class_docstr: Estimator for learning a linear support vector regressor by coordinate
  descent in the dual.
  
  Parameters
  ----------
  loss : str, 'epsilon_insensitive', 'squared_epsilon_insensitive'
      The loss function to be used.
  
  C : float
      Weight of the loss term.
  
  epsilon : float
      Parameter of the epsilon-insensitive loss.
  
  max_iter : int
      Maximum number of iterations to perform.
  
  tol : float
      Tolerance of the stopping criterion.
  
  fit_intercept : bool
      Whether to fit an intercept term or not.
  
  warm_start : bool
      Whether to activate warm-start or not.
  
  permute : bool
      Whether to permute coordinates or not before cycling.
  
  callback : callable
      Callback function.
  
  n_calls : int
      Frequency with which `callback` must be called.
  
  random_state : RandomState or int
      The seed of the pseudo random number generator to use.
  
  verbose : int
      Verbosity level.
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code: def __init__(
      self,
      C=1.0,
      epsilon=0,
      loss="epsilon_insensitive",
      max_iter=1000,
      tol=1e-3,
      fit_intercept=False,
      permute=True,
      warm_start=False,
      random_state=None,
      callback=None,
      n_calls=100,
      verbose=0,
  ):
      self.C = C
      self.epsilon = epsilon
      self.loss = loss
      self.max_iter = max_iter
      self.tol = tol
      self.fit_intercept = fit_intercept
      self.permute = permute
      self.warm_start = warm_start
      self.random_state = random_state
      self.callback = callback
      self.n_calls = n_calls
      self.verbose = verbose
      self.coef_ = None
  
file_path: lightning/lightning/impl/dual_cd.py
imports: import numpy
  from sklearn.preprocessing import add_dummy_feature
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .dual_cd_fast import _dual_cd
  from .dual_cd_fast import _dual_cd_auc
  from .dual_cd_fast import _dual_cd_svr
label_desc: Use image node_id 1 to create a new LinearSVR object from inherited base classes: BaseRegressor with example: obj = LinearSVR(C, epsilon, loss, max_iter, tol, fit_intercept, permute, warm_start, random_state, callback, n_calls, verbose)