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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docstr: Fit model according to X and y.
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples
      and n_features is the number of features.
  
  y : array-like, shape = [n_samples]
      Target values.
  
  Returns
  -------
  self : regressor
      Returns self.
docstr_tok: ["Fit","model","according","to","X","and","y.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","[","n_samples",",","n_features","]","Training","vectors",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","y",":","array-like",",","shape","=","[","n_samples","]","Target","values",".","Returns","--","--","--","-","self",":","regressor","Returns","self","."]
code: def fit(self, X, y):
      """Fit model according to X and y.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples
          and n_features is the number of features.
  
      y : array-like, shape = [n_samples]
          Target values.
  
      Returns
      -------
      self : regressor
          Returns self.
      """
      if self.fit_intercept:
          X = add_dummy_feature(X)
  
      n_samples, n_features = X.shape
      rs = self._get_random_state()
  
      self.outputs_2d_ = len(y.shape) == 2
      if self.outputs_2d_:
          Y = y
      else:
          Y = y.reshape(-1, 1)
      Y = np.asfortranarray(Y)
      n_vectors = Y.shape[1]
  
      ds = get_dataset(X)
  
      if not self.warm_start or self.coef_ is None:
          self.coef_ = np.zeros(
              (n_vectors, n_features), dtype=np.float64
          )
          self.dual_coef_ = np.zeros(
              (n_vectors, n_samples), dtype=np.float64
          )
  
      for i in range(n_vectors):
          _dual_cd_svr(
              self,
              self.coef_[i],
              self.dual_coef_[i],
              ds,
              Y[:, i],
              self.permute,
              self.C,
              self.epsilon,
              self._get_loss(),
              self.max_iter,
              rs,
              self.tol,
              self.callback,
              self.n_calls,
              verbose=self.verbose,
          )
  
      if self.fit_intercept:
          self.intercept_ = self.coef_[:, 0]
          self.coef_ = self.coef_[:, 1:]
  
      return self
  
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 3 for calling the LinearSVR obj's underlying member method code with example usage: obj.fit(X, y) and returns: self