739000e8c6
Standard CRX over-approximates when Hasse diagram is non-linear (24% of RAGSAK packages). Cluster-then-infer groups sequences by (first, last, length), infers per-cluster, picks largest cluster's grammar. Results on RAGSAK: Avg max disjunction: 2.8 → 1.7 (39% tighter) Packages improved: 6/10 Tradeoff: cluster granularity (too coarse = over-approximation, too fine = no generalization). Current: (first, last, length_bucket). Exports crx_refined() and crx_with_confidence() from bex package. 20 new tests. All 199 tests pass.
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
"""
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bex — Paper-faithful implementation of BEX inference algorithms.
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Papers:
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- Bex et al. 2010 (TODS): Inference of Concise Regular Expressions and DTDs
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- Bex et al. 2010 (arXiv 1004.2372): Learning Deterministic Regular Expressions
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Algorithms implemented:
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TODS 2010: 2T-INF, REWRITE, RWR, RWR², RWR₀, CRX
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arXiv 2010: iKoa, Disambiguate, rwr², iDRegEx
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"""
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from .soa import SOA
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from .twotinf import build_soa
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from .rwr0 import rwr0
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from .crx import CRX
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from .ikoa import ikoa
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from .rwrsq import rwr_sq
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from .idregex import idregex
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from .kore import kOREInference, validate_k_ore
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from .koa import KOA, build_complete_koa
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from .expr import concat, disj, star, optional, alphabet, strip_k
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from .marking import mark_koa
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from .tokenizer import YAMLTokenizer
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from .ensemble import infer_ensemble
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from .template import generate_template
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from .reduce import reduce_contexts, soa_distance, build_soa_with_support, minimize_contexts, reduce_and_infer
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from .gbnf import to_gbnf, to_gbnf_with_rules
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from .crx_refined import crx_refined, crx_with_confidence
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__version__ = "0.2.0"
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