class_docstr: Ridge regression with built-in Generalized Cross-Validation
  
  It allows efficient Leave-One-Out cross-validation.
  
  This class is not intended to be used directly. Use RidgeCV instead.
  
  Notes
  -----
  
  We want to solve (K + alpha*Id)c = y,
  where K = X X^T is the kernel matrix.
  
  Let G = (K + alpha*Id)^-1.
  
  Dual solution: c = Gy
  Primal solution: w = X^T c
  
  Compute eigendecomposition K = Q V Q^T.
  Then G = Q (V + alpha*Id)^-1 Q^T,
  where (V + alpha*Id) is diagonal.
  It is thus inexpensive to inverse for many alphas.
  
  Let loov be the vector of prediction values for each example
  when the model was fitted with all examples but this example.
  
  loov = (KGY - diag(KG)Y) / diag(I-KG)
  
  Let looe be the vector of prediction errors for each example
  when the model was fitted with all examples but this example.
  
  looe = y - loov = c / diag(G)
  
  References
  ----------
  http://cbcl.mit.edu/projects/cbcl/publications/ps/MIT-CSAIL-TR-2007-025.pdf
  http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
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code: def _values(self, alpha, y, v, Q, QT_y):
      G_diag, c = self._errors_and_values_helper(alpha, y, v, Q, QT_y)
      return y - (c / G_diag), c
  
imports: from abc import ABCMeta, abstractmethod
  import warnings
  import numpy
  from scipy import linalg
  from scipy import sparse
  from scipy.sparse import linalg
  from .base import LinearClassifierMixin, LinearModel, _rescale_data
  from .sag import sag_solver
  from ..base import RegressorMixin
  from ..utils.extmath import safe_sparse_dot
  from ..utils.extmath import row_norms
  from ..utils import check_X_y
  from ..utils import check_array
  from ..utils import check_consistent_length
  from ..utils import compute_sample_weight
  from ..utils import column_or_1d
  from ..preprocessing import LabelBinarizer
  from ..model_selection import GridSearchCV
  from ..externals import six
  from ..metrics.scorer import check_scoring
label_desc: Use image node_id 7 for calling the _RidgeGCV obj's underlying member method code with example usage: obj._values(alpha, y, v, Q, QT_y) and returns: unknown,  c