def prepare_dist(dist):
if not isinstance(dist._sample_space, dit.samplespace.CartesianProduct):
dist = dit.expanded_samplespace(dist, union=True)
if not dist.is_dense():
if len(dist._sample_space) > 1e4: # pragma: no cover
import warnings
msg = "Sample space has more than 10k elements."
msg += " This could be slow."
warnings.warn(msg)
dist.make_dense()
# We also need linear probabilities.
dist.set_base('linear')
return dist
def prepare_dist(dist):
if not isinstance(dist._sample_space, dit.samplespace.CartesianProduct):
dist = dit.expanded_samplespace(dist, union=True)
if not dist.is_dense():
if len(dist._sample_space) > 1e4:
import warnings
msg = "Sample space has more than 10k elements."
msg += " This could be slow."
warnings.warn(msg)
dist.make_dense()
# We also need linear probabilities.
dist.set_base('linear')
return dist
def test_ilr_inv_underflow():
"""
Make sure ilv_inv still works in potential underflow situations.
"""
# We need a lot of small probabilities so their products will underflow
# if we don't use logarithms properly.
d = dit.example_dists.summed_dice(.5, 1)
d = dit.expanded_samplespace(d, union=True)
d.make_dense()
prng = d.prng
prng.seed(0)
tol = 1e-5
pmf_ = dit.math.pmfops.replace_zeros(d.pmf, tol, prng=prng)
ilrpmf = dit.math.aitchison.ilr(pmf_)
pmf = dit.math.aitchison.ilr_inv(ilrpmf)
assert np.allclose(pmf, pmf_)
def test_ilr_inv_underflow():
"""
Make sure ilv_inv still works in potential underflow situations.
"""
# We need a lot of small probabilities so their products will underflow
# if we don't use logarithms properly.
d = dit.example_dists.summed_dice(.5, 1)
d = dit.expanded_samplespace(d, union=True)
d.make_dense()
prng = d.prng
prng.seed(0)
tol = 1e-5
pmf_ = dit.math.pmfops.replace_zeros(d.pmf, tol, prng=prng)
ilrpmf = dit.math.aitchison.ilr(pmf_)
pmf = dit.math.aitchison.ilr_inv(ilrpmf)
np.testing.assert_allclose(pmf, pmf_)