class_docstr: Abstract base class for naive Bayes estimators
class_docstr_tok: ["Abstract","base","class","for","naive","Bayes","estimators"]
docstr: Compute the unnormalized posterior log probability of X
  
  I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of
  shape [n_classes, n_samples].
  
  Input is passed to _joint_log_likelihood as-is by predict,
  predict_proba and predict_log_proba.
docstr_tok: ["Compute","the","unnormalized","posterior","log","probability","of","X","I.e",".","``","log","P","(","c",")","+","log","P","(","x|c",")","``","for","all","rows","x","of","X",",","as","an","array-like","of","shape","[","n_classes",",","n_samples","]",".","Input","is","passed","to","_joint_log_likelihood","as-is","by","predict",",","predict_proba","and","predict_log_proba","."]
code: def _joint_log_likelihood(self, X):
      """Compute the unnormalized posterior log probability of X
  
      I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of
      shape [n_classes, n_samples].
  
      Input is passed to _joint_log_likelihood as-is by predict,
      predict_proba and predict_log_proba.
      """
  
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 1 for calling the BaseNB obj's underlying member method code with example usage: obj._joint_log_likelihood(X) without return types