def test_empty_y():
with pytest.raises(RuntimeError):
cuspatial.directed_hausdorff_distance(
[0.0],
[],
[0],
)
def test_values():
in_trajs = []
in_trajs.append(np.array([[1, 0], [2, 1], [3, 2], [5, 3], [7, 1]]))
in_trajs.append(np.array([[0, 3], [2, 5], [3, 6], [6, 5]]))
in_trajs.append(np.array([[1, 4], [3, 7], [6, 4]]))
out_trajs = np.concatenate([np.asarray(traj) for traj in in_trajs], 0)
py_x = np.array(out_trajs[:, 0])
py_y = np.array(out_trajs[:, 1])
py_cnt = []
for traj in in_trajs:
py_cnt.append(len(traj))
pnt_x = cudf.Series(py_x)
pnt_y = cudf.Series(py_y)
cnt = cudf.Series(py_cnt)
distance = cuspatial.directed_hausdorff_distance(pnt_x, pnt_y, cnt)
assert_eq(
distance,
cudf.DataFrame(
{
0: [0, 3.605551, 4.472136],
1: [4.123106, 0.0, 1.414214],
2: [4.0, 1.414214, 0.0],
}
),
)
def _test_hausdorff_from_list_of_spaces(spaces):
lengths = [len(space) for space in spaces]
offsets = np.cumsum([0, *lengths])[:-1]
return cuspatial.directed_hausdorff_distance(
[x for space in spaces for (x, y) in space],
[y for space in spaces for (x, y) in space],
offsets,
)
def test_count_two():
distance = cuspatial.directed_hausdorff_distance(
cudf.Series([0.0, 0.0, 1.0, 0.0]),
cudf.Series([0.0, -1.0, 1.0, -1.0]),
cudf.Series([2, 2]),
)
assert_eq(
distance, cudf.DataFrame({0: [0, 1.4142135623730951], 1: [1, 0.0]})
)
def test_large():
in_trajs = []
in_trajs.append(np.array([[0, 0], [1, 0]]))
in_trajs.append(np.array([[0, -1], [1, -1]]))
out_trajs = np.concatenate([np.asarray(traj) for traj in in_trajs], 0)
py_x = np.array(out_trajs[:, 0])
py_y = np.array(out_trajs[:, 1])
py_cnt = []
for traj in in_trajs:
py_cnt.append(len(traj))
pnt_x = cudf.Series(py_x)
pnt_y = cudf.Series(py_y)
cnt = cudf.Series(py_cnt)
distance = cuspatial.directed_hausdorff_distance(pnt_x, pnt_y, cnt)
assert_eq(distance, cudf.DataFrame({0: [0, 1.0], 1: [1.0, 0]}))
def test_count_one():
distance = cuspatial.directed_hausdorff_distance(
cudf.Series([0.0, 0.0]), cudf.Series([0.0, 1.0]), cudf.Series([1, 1])
)
assert_eq(distance, cudf.DataFrame({0: [0, 1.0], 1: [1.0, 0]}))
def test_no_spaces():
result = cuspatial.directed_hausdorff_distance(
cudf.Series([0]), cudf.Series([0]), cudf.Series()
)
assert_eq(cudf.DataFrame([]), result)
def test_empty_y():
with pytest.raises(RuntimeError):
cuspatial.directed_hausdorff_distance(
cudf.Series([0]), cudf.Series(), cudf.Series([0])
)
def test_zeros():
distance = cuspatial.directed_hausdorff_distance(
cudf.Series([0.0]), cudf.Series([0.0]), cudf.Series([1])
)
assert_eq(distance, cudf.DataFrame([0.0]))
import cuspatial
in_trajs = []
in_trajs.append(np.array([[1, 0], [2, 1], [3, 2], [5, 3], [7, 1]]))
in_trajs.append(np.array([[0, 3], [2, 5], [3, 6], [6, 5]]))
in_trajs.append(np.array([[1, 4], [3, 7], [6, 4]]))
out_trajs = np.concatenate([np.asarray(traj) for traj in in_trajs], 0)
py_x = np.array(out_trajs[:, 0])
py_y = np.array(out_trajs[:, 1])
py_cnt = []
for traj in in_trajs:
py_cnt.append(len(traj))
pnt_x = Series(py_x)
pnt_y = Series(py_y)
cnt = Series(py_cnt)
distance = cuspatial.directed_hausdorff_distance(pnt_x, pnt_y, cnt)
matrix = distance.as_matrix()
# clustering using AgglomerativeClustering
agg1 = AgglomerativeClustering(n_clusters=2,
affinity="precomputed",
linkage="average")
label1 = agg1.fit(matrix)
print("AgglomerativeClustering results={}".format(label1.labels_))
# clustering using DBSCAN; as the minimum distanance is ~1.4,
# using eps=1.5 will generate the same two clasters as AgglomerativeClustering
agg2 = DBSCAN(eps=1.5, min_samples=1, metric="precomputed")
label2 = agg2.fit(matrix)
print("DBSCAN clustering results={}".format(label2.labels_))
def test_empty_counts():
with pytest.raises(RuntimeError):
distance = cuspatial.directed_hausdorff_distance( # noqa: F841
cudf.Series([0]), cudf.Series([0]), cudf.Series())
