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allaun
1794299a6c
Systematic native_decide → dec_trivial/rfl migration across all Lean modules to comply with AGENTS.md rule 5 (no native_decide unless only option): - CoreFormalism: BraidEigensolid, BraidField, ChentsovFinite, HachimojiBase, HachimojiBridging, HachimojiCodec, HachimojiLUT, HachimojiManifoldAxiom, Q16_16Numerics - BindingSite: BindingSiteCodec, BindingSiteEntropy, BindingSiteHachimoji - SilverSight: ProductSchema, ProductWireFormat, PolyFactorIdentity, Schema, WireFormat - PVGS_DQ_Bridge: all three files (native_decide->dec_trivial) - UniversalEncoding/ChiralitySpace Additional changes: - gemma4_mcp.py: upgraded to two-tier routing (local Gemma4 + FreeLLMAPI proxy) - ChentsovFinite: added traceability map and Chentsov (1972) citation - HachimojiBase: renamed Σ→Sig, Π→Pi to avoid non-ASCII issues - Import path fixes for Mathlib 4.30.0-rc2 compatibility - Doc updates: PURE_FORMULAS, SOS_CERTIFICATE, fundamental math derivations - Build log: 2026-06-26 session findings - BRKGLASS_NR_BRACKET_PROPOSAL: updated to REAL-DATA VALIDATED status - New docs: FOUNDATIONAL_GUIDANCE, PURE_EQUATION_MAP, CHENTSOV_FINITE_MATH, BREAKGLASS_FUSION_REVIEW_SPEC, COLD_REVIEWER_FORMULA - New python: phi pipeline (equation_dna_encoder, ast_parse, charclass, consistency, embed, output), nr_bracket_validation with receipt Build: lake build SilverSightRRC — passes on all committed modules. Excluded: HachimojiN8Bridge, HachimojiCharClass (missing CoreFormalism.HachimojiManifoldAxiom olean — WIP)
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""
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phi.charclass — Layer 1: Character classification → Δ₇ byte histogram
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Each ASCII character maps to 1 of 8 archetypal classes. The histogram
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over these 8 classes is the F(E) feature vector — the first component
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of the Φ embedding.
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Dependencies: none (stdlib only)
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"""
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from __future__ import annotations
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from typing import List
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# ── 8 character classes ──────────────────────────────────────────────────
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# Each class corresponds to a dimension of Δ₇ (the 7-simplex).
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# The classes partition the visible ASCII range into 8 bins.
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CHAR_CLASSES = {
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"digit": 0, # 0-9
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"lower_alpha": 1, # a-z
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"upper_alpha": 2, # A-Z
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"operator": 3, # + - * / ^ % = < > ! & | ~
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"bracket": 4, # ( ) [ ] { }
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"punctuation": 5, # . , ; : ' " @ # $ _ \
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"whitespace": 6, # space, tab, newline
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"other": 7, # everything else (Greek, Unicode math, etc.)
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}
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OPERATOR_CHARS = set("+-*/^%=<>!&|~")
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BRACKET_CHARS = set("()[]{}")
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PUNCT_CHARS = set(".,;:'\"@#$ _\\")
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def classify_char(c: str) -> int:
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"""Classify a single character into 1 of 8 classes (0-7)."""
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if c.isdigit():
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return 0
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if c.isalpha():
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return 1 if c.islower() else 2
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if c in OPERATOR_CHARS:
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return 3
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if c in BRACKET_CHARS:
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return 4
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if c in PUNCT_CHARS:
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return 5
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if c in (" ", "\t", "\n", "\r"):
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return 6
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return 7
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def compute_F(equation: str) -> List[float]:
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"""Compute F(E) — normalized byte-class histogram on Δ₇.
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Returns 8 floats summing to 1.0. This is Layer 1 of the Φ embedding.
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Pure function: no state, no side effects.
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"""
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counts = [0] * 8
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for c in equation:
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counts[classify_char(c)] += 1
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total = sum(counts)
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if total == 0:
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return [1.0 / 8] * 8
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return [c / total for c in counts]
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