def test_compute_concave_hulls(self):
data = Table("iris")[:, 2:4]
clusters = Table(
Domain([DiscreteVariable("cl", values=["1", "2", "3"])]),
[[0]] * 50 + [[1]] * 50 + [[2]] * 50)
hulls = compute_concave_hulls(data, clusters, epsilon=0.5)
self.assertEqual(3, len(hulls))
self.assertEqual(2, hulls["1"].shape[1]) # hull have x and y
self.assertEqual(2, hulls["2"].shape[1]) # hull have x and y
self.assertEqual(2, hulls["3"].shape[1]) # hull have x and y
def test_compute_concave_hulls_triangle(self):
"""
Concave hull must also work for tree points - it is a special case
"""
data = Table(
Domain([ContinuousVariable("x"),
ContinuousVariable("y")]),
np.array([[1, 1], [1, 2], [2, 1]]))
clusters = Table(Domain([DiscreteVariable("cl", values=["1"])]),
[[0]] * 3)
hulls = compute_concave_hulls(data, clusters, epsilon=0.5)
self.assertEqual(1, len(hulls))
self.assertEqual(2, hulls["1"].shape[1]) # hull have x and y
def test_compute_concave_hulls_subsampling(self):
"""
When more than 1000 points passed they are sub-sampled in order to
compute a concave hull
"""
iris = Table("iris")
data = Table( # here we pass 1500 points
Domain(iris.domain.attributes[2:4]),
np.repeat(iris.X[:, 2:4], 10, axis=0))
clusters = Table(
Domain([DiscreteVariable("cl", values=["1", "2", "3"])]),
[[0]] * 50 * 10 + [[1]] * 50 * 10 + [[2]] * 50 * 10,
)
hulls = compute_concave_hulls(data, clusters, epsilon=0.5)
self.assertEqual(3, len(hulls))
self.assertEqual(2, hulls["1"].shape[1]) # hull have x and y
self.assertEqual(2, hulls["2"].shape[1]) # hull have x and y
self.assertEqual(2, hulls["3"].shape[1]) # hull have x and y