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allaun f0466be09c docs(compression): add pi-as-tape-LUT coda (offset = data size, base conversion) 
Measured on real pi (1e6 digits): first-occurrence position of a k-digit string
~10^k, so the offset needs ~k digits — same size as the data, slope 1. BBP gives
pi free random access (never store the tape) but the address still carries all
the bits. Closes the findings doc on the cleanest single proof of the
base-conversion law. Adds scripts/compression/pi_tape_lut.py.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-03 15:56:01 -05:00
..
ac_roundtrip.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00
entropy_full.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00
frozen_model_compress.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00
gw_honest_test.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00
mass_number_compress.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00
pi_tape_lut.py docs(compression): add pi-as-tape-LUT coda (offset = data size, base conversion) 2026-07-03 15:56:01 -05:00
README.md docs(compression): add pi-as-tape-LUT coda (offset = data size, base conversion) 2026-07-03 15:56:01 -05:00
spectral_vs_empirical.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00
superposition_recovery.py docs(compression): honest-findings writeup + reproducible scripts 2026-07-03 15:52:27 -05:00

README.md

Compression — honest-measurement scripts

Reproducible experiments behind docs/research/COMPRESSION_HONEST_FINDINGS.md. Every claim is checked with a real coder and a byte-exact lossless round-trip; no straw baselines. Python 3 + numpy only (stdlib lzma/gzip for baselines).

script what it measures
spectral_vs_empirical.py Wall 1: rank-k reconstruction of the byte transition matrix never beats raw counts
entropy_full.py order-0/1/2 empirical entropy of a corpus (adaptive-AC target rate)
ac_roundtrip.py real Witten–Neal–Cleary order-2 arithmetic coder; lossless round-trip + bits/byte
gw_honest_test.py GW ringdown parametric fit (handed TRUE params) vs LPC/xz across SNRs — the "583×" gate
frozen_model_compress.py conservation law: frozen model + AC, model-column vs tape-column, self-contained vs amortized
mass_number_compress.py Semantic Mass Number = base conversion (bijection), M1/M2/M3 vs xz
superposition_recovery.py capstone: compressed-sensing recovery vs sparsity — recoverable ⟺ sparse cliff
pi_tape_lut.py π as a tape LUT: offset-into-π (real π digits) is the same size as the data — base conversion

Most default to a local text corpus path; pass a directory as argv[1] to point elsewhere. entropy_full.py/ac_roundtrip.py expect an enwik8 file (re-fetch from mattmahoney.net/dc/enwik8.zip).