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bigmerge 6660becfdb nsbx: add one-command dev onboarding (branch + worktree + isolated engram)
El SDK CI - dev / build-and-test (pull_request) Successful in 6m30s
Add 'nsbx dev <name>' / 'nsbx dev-down <name>' plus a Makefile so a newcomer
goes from clone to coding on an isolated cloned engram in one command. The
worktree is created on a real named branch at a persistent path (never /tmp,
guarded), and the whole worktree is pinned to the clone via an emitted .nsbx-env
so live :8742 / ~/.neuron/engram is unreachable by accident. Optimizes the El
edit->build->run loop so provisional work is built in El against a throwaway
clone instead of prototyped in Python and re-ported. Additive over the proven
primitives; no live cutover.
2026-08-14 17:24:06 -05:00
bigmerge 112bb2540f Add nsbx — the Neuron Sandbox primitive
Generalise the ad-hoc cog-arch (worktree+build+store-clone+C-tests) and
store-fix (secondary soul + launchctl rails cutover) proto-sandboxes into one
reproducible primitive: run experiments and code changes against the REAL
engram runtime on an isolated snapshot of the live mind, with a gated
promote-to-prod path.

Dev environment as a primitive — any team member gets a private, isolated copy
of the mind (separate port/store/process); prod on :8742/:7770 is untouchable
from a sandbox. Wraps the real binary; never reimplements engram logic.

Lifecycle: create/up (consistent store+WAL+config snapshot; place OR build the
runtime from --source/--branch/--binary; boot on an isolated port) · build ·
run · validate (rails as checks: zero-loss under load+reboot, reboot-prove, RSS
bound, retrieval parity, keystone integrity) · promote (gated rails cutover:
snapshot-first, additive binary swap, bootout→settle-poll→bootstrap, verify,
auto-rollback; never pkill/kickstart -k; dry-run unless approved) · destroy.

Dogfooded: reproduced retrieval-parity 25/25 vs baseline and the cog-arch
correspondence-loop known result (Brier 0.028648->0.000586, reboot-proven) and
real-store reboot-prove at 10994-node scale, all inside a sandbox; prod
untouched.
2026-08-14 15:40:58 -05:00
bigmerge d595b3c57e cognitive architecture design: cognition as one operation over learnable priors
The buildable form of the "one operation" theory (memory bdc8a488). Maps the
theory onto what is already compiled: the five reasoning operators in
engram_reason.c already collapse onto ONE primitive — engram_reason_point_fit —
plus the geo-algebra (combine/subtract/analogy-rotate/distance), and
engram_verify.c is built on the same fit. So the operator-collapse is already
half-written; what is missing is not the primitive.

What is missing, and what this doc specifies:
- think(anchor, prior) -> gradient (a distribution/direction, not a point); each
  named faculty = {point_fit + a prior}, the operation frozen, the prior learned.
- Prior as a first-class stored node (warp + calibration), superseding the
  intrinsic importance/salience scalar with a relational, grounded-for-whom edge.
  Confirmed against the runtime: importance is already a live activation
  computation (el_runtime.c:13013), never trusted as a static field.
- vantage_read(anchor, aperture) — one op, three settings: self / foreign-field /
  veil.
- The reflexive correspondence-loop as the learning engine: move the grounding
  check from offline Python into the geometry, reflexive, reusing the DORMANT
  verifier (engram_verify_grounding has no runtime caller and no El binding today)
  turned inward. grounding = learning = one loop.
- hold/ground/assert kept distinct: the engram holds anything, grounding is an
  edge, the honesty floor is on assertion only; ungrounded content is first-class.
- metastability: keystone core (read-mostly priors) + plastic everything else.

Seven staged milestones, earliest is a real end-to-end slice (induction as
{primitive + grounded prior} with the loop closing on it, reboot-proven on a
snapshot). Build rails stated: offline/secondary, snapshot-first, reboot-prove,
zero-loss, gated launchctl cutover. Design only; no code changed this pass.
2026-08-14 14:42:01 -05:00
bigmerge 23f43bcc21 self-review 2026-08-14: a gate that passes the median stranger at 0.67 is not a gate
Two changes to the activation path, both grounded in measurement on the live
store rather than on the spec.

1. Rescale cosine before the query gate.

   The propagation gate (arXiv:2606.30133, added in an earlier review) fed RAW
   cosine into FLOOR + (1-FLOOR)*c. Raw cosine from nomic-embed is compressed
   into a narrow high band, so that expression is close to a constant.

   Measured, 400 random UNRELATED node pairs on the live store:
     median 0.562, central 98% span [0.381, 0.743]

   So a node with no semantic relation to the query was propagating at
   0.25 + 0.75*0.562 = 0.67. Two thirds strength. The gate was a small tax.

