| import torch |
| from torch.distributions import constraints |
| from torch.distributions.normal import Normal |
| from torch.distributions.transformed_distribution import TransformedDistribution |
| from torch.distributions.transforms import StickBreakingTransform |
| |
| |
| class LogisticNormal(TransformedDistribution): |
| r""" |
| Creates a logistic-normal distribution parameterized by :attr:`loc` and :attr:`scale` |
| that define the base `Normal` distribution transformed with the |
| `StickBreakingTransform` such that:: |
| |
| X ~ LogisticNormal(loc, scale) |
| Y = log(X / (1 - X.cumsum(-1)))[..., :-1] ~ Normal(loc, scale) |
| |
| Args: |
| loc (float or Tensor): mean of the base distribution |
| scale (float or Tensor): standard deviation of the base distribution |
| |
| Example:: |
| |
| >>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1) |
| >>> # of the base Normal distribution |
| >>> m = distributions.LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3)) |
| >>> m.sample() |
| tensor([ 0.7653, 0.0341, 0.0579, 0.1427]) |
| |
| """ |
| arg_constraints = {'loc': constraints.real, 'scale': constraints.positive} |
| support = constraints.simplex |
| has_rsample = True |
| |
| def __init__(self, loc, scale, validate_args=None): |
| base_dist = Normal(loc, scale) |
| super(LogisticNormal, self).__init__(base_dist, |
| StickBreakingTransform(), |
| validate_args=validate_args) |
| # Adjust event shape since StickBreakingTransform adds 1 dimension |
| self._event_shape = torch.Size([s + 1 for s in self._event_shape]) |
| |
| def expand(self, batch_shape, _instance=None): |
| new = self._get_checked_instance(LogisticNormal, _instance) |
| return super(LogisticNormal, self).expand(batch_shape, _instance=new) |
| |
| @property |
| def loc(self): |
| return self.base_dist.loc |
| |
| @property |
| def scale(self): |
| return self.base_dist.scale |