def test_step_categorical(self):
start, model, (mu, C) = simple_categorical()
unc = C ** 0.5
check = (("x", np.mean, mu, unc / 10.0), ("x", np.std, unc, unc / 10.0))
with model:
steps = (
CategoricalGibbsMetropolis(model.x, proposal="uniform"),
CategoricalGibbsMetropolis(model.x, proposal="proportional"),
)
for step in steps:
trace = sample(8000, tune=0, step=step, start=start, model=model, random_seed=1)
self.check_stat(check, trace, step.__class__.__name__)
def test_step_categorical(self):
start, model, (mu, C) = simple_categorical()
unc = C ** .5
check = (('x', np.mean, mu, unc / 10.),
('x', np.std, unc, unc / 10.))
with model:
steps = (
CategoricalGibbsMetropolis(model.x, proposal='uniform'),
CategoricalGibbsMetropolis(model.x, proposal='proportional'),
)
for step in steps:
trace = sample(8000, step=step, start=start, model=model, random_seed=1)
yield self.check_stat, check, trace, step.__class__.__name__