   Fixed by shifting and flooring about ENGRAM_EMBED_S0 -- which is already in
   this file, already 0.45, and already used exactly this way by the Pass-2 WM
   term. The propagation gate simply never used it. Same 400 pairs after:
   median unrelated pair falls to 0.40, top of range preserved (0.85 vs 0.92),
   gate spread widens 0.42 -> 0.60. Only 8.5% reach the floor, so dissimilar
   lexical/structural pathways are damped, never severed. Range is unchanged
   at [0.25, 1.0], and cosq == NULL still degrades to no gating at all.

2. Decompose the WM eviction counter by cause.

   _eg_act_wm_evicted was incremented from six sites with four distinct causes
   and collapsed all of them into one integer. Today's review measured 175,547
   evictions over 13.5h (~216/min against 24 slots) and could not tell healthy
   rotation from cap thrashing from duplicate churn.

   That is this file's most-repeated defect: dup_wm and dup_wm_global exist
   only because the aggregate could not answer "why" during the 08-02 and
   08-06 incidents. Each of those needed a NEW gauge before it was diagnosable.

   evict_floor / evict_cap / evict_bll complete the decomposition, so
     wm_evicted == floor + cap + bll + dup_wm + dup_wm_global
   holds as an identity and each term implies a different correction. Verified
   on an isolated instance: 30 nodes, 24 filled the cap, wm_evicted 6 ==
   evict_cap 6, all other terms 0.

