DOC Add missing parameter defaults for Naive Bayes classes

Author: proinsias

sklearn/naive_bayes.py

@@ -168,7 +168,7 @@ def fit(self, X, y, sample_weight=None):
         y : array-like, shape (n_samples,)
             Target values.
 
-        sample_weight : array-like, shape (n_samples,), optional
+        sample_weight : array-like, shape (n_samples,), optional (default=None)
             Weights applied to individual samples (1. for unweighted).
 
             .. versionadded:: 0.17
@@ -212,7 +212,7 @@ def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
         var : array-like, shape (number of Gaussians,)
             Variances for Gaussians in original set.
 
-        sample_weight : array-like, shape (n_samples,), optional
+        sample_weight : array-like, shape (n_samples,), optional (default=None)
             Weights applied to individual samples (1. for unweighted).
 
         Returns
@@ -282,13 +282,13 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
         y : array-like, shape (n_samples,)
             Target values.
 
-        classes : array-like, shape (n_classes,)
+        classes : array-like, shape (n_classes,), optional (default=None)
             List of all the classes that can possibly appear in the y vector.
 
             Must be provided at the first call to partial_fit, can be omitted
             in subsequent calls.
 
-        sample_weight : array-like, shape (n_samples,), optional
+        sample_weight : array-like, shape (n_samples,), optional (default=None)
             Weights applied to individual samples (1. for unweighted).
 
             .. versionadded:: 0.17
@@ -314,17 +314,17 @@ def _partial_fit(self, X, y, classes=None, _refit=False,
         y : array-like, shape (n_samples,)
             Target values.
 
-        classes : array-like, shape (n_classes,)
+        classes : array-like, shape (n_classes,), optional (default=None)
             List of all the classes that can possibly appear in the y vector.
 
             Must be provided at the first call to partial_fit, can be omitted
             in subsequent calls.
 
-        _refit: bool
+        _refit: bool, optional (default=False)
             If true, act as though this were the first time we called
             _partial_fit (ie, throw away any past fitting and start over).
 
-        sample_weight : array-like, shape (n_samples,), optional
+        sample_weight : array-like, shape (n_samples,), optional (default=None)
             Weights applied to individual samples (1. for unweighted).
 
         Returns
@@ -480,13 +480,13 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
         y : array-like, shape = [n_samples]
             Target values.
 
-        classes : array-like, shape = [n_classes]
+        classes : array-like, shape = [n_classes], optional (default=None)
             List of all the classes that can possibly appear in the y vector.
 
             Must be provided at the first call to partial_fit, can be omitted
             in subsequent calls.
 
-        sample_weight : array-like, shape = [n_samples], optional
+        sample_weight : array-like, shape = [n_samples], optional (default=None)
             Weights applied to individual samples (1. for unweighted).
 
         Returns
@@ -551,7 +551,7 @@ def fit(self, X, y, sample_weight=None):
         y : array-like, shape = [n_samples]
             Target values.
 
-        sample_weight : array-like, shape = [n_samples], optional
+        sample_weight : array-like, shape = [n_samples], optional (default=None)
             Weights applied to individual samples (1. for unweighted).
 
         Returns
@@ -620,11 +620,11 @@ class MultinomialNB(BaseDiscreteNB):
         Additive (Laplace/Lidstone) smoothing parameter
         (0 for no smoothing).
 
-    fit_prior : boolean
+    fit_prior : boolean, optional (default=True)
         Whether to learn class prior probabilities or not.
         If false, a uniform prior will be used.
 
-    class_prior : array-like, size (n_classes,)
+    class_prior : array-like, size (n_classes,), optional (default=None)
         Prior probabilities of the classes. If specified the priors are not
         adjusted according to the data.
 
@@ -723,15 +723,15 @@ class BernoulliNB(BaseDiscreteNB):
         Additive (Laplace/Lidstone) smoothing parameter
         (0 for no smoothing).
 
-    binarize : float or None, optional
+    binarize : float or None, optional (default=0.0)
         Threshold for binarizing (mapping to booleans) of sample features.
         If None, input is presumed to already consist of binary vectors.
 
-    fit_prior : boolean
+    fit_prior : boolean, optional (default=True)
         Whether to learn class prior probabilities or not.
         If false, a uniform prior will be used.
 
-    class_prior : array-like, size=[n_classes,]
+    class_prior : array-like, size=[n_classes,], optional (default=None)
         Prior probabilities of the classes. If specified the priors are not
         adjusted according to the data.