class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Distance of the samples X to the separating hyperplane.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
  
  Returns
  -------
  X : array-like, shape (n_samples, n_classes * (n_classes-1) / 2)
      Returns the decision function of the sample for each class
      in the model.
      If decision_function_shape='ovr', the shape is (n_samples,
      n_classes)
docstr_tok: ["Distance","of","the","samples","X","to","the","separating","hyperplane",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","(","n_samples",",","n_features",")","Returns","--","--","--","-","X",":","array-like",",","shape","(","n_samples",",","n_classes","*","(","n_classes-1",")","\/","2",")","Returns","the","decision","function","of","the","sample","for","each","class","in","the","model",".","If","decision_function_shape='ovr","'",",","the","shape","is","(","n_samples",",","n_classes",")"]
code: def decision_function(self, X):
      """Distance of the samples X to the separating hyperplane.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
  
      Returns
      -------
      X : array-like, shape (n_samples, n_classes * (n_classes-1) / 2)
          Returns the decision function of the sample for each class
          in the model.
          If decision_function_shape='ovr', the shape is (n_samples,
          n_classes)
      """
      dec = self._decision_function(X)
      if (
          self.decision_function_shape is None
          and len(self.classes_) > 2
      ):
          warnings.warn(
              "The decision_function_shape default value will "
              "change from 'ovo' to 'ovr' in 0.19. This will change "
              "the shape of the decision function returned by "
              "SVC.",
              ChangedBehaviorWarning,
          )
      if (
          self.decision_function_shape == "ovr"
          and len(self.classes_) > 2
      ):
          return _ovr_decision_function(
              dec < 0, -dec, len(self.classes_)
          )
      return dec
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 3 for calling the BaseSVC obj's underlying member method code with example usage: obj.decision_function(X) and returns: dec, _ovr_decision_function