Commit d3601a40 authored by Dominique Benielli's avatar Dominique Benielli
Browse files

change cumbo to combo

parent 65e5c1a5
Pipeline #5363 failed with stage
in 5 minutes and 39 seconds
......@@ -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:
......
......@@ -23,7 +23,7 @@ multimodal.boosting.mumbo
multimodal.boosting.cumbo
+++++++++++++++++++++++++
.. automodule:: multimodal.boosting.cumbo
.. automodule:: multimodal.boosting.combo
:members:
:inherited-members:
......
......@@ -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 '
......
# -*- 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 '
......
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