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, loc, scale, name, learnable=False):
self._type = "Log 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.LogNormalDistribution()
def __init__(self, mu, sigma, name, learnable=False):
self._type = "Log 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.LogNormalDistribution()