def binomial_model():
n_samples = 100
xs = intX(np.random.binomial(n=1, p=0.2, size=n_samples))
with pm.Model() as model:
p = pm.Beta("p", alpha=1, beta=1)
pm.Binomial("xs", n=1, p=p, observed=xs)
return model
def dist(
cls,
distribution,
lower=None,
upper=None,
size=None,
shape=None,
**kwargs,
):
cls._argument_checks(distribution, **kwargs)
lower, upper, initval = cls._set_values(lower,
upper,
size,
shape,
initval=None)
distribution.tag.ignore_logprob = True
if isinstance(distribution.owner.op, Continuous):
res = _ContinuousBounded.dist(
[distribution, lower, upper],
size=size,
shape=shape,
**kwargs,
)
res.tag.test_value = floatX(initval)
else:
res = _DiscreteBounded.dist(
[distribution, lower, upper],
size=size,
shape=shape,
**kwargs,
)
res.tag.test_value = intX(initval)
return res
def dist(
cls, mu=0.0, sigma=1.0, *, init=None, steps=None, size=None, **kwargs
) -> at.TensorVariable:
mu = at.as_tensor_variable(floatX(mu))
sigma = at.as_tensor_variable(floatX(sigma))
steps = get_steps(
steps=steps,
shape=kwargs.get("shape", None),
step_shape_offset=1,
)
if steps is None:
raise ValueError("Must specify steps or shape parameter")
steps = at.as_tensor_variable(intX(steps))
# If no scalar distribution is passed then initialize with a Normal of same mu and sigma
if init is None:
init = Normal.dist(0, 1)
else:
if not (
isinstance(init, at.TensorVariable)
and init.owner is not None
and isinstance(init.owner.op, RandomVariable)
and init.owner.op.ndim_supp == 0
):
raise TypeError("init must be a univariate distribution variable")
check_dist_not_registered(init)
# Ignores logprob of init var because that's accounted for in the logp method
init = ignore_logprob(init)
return super().dist([mu, sigma, init, steps], size=size, **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,
distribution,
lower=None,
upper=None,
size=None,
shape=None,
initval=None,
dims=None,
**kwargs,
):
cls._argument_checks(distribution, **kwargs)
if dims is not None:
model = modelcontext(None)
if dims in model.coords:
dim_obj = np.asarray(model.coords[dims])
size = dim_obj.shape
else:
raise ValueError(
"Given dims do not exist in model coordinates.")
lower, upper, initval = cls._set_values(lower, upper, size, shape,
initval)
distribution.tag.ignore_logprob = True
if isinstance(distribution.owner.op, Continuous):
res = _ContinuousBounded(
name,
[distribution, lower, upper],
initval=floatX(initval),
size=size,
shape=shape,
**kwargs,
)
else:
res = _DiscreteBounded(
name,
[distribution, lower, upper],
initval=intX(initval),
size=size,
shape=shape,
**kwargs,
)
return res
def dist(cls, mu=0.0, sigma=1.0, *, init_dist=None, steps=None, **kwargs) -> at.TensorVariable:
mu = at.as_tensor_variable(floatX(mu))
sigma = at.as_tensor_variable(floatX(sigma))
steps = get_steps(
steps=steps,
shape=kwargs.get("shape"),
step_shape_offset=1,
)
if steps is None:
raise ValueError("Must specify steps or shape parameter")
steps = at.as_tensor_variable(intX(steps))
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")
# If no scalar distribution is passed then initialize with a Normal of same mu and sigma
if init_dist is None:
warnings.warn(
"Initial distribution not specified, defaulting to `Normal.dist(0, 100)`."
"You can specify an init_dist manually to suppress this warning.",
UserWarning,
)
init_dist = Normal.dist(0, 100)
else:
if not (
isinstance(init_dist, at.TensorVariable)
and init_dist.owner is not None
and isinstance(init_dist.owner.op, RandomVariable)
and init_dist.owner.op.ndim_supp == 0
):
raise TypeError("init must be a univariate distribution variable")
check_dist_not_registered(init_dist)
# Ignores logprob of init var because that's accounted for in the logp method
init_dist = ignore_logprob(init_dist)
return super().dist([mu, sigma, init_dist, steps], **kwargs)