def test_mixture_list_of_poissons(self):
with Model() as model:
w = Dirichlet("w",
floatX(np.ones_like(self.pois_w)),
shape=self.pois_w.shape)
mu = Gamma("mu", 1.0, 1.0, shape=self.pois_w.size)
Mixture(
"x_obs",
w,
[Poisson.dist(mu[0]), Poisson.dist(mu[1])],
observed=self.pois_x)
step = Metropolis()
trace = sample(5000,
step,
random_seed=self.random_seed,
progressbar=False,
chains=1)
assert_allclose(np.sort(trace["w"].mean(axis=0)),
np.sort(self.pois_w),
rtol=0.1,
atol=0.1)
assert_allclose(np.sort(trace["mu"].mean(axis=0)),
np.sort(self.pois_mu),
rtol=0.1,
atol=0.1)
def test_mixture_list_of_poissons(self):
with Model() as model:
w = Dirichlet('w', np.ones_like(self.pois_w))
mu = Gamma('mu', 1., 1., shape=self.pois_w.size)
x_obs = Mixture(
'x_obs',
w,
[Poisson.dist(mu[0]), Poisson.dist(mu[1])],
observed=self.pois_x)
step = Metropolis()
trace = sample(5000,
step,
random_seed=self.random_seed,
progressbar=False)
assert_allclose(np.sort(trace['w'].mean(axis=0)),
np.sort(self.pois_w),
rtol=0.1,
atol=0.1)
assert_allclose(np.sort(trace['mu'].mean(axis=0)),
np.sort(self.pois_mu),
rtol=0.1,
atol=0.1)
def test_mixture_list_of_poissons(self):
with Model() as model:
w = Dirichlet('w', np.ones_like(self.pois_w))
mu = Gamma('mu', 1., 1., shape=self.pois_w.size)
Mixture('x_obs', w,
[Poisson.dist(mu[0]), Poisson.dist(mu[1])],
observed=self.pois_x)
step = Metropolis()
trace = sample(5000, step, random_seed=self.random_seed, progressbar=False)
assert_allclose(np.sort(trace['w'].mean(axis=0)),
np.sort(self.pois_w),
rtol=0.1, atol=0.1)
assert_allclose(np.sort(trace['mu'].mean(axis=0)),
np.sort(self.pois_mu),
rtol=0.1, atol=0.1)