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()