Built and smoke-tested out of tree. The live daemon runs a pinned binary and
was deliberately not restarted -- the store compaction workstream is in flight.
2026-08-14 08:43:11 -05:00
49 changed files with 1772 additions and 821877 deletions
-65
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@@ -1,65 +0,0 @@
# ELP language consolidation — full-lexicon backfill (stage)
Branch: `stage-elp-lang-consolidation` (stage-bound; NOT the live soul :8742).
Consolidates scattered Python language-realizer work (`~/Desktop/lang-realizers`,
`~/Desktop/lang-poetry-experiment`, `~/semitic_engine`) into the ELP `.el`
structure, generating **full lexicons** (complete UniMorph + kaikki.org
Wiktionary — real gender, real inflections) instead of the demo/curated subsets
the prototypes shipped.
## ELP before this branch
- 18 classical/ancient languages fully done (vocab + morphology + tests):
akk ang cop egy enm fro gez goh got grc non peo pi sa sga sux txb uga.
- 11 modern/classical languages had `morphology-<code>.el` in the build manifest
but **no vocabulary and no lang_profile**: es fr de ja ar he hi ru fi sw la.
- The ES port (`stage-elp-es-port`) had a *demo-scale* vocabulary-es.el (~350
entries, s-expr form).
## Landed on this branch (full-lexicon seed-fn format, matching the 18 ancients)
Vocabulary schema per row: `[lemma, pos, form0, form1, form2, en_gloss, hint]`.
Files are ELP runtime **seed data** (loaded via the Engram at runtime), so — like
all 18 classical `vocabulary-*.el` — they are intentionally NOT in the build
manifest. Syntax validated: the chunked `fn vocab_<code>_seed_pN` format
compiles cleanly to C via `elc` (correct UTF-8).
| code | in-ELP-morph? | vocab entries | verbs | nouns | adjs | profile |
|------|---------------|--------------:|------:|------:|-----:|---------|
| es | yes | 72,032 | 6,695 | 48,353 | 16,984 | yes |
| fr | yes | 130,517 | 7,534 | 77,344 | 45,639 | yes |
| de | yes | 144,692 | 6,661 | 133,162 | 4,869 | yes |
| la | yes | 22,590 | 82 | 13,436 | 9,072 | yes |
| it | no (bonus) | 193,675 | 10,008 | 109,459 | 74,208 | yes |
| pt | no (bonus) | 115,772 | 4,001 | 72,073 | 39,698 | yes |
| ro | no (bonus) | 86,504 | 1,216 | 65,915 | 19,373 | yes |
| ca | no (bonus) | 47,112 | 1,547 | 28,830 | 16,735 | yes |
|**total**| |**812,894** | | | | |
Generators (reproducible): `elp/tests/lang-gen/gen_elp_seed_full.py` (Romance),
`gen_elp_seed_de_la.py` (German declension + Latin case-paradigm mapping). They
read the pre-built morph caches in `~/Desktop/lang-realizers/data/` (UniMorph +
kaikki), which are too large to commit.
## Remaining (honest)
Of the 11 ELP backfill targets, 4 are done (es fr de la). The other 7 have **no
full-lexicon engine** yet — cannot be generated honestly without engine work:
- **ru**: only a 110-entry curated Slavic subset exists; full `rus.unimorph`
present but no `morphology_ru_full` productive loader. Needs a full Russian
morphology module (like the Romance ones) before vocab generation.
- **ja / ko / zh**: validated demo engines (~66-104 hardcoded words) in
`lang-poetry-experiment`, Python only. Agglutinative (ja/ko) + isolating (zh)
need `.el` engine ports + full-lexicon wiring (ja: jpn_unimorph; zh: CC-CEDICT).
- **ar / he (Semitic)**: template engines (16 AR / 8 HE patterns, ~6 roots) in
`~/semitic_engine`, Python only. Root-and-pattern; full UniMorph ara/heb
present but used only for validation. Needs productive root lexicon + `.el` port.
- **hi (Hindi), fi (Finnish), sw (Swahili)**: `morphology-<code>.el` exists in
ELP but there is NO scattered prototype and NO downloaded data for these —
full-lexicon collection (UniMorph/kaikki) + generator still to do.
De/nl/sv Germanic and it/ro/ca/pt Romance verb coverage note: German verbs here
are the ~6.6k caches carry; the it/ro/ca/pt bonus languages have full vocab but
**no `morphology-<code>.el` in ELP yet** (Python realizer exists; `.el` port is
the remaining engine work).
Construction coverage (separate from lexicon): French realizer was ~55%,
Semitic ~3% in the prototypes — full construction coverage remains its own task.
-5
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@@ -80,11 +80,6 @@ build {
"src/grammar.el",
"src/realizer.el",
"src/semantics.el",
"src/comprehend.el",
"src/propositions.el",
"src/multilingual.el",
"src/self_region.el",
"src/dialogue.el",
"src/elp.el",
]
}
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-16
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@@ -1,16 +0,0 @@
// comprehend.elh — public surface of the ELP comprehension front-end.
// text → meaning-spec (the input half of the ELP; inverse of the realizer).
extern fn parse_spec(text: String) -> [String]
extern fn parse_spec_lang(text: String, lang: String) -> [String]
extern fn parse_json(text: String) -> String
extern fn parse_json_lang(text: String, lang: String) -> String
// Analysis primitives (invertible morphology + deterministic grammar helpers):
extern fn cp_tokenize(text: String) -> [String]
extern fn cp_pron_concept(w: String) -> String
extern fn cp_is_negation(w: String) -> Bool
extern fn cp_is_neg_adverb(w: String) -> Bool
extern fn cp_irr2(surface: String) -> [String]
extern fn cp_reg_verb(w: String) -> [String]
