def pymc3_random_discrete(dist, paramdomains,
valuedomain=Domain([0]), ref_rand=None,
size=100000, alpha=0.05, fails=20):
model = build_model(dist, valuedomain, paramdomains)
domains = paramdomains.copy()
for pt in product(domains):
pt = Point(pt, model=model)
p = alpha
# Allow Chisq test to fail (i.e., the samples be different)
# a certain number of times.
f = fails
while p <= alpha and f > 0:
o = model.named_vars['value'].random(size=size, point=pt)
e = ref_rand(size=size, **pt)
o = np.atleast_1d(o).flatten()
e = np.atleast_1d(e).flatten()
observed = dict(zip(*np.unique(o, return_counts=True)))
expected = dict(zip(*np.unique(e, return_counts=True)))
for e in expected.keys():
expected[e] = (observed.get(e, 0), expected[e])
k = np.array([v for v in expected.values()])
if np.all(k[:, 0] == k[:, 1]):
p = 1.
else:
_chi, p = st.chisquare(k[:, 0], k[:, 1])
f -= 1
assert p > alpha, str(pt)
def pymc3_random(dist, paramdomains,
ref_rand=None, valuedomain=Domain([0]),
size=10000, alpha=0.05, fails=10):
model = build_model(dist, valuedomain, paramdomains)
domains = paramdomains.copy()
for pt in product(domains):
pt = Point(pt, model=model)
p = alpha
# Allow KS test to fail (i.e., the samples be different)
# a certain number of times. Crude, but necessary.
f = fails
while p <= alpha and f > 0:
s0 = model.named_vars['value'].random(size=size, point=pt)
s1 = ref_rand(size=size, **pt)
_, p = st.ks_2samp(np.atleast_1d(s0).flatten(), np.atleast_1d(s1).flatten())
f -= 1
assert p > alpha, str(pt)
def pymc3_random(dist, paramdomains,
ref_rand=None, valuedomain=Domain([0]),
size=10000, alpha=0.05, fails=10):
model = build_model(dist, valuedomain, paramdomains)
domains = paramdomains.copy()
for pt in product(domains):
pt = Point(pt, model=model)
p = alpha
# Allow KS test to fail (i.e., the samples be different)
# a certain number of times. Crude, but necessary.
f = fails
while p <= alpha and f > 0:
s0 = model.named_vars['value'].random(size=size, point=pt)
s1 = ref_rand(size=size, **pt)
_, p = st.ks_2samp(np.atleast_1d(s0).flatten(),
np.atleast_1d(s1).flatten())
f -= 1
assert p > alpha, str(pt)
