def __init__(self, rho, sigma=None, tau=None, constant=False, init=None, *args, **kwargs):
super().__init__(*args, **kwargs)
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.sigma = at.as_tensor_variable(sigma)
self.tau = at.as_tensor_variable(tau)
self.mean = at.as_tensor_variable(0.0)
if isinstance(rho, list):
p = len(rho)
else:
try:
shape_ = rho.shape.tag.test_value
except AttributeError:
shape_ = rho.shape
if hasattr(shape_, "size") and shape_.size == 0:
p = 1
else:
p = shape_[0]
if constant:
self.p = p - 1
else:
self.p = p
self.constant = constant
self.rho = rho = at.as_tensor_variable(rho)
self.init = init or Flat.dist()
def __init__(self, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), *args, **kwargs):
if sd is not None:
sigma = sd
_, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.mu = mu = at.as_tensor_variable(mu)
self.sigma = self.sd = sigma = at.as_tensor_variable(sigma)
super().__init__(w, Normal.dist(mu, sigma=sigma, shape=comp_shape), *args, **kwargs)
def dist(
cls,
rho,
sigma=None,
tau=None,
*,
init_dist=None,
steps=None,
constant=False,
ar_order=None,
**kwargs,
):
_, sigma = get_tau_sigma(tau=tau, sigma=sigma)
sigma = at.as_tensor_variable(floatX(sigma))
rhos = at.atleast_1d(at.as_tensor_variable(floatX(rho)))
if "init" in kwargs:
warnings.warn(
"init parameter is now called init_dist. Using init will raise an error in a future release.",
FutureWarning,
)
init_dist = kwargs.pop("init")
ar_order = cls._get_ar_order(rhos=rhos, constant=constant, ar_order=ar_order)
steps = get_steps(steps=steps, shape=kwargs.get("shape", None), step_shape_offset=ar_order)
if steps is None:
raise ValueError("Must specify steps or shape parameter")
steps = at.as_tensor_variable(intX(steps), ndim=0)
if init_dist is not None:
if not isinstance(init_dist, TensorVariable) or not isinstance(
init_dist.owner.op, RandomVariable
):
raise ValueError(
f"Init dist must be a distribution created via the `.dist()` API, "
f"got {type(init_dist)}"
)
check_dist_not_registered(init_dist)
if init_dist.owner.op.ndim_supp > 1:
raise ValueError(
"Init distribution must have a scalar or vector support dimension, ",
f"got ndim_supp={init_dist.owner.op.ndim_supp}.",
)
else:
warnings.warn(
"Initial distribution not specified, defaulting to "
"`Normal.dist(0, 100, shape=...)`. You can specify an init_dist "
"manually to suppress this warning.",
UserWarning,
)
init_dist = Normal.dist(0, 100, shape=(*sigma.shape, ar_order))
# Tell Aeppl to ignore init_dist, as it will be accounted for in the logp term
init_dist = ignore_logprob(init_dist)
return super().dist([rhos, sigma, init_dist, steps, ar_order, constant], **kwargs)
def __new__(cls,
name,
w,
mu,
sigma=None,
tau=None,
comp_shape=(),
**kwargs):
_, sigma = get_tau_sigma(tau=tau, sigma=sigma)
return Mixture(name, w, Normal.dist(mu, sigma=sigma, size=comp_shape),
**kwargs)
def __init__(self, tau=None, init=None, sigma=None, mu=0.0, *args, **kwargs):
kwargs.setdefault("shape", 1)
super().__init__(*args, **kwargs)
if sum(self.shape) == 0:
raise TypeError("GaussianRandomWalk must be supplied a non-zero shape argument!")
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.tau = at.as_tensor_variable(tau)
sigma = at.as_tensor_variable(sigma)
self.sigma = sigma
self.mu = at.as_tensor_variable(mu)
self.init = init or Flat.dist()
self.mean = at.as_tensor_variable(0.0)