class_docstr: Abstract base class for naive Bayes estimators
class_docstr_tok: ["Abstract","base","class","for","naive","Bayes","estimators"]
docstr: Perform classification on an array of test vectors X.
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
  
  Returns
  -------
  C : array, shape = [n_samples]
      Predicted target values for X
docstr_tok: ["Perform","classification","on","an","array","of","test","vectors","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","[","n_samples",",","n_features","]","Returns","--","--","--","-","C",":","array",",","shape","=","[","n_samples","]","Predicted","target","values","for","X"]
code: def predict(self, X):
      """
      Perform classification on an array of test vectors X.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
  
      Returns
      -------
      C : array, shape = [n_samples]
          Predicted target values for X
      """
      jll = self._joint_log_likelihood(X)
      return self.classes_[np.argmax(jll, axis=1)]
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse
  from .base import BaseEstimator, ClassifierMixin
  from .preprocessing import binarize
  from .preprocessing import LabelBinarizer
  from .preprocessing import label_binarize
  from .utils import check_X_y, check_array
  from .utils.extmath import safe_sparse_dot, logsumexp
  from .utils.multiclass import _check_partial_fit_first_call
  from .utils.fixes import in1d
  from .utils.validation import check_is_fitted
  from .externals import six
label_desc: Use image node_id 2 for calling the BaseNB obj's underlying member method code with example usage: obj.predict(X) and returns: self