def symbolic_logq_not_scaled(self):
z0 = self.symbolic_initial
std = rho2sigma(self.rho)
logdet = at.log(std)
quaddist = -0.5 * z0**2 - at.log((2 * np.pi)**0.5)
logq = quaddist - logdet
return logq.sum(range(1, logq.ndim))
def L(self):
if self.batched:
L = at.zeros((self.ddim, self.ddim, self.bdim))
L = at.set_subtensor(L[self.tril_indices],
self.params_dict["L_tril"].T)
L = L.dimshuffle(2, 0, 1)
else:
L = at.zeros((self.ddim, self.ddim))
L = at.set_subtensor(L[self.tril_indices],
self.params_dict["L_tril"])
Ld = L[..., np.arange(self.ddim), np.arange(self.ddim)]
L = at.set_subtensor(Ld, rho2sigma(Ld))
return L
def std(self):
return rho2sigma(self.rho)
def cov(self):
var = rho2sigma(self.rho)**2
if self.batched:
return batched_diag(var)
else:
return at.diag(var)
def symbolic_logq_not_scaled(self):
z0 = self.symbolic_initial
std = rho2sigma(self.rho)
logdet = at.log(std)
logq = pm.Normal.dist().logp(z0) - logdet
return logq.sum(range(1, logq.ndim))
def cov(self):
var = rho2sigma(self.rho) ** 2
return at.diag(var)
def L(self):
L = at.zeros((self.ddim, self.ddim))
L = at.set_subtensor(L[self.tril_indices], self.params_dict["L_tril"])
Ld = L[..., np.arange(self.ddim), np.arange(self.ddim)]
L = at.set_subtensor(Ld, rho2sigma(Ld))
return L
def __init__(self, rho=None, **kwargs):
super().__init__(**kwargs)
rho = self.add_param(rho, "rho")
self.scale = rho2sigma(rho)
self.shared_params = dict(rho=rho)