ncalab.autostepper

Classes

AutoStepper

Helps selecting number of timesteps based on NCA activity.

Module Contents

class ncalab.autostepper.AutoStepper(min_steps: int = 10, max_steps: int = 100, plateau: int = 5, verbose: bool = False, threshold: float = 0.01)

Helps selecting number of timesteps based on NCA activity.

Parameters:

min_steps (int, optional) – Minimum number of timesteps to always execute, defaults to 10.
max_steps (int, optional) – Terminate after maximum number of steps, defaults to 100.
plateau (int) – Number of steps that is considered a plateau, defaults to 5.
verbose (bool) – Whether to log interruption to stdout, defaults to False.
threshold (float) – Score threshold, defaults to 1e-2.

min_steps = 10

max_steps = 100

plateau = 5

verbose = False

threshold = 0.01

cooldown = 0

hidden_i: torch.Tensor | None = None

hidden_i_1: torch.Tensor | None = None

_score() → torch.Tensor

Calculates activity score.

Method check() uses this score to determine if the NCA is inactive.

Returns:: Activity score estimate.
Return type:: torch.Tensor

_check(step: int) → bool

Checks whether to interrupt inference after the current step.

Parameters:: step (int) – Current NCA inference step.
Returns:: Whether to interrupt inference after the current step.
Return type:: bool

run(nca: ncalab.models.AbstractNCAModel, x)

__call__(*args: Any, **kwargs: Any) → Any