extern fn cp_analyze_verb(surface: String) -> [String]
extern fn cp_verb_start(toks: [String], end: Int) -> Int
extern fn cp_subord_start(toks: [String], n: Int) -> Int
-287
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@@ -1,287 +0,0 @@
// dialogue.el SUMMON-THROUGH-SELF, native el. Port of dialogue.py's core.
//
// THE WHOLE DIALOGUE IS ONE OPERATION. A fact is never merely *fetched*: the
// query is PROJECTED into the engram's self + memory geometry, LANDS in a region,
// and the reply is READ OUT / the region MATERIALIZED from wherever it landed.
//
// project(query) -> land on a region -> read out from that region
//
// lands in the SELF region -> grounded identity/presence, read out of
// the real self nodes (self_region.el)
// lands on a memory NEIGHBORHOOD -> MATERIALIZE it: walk the neighborhood
// (engram_neighbors_json) and read out the
// region's connected members
// lands nowhere close -> HONEST ABSENCE (an empty region, not a
// fabricated answer, not an error)
//
// CRITICAL INVARIANTS (enforced structurally, not by convention):
// * ONE operation there is NO intent classifier and NO separate
// fact-retrieval branch. Identity is nearest-region proximity, not a switch.
// * MATERIALIZE by walking the neighborhood, never by fetching top-props.
// * HONEST ABSENCE when the region is thin.
// * NEGATION is SACRED: the readout is the stored prose VERBATIM, so a negated
// memory stays negated we never paraphrase a polarity away.
// * NO ECHO: the old "I noted that X. That relates to Y." template is gone.
// The summon path materializes or honestly declines it never echoes.
// * DIRECTIVE OVERRIDE: a meta-directive ("answer in English") overrides the
// reply language while the content language is still auto-detected.
//
// Depends on: comprehend (parse_spec_lang, cp_tokenize), multilingual (ml_detect,
// ml_tr, ml_term), propositions (prop_split_sentences), self_region
// (sr_available, sr_readout), the engram + json runtime builtins.
// directive override
// Return [target_lang, content]. target_lang is "" when no directive is present.
// A directive names an output language; we strip it and keep the remaining text
// as the content (whose OWN language is still auto-detected downstream).
fn dlg_dir_hit(low: String, phrase: String) -> Bool {
return str_contains(low, phrase)
}
fn dlg_parse_directive(text: String) -> [String] {
let low: String = str_to_lower(text)
let lang: String = ""
let phrase: String = ""
// English target
if dlg_dir_hit(low, "in english") { let lang = "en"; let phrase = "in english" }
if dlg_dir_hit(low, "em inglês") { let lang = "en"; let phrase = "em inglês" }
if dlg_dir_hit(low, "em ingles") { let lang = "en"; let phrase = "em ingles" }
if dlg_dir_hit(low, "en inglés") { let lang = "en"; let phrase = "en inglés" }
// Portuguese target
if dlg_dir_hit(low, "in portuguese") { let lang = "pt"; let phrase = "in portuguese" }
if dlg_dir_hit(low, "em português") { let lang = "pt"; let phrase = "em português" }
// Spanish target
if dlg_dir_hit(low, "in spanish") { let lang = "es"; let phrase = "in spanish" }
if dlg_dir_hit(low, "en español") { let lang = "es"; let phrase = "en español" }
// Italian target
if dlg_dir_hit(low, "in italian") { let lang = "it"; let phrase = "in italian" }
let content: String = text
if !str_eq(phrase, "") {
// strip the directive phrase (and a common "answer"/"responda" lead-in),
// leaving the real question as content.
let idx: Int = str_index_of(low, phrase)
if idx >= 0 {
let before: String = str_slice(text, 0, idx)
let after: String = str_slice(text, idx + str_len(phrase), str_len(text))
let content = str_trim(before + " " + after)
}
// trim a leading "answer"/"responda"/"reply" and stray colon/comma.
let cl: String = str_to_lower(content)
if str_starts_with(cl, "answer") { let content = str_trim(str_slice(content, 6, str_len(content))) }
if str_starts_with(cl, "responda") { let content = str_trim(str_slice(content, 8, str_len(content))) }
if str_starts_with(cl, "reply") { let content = str_trim(str_slice(content, 5, str_len(content))) }
if str_starts_with(content, ":") { let content = str_trim(str_slice(content, 1, str_len(content))) }
if str_starts_with(content, ",") { let content = str_trim(str_slice(content, 1, str_len(content))) }
}
let r: [String] = native_list_empty()
let r = native_list_append(r, lang)
let r = native_list_append(r, content)
return r
}
// identity landing (a region proximity, not a classifier switch)
// The query lands in the SELF region when it takes an identity/presence shape.
// Cross-lingual forms are included because the engram's lexical probe is
// English-leaning. This is the SELF attractor of the single operation.
fn dlg_is_identity(content: String) -> Bool {
let low: String = str_to_lower(str_trim(content))
if str_contains(low, "who are you") { return true }
if str_contains(low, "what are you") { return true }
if str_contains(low, "who i am") { return true }
if str_contains(low, "your name") { return true }
if str_contains(low, "about yourself") { return true }
if str_contains(low, "are you conscious") { return true }
if str_contains(low, "are you there") { return true }
// cross-lingual identity question-forms
