@@ -53,8 +53,8 @@ def _joint_log_likelihood(self, X):
5353 I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of
5454 shape (n_samples, n_classes).
5555
56- Input is passed to _joint_log_likelihood as-is by predict,
57- predict_proba and predict_log_proba .
56+ predict, predict_proba, and predict_log_proba pass the input through
57+ _check_X and handle it over to _joint_log_likelihood .
5858 """
5959
6060 @abstractmethod
@@ -140,7 +140,7 @@ class GaussianNB(_BaseNB):
140140 Parameters
141141 ----------
142142 priors : array-like of shape (n_classes,)
143- Prior probabilities of the classes. If specified the priors are not
143+ Prior probabilities of the classes. If specified, the priors are not
144144 adjusted according to the data.
145145
146146 var_smoothing : float, default=1e-9
@@ -423,13 +423,13 @@ def _partial_fit(self, X, y, classes=None, _refit=False, sample_weight=None):
423423 # Take into account the priors
424424 if self .priors is not None :
425425 priors = np .asarray (self .priors )
426- # Check that the provide prior match the number of classes
426+ # Check that the provided prior matches the number of classes
427427 if len (priors ) != n_classes :
428428 raise ValueError ("Number of priors must match number of classes." )
429429 # Check that the sum is 1
430430 if not np .isclose (priors .sum (), 1.0 ):
431431 raise ValueError ("The sum of the priors should be 1." )
432- # Check that the prior are non-negative
432+ # Check that the priors are non-negative
433433 if (priors < 0 ).any ():
434434 raise ValueError ("Priors must be non-negative." )
435435 self .class_prior_ = priors
@@ -523,6 +523,12 @@ def _check_X_y(self, X, y, reset=True):
523523 return self ._validate_data (X , y , accept_sparse = "csr" , reset = reset )
524524
525525 def _update_class_log_prior (self , class_prior = None ):
526+ """Update class log priors.
527+
528+ The class log priors are based on `class_prior`, class count or the
529+ number of classes. This method is called each time `fit` or
530+ `partial_fit` update the model.
531+ """
526532 n_classes = len (self .classes_ )
527533 if class_prior is not None :
528534 if len (class_prior ) != n_classes :
@@ -733,7 +739,7 @@ class MultinomialNB(_BaseDiscreteNB):
733739 If false, a uniform prior will be used.
734740
735741 class_prior : array-like of shape (n_classes,), default=None
736- Prior probabilities of the classes. If specified the priors are not
742+ Prior probabilities of the classes. If specified, the priors are not
737743 adjusted according to the data.
738744
739745 Attributes
@@ -988,7 +994,7 @@ class BernoulliNB(_BaseDiscreteNB):
988994 If false, a uniform prior will be used.
989995
990996 class_prior : array-like of shape (n_classes,), default=None
991- Prior probabilities of the classes. If specified the priors are not
997+ Prior probabilities of the classes. If specified, the priors are not
992998 adjusted according to the data.
993999
9941000 Attributes
@@ -1136,7 +1142,7 @@ class CategoricalNB(_BaseDiscreteNB):
11361142 If false, a uniform prior will be used.
11371143
11381144 class_prior : array-like of shape (n_classes,), default=None
1139- Prior probabilities of the classes. If specified the priors are not
1145+ Prior probabilities of the classes. If specified, the priors are not
11401146 adjusted according to the data.
11411147
11421148 min_categories : int or array-like of shape (n_features,), default=None
0 commit comments