botorch.distributions

class botorch.distributions.Kumaraswamy(concentration1, concentration0, validate_args=False)[source]

Bases: torch.distributions.transformed_distribution.TransformedDistribution

A Kumaraswamy distribution.

Example:

>>> m = Kumaraswamy(torch.Tensor([1.0]), torch.Tensor([1.0]))
>>> m.sample()  # sample from a Kumaraswamy distribution
tensor([ 0.1729])
Parameters

  • concentration1 (Union[float, Tensor]) – 1st concentration parameter of the distribution (often referred to as alpha)

  • concentration0 (Union[float, Tensor]) – 2nd concentration parameter of the distribution (often referred to as beta)

arg_constraints = {'concentration0': GreaterThan(lower_bound=0.0), 'concentration1': GreaterThan(lower_bound=0.0)}
support = Interval(lower_bound=0.0, upper_bound=1.0)
has_rsample = True
expand(batch_shape, _instance=None)[source]

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 expand on 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 (Optional[Kumaraswamy]) – new instance provided by subclasses that need to override .expand.

Return type

Kumaraswamy

Returns

New distribution instance with batch dimensions expanded to batch_size.

property mean

Returns the mean of the distribution.

Return type

None

property variance

Returns the variance of the distribution.

Return type

None

entropy()[source]

Returns entropy of distribution, batched over batch_shape.

Return type

None

Returns

Tensor of shape batch_shape.

Distributions

Probability Distributions.

This is modified from https://github.com/probtorch/pytorch/pull/143 and https://github.com/tensorflow/probability/blob/v0.11.1/ tensorflow_probability/python/distributions/kumaraswamy.py.

TODO: replace with PyTorch version once the PR is up and landed.

class botorch.distributions.distributions.Kumaraswamy(concentration1, concentration0, validate_args=False)[source]

Bases: torch.distributions.transformed_distribution.TransformedDistribution

A Kumaraswamy distribution.

Example:

>>> m = Kumaraswamy(torch.Tensor([1.0]), torch.Tensor([1.0]))
>>> m.sample()  # sample from a Kumaraswamy distribution
tensor([ 0.1729])
Parameters

  • concentration1 (Union[float, Tensor]) – 1st concentration parameter of the distribution (often referred to as alpha)

  • concentration0 (Union[float, Tensor]) – 2nd concentration parameter of the distribution (often referred to as beta)

arg_constraints = {'concentration0': GreaterThan(lower_bound=0.0), 'concentration1': GreaterThan(lower_bound=0.0)}
support = Interval(lower_bound=0.0, upper_bound=1.0)
has_rsample = True
expand(batch_shape, _instance=None)[source]

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 expand on 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 (Optional[Kumaraswamy]) – new instance provided by subclasses that need to override .expand.

Return type

Kumaraswamy

Returns

New distribution instance with batch dimensions expanded to batch_size.

property mean

Returns the mean of the distribution.

Return type

None

property variance

Returns the variance of the distribution.

Return type

None

entropy()[source]

Returns entropy of distribution, batched over batch_shape.

Return type

None

Returns

Tensor of shape batch_shape.