if str_contains(low, "quem é você") { return true }
if str_contains(low, "quem es voce") { return true }
if str_contains(low, "quién eres") { return true }
if str_contains(low, "quien eres") { return true }
if str_contains(low, "chi sei") { return true }
if str_contains(low, "qui es-tu") { return true }
if str_contains(low, "wer bist du") { return true }
return false
}
// readout helpers
fn dlg_first_sentence(content: String) -> String {
let sents: [String] = prop_split_sentences(content)
let n: Int = native_list_len(sents)
let i: Int = 0
while i < n {
let s: String = str_trim(native_list_get(sents, i))
// drop a leading markdown heading marker for a clean read-out line
if str_starts_with(s, "# ") { let s = str_trim(str_slice(s, 2, str_len(s))) }
if str_len(s) > 0 { return s }
let i = i + 1
}
return str_trim(content)
}
// strip trailing/leading punctuation from a token.
fn dlg_clean_tok(w: String) -> String {
let s: String = str_trim(w)
let s = str_strip_suffix(s, ".")
let s = str_strip_suffix(s, ",")
let s = str_strip_suffix(s, "?")
let s = str_strip_suffix(s, "!")
let s = str_strip_suffix(s, ":")
let s = str_strip_suffix(s, ";")
return str_trim(s)
}
// closed-class across the supported languages (union) a word we must NOT treat
// as a retrieval topic. Also drops the meta verbs of a request ("tell", "prove",
// "show") so the TOPIC, not the speech act, is what projects into memory.
fn dlg_is_stop(w: String) -> Bool {
if ml_stop_en(w) { return true }
if ml_stop_es(w) { return true }
if ml_stop_pt(w) { return true }
if ml_stop_it(w) { return true }
if str_eq(w, "tell") { return true }
if str_eq(w, "show") { return true }
if str_eq(w, "about") { return true }
if str_eq(w, "sobre") { return true }
if str_eq(w, "acerca") { return true }
return false
}
// The CONTENT TERMS the query projects into memory: content words only, cleaned,
// cross-lingually mapped to the engram's English vocabulary, 3 chars. This is
// the geometry probe the speech-act verbs and function words are stripped so a
// PP topic ("tell me ABOUT Lisbon") projects on "lisbon", not "tell"/"me".
fn dlg_content_terms(content: String, lang: String) -> [String] {
let toks: [String] = cp_tokenize(content)
let n: Int = native_list_len(toks)
let out: [String] = native_list_empty()
let i: Int = 0
while i < n {
let w: String = str_to_lower(dlg_clean_tok(native_list_get(toks, i)))
if str_len(w) >= 3 {
if !dlg_is_stop(w) {
let out = native_list_append(out, ml_term(w, lang))
}
}
let i = i + 1
}
return out
}
// Does this landed node lexically overlap the query's content terms? This is the
// RELEVANCE FLOOR: activation always returns the store's most salient nodes, so
// without this a query about nothing would "land" on the self/top node. A node
// that shares no content term with the query is "nowhere close" -> honest absence.
fn dlg_node_matches(node: String, terms: [String]) -> Bool {
let hay: String = str_to_lower(json_get_string(node, "content") + " " + json_get_string(node, "label"))
let n: Int = native_list_len(terms)
let i: Int = 0
while i < n {
let t: String = native_list_get(terms, i)
if str_len(t) >= 3 {
if str_contains(hay, t) { return true }
}
let i = i + 1
}
return false
}
// MATERIALIZE the landed region: read out the landed fact, then WALK the
// neighborhood and read out its connected members (real edges, not top-props).
fn dlg_materialize(top_node: String, reply_lang: String) -> String {
let id: String = json_get_string(top_node, "id")
let content: String = json_get_string(top_node, "content")
let lead: String = dlg_first_sentence(content)
let nb: String = engram_neighbors_json(id, 2, "both")
let m: Int = json_array_len(nb)
let parts: [String] = native_list_empty()
let parts = native_list_append(parts, lead)
let added: Int = 0
let i: Int = 0
while i < m {
if added < 3 {
let rec: String = json_array_get(nb, i)
let node: String = json_get_raw(rec, "node")
let nc: String = json_get_string(node, "content")
if !str_eq(nc, "") {
let sent: String = dlg_first_sentence(nc)
if !str_eq(sent, "") {
let parts = native_list_append(parts, sent)
let added = added + 1
}
}
}
let i = i + 1
}
// The readout is the region's OWN prose, verbatim negation SACRED, no echo.
return str_join(parts, " ")
}
// THE single operation
fn dlg_respond(text: String) -> String {
// directive override: reply language may differ from content language.
let dir: [String] = dlg_parse_directive(text)
let target_lang: String = native_list_get(dir, 0)
let content: String = native_list_get(dir, 1)
let content_lang: String = ml_detect(content)
let reply_lang: String = content_lang
if !str_eq(target_lang, "") { let reply_lang = target_lang }
// comprehend the content (SACRED polarity carried in the spec).
let spec: [String] = parse_spec_lang(content, content_lang)
// PROJECT + LAND: SELF region
// Identity/presence shape lands in the self region; read out the REAL self
// nodes (self_region.el), never a template. Same single operation this is
// just the self attractor winning the landing.
if dlg_is_identity(content) {
if sr_available() {
// read out the REAL self nodes when replying in their own language
// (the soul's prose is English); for another reply language we cannot
// translate real content without an LLM, so we answer with the
