def test_3():
# test using a callable metric. should get same results
model1 = LandmarkAgglomerative(n_clusters=10, n_landmarks=20, metric='euclidean')
model2 = LandmarkAgglomerative(n_clusters=10, n_landmarks=20, metric=lambda target, ref, i: np.sqrt(np.sum((target-ref[i])**2, axis=1)))
data = np.random.RandomState(0).randn(100, 2)
eq(model1.fit_predict([data])[0], model2.fit_predict([data])[0])
def test_2():
# this should be a really easy clustering problem
x = [random.randn(20,2)+10, random.randn(20,2)]
n_clusters = 2
model1 = LandmarkAgglomerative(n_clusters=n_clusters)
model2 = LandmarkAgglomerative(n_clusters=n_clusters,
landmark_strategy='random', random_state=random, n_landmarks=20)
labels1 = model1.fit_predict(x)
labels2 = model2.fit_predict(x)
assert adjusted_rand_score(np.concatenate(labels1), np.concatenate(labels2)) == 1.0
def test_1():
x = [random.randn(10,2), random.randn(10,2)]
n_clusters = 2
model1 = LandmarkAgglomerative(n_clusters=n_clusters)
model2 = LandmarkAgglomerative(n_clusters=n_clusters,
n_landmarks=sum(len(s) for s in x))
labels0 = clone(model1).fit(x).predict(x)
labels1 = model1.fit_predict(x)
labels2 = model2.fit_predict(x)
assert len(labels0) == 2
assert len(labels1) == 2
assert len(labels2) == 2
eq(labels0[0], labels1[0])
eq(labels0[1], labels1[1])
eq(labels0[0], labels2[0])
eq(labels0[1], labels2[1])
assert len(np.unique(np.concatenate(labels0))) == n_clusters
