torchfilter.base._particle_filter_measurement_model

Private module; avoid importing from directly.

Module Contents

Classes

ParticleFilterMeasurementModel	Observation model base class for a generic differentiable particle
ParticleFilterMeasurementModelWrapper	Helper class for creating a particle filter measurement model (states,

class torchfilter.base._particle_filter_measurement_model.ParticleFilterMeasurementModel(state_dim: int)

Bases: abc.ABC, torch.nn.Module

Observation model base class for a generic differentiable particle filter; maps (state, observation) pairs to the log-likelihood of the observation given the state ( \(\log p(z | x)\) ).

state_dim

Dimensionality of our state.

Type:

int

abstract forward(self, *, states: types.StatesTorch, observations: types.ObservationsTorch) → torch.Tensor

Observation model forward pass, over batch size N. For each member of a batch, we expect M separate states (particles) and one unique observation.

Parameters:

• states (torch.Tensor) – States to pass to our observation model. Shape should be (N, M, state_dim).

• observations (dict or torch.Tensor) – Measurement inputs. Should be either a dict of tensors or tensor of size (N, ...).

Returns:

torch.Tensor – Log-likelihoods of each state, conditioned on a corresponding observation. Shape should be (N, M).

class torchfilter.base._particle_filter_measurement_model.ParticleFilterMeasurementModelWrapper(kalman_filter_measurement_model: KalmanFilterMeasurementModel)

Bases: torchfilter.base.ParticleFilterMeasurementModel

Helper class for creating a particle filter measurement model (states, observations -> log-likelihoods) from a Kalman filter one (states -> observations).

Parameters:

kalman_filter_measurement_model (torchfilter.base.KalmanFilterMeasurementModel) – Kalman filter measurement model instance to wrap.

forward(self, *, states: types.StatesTorch, observations: types.ObservationsTorch) → torch.Tensor

Observation model forward pass, over batch size N. For each member of a batch, we expect M separate states (particles) and one unique observation.

Parameters:

• states (torch.Tensor) – States to pass to our observation model. Shape should be (N, M, state_dim).

• observations (torch.Tensor) – Measurement inputs. Should be either a dict of tensors or tensor of size (N, ...).

Returns:

torch.Tensor – Log-likelihoods of each state, conditioned on a corresponding observation. Shape should be (N, M).