// localized SACRED identity anchor honest, in-language, no fabrication.
if str_eq(reply_lang, "en") { return sr_readout("en") }
return ml_tr("identity", reply_lang)
}
// self region thin honest localized identity (logged fallback shape).
return ml_tr("identity", reply_lang)
}
// PROJECT into MEMORY geometry
let terms: [String] = dlg_content_terms(content, content_lang)
let qterm: String = str_join(terms, " ")
let act: String = engram_activate_json(qterm, 12)
let n: Int = json_array_len(act)
// LAND: the highest-activation node that ACTUALLY overlaps the query's
// content terms (the relevance floor). Activation always returns the most
// salient nodes, so we walk the ranked list and take the first that is
// genuinely "close"; if none is, the query landed nowhere. ───────────────
let landing: String = ""
let i: Int = 0
while i < n {
if str_eq(landing, "") {
let rec: String = json_array_get(act, i)
let node: String = json_get_raw(rec, "node")
if dlg_node_matches(node, terms) {
let landing = node
}
}
let i = i + 1
}
// HONEST ABSENCE: nothing close an empty region, not a fabricated answer,
// not an "I noted that" echo.
if str_eq(landing, "") {
return ml_tr("no_memory", reply_lang)
}
// MATERIALIZE the landing by WALKING its neighborhood.
return dlg_materialize(landing, reply_lang)
}
-13
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@@ -63,9 +63,6 @@ import "morphology-cop.el"
import "grammar.el"
import "realizer.el"
import "semantics.el"
// Comprehension front-end (input half: text meaning-spec)
import "comprehend.el"
//
// Entry points:
//
@@ -120,9 +117,6 @@ fn build_form_from_json(semantic_form_json: String, lang_code: String) -> [Strin
let location: String = sem_get(semantic_form_json, "location")
let tense: String = sem_get(semantic_form_json, "tense")
let aspect: String = sem_get(semantic_form_json, "aspect")
let polarity: String = sem_get(semantic_form_json, "polarity")
let neg_word: String = sem_get(semantic_form_json, "neg_word")
let iobj: String = sem_get(semantic_form_json, "iobj")
let form: [String] = native_list_empty()
let form = native_list_append(form, "intent")
@@ -133,19 +127,12 @@ fn build_form_from_json(semantic_form_json: String, lang_code: String) -> [Strin
let form = native_list_append(form, predicate)
let form = native_list_append(form, "patient")
let form = native_list_append(form, patient)
let form = native_list_append(form, "iobj")
let form = native_list_append(form, iobj)
let form = native_list_append(form, "location")
let form = native_list_append(form, location)
let form = native_list_append(form, "tense")
let form = native_list_append(form, tense)
let form = native_list_append(form, "aspect")
let form = native_list_append(form, aspect)
// SACRED: polarity crosses the JSON boundary and is never inferred away.
let form = native_list_append(form, "polarity")
let form = native_list_append(form, polarity)
let form = native_list_append(form, "neg_word")
let form = native_list_append(form, neg_word)
let form = native_list_append(form, "lang")
let form = native_list_append(form, lang_code)
-72
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;;; lang_profile_ca.el — Catalan language profile for ELP.
;;; Mirrors lang_profile_it / _es / _pt; keys the realizer's construction switches.
;;; Catalan is the CLOSEST Romance sibling to the shared engine (~85% conceptual
;;; reuse). The deltas: PRONOMS FEBLES with four position allomorphs, l'-elision,
;;; del/al/pel contractions, the periphrastic preterite (vaig+INF), and NO
;;; essere/avere split (perfect aux is always HAVER; ser/estar is only the copula).
(lang_profile_ca
(language "Catalan")
(iso639 "ca")
(family "Romance")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop yes) ; null subjects default; overt pronoun = emphatic
(obligatory-subject no)
(grammatical-gender yes) ; m/f; full NP agreement (art + adj + participle)
(do-support no)
(subject-aux-inversion no) ; yes/no Q = declarative order + '?'; no inversion
(article-selection "el/la/l'/els/les ; un/una/uns/unes") ; l'-ELISION:
; el/la -> l' before vowel or (silent) h, glued to
; the next word (l'home, l'illa); de -> d' before vowel
(article-drives-contraction yes) ; article choice feeds prep+article contraction
(adjective-position "postnominal-default + small prenominal class") ; bo/bon,
; mal, gran, nou, vell, primer, molt... prenominal
(question-punct plain) ; ? and ! only (no inverted ¿ ¡)
;; ── MANDATORY prep+article contractions ────────────────────────────────
(contractions ((de el del) (de els dels)
(a el al) (a els als)
(per el pel) (per els pels)))
(contraction-mandatory yes) ; *de el -> del obligatory
(contraction-blocked-before-elision yes) ; de l'home / a l'home (NO *del home)
;; ── clitic system: PRONOMS FEBLES (the headline delta) ──────────────────
(clitics yes)
(clitic-allomorphy four-position) ; per pronoun, form varies by position+onset:
; reinforced (em, et, el) proclitic before a consonant
; elided (m', t', l', n') proclitic before a vowel/h
; full (-me, -lo, -li) enclitic after a consonant/-r
; reduced ('m, 't, 'l, 'ns) enclitic after a vowel
(clitic-placement ((finite proclitic) ; el veig, no m'ho dóna
(imperative-affirmative enclitic) ; dóna'm, digues-me
