def test_final_centroids_no_consensus():
with pytest.raises(ConvergenceWarning):
kmeans = MultiviewKMeans(random_state=RANDOM_SEED)
view1 = np.array([[0, 1], [1, 0]])
view2 = np.array([[1, 0], [0, 1]])
v1_centroids = np.array([[0, 1],[1, 0]])
v2_centroids = np.array([[0, 1],[1, 0]])
centroids = [v1_centroids, v2_centroids]
kmeans._final_centroids([view1, view2], centroids)
def test_final_centroids_less_than_n_clusters():
with pytest.raises(ConvergenceWarning):
kmeans = MultiviewKMeans(n_clusters=3, random_state=RANDOM_SEED)
view1 = np.random.random((2,5))
view2 = np.random.random((2,6))
v1_centroids = np.random.random((3, 5))
v2_centroids = np.random.random((3, 6))
centroids = [v1_centroids, v2_centroids]
kmeans._final_centroids([view1, view2], centroids)
def test_predict_no_centroids2():
kmeans = MultiviewKMeans()
with pytest.raises(ConvergenceWarning):
view1 = np.array([[0, 1], [1, 0]])
view2 = np.array([[1, 0], [0, 1]])
v1_centroids = np.array([[0, 1],[1, 0]])
v2_centroids = np.array([[0, 1],[1, 0]])
centroids = [v1_centroids, v2_centroids]
kmeans._final_centroids([view1, view2], centroids)
with pytest.raises(AttributeError):
kmeans.predict([view1, view2])