def setUp(self):
n_points_per_cluster = 250
np.random.seed(0)
C1 = np.zeros((n_points_per_cluster, 3))
C2 = np.zeros((n_points_per_cluster, 3))
C3 = np.zeros((n_points_per_cluster, 3))
C4 = np.zeros((n_points_per_cluster, 3))
C5 = np.zeros((n_points_per_cluster, 3))
C6 = np.zeros((n_points_per_cluster, 3))
C1[:, 1:3] = ([-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2))
C2[:, 1:3] = ([4, -1] + .1 * np.random.randn(n_points_per_cluster, 2))
C3[:, 1:3] = ([0, -2] + .2 * np.random.randn(n_points_per_cluster, 2))
C4[:, 1:3] = ([-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2))
C5[:, 1:3] = ([3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2))
C6[:, 1:3] = ([5, 6] + 2 * np.random.randn(n_points_per_cluster, 2))
X = np.vstack(
(C1[:, 1:3], C2[:, 1:3], C3[:, 1:3], C4[:, 1:3], C5[:,
1:3], C6[:,
1:3]))
clust = OPTICS(min_samples=50, xi=.05, min_cluster_size=.05)
# Run the fit
clust.fit(X)
self.tbhg = modeling.TBH()
self.tbhg.optics = clust
self.tbhg.locH = (C1, C2, C3, C4, C5, C6)
# self.tbhg = TBHG(clust)
pass
def test_build_hierarchy(self):
tbhg = modeling.TBH()
optics = OPTICS_()
optics.cluster_hierarchy_ = [[0, 4], [6, 8], [5, 8], [8, 10], [0, 10]]
optics.ordering_ = [x for x in range(11)]
tbhg.optics = optics
r = tbhg._build_tree()
h = tbhg._build_hierarchy(r)
for i in h:
print(i)
def build_tbh(optics, locH):
return modeling.TBH(optics, locH)
def build_tree(optics):
tbhg = modeling.TBH()
tbhg.optics = optics
return tbhg._build_tree()
def build_hierarchy(optics):
tbhg = modeling.TBH(optics)
return tbhg.hierarchy