How to use the persim.sliced_wasserstein function in persim
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scikit-tda / persim / test / test_distances.py View on Github 
def test_different_size(self):
d = sliced_wasserstein(
np.array([
[0.5, 1],
[0.6, 1.1]
]),
np.array([
[0.6, 1.2]
])
)
# These are very loose bounds
assert d == pytest.approx(0.314, 0.1)def test_sliced_single():
d = sliced_wasserstein(
np.array([[0.5, 1]]),
np.array([[0.5, 1.1]])
)
# These are very loose bounds
assert d == pytest.approx(0.1, 0.01)def test_sliced_some():
d = sliced_wasserstein(
np.array([
[0.5, 1],
[0.6, 1.1]
]),
np.array([
[0.5, 1.1],
[0.6, 1.2]
])
)
# These are very loose bounds
assert d == pytest.approx(0.19, 0.02)def test_some(self):
d = sliced_wasserstein(
np.array([
[0.5, 1],
[0.6, 1.1]
]),
np.array([
[0.5, 1.1],
[0.6, 1.2]
])
)
# These are very loose bounds
assert d == pytest.approx(0.19, 0.02)def test_single(self):
d = sliced_wasserstein(
np.array([[0.5, 1]]),
np.array([[0.5, 1.1]])
)
# These are very loose bounds
assert d == pytest.approx(0.1, 0.01)persim
Distances and representations of persistence diagrams
