rbnet.pcfg.DiscretePrior

class rbnet.pcfg.DiscretePrior(struc_weights, prior_weights, prob_rep=<class 'rbnet.util.LogProb'>, *args, **kwargs)[source]

Bases: Prior, ConstrainedModuleMixin

A prior distribution over discrete non-terminal variables.

Initialise prior with structural distribution p(z) and individual prior distributions p(a1), ..., p(an)

for n non-terminal variables a1, ..., an. The cardinality of z is n.

Parameters:

  • struc_weights – array of shape (n,) with weights for the structural distribution

  • prior_weights – iterable over n arrays if shapes (K1,), ..., (Kn,) with weights for the prior distribution of the n non-terminal variables.

Public Data Attributes:

Inherited from Prior

dump_patches

training

call_super_init

forward(*input)

Define the computation performed at every call.
Inherited from Module

dump_patches

call_super_init

T_destination

training

Public Methods:

__init__(struc_weights, prior_weights[, ...])

Initialise prior with structural distribution p(z) and individual prior distributions p(a1), ..., p(an)

marginal_likelihood(root_location, ...)

Compute the marginal data likelihood
Inherited from Prior

__init__(*args, **kwargs)

marginal_likelihood(root_location, ...)

Compute the marginal data likelihood
Inherited from Module

__init__(*args, **kwargs)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(*input)

Define the computation performed at every call.

register_buffer(name, tensor[, persistent])

Add a buffer to the module.

register_parameter(name, param)

Add a parameter to the module.

add_module(name, module)

Add a child module to the current module.

register_module(name, module)

Alias for add_module().

get_submodule(target)

Return the submodule given by target if it exists, otherwise throw an error.

set_submodule(target, module)

Set the submodule given by target if it exists, otherwise throw an error.

get_parameter(target)

Return the parameter given by target if it exists, otherwise throw an error.

get_buffer(target)

Return the buffer given by target if it exists, otherwise throw an error.

get_extra_state()

Return any extra state to include in the module's state_dict.

set_extra_state(state)

Set extra state contained in the loaded state_dict.

apply(fn)

Apply fn recursively to every submodule (as returned by .children()) as well as self.

cuda([device])

Move all model parameters and buffers to the GPU.

ipu([device])

Move all model parameters and buffers to the IPU.

xpu([device])

Move all model parameters and buffers to the XPU.

mtia([device])

Move all model parameters and buffers to the MTIA.

cpu()

Move all model parameters and buffers to the CPU.

type(dst_type)

Casts all parameters and buffers to dst_type.

float()

Casts all floating point parameters and buffers to float datatype.

double()

Casts all floating point parameters and buffers to double datatype.

half()

Casts all floating point parameters and buffers to half datatype.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

to_empty(*, device[, recurse])

Move the parameters and buffers to the specified device without copying storage.

to(*args, **kwargs)

Move and/or cast the parameters and buffers.

register_full_backward_pre_hook(hook[, prepend])

Register a backward pre-hook on the module.

register_backward_hook(hook)

Register a backward hook on the module.

register_full_backward_hook(hook[, prepend])

Register a backward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Register a forward pre-hook on the module.

register_forward_hook(hook, *[, prepend, ...])

Register a forward hook on the module.

__call__(*args, **kwargs)

Call self as a function.

__getstate__()

__setstate__(state)

__getattr__(name)

__setattr__(name, value)

Implement setattr(self, name, value).

__delattr__(name)

Implement delattr(self, name).

register_state_dict_post_hook(hook)

Register a post-hook for the state_dict() method.

register_state_dict_pre_hook(hook)

Register a pre-hook for the state_dict() method.

state_dict(*args[, destination, prefix, ...])

Return a dictionary containing references to the whole state of the module.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module's load_state_dict() is called.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module's load_state_dict() is called.

load_state_dict(state_dict[, strict, assign])

Copy parameters and buffers from state_dict into this module and its descendants.

parameters([recurse])

Return an iterator over module parameters.

named_parameters([prefix, recurse, ...])

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

buffers([recurse])

Return an iterator over module buffers.

named_buffers([prefix, recurse, ...])

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

children()

Return an iterator over immediate children modules.

named_children()

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

modules()

Return an iterator over all modules in the network.

named_modules([memo, prefix, remove_duplicate])

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

train([mode])

Set the module in training mode.

eval()

Set the module in evaluation mode.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

zero_grad([set_to_none])

Reset gradients of all model parameters.

share_memory()

See torch.Tensor.share_memory_().

extra_repr()

Set the extra representation of the module.

__repr__()

Return repr(self).

__dir__()

Default dir() implementation.

compile(*args, **kwargs)

Compile this Module's forward using torch.compile().
Inherited from ConstrainedModuleMixin

enforce_constraints([recurse])

Enforce constraints for module parameters and child modules.

remap(param[, _top_level, prefix])

Private Data Attributes:

_abc_impl

Inherited from Prior

_abc_impl

_version

This allows better BC support for load_state_dict().

_parameters

_buffers

_non_persistent_buffers_set

_backward_pre_hooks

_backward_hooks

_is_full_backward_hook

_forward_hooks

_forward_hooks_with_kwargs

_forward_hooks_always_called

_forward_pre_hooks

_forward_pre_hooks_with_kwargs

_state_dict_hooks

_load_state_dict_pre_hooks

_state_dict_pre_hooks

_load_state_dict_post_hooks

_modules

_compiled_call_impl

Inherited from ABC

_abc_impl

Inherited from Module

_version

This allows better BC support for load_state_dict().

_compiled_call_impl

_parameters

_buffers

_non_persistent_buffers_set

_backward_pre_hooks

_backward_hooks

_is_full_backward_hook

_forward_hooks

_forward_hooks_with_kwargs

_forward_hooks_always_called

_forward_pre_hooks

_forward_pre_hooks_with_kwargs

_state_dict_hooks

_load_state_dict_pre_hooks

_state_dict_pre_hooks

_load_state_dict_post_hooks

_modules

Private Methods:

Inherited from Module

_apply(fn[, recurse])

_get_backward_hooks()

Return the backward hooks for use in the call function.

_get_backward_pre_hooks()

_maybe_warn_non_full_backward_hook(inputs, ...)

_slow_forward(*input, **kwargs)

_wrapped_call_impl(*args, **kwargs)

_call_impl(*args, **kwargs)

_register_state_dict_hook(hook)

Register a post-hook for the state_dict() method.

_save_to_state_dict(destination, prefix, ...)

Save module state to the destination dictionary.

_register_load_state_dict_pre_hook(hook[, ...])

See register_load_state_dict_pre_hook() for details.

_load_from_state_dict(state_dict, prefix, ...)

Copy parameters and buffers from state_dict into only this module, but not its descendants.

_named_members(get_members_fn[, prefix, ...])

Help yield various names + members of modules.

_get_name()

_replicate_for_data_parallel()

marginal_likelihood(root_location, inside_chart, **kwargs)[source]

Compute the marginal data likelihood

as described in more detail here.

Parameters:

  • root_location – location of the root variables

  • inside_chart – chart of inside probabilities

Returns:

marginal likelihood