def __init__(
self, rho, sigma=None, tau=None, constant=False, init=Flat.dist(), sd=None, *args, **kwargs
):
super().__init__(*args, **kwargs)
if sd is not None:
sigma = sd
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.sigma = self.sd = tt.as_tensor_variable(sigma)
self.tau = tt.as_tensor_variable(tau)
self.mean = tt.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 = tt.as_tensor_variable(rho)
self.init = init
def __init__(self,
tau=None,
init=Flat.dist(),
sigma=None,
mu=0.0,
sd=None,
*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!"
)
if sd is not None:
sigma = sd
warnings.warn("sd is deprecated, use sigma instead",
DeprecationWarning)
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.tau = tt.as_tensor_variable(tau)
sigma = tt.as_tensor_variable(sigma)
self.sigma = self.sd = sigma
self.mu = tt.as_tensor_variable(mu)
self.init = init
self.mean = tt.as_tensor_variable(0.0)
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 __init__(self, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), *args, **kwargs):
if sd is not None:
sigma = sd
warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
_, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.mu = mu = tt.as_tensor_variable(mu)
self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
super().__init__(w, Normal.dist(mu, sigma=sigma, shape=comp_shape), *args, **kwargs)
def __init__(self,
tau=None,
init=Flat.dist(),
sigma=None,
mu=0.,
sd=None,
*args,
**kwargs):
kwargs.setdefault('shape', 1)
super().__init__(*args, **kwargs)
if sum(self.shape) == 0:
raise TypeError("RW2 must be supplied a non-zero shape argument!")
if sd is not None:
sigma = sd
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.tau = tt.as_tensor_variable(tau)
sigma = tt.as_tensor_variable(sigma)
self.sigma = self.sd = sigma
self.mu = tt.as_tensor_variable(mu)
self.init = init
self.mean = tt.as_tensor_variable(0.)