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()