class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
code: def _dense_predict_proba(self, X):
      X = self._compute_kernel(X)
  
      kernel = self.kernel
      if callable(kernel):
          kernel = "precomputed"
  
      svm_type = LIBSVM_IMPL.index(self._impl)
      pprob = libsvm.predict_proba(
          X,
          self.support_,
          self.support_vectors_,
          self.n_support_,
          self._dual_coef_,
          self._intercept_,
          self.probA_,
          self.probB_,
          svm_type=svm_type,
          kernel=kernel,
          degree=self.degree,
          cache_size=self.cache_size,
          coef0=self.coef0,
          gamma=self._gamma,
      )
  
      return pprob
  
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 10 for calling the BaseSVC obj's underlying member method code with example usage: obj._dense_predict_proba(X) and returns: pprob