|  | 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 |