beanmachine.ppl.distributions.unit module
- class beanmachine.ppl.distributions.unit.Unit(log_factor, validate_args=None)
Bases:
torch.distributions.distribution.DistributionTrivial nonnormalized distribution representing the unit type.
The unit type has a single value with no data, i.e.
value.numel() == 0.This is used for
pyro.factor()statements.- arg_constraints = {'log_factor': Real()}
- expand(batch_shape, _instance=None)
Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls
expandon the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created.- Parameters
batch_shape (torch.Size) – the desired expanded size.
_instance – new instance provided by subclasses that need to override .expand.
- Returns
New distribution instance with batch dimensions expanded to batch_size.
- log_prob(value)
Returns the log of the probability density/mass function evaluated at value.
- Parameters
value (Tensor) –
- sample(sample_shape=torch.Size([]))
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.
- support = Real()
- beanmachine.ppl.distributions.unit.broadcast_shape(*shapes, **kwargs)
Similar to
np.broadcast()but for shapes. Equivalent tonp.broadcast(*map(np.empty, shapes)).shape. :param tuple shapes: shapes of tensors. :param bool strict: whether to use extend-but-not-resize broadcasting. :returns: broadcasted shape :rtype: tuple :raises: ValueError