test passed

multimodal/boosting/cumbo.py

@@ -113,23 +113,29 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
     >>> views_ind = [0, 2, 4]  # view 0: sepal data, view 1: petal data
     >>> clf = MuCumboClassifier(random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-
+    MuCumboClassifier(base_estimator=None, n_estimators=50, random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
-    [1]
+    [0]
     >>> views_ind = [[0, 2], [1, 3]]  # view 0: length data, view 1: width data
     >>> clf = MuCumboClassifier(random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-
+    MuCumboClassifier(base_estimator=None, n_estimators=50, random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
-    [1]
+    [0]
 
     >>> from sklearn.tree import DecisionTreeClassifier
     >>> base_estimator = DecisionTreeClassifier(max_depth=2)
     >>> clf = MuCumboClassifier(base_estimator=base_estimator, random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-
+    MuCumboClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,
+                max_features=None, max_leaf_nodes=None,
+                min_impurity_decrease=0.0, min_impurity_split=None,
+                min_samples_leaf=1, min_samples_split=2,
+                min_weight_fraction_leaf=0.0, presort=False, random_state=None,
+                splitter='best'),
+             n_estimators=50, random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
-    [1]
+    [0]
 
     See also
     --------