torchfilter.data._single_step_dataset

Private module; avoid importing from directly.

Module Contents

Classes

SingleStepDataset

A dataset interface that returns single-step training examples:

class torchfilter.data._single_step_dataset.SingleStepDataset(trajectories: List[types.TrajectoryNumpy])

Bases: torch.utils.data.Dataset

A dataset interface that returns single-step training examples: (previous_state, state, observation, control)

By default, extracts these examples from a list of trajectories.

Parameters:

trajectories (List[torchfilter.types.TrajectoryNumpy]) – List of trajectories.

__getitem__(self, index: int) → Tuple[types.StatesNumpy, types.StatesNumpy, types.ObservationsNumpy, types.ControlsNumpy]

Get a single-step prediction sample from our dataset.

Parameters:

index (int) – Subsequence number in our dataset.

Returns:

tuple – (previous_state, state, observation, control) tuple that contains data for a single subsequence. Each tuple member should be either a numpy array or dict of numpy arrays with shape (subsequence_length, ...).

__len__(self) → int

Total number of subsequences in the dataset.

Returns:

int – Length of dataset.