def test_double_fit_agnes():
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=79)
impl = agnes.Agnes(6, 'wards')
impl.Fit(data1)
t1 = t.check_clusters(r_labels1, impl.GetLabels(data1.shape[0]), 6,)
impl.Fit(data2)
t2 = t.check_clusters(r_labels2, impl.GetLabels(data2.shape[0]), 6)
assert t1 == True and t2 == True
def test_duplicate_dual_kmeans():
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 = kmeans.KMeans(2, max_iter, rstate)
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_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_kmeans():
data, r_labels = datasets.make_blobs(n_samples=1000, centers=1)
impl = kmeans.KMeans(1, max_iter, rstate)
impl.Fit(data)
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 1) == True
def test_clear_blobs_kmeans():
centers = ((-5, -5), (5, 5))
data, r_labels = datasets.make_blobs(n_samples=100, centers=centers)
impl = kmeans.KMeans(2, max_iter, rstate)
impl.Fit(data)
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 2) == True
def test_dimensionality_agnes(triple):
data, r_labels = datasets.make_blobs(n_samples=60, n_features=16, cluster_std=0.2, random_state=31)
impl = triple
impl.Fit(data)
assert True == t.check_clusters(r_labels, impl.GetLabels(data.shape[0]), 3)
def test_clear_blobs_agnes(dual):
centers = ((-5, -5), (5, 5))
data, r_labels = datasets.make_blobs(n_samples=100, centers=centers)
impl = dual
impl.Fit(data)
i_labels = impl.GetLabels(data.shape[0])
assert t.check_clusters(r_labels, i_labels, 2) == True
def test_dimensionality_kmeans():
data, r_labels = datasets.make_blobs(n_samples=288,
n_features=16,
cluster_std=0.2,
random_state=31)
impl = kmeans.KMeans(3, max_iter, rstate)
impl.Fit(data)
assert True == t.check_clusters(r_labels, impl.GetLabels(data.shape[0]), 3)
def test_simple_single_agnes(single):
data = np.array([[1, 1],])
impl = single
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_duplicate_dual_agnes(dual):
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 = dual
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_simple_single_kmeans():
data = np.array([
[1, 1],
])
impl = kmeans.KMeans(1, max_iter, rstate)
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_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_large_values_kmeans():
max_int = 1 << 25
data, r_labels = datasets.make_blobs(n_samples=144,
centers=(
(max_int, max_int),
(0, 0),
(-max_int, -max_int),
))
impl = kmeans.KMeans(3, max_iter, rstate)
impl.Fit(data)
assert True == t.check_clusters(r_labels, impl.GetLabels(data.shape[0]), 3)
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
