def test_affinity_propagation_convergence_warning_dense_sparse(centers):
"""Non-regression, see #13334"""
rng = np.random.RandomState(42)
X = rng.rand(40, 10)
y = (4 * rng.rand(40)).astype(np.int)
ap = AffinityPropagation()
ap.fit(X, y)
ap.cluster_centers_ = centers
with pytest.warns(None) as record:
assert_array_equal(ap.predict(X), np.zeros(X.shape[0], dtype=int))
assert len(record) == 0
def test_affinity_propagation_predict_non_convergence():
# In case of non-convergence of affinity_propagation(), the cluster
# centers should be an empty array
X = np.array([[0, 0], [1, 1], [-2, -2]])
# Force non-convergence by allowing only a single iteration
af = AffinityPropagation(preference=-10, max_iter=1).fit(X)
# At prediction time, consider new samples as noise since there are no
# clusters
assert_array_equal(np.array([-1, -1, -1]),
af.predict(np.array([[2, 2], [3, 3], [4, 4]])))
def test_affinity_propagation_convergence_warning_dense_sparse(centers):
"""Non-regression, see #13334"""
rng = np.random.RandomState(42)
X = rng.rand(40, 10)
y = (4 * rng.rand(40)).astype(np.int)
ap = AffinityPropagation()
ap.fit(X, y)
ap.cluster_centers_ = centers
with pytest.warns(None) as record:
assert_array_equal(ap.predict(X),
np.zeros(X.shape[0], dtype=int))
assert len(record) == 0
def test_affinity_propagation_predict_non_convergence():
# In case of non-convergence of affinity_propagation(), the cluster
# centers should be an empty array
X = np.array([[0, 0], [1, 1], [-2, -2]])
# Force non-convergence by allowing only a single iteration
af = AffinityPropagation(preference=-10, max_iter=1).fit(X)
# At prediction time, consider new samples as noise since there are no
# clusters
assert_array_equal(np.array([-1, -1, -1]),
af.predict(np.array([[2, 2], [3, 3], [4, 4]])))
def test_affinity_propagation_predict():
"""Test AffinityPropagation.predict"""
af = AffinityPropagation(affinity="euclidean")
labels = af.fit_predict(X)
labels2 = af.predict(X)
assert_array_equal(labels, labels2)
def test_affinity_propagation_predict():
"""Test AffinityPropagation.predict"""
af = AffinityPropagation(affinity="euclidean")
labels = af.fit_predict(X)
labels2 = af.predict(X)
assert_array_equal(labels, labels2)
