def true_nll_gaussian(x, mu, sigma):
x = convert_to_container(x, container=tuple)
mu = convert_to_container(mu, container=tuple)
sigma = convert_to_container(sigma, container=tuple)
constraint = z.constant(0.0)
if not len(x) == len(mu) == len(sigma):
raise ValueError("params, mu and sigma have to have the same length.")
for x_, mean, sig in zip(x, mu, sigma):
constraint += z.reduce_sum(z.square(x_ - mean) / (2.0 * z.square(sig)))
return constraint
def indefinite_integral(limits):
max_degree = model.degree + 1
polys = do_recurrence(x=limits,
polys=chebyshev_polys,
degree=max_degree,
recurrence=chebyshev_recurrence)
one_limit_integrals = []
for degree in range(2, max_degree):
coeff = model.params[f"c_{degree}"]
n_float = z.convert_to_tensor(degree)
integral = (n_float * polys[degree + 1] /
(z.square(n_float) - 1) - limits * polys[degree] /
(n_float - 1))
one_limit_integrals.append(coeff * integral)
return z.reduce_sum(one_limit_integrals, axis=0)
def _unnormalized_pdf(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def _func(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def _func(self, x):
mu = self.params["mu"]
sigma = self.params["sigma"]
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))