def __init__(self, loc, scale, name, learnable=False, has_bias=False, is_observed=False, is_policy=False, is_reward=False):
self._type = "Normal"
ranges = {"loc": geometric_ranges.UnboundedRange(),
"scale": geometric_ranges.RightHalfLine(0.)}
super().__init__(name, loc=loc, scale=scale, learnable=learnable, has_bias=has_bias,
ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.NormalDistribution()
def __init__(self, loc, scale, name, learnable=False):
self._type = "Normal"
ranges = {
"loc": geometric_ranges.UnboundedRange(),
"scale": geometric_ranges.RightHalfLine(0.)
}
super().__init__(name,
loc=loc,
scale=scale,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.NormalDistribution()
def __init__(self, mu, sigma, name, learnable=False):
self._type = "Normal"
ranges = {
"mu": geometric_ranges.UnboundedRange(),
"sigma": geometric_ranges.RightHalfLine(0.)
}
super().__init__(name,
mu=mu,
sigma=sigma,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.NormalDistribution()
def __init__(self,
mu,
cov=None,
chol_cov=None,
diag_cov=None,
name="Multivariate Normal",
learnable=False):
self._type = "Multivariate Normal"
if chol_cov is not None and diag_cov is None:
ranges = {
"mu": geometric_ranges.UnboundedRange(),
"chol_cov": geometric_ranges.UnboundedRange()
}
super().__init__(name,
mu=mu,
chol_cov=chol_cov,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.CholeskyMultivariateNormal()
elif diag_cov is not None and chol_cov is None:
ranges = {
"mean": geometric_ranges.UnboundedRange(),
"var": geometric_ranges.RightHalfLine(0.)
}
super().__init__(name,
mean=mu,
var=diag_cov,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.NormalDistribution()
else:
raise ValueError(
"Either chol_cov (cholesky factor of the covariance matrix) or "
+
"diag_cov (diagonal of the covariance matrix) need to be provided as input"
)