def test_predict_no_centroids1():
with pytest.raises(AttributeError):
kmeans = MultiviewSphericalKMeans()
kmeans.centroids_ = [None, None]
view1 = np.random.random((10,11))
view2 = np.random.random((10,10))
kmeans.predict([view1, view2])
def test_predict_no_centroids2():
kmeans = MultiviewSphericalKMeans()
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])
def test_predict_random_small(data_random):
kmeans = MultiviewSphericalKMeans()
input_data = [data_random['fit_data'][0][:2],data_random['fit_data'][1][:2]]
kmeans.fit(input_data)
cluster_pred = kmeans.predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_predict_not_fit():
with pytest.raises(NotFittedError):
kmeans = MultiviewSphericalKMeans()
view1 = np.random.random((10,11))
view2 = np.random.random((10,10))
kmeans.predict([view1, view2])