class_docstr: Martrix Profile (MP) classifier.
  
  This classifier simply transforms the input data using the MatrixProfile [1]
  transformer and builds a provided estimator using the transformed data.
  
  Parameters
  ----------
  subsequence_length : int, default=10
      The subsequence length for the MatrixProfile transformer.
  estimator : sklearn classifier, default=None
      An sklearn estimator to be built using the transformed data. Defaults to a
      1-nearest neighbour classifier.
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors. Currently available for the classifier
      portion only.
  random_state : int or None, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes_)
      Holds the label for each class.
  
  See Also
  --------
  MatrixProfile
  
  References
  ----------
  .. [1] Yeh, Chin-Chia Michael, et al. "Time series joins, motifs, discords and
      shapelets: a unifying view that exploits the matrix profile." Data Mining and
      Knowledge Discovery 32.1 (2018): 83-123.
      https://link.springer.com/article/10.1007/s10618-017-0519-9
  
  Examples
  --------
  >>> from sktime.classification.feature_based import MatrixProfileClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
  >>> X_test, y_test = load_unit_test(split="test", return_X_y=True)  # doctest: +SKIP
  >>> clf = MatrixProfileClassifier()  # doctest: +SKIP
  >>> clf.fit(X_train, y_train)  # doctest: +SKIP
  MatrixProfileClassifier(...)  # doctest: +SKIP
  >>> y_pred = clf.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.
      For classifiers, a "default" set of parameters should be provided for
      general testing, and a "results_comparison" set for comparing against
      previously recorded results if the general set does not produce suitable
      probabilities to compare against.
  
  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.
          For classifiers, a "default" set of parameters should be provided for
          general testing, and a "results_comparison" set for comparing against
          previously recorded results if the general set does not produce suitable
          probabilities to compare against.
  
      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 {"subsequence_length": 4}
  
file_path: sktime/sktime/classification/feature_based/_matrix_profile_classifier.py
imports: import numpy
  from sklearn.neighbors import KNeighborsClassifier
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.matrix_profile import MatrixProfile
label_desc: Use image node_id 5 for calling the MatrixProfileClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict