def guide_3DA(data):
# Hyperparameters
a_psi_1 = pyro.param('a_psi_1', torch.tensor(-np.pi), constraint=constraints.greater_than(-3.15))
b_psi_1 = pyro.param('b_psi_1', torch.tensor(np.pi), constraint=constraints.less_than(3.15))
x_psi_1 = pyro.param('x_psi_1', torch.tensor(2.), constraint=constraints.positive)
a_phi_2 = pyro.param('a_phi_2', torch.tensor(-np.pi), constraint=constraints.greater_than(-3.15))
b_phi_2 = pyro.param('b_phi_2', torch.tensor(np.pi), constraint=constraints.less_than(3.15))
x_phi_2 = pyro.param('x_phi_2', torch.tensor(2.), constraint=constraints.positive)
a_psi_2 = pyro.param('a_psi_2', torch.tensor(-np.pi), constraint=constraints.greater_than(-3.15))
b_psi_2 = pyro.param('b_psi_2', torch.tensor(np.pi), constraint=constraints.less_than(3.15))
x_psi_2 = pyro.param('x_psi_2', torch.tensor(2.), constraint=constraints.positive)
a_phi_3 = pyro.param('a_phi_3', torch.tensor(-np.pi), constraint=constraints.greater_than(-3.15))
b_phi_3 = pyro.param('b_phi_3', torch.tensor(np.pi), constraint=constraints.less_than(3.15))
x_phi_3 = pyro.param('x_phi_3', torch.tensor(2.), constraint=constraints.positive)
# Sampling mu and kappa
pyro.sample("mu_psi_1", dist.Uniform(a_psi_1, b_psi_1))
pyro.sample("inv_kappa_psi_1", dist.HalfNormal(x_psi_1))
pyro.sample("mu_phi_2", dist.Uniform(a_phi_2, b_phi_2))
pyro.sample("inv_kappa_phi_2", dist.HalfNormal(x_phi_2))
pyro.sample("mu_psi_2", dist.Uniform(a_psi_2, b_psi_2))
pyro.sample("inv_kappa_psi_2", dist.HalfNormal(x_psi_2))
pyro.sample("mu_phi_3", dist.Uniform(a_phi_3, b_phi_3))
pyro.sample("inv_kappa_phi_3", dist.HalfNormal(x_phi_3))
def build_support(
lower_bound: Optional[Tensor] = None,
upper_bound: Optional[Tensor] = None) -> constraints.Constraint:
"""Return support for prior distribution, depending on available bounds.
Args:
lower_bound: lower bound of the prior support, can be None
upper_bound: upper bound of the prior support, can be None
Returns:
support: Pytorch constraint object.
"""
# Support is real if no bounds are passed.
if lower_bound is None and upper_bound is None:
support = constraints.real
warnings.warn(
"""No prior bounds were passed, consider passing lower_bound
and / or upper_bound if your prior has bounded support.""")
# Only lower bound is specified.
elif upper_bound is None:
num_dimensions = lower_bound.numel() # type: ignore
if num_dimensions > 1:
support = constraints._IndependentConstraint(
constraints.greater_than(lower_bound),
1,
)
else:
support = constraints.greater_than(lower_bound)
# Only upper bound is specified.
elif lower_bound is None:
num_dimensions = upper_bound.numel()
if num_dimensions > 1:
support = constraints._IndependentConstraint(
constraints.less_than(upper_bound),
1,
)
else:
support = constraints.less_than(upper_bound)
# Both are specified.
else:
num_dimensions = lower_bound.numel()
assert (num_dimensions == upper_bound.numel()
), "There must be an equal number of independent bounds."
if num_dimensions > 1:
support = constraints._IndependentConstraint(
constraints.interval(lower_bound, upper_bound),
1,
)
else:
support = constraints.interval(lower_bound, upper_bound)
return support
def __init__(self,
a,
b,
sigma=0.01,
log_transform=False,
validate_args=False):
TModule.__init__(self)
_a = torch.tensor(float(a)) if isinstance(a, Number) else a
_a = _a.view(-1) if _a.dim() < 1 else _a
_a, _b, _sigma = broadcast_all(_a, b, sigma)
if not torch.all(constraints.less_than(_b).check(_a)):
raise ValueError("must have that a < b (element-wise)")
# TODO: Proper argument validation including broadcasting
batch_shape, event_shape = _a.shape[:-1], _a.shape[-1:]
# need to assign values before registering as buffers to make argument validation work
self.a, self.b, self.sigma = _a, _b, _sigma
super(SmoothedBoxPrior, self).__init__(batch_shape,
event_shape,
validate_args=validate_args)
# now need to delete to be able to register buffer
del self.a, self.b, self.sigma
self.register_buffer("a", _a)
self.register_buffer("b", _b)
self.register_buffer("sigma", _sigma)
self.tails = NormalPrior(torch.zeros_like(_a),
_sigma,
validate_args=validate_args)
self._log_transform = log_transform