from sktime.clustering.metrics.averaging import _resolve_average_callable
  from sktime.clustering.partitioning import TimeSeriesLloyds
  from sktime.distances import pairwise_distance
label_desc: Use image node_id 4 for calling the TimeSeriesKMeans obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list