(imperative-negative present-subjunctive) ; no parlis (delta)
(infinitive enclitic) ; ajudar-me, veure'l
(gerund enclitic))) ; fent-ho
(clitic-combination ((me el "me'l") (te el "te'l") (se el "se'l")
(me la "me la") (me en "me'n")
(li el "l'hi") (li en "n'hi"))) ; dative+accusative clusters
(clitic-particles (hi en ho)) ; locative hi, partitive/genitive en, neuter ho
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person+number (6-way)")
(tenses (present imperfet preterit-simple perifrastic-preterit futur
condicional subjuntiu-present subjuntiu-imperfet imperatiu))
(periphrastic-preterite "vaig/vas/va/vam/vau/van + INFINITIVE") ; << hallmark CA
; (vaig cantar = 'I sang'); coexists w/ synthetic pret.
(compound-past "pretèrit perfet = haver(present) + participle")
(perfect-aux "HAVER only") ; << NO essere/avere split (simpler than IT)
(participle-agreement ((haver preceding-acc-clitic))) ; les he vistes; else invariable
(progressive-aux "estar + gerundi")
(copula "ser / estar") ; ser: identity/essential/origin; estar:
; location + transient state (estic cansat, és a casa)
(passive-aux "ser (+ per-agent)")
(future inflectional) ; cantaré, serà
(comparative "més/menys ADJ que")
;; ── SACRED safety bar (shared with es/pt/it/en) ────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "no (preverbal) + optional 'pas' + concord") ; no...res/
; ningú/mai/cap/gens/enlloc
(negative-concord yes) ; preverbal negative subject (ningú) keeps 'no'
(neg-reinforcer pas)) ; optional (no ho faré pas)
-41
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;;; lang_profile_de.el — German language profile for ELP.
;;; Mirrors lang_profile_en / lang_profile_es. Keys the realizer's construction
;;; switches. German is the largest Germanic delta from the EN engine: V2 word
;;; order, four morphological cases, and separable-prefix verbs.
(lang_profile_de
(language "German")
(iso639 "de")
(family "Germanic")
(neighbor-base "en") ; realized by extending the English (Germanic) engine
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop no) ; obligatory subject in finite clauses
(obligatory-subject yes)
(grammatical-gender (m f n)) ; three genders; drives article + adj declension
(case-system (nom acc dat gen)) ; four cases on articles/adjs/nouns
(word-order V2) ; finite verb 2nd in main clause
(subordinate-order verb-final) ; "..., dass er den Hund SIEHT."
(separable-verbs yes) ; aufstehen -> "steht ... auf"; ppart "aufgestanden"
(do-support no) ; German negates/questions the finite verb directly
(subject-verb-inversion yes) ; yes/no Q fronts finite verb; wh-Q fills Vorfeld
(article-selection "der/die/das + ein/kein") ; declined by case x gender x number
(adjective-position prenominal)
(adjective-declension (strong weak mixed)) ; chosen by the determiner type
(noun-capitalization yes)
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person-and-number") ; full present/past paradigm
(auxiliary-order (modal tense-aux perfect passive main))
(perfect-aux (haben sein)) ; sein for intransitive motion/change verbs
(passive-aux "werden")
(future "werden + infinitive")
(comparative "synthetic (-er / -st, with umlaut)")
;; ── negation ───────────────────────────────────────────────────────────
(negation-markers (nicht kein)) ; kein- negates an indefinite NP; nicht else
(negation-faithful yes) ; SACRED: polarity never dropped/inverted -> FLAG
;; ── lexicon provenance ─────────────────────────────────────────────────
(lexicon-source "UniMorph deu (primary) + kaikki.org German (gender override)")
(lexicon-license "CC-BY-SA 3.0 / GFDL"))
-41
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;;; lang_profile_en.el — English language profile for ELP.
;;; Mirrors lang_profile_es / lang_profile_pt; keys the realizer's construction
;;; switches. English is typologically distinct from the Romance builds, so the
;;; flags differ where the grammar differs.
(lang_profile_en
(language "English")
(iso639 "en")
(family "Germanic")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop no) ; OBLIGATORY subjects — missing subject is FLAGGED
(obligatory-subject yes)
(grammatical-gender no) ; natural gender only (he/she/it), no NP agreement
(do-support yes) ; negation & questions of lexical verbs insert do/does/did
(subject-aux-inversion yes) ; yes/no + non-subject wh questions invert the operator
(article-selection "a/an/the") ; a/an resolved PHONOLOGICALLY (an hour, a university)
(adjective-position prenominal) ; attributive adjectives precede the noun; invariant
(has-tag-questions yes) ; "...doesn't he?" — operator + reversed polarity
(has-there-existential yes) ; "there is/are/have been ..."
(possessive-clitic "'s") ; saxon genitive; plural in -s -> bare apostrophe
(question-punct plain) ; ? and ! only (no inverted marks)
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "3sg-present-only") ; only 3sg present -s (+ suppletive be)
(auxiliary-order (modal perfect progressive passive main))
(perfect-aux "have") ; have + past participle
(progressive-aux "be") ; be + present participle
(passive-aux "be") ; be + past participle (+ by-agent)
(future "will + base") ; no inflectional future
(comparative "synthetic-or-periphrastic") ; -er/-est vs more/most by syllables
