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import numpy as np
from numpy.testing import assert_, assert_allclose
from astroML.clustering import HierarchicalClustering, get_graph_segments
def test_simple_clustering():
np.random.seed(0)
N = 10
X = np.random.random((N, 2))
model = HierarchicalClustering(8, edge_cutoff=0.5)
model.fit(X)
assert_(model.n_components_ == N / 2)
assert_(np.sum(model.full_tree_.toarray() > 0) == N - 1)
assert_(np.sum(model.cluster_graph_.toarray() > 0) == N / 2)
assert_allclose(np.unique(model.labels_), np.arange(N / 2))
def test_cluster_cutoff():
np.random.seed(0)
N = 100
X = np.random.random((N, 2))
model = HierarchicalClustering(8, edge_cutoff=0.9, min_cluster_size=10)
model.fit(X)
assert_allclose(np.unique(model.labels_),
np.arange(-1, model.n_components_))
def test_graph_segments():
np.random.seed(0)
N = 4
X = np.random.random((N, 2))
G = np.zeros([N, N])
G[0, 1] = 1
G[2, 1] = 1
G[2, 3] = 1
ind = np.array([[0, 2, 2],
[1, 1, 3]])
xseg_check = X[ind, 0]
yseg_check = X[ind, 1]
xseg, yseg = get_graph_segments(X, G)
assert_allclose(xseg, xseg_check)
assert_allclose(yseg, yseg_check)
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