sequince.steps.base

sequince.steps.base#

Classes#

FixedLengthMutation

Capability for mutation-like steps accepting a per-call transition count.

SMCStep

Base class for steps with explicit context and output returns.

Functions#

validate_n_steps(n_steps)

Validate a caller-supplied fixed mutation length.

Module Contents#

sequince.steps.base.validate_n_steps(n_steps)[source]#

Validate a caller-supplied fixed mutation length.

Parameters:

n_steps (int)

Return type:

int

class sequince.steps.base.FixedLengthMutation[source]#

Bases: Protocol

Capability for mutation-like steps accepting a per-call transition count.

n_steps is a host-side integer. Backends such as JAX therefore require it to be static while tracing or compiling a call.

apply_with_n_steps(state, context, rng, *, n_steps)[source]#
Parameters:
Return type:

tuple[sequince.state.ParticleState, sequince.steps.context.StepContext, Any]

class sequince.steps.base.SMCStep[source]#

Base class for steps with explicit context and output returns.

Functional steps request immediate stopping through StepContext. The runner conditionally skips later steps, which must have initialized, fixed-structure functional carries under JAX.

abstractmethod apply(state, context, rng)[source]#
Parameters:
Return type:

tuple[sequince.state.ParticleState, sequince.steps.context.StepContext, Any]

apply_functional(state, context, random_state)[source]#

Apply a functional step to particle step.

This returns the updated state, context, next random state, and any additional outputs.

This method is intended for use in functional SMC iterations where the random state is explicitly threaded through the computation.

Parameters:
Return type:

tuple[sequince.state.ParticleState, sequince.steps.context.StepContext, Any, Any]

validate_functional_configuration(config, state)[source]#

Validate host-side pipeline settings before initializing any step.

Parameters:

  • config (SMCConfig) – Read-only runner configuration, including the complete pipeline.

  • state (ParticleState) – Initial particle state, including the population shape.

Return type:

None

Notes

Override to reject incompatible pipeline settings. Metadata and cache preparation belong in the existing functional initialization hooks.

initialize_functional_metadata(state, metadata)[source]#

Return metadata with fields required by apply_functional.

Parameters:
Return type:

collections.abc.Mapping[str, Any]

initialize_functional_state_and_context(state, context)[source]#

Return shape-complete inputs for apply_functional.

Steps whose functional output adds array leaves should initialize matching leaves here so one compiled signature can be reused from the first iteration onward.

Parameters:
Return type:

tuple[sequince.state.ParticleState, sequince.steps.context.StepContext]

state_dict()[source]#

Return mutable state required by subsequent SMC iterations.

Return type:

dict[str, Any]

load_state_dict(state)[source]#

Restore state produced by state_dict().

Parameters:

state (collections.abc.Mapping[str, Any])

Return type:

None