pydpf.model_based_api.FilteringModel

class pydpf.model_based_api.FilteringModel(*, dynamic_model: Module | Distribution, observation_model: Module | Distribution, prior_model: Module | Distribution, initial_proposal_model: Module | Distribution | None = None, proposal_model: Module | Distribution | None = None)

Bases: Module

The class for grouping model components to define a state-space model with optional proposal distributions.

Parameters:

dynamic_model:Module|Distribution

The dynamic model.

observation_model:Module|Distribution

The observation model.

prior_model:Module|Distribution

The prior model.

initial_proposal_model:Module|Distribution|None. Default None

The initial proposal model.

proposal_model:Module|Distribution|None. Default None

The proposal model.

Notes

Any of the model components can be Distribution objects. However, distributions defined by Distribution objects can only be dependent on at most one variable. That variable is: For the prior_model and initial_proposal_model: None. For the dynamic_model and proposal_model: prev_state. For the observation_model: state.

If the components are not Distributions then they must be Modules with that will be accessed through specific methods. Not all of these methods need to be defined depending on the use case. Let B be the size of the batch dimension, K be the size of the particle dimension, D-x be the size of the inherent dimension for data type x. Starred (*) arguments are always passed, unstarred arguments are only passed if they exist.

prior_model:

log_density() Parameters: *state, time, control, series_metadata. Output: the probability density of the state, Tensor of size (B X K). Required for non-bootstrap filtering.

sample() Parameters: *batch_size := B, *n_particles := K, time, control, series_metadata. Output a sample from the prior, Tensor of size (B X K X D-state). Required for data generation and bootstrap filtering.

dynamic_model:

log_density() Parameters: *prev_state, *state, prev_time, time, control, series_metadata, *t. Output: the probability density of the state, Tensor of size (B X K). Required for non-bootstrap filtering.

sample() Parameters: *prev_state, prev_time, time, control, series_metadata, *t. Output: a sample from the dynamic model, Tensor of size (B X K X D-state). Required for data generation and bootstrap filtering.

observation_model:

score() Parameters: *state, *observation, prev_time, time, control, series_metadata, *t. Output: The score of an observation given the latent state, usually the log-density, Tensor fo size (B X K). Required for filtering.

sample() Parameters: *state, prev_time, time, control, series_metadata, *t. Output: a sample from the dynamic model, Tensor of size (B X K X D-observation). Required for data generation.

inital_proposal_model:

log_density() Parameters: *state, *observation, time, control, series_metadata. Output: the probability density of the state under the initial proposal, Tensor of size (B X K). Required for non-bootstrap filtering.

sample() Parameters: *batch_size := B, *n_particles := K, *observation, time, control, series_metadata. Output a sample from the initial proposal, Tensor of size (B X K X D-state). Required non-bootstrap filtering.

proposal_model:

log_density() Parameters: *prev_state, *state, *observation, prev_time, time, control, series_metadata, *t. Output: the probability density of the state under the proposal, Tensor of size (B X K). Required for non-bootstrap filtering.

sample() Parameters: *prev_state, *observation, prev_time, time, control, series_metadata, *t. Output: a sample from the proposal model, Tensor of size (B X K X D-state). Required for non-bootstrap filtering.

We check that the components have the components required for SIRS particle filtering. Additional components may be required depending on the loss function or when generating data and these will not be caught.

__init__(*, dynamic_model: Module | Distribution, observation_model: Module | Distribution, prior_model: Module | Distribution, initial_proposal_model: Module | Distribution | None = None, proposal_model: Module | Distribution | None = None)

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

Methods

__init__(*, dynamic_model, ...[, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward()
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.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
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.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
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.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
update()	Update all constrained_parameters and cached_properties belonging to this Module.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
has_initial_proposal	True if the model has an initial_proposal_model
has_proposal	True if the model has a proposal_model
is_bootstrap	True if the model doesn't have either an initial_proposal_model or proposal_model
training

property has_initial_proposal

True if the model has an initial_proposal_model

property has_proposal

True if the model has a proposal_model

property is_bootstrap

True if the model doesn’t have either an initial_proposal_model or proposal_model