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
code: def __init__(
      self,
      subsequence_length=10,
      estimator=None,
      n_jobs=1,
      random_state=None,
  ):
      self.subsequence_length = subsequence_length
      self.estimator = estimator
  
      self.n_jobs = n_jobs
      self.random_state = random_state
  
      self._transformer = None
      self._estimator = None
  
      super().__init__()
  
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 1 to create a new MatrixProfileClassifier object from inherited base classes: BaseClassifier with example: obj = MatrixProfileClassifier(subsequence_length, estimator, n_jobs, random_state)