class_docstr: DBSCAN for time series distances.
  
  Interface to sklearn DBSCAN with sktime time series distances.
  
  Parameters
  ----------
  distance : str, or callable, default='euclidean'
      The metric to use when calculating distance between instances in a
      feature array. If metric is a string or callable, it must be one of
      the options allowed by :func:`sklearn.metrics.pairwise_distances` for
      its metric parameter.
      If metric is "precomputed", X is assumed to be a distance matrix and
      must be square. X may be a :term:`Glossary <sparse graph>`, in which
      case only "nonzero" elements may be considered neighbors for DBSCAN.
  eps : float, default=0.5
      The maximum distance between two samples for one to be considered
      as in the neighborhood of the other. This is not a maximum bound
      on the distances of points within a cluster. This is the most
      important DBSCAN parameter to choose appropriately for your data set
      and distance function.
  min_samples : int, default=5
      The number of samples (or total weight) in a neighborhood for a point
      to be considered as a core point. This includes the point itself.
  algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
      The algorithm to be used by the NearestNeighbors module
      to compute pointwise distances and find nearest neighbors.
      See NearestNeighbors module documentation for details.
  leaf_size : int, default=30
      Leaf size passed to BallTree or cKDTree. This can affect the speed
      of the construction and query, as well as the memory required
      to store the tree. The optimal value depends
      on the nature of the problem.
  n_jobs : int, default=None
      The number of parallel jobs to run.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  Attributes
  ----------
  core_sample_indices_ : ndarray of shape (n_core_samples,)
      Indices of core samples.
  components_ : ndarray of shape (n_core_samples, n_features)
      Copy of each core sample found by training.
  labels_ : ndarray of shape (n_samples)
      Cluster labels for each point in the dataset given to fit().
      Noisy samples are given the label -1.
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docstr: Predicts labels probabilities for sequences in X.
  
  Default behaviour is to call _predict and set the predicted class probability
  to 1, other class probabilities to 0. Override if better estimates are
  obtainable.
  
  Parameters
  ----------
  X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
      if self.get_tag("X_inner_mtype") = "numpy3D":
          3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
      if self.get_tag("X_inner_mtype") = "nested_univ":
          pd.DataFrame with each column a dimension, each cell a pd.Series
      for list of other mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
  Returns
  -------
  y : 2D array of shape [n_instances, n_classes] - predicted class probabilities
      1st dimension indices correspond to instance indices in X
      2nd dimension indices correspond to possible labels (integers)
      (i, j)-th entry is predictive probability that i-th instance is of class j
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code: def _predict_proba(self, X):
      """Predicts labels probabilities for sequences in X.
  
      Default behaviour is to call _predict and set the predicted class probability
      to 1, other class probabilities to 0. Override if better estimates are
      obtainable.
  
      Parameters
      ----------
      X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
          if self.get_tag("X_inner_mtype") = "numpy3D":
              3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
          if self.get_tag("X_inner_mtype") = "nested_univ":
              pd.DataFrame with each column a dimension, each cell a pd.Series
          for list of other mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
      Returns
      -------
      y : 2D array of shape [n_instances, n_classes] - predicted class probabilities
          1st dimension indices correspond to instance indices in X
          2nd dimension indices correspond to possible labels (integers)
          (i, j)-th entry is predictive probability that i-th instance is of class j
      """
      preds = self._predict(X)
      n_instances = len(preds)
      n_clusters = max(preds) + 1
      dists = np.zeros((X.shape[0], n_clusters))
      for i in range(n_instances):
          # preds[i] can be -1 for DBSCAN
          if preds[i] > -1:
              dists[i, preds[i]] = 1
      return dists
  
file_path: sktime/sktime/clustering/dbscan.py
imports: import numpy
  from sklearn.cluster import DBSCAN
  from sktime.clustering.base import BaseClusterer
  from sktime.datatypes import update_data
  from sktime.dists_kernels.base import BasePairwiseTransformerPanel
  from sktime.utils.warnings import warn
label_desc: Use image node_id 4 for calling the TimeSeriesDBSCAN obj's underlying member method code with example usage: obj._predict_proba(X) and returns: dists