class_docstr: Time-series specific Support Vector Classifier.
  
  Parameters
  ----------
  C : float, optional (default=1.0)
      Penalty parameter C of the error term.
  
  kernel : string, optional (default='gak')
       Specifies the kernel type to be used in the algorithm.
       It must be one of 'gak' or a kernel accepted by ``sklearn.svm.SVC``.
       If none is given, 'gak' will be used. If a callable is given it is
       used to pre-compute the kernel matrix from data matrices; that matrix
       should be an array of shape ``(n_samples, n_samples)``.
  
  degree : int, optional (default=3)
      Degree of the polynomial kernel function ('poly').
      Ignored by all other kernels.
  
  gamma : float, optional (default='auto')
      Kernel coefficient for 'gak', 'rbf', 'poly' and 'sigmoid'.
      If gamma is 'auto' then:
  
      - for 'gak' kernel, it is computed based on a sampling of the training
        set (cf :ref:`tslearn.metrics.gamma_soft_dtw <fun-tslearn.metrics.gamma_soft_dtw>`)
      - for other kernels (eg. 'rbf'), 1/n_features will be used.
  
  coef0 : float, optional (default=0.0)
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  shrinking : boolean, optional (default=True)
      Whether to use the shrinking heuristic.
  
  probability : boolean, optional (default=False)
      Whether to enable probability estimates. This must be enabled prior
      to calling `fit`, and will slow down that method.
      Also, probability estimates are not guaranteed to match predict output.
      See our :ref:`dedicated user guide section <kernels-ml>`
      for more details.
  
  tol : float, optional (default=1e-3)
      Tolerance for stopping criterion.
  
  cache_size : float, optional (default=200.0)
      Specify the size of the kernel cache (in MB).
  
  class_weight : {dict, 'balanced'}, optional
      Set the parameter C of class i to class_weight[i]*C for
      SVC. If not given, all classes are supposed to have
      weight one.
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``
  
  n_jobs : int or None, optional (default=None)
      The number of jobs to run in parallel for GAK cross-similarity matrix
      computations.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See scikit-learns'
      `Glossary <https://scikit-learn.org/stable/glossary.html#term-n-jobs>`_
      for more details.
  
  verbose : int, default: 0
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in libsvm that, if enabled, may not work
      properly in a multithreaded context.
  
  max_iter : int, optional (default=-1)
      Hard limit on iterations within solver, or -1 for no limit.
  
  decision_function_shape : 'ovo', 'ovr', default='ovr'
      Whether to return a one-vs-rest ('ovr') decision function of shape
      (n_samples, n_classes) as all other classifiers, or the original
      one-vs-one ('ovo') decision function of libsvm which has shape
      (n_samples, n_classes * (n_classes - 1) / 2).
  
  random_state : int, RandomState instance or None, optional (default=None)
      The seed of the pseudo random number generator to use when shuffling
      the data.  If int, random_state is the seed used by the random number
      generator; If RandomState instance, random_state is the random number
      generator; If None, the random number generator is the RandomState
      instance used by `np.random`.
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
  
  n_support_ : array-like, dtype=int32, shape = [n_class]
      Number of support vectors for each class.
      
  support_vectors_ : list of arrays of shape [n_SV, sz, d]
      List of support vectors in tslearn dataset format, one array per class
  
  dual_coef_ : array, shape = [n_class-1, n_SV]
      Coefficients of the support vector in the decision function.
      For multiclass, coefficient for all 1-vs-1 classifiers.
      The layout of the coefficients in the multiclass case is somewhat
      non-trivial. See the section about multi-class classification in the
      SVM section of the User Guide of ``sklearn`` for details.
  
  coef_ : array, shape = [n_class-1, n_features]
      Weights assigned to the features (coefficients in the primal
      problem). This is only available in the case of a linear kernel.
      `coef_` is a readonly property derived from `dual_coef_` and
      `support_vectors_`.
  
  intercept_ : array, shape = [n_class * (n_class-1) / 2]
      Constants in decision function.
  
  svm_estimator_ : sklearn.svm.SVC
      The underlying sklearn estimator
  
  Examples
  --------
  >>> from tslearn.generators import random_walk_blobs
  >>> X, y = random_walk_blobs(n_ts_per_blob=10, sz=64, d=2, n_blobs=2)
  >>> clf = TimeSeriesSVC(kernel="gak", gamma="auto", probability=True)
  >>> clf.fit(X, y).predict(X).shape
  (20,)
  >>> sv = clf.support_vectors_
  >>> len(sv)  # should be equal to the nr of classes in the clf problem
  2
  >>> sv[0].shape  # doctest: +ELLIPSIS
  (..., 64, 2)
  >>> sv_sum = sum([sv_i.shape[0] for sv_i in sv])
  >>> sv_sum == clf.svm_estimator_.n_support_.sum()
  True
  >>> clf.decision_function(X).shape
  (20,)
  >>> clf.predict_log_proba(X).shape
  (20, 2)
  >>> clf.predict_proba(X).shape
  (20, 2)
  
  References
  ----------
  Fast Global Alignment Kernels.
  Marco Cuturi.
  ICML 2011.
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docstr: Predict class for a given set of time series.
  
  Parameters
  ----------
  X : array-like of shape=(n_ts, sz, d)
      Time series dataset.
  
  Returns
  -------
  array of shape=(n_ts, ) or (n_ts, n_classes), depending on the shape
  of the label vector provided at training time.
      Index of the cluster each sample belongs to or class probability
      matrix, depending on what was provided at training time.
docstr_tok: ["Predict","class","for","a","given","set","of","time","series",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape=","(","n_ts",",","sz",",","d",")","Time","series","dataset",".","Returns","--","--","--","-","array","of","shape=","(","n_ts",",",")","or","(","n_ts",",","n_classes",")",",","depending","on","the","shape","of","the","label","vector","provided","at","training","time",".","Index","of","the","cluster","each","sample","belongs","to","or","class","probability","matrix",",","depending","on","what","was","provided","at","training","time","."]
code: def predict(self, X):
      """Predict class for a given set of time series.
  
      Parameters
      ----------
      X : array-like of shape=(n_ts, sz, d)
          Time series dataset.
  
      Returns
      -------
      array of shape=(n_ts, ) or (n_ts, n_classes), depending on the shape
      of the label vector provided at training time.
          Index of the cluster each sample belongs to or class probability
          matrix, depending on what was provided at training time.
      """
      sklearn_X = self._preprocess_sklearn(X, fit_time=False)
      return self.svm_estimator_.predict(sklearn_X)
  
imports: from sklearn.svm import SVC, SVR
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.utils import deprecated
  from sklearn.utils import check_array, check_X_y
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from ..metrics import cdist_gak, gamma_soft_dtw, VARIABLE_LENGTH_METRICS
  from ..utils import to_time_series_dataset, check_dims, to_sklearn_dataset
  from ..bases import TimeSeriesBaseEstimator
  import warnings
label_desc: Use image node_id 5 for calling the TimeSeriesSVC obj's underlying member method code with example usage: obj.predict(X) and returns: self