def test_multiple_simulators(self):
true_a = 2
true_b = -2
data1 = np.random.normal(true_a, 0.1, size=1000)
data2 = np.random.normal(true_b, 0.1, size=1000)
with pm.Model() as m:
a = pm.Normal("a", mu=0, sigma=3)
b = pm.Normal("b", mu=0, sigma=3)
sim1 = pm.Simulator(
"sim1",
self.normal_sim,
a,
0.1,
distance="gaussian",
sum_stat="sort",
observed=data1,
)
sim2 = pm.Simulator(
"sim2",
self.normal_sim,
b,
0.1,
distance="laplace",
sum_stat="mean",
epsilon=0.1,
observed=data2,
)
assert self.count_rvs(m.logpt) == 2
# Check that the logps use the correct methods
a_val = m.rvs_to_values[a]
sim1_val = m.rvs_to_values[sim1]
logp_sim1 = pm.logp(sim1, sim1_val)
logp_sim1_fn = aesara.function([sim1_val, a_val], logp_sim1)
b_val = m.rvs_to_values[b]
sim2_val = m.rvs_to_values[sim2]
logp_sim2 = pm.logp(sim2, sim2_val)
logp_sim2_fn = aesara.function([sim2_val, b_val], logp_sim2)
assert any(node for node in logp_sim1_fn.maker.fgraph.toposort()
if isinstance(node.op, SortOp))
assert not any(node for node in logp_sim2_fn.maker.fgraph.toposort()
if isinstance(node.op, SortOp))
with m:
trace = pm.sample_smc(return_inferencedata=False)
assert abs(true_a - trace["a"].mean()) < 0.05
assert abs(true_b - trace["b"].mean()) < 0.05
def test_potential(self):
with pm.Model():
x = pm.Normal("x", 0.0, 1.0)
pm.Potential("z", pm.logp(pm.Normal.dist(x, 1.0), np.random.randn(10)))
inference_data = pm.sample(100, chains=2, return_inferencedata=True)
assert inference_data
def test_gaussian_random_walk_init_dist_logp(self, init):
grw = pm.GaussianRandomWalk.dist(init=init, steps=1)
assert np.isclose(
pm.logp(grw, [0, 0]).eval(),
pm.logp(init, 0).eval() + scipy.stats.norm.logpdf(0),
)
def logp(x):
out = pm.logp(normal_dist, x)
return out
def logp(x):
out = pm.logp(normal_dist, x, transformed=False)
return out
def logp(x, mu):
normal_dist = pm.Normal.dist(mu, 1, size=N)
out = pm.logp(normal_dist, x)
return out