def test_duplicate_dual_dbscan():
data = np.array([
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[-1, -1],
[-1, -1],
[-1, -1],
[-1, -1],
[-1, -1],
[-1, -1],
[-1, -1],
])
impl = DBSCAN(0.1, 5)
impl.Fit(data)
r_labels = np.array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 2) == True
def test_large_dbscan():
data, r_labels = datasets.make_blobs(n_samples=1000, centers=1)
impl = DBSCAN(0.6, 4)
ref = cluster.DBSCAN(0.6, 4)
impl.Fit(data)
ref.fit(data)
r_labels = ref.labels_
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 1) == True
def test_double_fit_dbscan():
data1, r_labels1 = datasets.make_blobs(n_samples=288,
centers=6,
cluster_std=0.2,
random_state=31)
data2, r_labels2 = datasets.make_blobs(n_samples=288,
centers=6,
cluster_std=0.2,
random_state=31)
impl = DBSCAN(0.6, 4)
impl.Fit(data1)
i_labels1 = impl.GetLabels(data1.shape[0])
impl.Fit(data2)
i_labels2 = impl.GetLabels(data2.shape[0])
t2 = t.check_clusters_with_allowance(i_labels2, i_labels1, 6, .01)
assert t2 == True
def test_simple_single_dbscan():
data = np.array([
[1, 1],
])
impl = DBSCAN(1, 1)
impl.Fit(data)
r_labels = np.array([0])
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 1) == True
def test_dimensionality_dbscan():
data, r_labels = datasets.make_blobs(n_samples=288,
n_features=16,
cluster_std=0.2,
random_state=31)
impl = DBSCAN(0.6, 4)
impl.Fit(data)
ref = cluster.DBSCAN(0.6, 4)
ref.fit(data)
assert True == t.check_clusters(ref.labels_, impl.GetLabels(data.shape[0]),
3)
def test_clear_blobs_dbscan():
centers = ((-5, -5), (5, 5))
data, _ = datasets.make_blobs(n_samples=100,
centers=centers,
cluster_std=0.1)
ref = cluster.DBSCAN(0.6, 4)
impl = DBSCAN(0.6, 4)
impl.Fit(data)
ref.fit(data)
r_labels = ref.labels_
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 2) == True
def test_epsilon_dbscan(dbscanparams):
data, _ = datasets.make_blobs(n_samples=512, n_features=2, random_state=73)
impl = DBSCAN(dbscanparams[0], dbscanparams[1])
ref = cluster.DBSCAN(dbscanparams[0], dbscanparams[1])
impl.Fit(data)
ref.fit(data)
i_labels = impl.GetLabels(data.shape[0])
r_labels = ref.labels_
assert np.unique(i_labels).shape[0] == np.unique(r_labels).shape[0]
assert True == t.check_clusters_with_allowance(
r_labels, i_labels,
np.unique(i_labels).shape[0], 0.05)
