class_docstr: Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1.
  
  An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature
  representations using the CAWPE structure as described in [1]_. The default
  implementation differs from the one described in [1]_, in that the STC component
  uses the out of bag error (OOB) estimates for weights (described in [2]_) rather
  than the cross validation estimate. OOB is an order of magnitude faster and on
  average as good as CV. This means that this version of HIVE COTE is a bit faster
  than HC2, although less accurate on average.
  
  Parameters
  ----------
  stc_params : dict or None, default=None
      Parameters for the ShapeletTransformClassifier module. If None, uses the
      default parameters with a 2 hour transform contract.
  tsf_params : dict or None, default=None
      Parameters for the TimeSeriesForestClassifier module. If None, uses the default
      parameters with n_estimators set to 500.
  rise_params : dict or None, default=None
      Parameters for the RandomIntervalSpectralForest module. If None, uses the
      default parameters with n_estimators set to 500.
  cboss_params : dict or None, default=None
      Parameters for the ContractableBOSS module. If None, uses the default
      parameters.
  verbose : int, default=0
      Level of output printed to the console (for information only).
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random number generation.
  
  Attributes
  ----------
  n_classes_ : int
      The number of classes.
  classes_ : list
      The unique class labels.
  stc_weight_ : float
      The weight for STC probabilities.
  tsf_weight_ : float
      The weight for TSF probabilities.
  rise_weight_ : float
      The weight for RISE probabilities.
  cboss_weight_ : float
      The weight for cBOSS probabilities.
  
  See Also
  --------
  HIVECOTEV2, ShapeletTransformClassifier, TimeSeriesForestClassifier,
  RandomIntervalSpectralForest, ContractableBOSS
  
  Notes
  -----
  For the Java version, see
  `https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
  tsml/classifiers/hybrids/HIVE_COTE.java`_.
  
  References
  ----------
  .. [1] Anthony Bagnall, Michael Flynn, James Large, Jason Lines and
     Matthew Middlehurst. "On the usage and performance of the Hierarchical Vote
     Collective of Transformation-based Ensembles version 1.0 (hive-cote v1.0)"
     International Workshop on Advanced Analytics and Learning on Temporal Data 2020
  .. [2] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom,
     and Anthony Bagnall. "HIVE-COTE 2.0: a new meta ensemble for time series
     classification." Machine Learning (2021).
docstr: Predicts labels probabilities for sequences in X.
  
  Parameters
  ----------
  X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
      The data to make predict probabilities for.
  
  Returns
  -------
  y : array-like, shape = [n_instances, n_classes_]
      Predicted probabilities using the ordering in classes_.
docstr_tok: ["Predicts","labels","probabilities","for","sequences","in","X.","Parameters","--","--","--","--","--","X",":","3D","np.array","of","shape","=","[","n_instances",",","n_dimensions",",","series_length","]","The","data","to","make","predict","probabilities","for",".","Returns","--","--","--","-","y",":","array-like",",","shape","=","[","n_instances",",","n_classes_","]","Predicted","probabilities","using","the","ordering","in","classes_","."]
code: def _predict_proba(self, X) -> np.ndarray:
      """Predicts labels probabilities for sequences in X.
  
      Parameters
      ----------
      X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
          The data to make predict probabilities for.
  
      Returns
      -------
      y : array-like, shape = [n_instances, n_classes_]
          Predicted probabilities using the ordering in classes_.
      """
      dists = np.zeros((X.shape[0], self.n_classes_))
  
      # Call predict proba on each classifier, multiply the probabilities by the
      # classifiers weight then add them to the current HC1 probabilities
      dists = np.add(
          dists,
          self._stc.predict_proba(X)
          * (np.ones(self.n_classes_) * self.stc_weight_),
      )
      dists = np.add(
          dists,
          self._tsf.predict_proba(X)
          * (np.ones(self.n_classes_) * self.tsf_weight_),
      )
      dists = np.add(
          dists,
          self._rise.predict_proba(X)
          * (np.ones(self.n_classes_) * self.rise_weight_),
      )
      dists = np.add(
          dists,
          self._cboss.predict_proba(X)
          * (np.ones(self.n_classes_) * self.cboss_weight_),
      )
  
      # Make each instances probability array sum to 1 and return
      return dists / dists.sum(axis=1, keepdims=True)
  
file_path: sktime/sktime/classification/hybrid/_hivecote_v1.py
imports: from datetime import datetime
  import numpy
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import cross_val_predict
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.classification.dictionary_based import ContractableBOSS
  from sktime.classification.interval_based import RandomIntervalSpectralEnsemble, TimeSeriesForestClassifier
  from sktime.classification.shapelet_based import ShapeletTransformClassifier
label_desc: Use image node_id 4 for calling the HIVECOTEV1 obj's underlying member method code with example usage: obj._predict_proba(X) and returns: unknown