change cumbo to combo

copyrightstamp.txt

@@ -23,7 +23,7 @@ Description:
 -----------
 
 The multimodal package implement classifiers multiview, 
-MumboClassifier class, MuCumboClassifier class, MVML class, MKL class.
+MumboClassifier class, MuComboClassifier class, MVML class, MKL class.
 compatible with sklearn
 
 Version:

doc/reference/api.rst

@@ -23,7 +23,7 @@ multimodal.boosting.mumbo
 multimodal.boosting.cumbo
 +++++++++++++++++++++++++
 
-.. automodule:: multimodal.boosting.cumbo
+.. automodule:: multimodal.boosting.combo
    :members:
    :inherited-members:
 

examples/cumbo/plot_cumbo_2_views_2_classes.py → examples/combo/plot_combo_2_views_2_classes.py

@@ -23,7 +23,7 @@ rightly classify the points.
 """
 
 import numpy as np
-from multimodal.boosting.cumbo import MuCumboClassifier
+from multimodal.boosting.combo import MuComboClassifier
 from matplotlib import pyplot as plt
 
 
@@ -59,7 +59,7 @@ y[2*n_samples:] = 1
 views_ind = np.array([0, 2, 4])
 
 n_estimators = 3
-clf = MuCumboClassifier(n_estimators=n_estimators)
+clf = MuComboClassifier(n_estimators=n_estimators)
 clf.fit(X, y, views_ind)
 
 print('\nAfter 3 iterations, the MuCuMBo classifier reaches exact '

examples/cumbo/plot_cumbo_3_views_3_classes.py → examples/combo/plot_combo_3_views_3_classes.py

 # -*- coding: utf-8 -*-
 """
 ==================================
-MuCumbo 3 views, 3 classes example
+MuCombo 3 views, 3 classes example
 ==================================
 
 In this toy example, we generate data from three classes, split between three
@@ -12,12 +12,12 @@ well seperated, while the points for the third class are not seperated with
 the two other classes. That means that, taken separately, none of the single
 views allows for a good classification of the data.
 
-Nevertheless, the MuCuMbo algorithm take adavantage of the complementarity of
+Nevertheless, the MuCoMbo algorithm take adavantage of the complementarity of
 the views to rightly classify the points.
 """
 
 import numpy as np
-from multimodal.boosting.cumbo import MuCumboClassifier
+from multimodal.boosting.combo import MuComboClassifier
 from matplotlib import pyplot as plt
 
 
@@ -56,7 +56,7 @@ y[2*n_samples:] = 2
 views_ind = np.array([0, 2, 4, 6])
 
 n_estimators = 4
-clf = MuCumboClassifier(n_estimators=n_estimators)
+clf = MuComboClassifier(n_estimators=n_estimators)
 clf.fit(X, y, views_ind)
 
 print('\nAfter 4 iterations, the MuCuMBo classifier reaches exact '