def __init__(self, loc, covariance_matrix=None, precision_matrix=None,
scale_tril=None, name="Multivariate Normal", learnable=False, has_bias=False,
is_observed=False, is_policy=False, is_reward=False):
self._type = "Multivariate Normal"
if scale_tril is not None and covariance_matrix is None and precision_matrix is None:
ranges = {"loc": geometric_ranges.UnboundedRange(),
"scale_tril": geometric_ranges.UnboundedRange()}
super().__init__(name, loc=loc, scale_tril=scale_tril, learnable=learnable,
has_bias=has_bias, ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.MultivariateNormalDistribution()
elif scale_tril is None and covariance_matrix is not None and precision_matrix is None:
ranges = {"loc": geometric_ranges.UnboundedRange(),
"covariance_matrix": geometric_ranges.PositiveDefiniteMatrix()}
super().__init__(name, loc=loc, covariance_matrix=covariance_matrix, learnable=learnable,
has_bias=has_bias, ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.MultivariateNormalDistribution()
elif scale_tril is None and covariance_matrix is None and precision_matrix is not None:
ranges = {"loc": geometric_ranges.UnboundedRange(),
"precision_matrix": geometric_ranges.PositiveDefiniteMatrix()}
super().__init__(name, loc=loc, precision_matrix=precision_matrix, learnable=learnable,
has_bias=has_bias, ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.MultivariateNormalDistribution()
else:
raise ValueError("Either covariance_matrix or precision_matrix or"+
"scale_tril needs to be provided as input")
def __init__(self,
loc,
covariance_matrix=None,
precision_matrix=None,
cholesky_factor=None,
name="Multivariate Normal",
learnable=False):
self._type = "Multivariate Normal"
if cholesky_factor is not None and covariance_matrix is None and precision_matrix is None:
ranges = {
"loc": geometric_ranges.UnboundedRange(),
"cholesky_factor": geometric_ranges.UnboundedRange()
}
super().__init__(name,
loc=loc,
cholesky_factor=cholesky_factor,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.MultivariateNormalDistribution()
elif cholesky_factor is None and covariance_matrix is not None and precision_matrix is None:
ranges = {
"loc": geometric_ranges.UnboundedRange(),
"covariance_matrix": geometric_ranges.PositiveDefiniteMatrix()
}
super().__init__(name,
loc=loc,
covariance_matrix=covariance_matrix,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.MultivariateNormalDistribution()
elif cholesky_factor is None and covariance_matrix is None and precision_matrix is not None:
ranges = {
"loc": geometric_ranges.UnboundedRange(),
"precision_matrix": geometric_ranges.UnboundedRange()
}
super().__init__(name,
loc=loc,
precision_matrix=precision_matrix,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.MultivariateNormalDistribution()
else:
raise ValueError(
"Either covariance_matrix or precision_matrix or" +
"cholesky_factor needs to be provided as input")