def setUpClass(cls):
cls.test_point, cls.model, _ = models.non_normal(cls.shape)
with cls.model:
trace0 = cls.backend(cls.name)
trace1 = cls.backend(cls.name)
cls.draws = 5
trace0.setup(cls.draws, chain=0)
trace1.setup(cls.draws, chain=1)
varnames = list(cls.test_point.keys())
shapes = {varname: value.shape
for varname, value in cls.test_point.items()}
cls.expected = {0: {}, 1: {}}
for varname in varnames:
mcmc_shape = (cls.draws,) + shapes[varname]
values = np.arange(cls.draws * np.prod(shapes[varname]))
cls.expected[0][varname] = values.reshape(mcmc_shape)
cls.expected[1][varname] = values.reshape(mcmc_shape) * 100
for idx in range(cls.draws):
point0 = {varname: cls.expected[0][varname][idx, ...]
for varname in varnames}
point1 = {varname: cls.expected[1][varname][idx, ...]
for varname in varnames}
trace0.record(point=point0)
trace1.record(point=point1)
trace0.close()
trace1.close()
cls.mtrace = base.MultiTrace([trace0, trace1])
def test_leapfrog_reversible():
n = 3
np.random.seed(42)
start, model, _ = models.non_normal(n)
size = sum(start[n.name].size for n in model.value_vars)
scaling = floatX(np.random.rand(size))
class HMC(BaseHMC):
def _hamiltonian_step(self, *args, **kwargs):
pass
step = HMC(vars=model.value_vars, model=model, scaling=scaling)
step.integrator._logp_dlogp_func.set_extra_values({})
astart = DictToArrayBijection.map(start)
p = RaveledVars(floatX(step.potential.random()), astart.point_map_info)
q = RaveledVars(floatX(np.random.randn(size)), astart.point_map_info)
start = step.integrator.compute_state(p, q)
for epsilon in [0.01, 0.1]:
for n_steps in [1, 2, 3, 4, 20]:
state = start
for _ in range(n_steps):
state = step.integrator.step(epsilon, state)
for _ in range(n_steps):
state = step.integrator.step(-epsilon, state)
npt.assert_allclose(state.q.data, start.q.data, rtol=1e-5)
npt.assert_allclose(state.p.data, start.p.data, rtol=1e-5)
def setUpClass(cls):
cls.test_point, cls.model, _ = models.non_normal(cls.shape)
with cls.model:
trace0 = cls.backend(cls.name)
trace1 = cls.backend(cls.name)
cls.draws = 5
trace0.setup(cls.draws, chain=0)
trace1.setup(cls.draws, chain=1)
varnames = list(cls.test_point.keys())
shapes = {
varname: value.shape
for varname, value in cls.test_point.items()
}
cls.expected = {0: {}, 1: {}}
for varname in varnames:
mcmc_shape = (cls.draws, ) + shapes[varname]
values = np.arange(cls.draws * np.prod(shapes[varname]))
cls.expected[0][varname] = values.reshape(mcmc_shape)
cls.expected[1][varname] = values.reshape(mcmc_shape) * 100
for idx in range(cls.draws):
point0 = {
varname: cls.expected[0][varname][idx, ...]
for varname in varnames
}
point1 = {
varname: cls.expected[1][varname][idx, ...]
for varname in varnames
}
trace0.record(point=point0)
trace1.record(point=point1)
trace0.close()
trace1.close()
cls.mtrace = base.MultiTrace([trace0, trace1])
def test_accuracy_non_normal():
_, model, (mu, _) = non_normal(4)
with model:
newstart = find_MAP(Point(x=[0.5, 0.01, 0.95, 0.99]))
close_to(newstart["x"], mu,
select_by_precision(float64=1e-5, float32=1e-4))
def setUp(self):
self.test_point, self.model, _ = models.non_normal(self.shape)
with self.model:
self.trace = self.backend(self.name)
self.draws, self.chain = 3, 0
self.trace.setup(self.draws, self.chain)
def test_guess_scaling():
start, model, _ = models.non_normal(n=5)
a1 = scaling.guess_scaling(start, model=model)
assert all((a1 > 0) & (a1 < 1e200))
def setUp(self):
self.test_point, self.model, _ = models.non_normal(self.shape)
with self.model:
self.trace = self.backend(self.name)
self.draws, self.chain = 3, 0
self.trace.setup(self.draws, self.chain)
