def __init__(self, alpha, beta, name, learnable=False, has_bias=False, is_observed=False, is_policy=False, is_reward=False):
self._type = "Beta"
concentration1 = alpha
concentration0 = beta
ranges = {"concentration1": geometric_ranges.RightHalfLine(0.),
"concentration0": geometric_ranges.RightHalfLine(0.)}
super().__init__(name, concentration1=concentration1, concentration0=concentration0,
learnable=learnable, has_bias=has_bias, ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.BetaDistribution()
def __init__(self, alpha, beta, name, learnable=False):
self._type = "Logit Normal"
ranges = {
"alpha": geometric_ranges.RightHalfLine(0.),
"beta": geometric_ranges.RightHalfLine(0.)
}
super().__init__(name,
alpha=alpha,
beta=beta,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.BetaDistribution()
def __init__(self, alpha, beta, name, learnable=False, is_observed=False):
self._type = "Logit Normal"
concentration1 = alpha
concentration0 = beta
ranges = {
"concentration1": geometric_ranges.RightHalfLine(0.),
"concentration0": geometric_ranges.RightHalfLine(0.)
}
super().__init__(name,
concentration1=concentration1,
concentration0=concentration0,
learnable=learnable,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.BetaDistribution()
def __init__(self, loc, scale, name, learnable=False, has_bias=False, is_observed=False, is_policy=False, is_reward=False):
self._type = "Log 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.LogNormalDistribution()
def __init__(self, df, loc, scale, name, learnable=False, is_observed=False):
self._type = "StudentT"
ranges = {"df": geometric_ranges.UnboundedRange(),
"loc": geometric_ranges.UnboundedRange(),
"scale": geometric_ranges.RightHalfLine(0.)}
super().__init__(name, df=df, loc=loc, scale=scale, learnable=learnable, ranges=ranges, is_observed=is_observed)
self.distribution = distributions.StudentTDistribution()
def __init__(self, concentration, name, learnable=False, has_bias=False,
is_observed=False, is_policy=False, is_reward=False):
self._type = "Dirichlet"
ranges = {"concentration": geometric_ranges.RightHalfLine(0.)}
super().__init__(name, concentration=concentration,
learnable=learnable,
has_bias=has_bias, ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.DirichletDistribution()
def __init__(self, tau, p, name, learnable=False):
self._type = "Concrete"
ranges = {
"tau": geometric_ranges.RightHalfLine(0.),
"p": geometric_ranges.Simplex()
}
super().__init__(name,
tau=tau,
p=p,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.ConcreteDistribution()
def __init__(self, loc, scale, name, learnable=False):
self._type = "Logit 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.LogitNormalDistribution()
def __init__(self, mu, sigma, name, learnable=False):
self._type = "Logit 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.LogitNormalDistribution()
def __init__(self,
rate,
name,
learnable=False,
has_bias=False,
is_observed=False):
self._type = "Poisson"
ranges = {"rate": geometric_ranges.RightHalfLine(0.)}
super().__init__(name,
rate=rate,
learnable=learnable,
has_bias=has_bias,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.PoissonDistribution()
def __init__(self,
scale,
name,
learnable=False,
has_bias=False,
is_observed=False):
self._type = "HalfNormal"
ranges = {"scale": geometric_ranges.RightHalfLine(0.)}
super().__init__(name,
scale=scale,
learnable=learnable,
has_bias=has_bias,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.HalfNormalDistribution()
def __init__(self,
loc,
scale,
name,
learnable=False,
has_bias=False,
is_observed=False):
self._type = "Cauchy"
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)
self.distribution = distributions.CauchyDistribution()
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"
)
def __init__(self, rate, name, learnable=False, has_bias=False, is_observed=False, is_policy=False, is_reward=False):
self._type = "Exponential"
ranges = {"rate": geometric_ranges.RightHalfLine(0.)}
super().__init__(name, rate=rate, learnable=learnable,
has_bias=has_bias, ranges=ranges, is_observed=is_observed, is_policy=is_policy, is_reward=is_reward)
self.distribution = distributions.ExponentialDistribution()