class_docstr: Time-series specific Support Vector Regressor.
  
  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'.
  
  tol : float, optional (default=1e-3)
      Tolerance for stopping criterion.
  
  epsilon : float, optional (default=0.1)
       Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
       within which no penalty is associated in the training loss function
       with points predicted within a distance epsilon from the actual
       value.
  
  shrinking : boolean, optional (default=True)
      Whether to use the shrinking heuristic.
  
  cache_size :  float, optional (default=200.0)
      Specify the size of the kernel cache (in MB).
  
  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.
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
      
  support_vectors_ : array of shape [n_SV, sz, d]
      Support vectors in tslearn dataset format
  
  dual_coef_ : array, shape = [1, n_SV]
      Coefficients of the support vector in the decision function.
  
  coef_ : array, shape = [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 readonly property derived from `dual_coef_` and
      `support_vectors_`.
  
  intercept_ : array, shape = [1]
      Constants in decision function.
  
  sample_weight : array-like, shape = [n_samples]
      Individual weights for each sample
  
  svm_estimator_ : sklearn.svm.SVR
      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)
  >>> import numpy
  >>> y = y.astype(float) + numpy.random.randn(20) * .1
  >>> reg = TimeSeriesSVR(kernel="gak", gamma="auto")
  >>> reg.fit(X, y).predict(X).shape
  (20,)
  >>> sv = reg.support_vectors_
  >>> sv.shape  # doctest: +ELLIPSIS
  (..., 64, 2)
  >>> sv.shape[0] <= 20
  True
  
  
  References
  ----------
  Fast Global Alignment Kernels.
  Marco Cuturi.
  ICML 2011.
class_docstr_tok: ["Time-series","specific","Support","Vector","Regressor",".","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","'",".","tol",":","float",",","optional","(","default=1e-3",")","Tolerance","for","stopping","criterion",".","epsilon",":","float",",","optional","(","default=0.1",")","Epsilon","in","the","epsilon-SVR","model",".","It","specifies","the","epsilon-tube","within","which","no","penalty","is","associated","in","the","training","loss","function","with","points","predicted","within","a","distance","epsilon","from","the","actual","value",".","shrinking",":","boolean",",","optional","(","default=True",")","Whether","to","use","the","shrinking","heuristic",".","cache_size",":","float",",","optional","(","default=200.0",")","Specify","the","size","of","the","kernel","cache","(","in","MB",")",".","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",".","Attributes","--","--","--","--","--","support_",":","array-like",",","shape","=","[","n_SV","]","Indices","of","support","vectors",".","support_vectors_",":","array","of","shape","[","n_SV",",","sz",",","d","]","Support","vectors","in","tslearn","dataset","format","dual_coef_",":","array",",","shape","=","[","1",",","n_SV","]","Coefficients","of","the","support","vector","in","the","decision","function",".","coef_",":","array",",","shape","=","[","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","readonly","property","derived","from","`","dual_coef_","`","and","`","support_vectors_","`",".","intercept_",":","array",",","shape","=","[","1","]","Constants","in","decision","function",".","sample_weight",":","array-like",",","shape","=","[","n_samples","]","Individual","weights","for","each","sample","svm_estimator_",":","sklearn.svm.SVR","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",")",">",">",">","import","numpy",">",">",">","y","=","y.astype","(","float",")","+","numpy.random.randn","(","20",")","*",".1",">",">",">","reg","=","TimeSeriesSVR","(","kernel=","''","gak","''",",","gamma=","''","auto","''",")",">",">",">","reg.fit","(","X",",","y",")",".predict","(","X",")",".shape","(","20",",",")",">",">",">","sv","=","reg.support_vectors_",">",">",">","sv.shape","#","doctest",":","+ELLIPSIS","(","...",",","64",",","2",")",">",">",">","sv.shape","[","0","]","<","=","20","True","References","--","--","--","--","--","Fast","Global","Alignment","Kernels",".","Marco","Cuturi",".","ICML","2011","."]
code: def n_iter_(self):
      warnings.warn(
          "n_iter_ is always set to 1 for TimeSeriesSVR, since "
          "it is non-trivial to access the underlying libsvm"
      )
      return 1
  
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 2 for calling the TimeSeriesSVR obj's underlying member method code with example usage: obj.n_iter_() and returns: int