api_reference.md

PyTorch BP API Reference

This reference documents the public API exported from bpdecoderplus.pytorch_bp.

UAI Parsing

• read_model_file(path, factor_eltype=torch.float64) -> UAIModel Parse a UAI .uai model file.

• read_model_from_string(content, factor_eltype=torch.float64) -> UAIModel Parse a UAI model from an in-memory string.

• read_evidence_file(path) -> Dict[int, int] Parse a UAI .evid file and return evidence as 1-based indices.

Data Structures

• Factor(vars: List[int], values: torch.Tensor) Container for a factor scope and its tensor.

• UAIModel(nvars: int, cards: List[int], factors: List[Factor]) Holds all model metadata for BP.

Belief Propagation

• BeliefPropagation(uai_model: UAIModel) Builds factor graph adjacency for BP.

• initial_state(bp: BeliefPropagation) -> BPState Initialize messages to uniform vectors.

• collect_message(bp, state, normalize=True) Update factor-to-variable messages in place.

• process_message(bp, state, normalize=True, damping=0.2) Update variable-to-factor messages in place.

• belief_propagate(bp, max_iter=100, tol=1e-6, damping=0.2, normalize=True) Run the full BP loop and return (BPState, BPInfo).

• compute_marginals(state, bp) -> Dict[int, torch.Tensor] Compute marginal distributions after convergence.

• apply_evidence(bp, evidence: Dict[int, int]) -> BeliefPropagation Return a new BP object with evidence applied to factor tensors.