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 init_proposal_covariance(bij, vars, model, pop_size=1000):
"""
Create initial proposal covariance matrix based on random samples
from the solution space.
"""
population_array = num.zeros((pop_size, bij.ordering.size))
for i in range(pop_size):
point = Point({v.name: v.random() for v in vars}, model=model)
population_array[i, :] = bij.map(point)
return num.diag(population_array.var(0))
def test_errors():
_, model, _ = exponential_beta(2)
with model:
try:
newstart = find_MAP(Point(x=[-.5, .01], y=[.5, 4.4]))
except ValueError as e:
msg = str(e)
assert "x.logp" in msg, msg
assert "x.value" not in msg, msg
else:
assert False, newstart
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 test_accuracy_normal():
_, model, (mu, _) = simple_model()
with model:
newstart = find_MAP(Point(x=[-10.5, 100.5]))
close_to(newstart['x'], [mu, mu], 1e-5)
def test_accuracy_non_normal():
_, model, (mu, _) = non_normal(4)
with model:
newstart = find_MAP(Point(x=[.5, .01, .95, .99]))
close_to(newstart['x'], mu, 1e-5)
def test_accuracy_non_normal():
_, model, (mu, _) = non_normal(4)
with model:
newstart = find_MAP(Point(x=[.5, .01, .95, .99]))
close_to(newstart['x'], mu,
select_by_precision(float64=1e-5, float32=1E-4))
def test_accuracy_normal():
_, model, (mu, _) = simple_model()
with model:
newstart = find_MAP(Point(x=[-10.5, 100.5]))
close_to(newstart['x'], [mu, mu],
select_by_precision(float64=1e-5, float32=1E-4))