class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Perform classification on samples in X.
  
  For an one-class model, +1 or -1 is returned.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      For kernel="precomputed", the expected shape of X is
      [n_samples_test, n_samples_train]
  
  Returns
  -------
  y_pred : array, shape (n_samples,)
      Class labels for samples in X.
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code: def predict(self, X):
      """Perform classification on samples in X.
  
      For an one-class model, +1 or -1 is returned.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          For kernel="precomputed", the expected shape of X is
          [n_samples_test, n_samples_train]
  
      Returns
      -------
      y_pred : array, shape (n_samples,)
          Class labels for samples in X.
      """
      y = super(BaseSVC, self).predict(X)
      return self.classes_.take(np.asarray(y, dtype=np.intp))
  
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 4 for calling the BaseSVC obj's underlying member method code with example usage: obj.predict(X) and returns: self