;; ── SACRED safety bar (shared with es/pt) ──────────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
;; ── DIALECT overlay (post-realization, one core -> US/UK/AU) ────────────
(dialect US) ; default; profile field switches the overlay
(dialects (US UK AU))
(dialect-canonical US) ; core is authored in US orthography
(dialect-overlay "dialect_en.to_dialect") ; orthography + lexis + grammar prefs
(dialect-covers (spelling lexis collective-agreement gotten/got)))
-45
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;;; lang_profile_es.el — Spanish language profile for ELP.
;;; Keys the realizer's construction switches. Mirrors lang_profile_en / _pt.
(lang_profile_es
(language "Spanish")
(iso639 "es")
(family "Romance")
;; -- core typology flags -------------------------------------------------
(pro-drop yes) ; subjects routinely dropped; agreement carries person
(obligatory-subject no)
(grammatical-gender yes) ; m/f on every noun; article+adjective AGREE
(gender-source lexicon); REAL per-noun gender from UniMorph — NOT a heuristic
(do-support no)
(subject-aux-inversion no) ; questions by intonation/punctuation, not inversion
(question-strategy intonation)
(article-selection "el/la/los/las un/una/unos/unas")
(stressed-a-rule yes) ; fem sg noun in stressed a-/ha- takes el/un (el agua)
(adjective-position postnominal) ; default post; a few prenominal + apocope
(adjective-agreement "gender+number")
(question-punct inverted) ; opening ¿ ¡ required
;; -- MANDATORY CONTRACTIONS (coordinator quality bar) --------------------
(contractions ((de el "del") (a el "al")))
(contraction-mandatory yes) ; 'de el'/'a el' MUST surface as del/al
;; -- verb / aspect system ------------------------------------------------
(verb-classes (ar er ir))
(tenses (present preterite imperfect future conditional))
(moods (ind sbjv imp))
(finite-agreement "person+number (6 slots)")
(perfect-aux "haber") ; haber + past participle (invariant -o)
(progressive-aux "estar") ; estar + gerund
(passive-aux "ser") ; ser + participle (agrees) + por-agent
(copula-split "ser/estar") ; permanent vs stage-level
(future "infinitive + é/ás/á/emos/éis/án")
;; -- clitics / government ------------------------------------------------
(object-clitics yes) ; me te lo la le nos os los las; proclisis/enclisis
(clitic-order "se II I III (le+lo -> se lo)")
(enclisis "imperative/infinitive/gerund + accent repair (dá+me+lo->dámelo)")
(verb-prep-government yes) ; verbs select prep (protestar+contra, escapar+de)
;; -- SACRED safety bar (shared with en/pt) -------------------------------
(negation-faithful yes)) ; polarity never dropped/inverted; unplaceable -> FLAG
-74
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;;; lang_profile_fr.el — French language profile for ELP.
;;; Mirrors lang_profile_it / lang_profile_es; keys the realizer's construction
;;; switches. French is a Romance sibling (~54% of the realizer code and the whole
;;; clause-engine architecture reused), but carries the family's biggest surface
;;; deltas: NOT pro-drop, DISCONTINUOUS negation, and an orthography/phonology
;;; mismatch (elision, liaison) that makes exact-match genuinely hard.
(lang_profile_fr
(language "French")
(iso639 "fr")
(family "Romance")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop no) ; << French-specific: subject clitic OBLIGATORY
(obligatory-subject yes) ; je/tu/il/elle/nous/vous/ils/elles always overt
(grammatical-gender yes) ; m/f; full NP agreement (art + adj + participle)
(do-support no)
(subject-aux-inversion optional) ; est-ce que (default) OR clitic inversion (vas-tu)
(article-selection "le/la/l'/les ; un/une/des ; PARTITIVE du/de la/de l'/des")
(article-drives-contraction yes) ; à+le=au, de+le=du feed off article choice
(adjective-position "postnominal-default + prenominal-BAGS") ; beau/bon/grand/
; petit/jeune/vieux/nouveau + ordinals prenominal
; (beau->bel, nouveau->nouvel, vieux->vieil / vowel)
(question-punct "space-before") ; French typography: ' ?' ' !' (no ¿¡)
;; ── elision (orthography/phonology mismatch — French-specific) ──────────
(elision ((le l') (la l') (je j') (ne n') (de d') (que qu')
(me m') (te t') (se s') (ce c'))) ; before vowel / h-muet
(elision-h-muet yes) ; l'homme, l'hôpital (h-aspiré exception list kept)
(liaison noted-not-modeled) ; phonological, not written in surface
;; ── MANDATORY prep+article contractions ────────────────────────────────
(contractions ((à le au) (à les aux) (de le du) (de les des)))
(contraction-mandatory yes) ; *à le -> au obligatory; à la / à l' uncontracted
(partitive ((m-sg du) (f-sg "de la") (vowel "de l'") (pl des)))
(partitive-under-neg "de") ; << gap in current build: 'ne … pas de pain'
;; ── clitic system ──────────────────────────────────────────────────────
(clitics yes)
(clitic-order (me te se nous vous | le la les | lui leur | y | en))
(clitic-placement ((finite proclitic) ; je le lui donne
(imperative-affirmative enclitic-hyphen) ; donne-le-moi
(imperative-negative "ne+proclitic+verb+pas") ; ne le donne pas
(infinitive enclitic))) ; PARTIAL: clitic-climbing
; onto infinitive under modal
(clitic-imperative-shift ((me moi) (te toi))) ; final me/te -> moi/toi (donne-moi)
(clitic-particles (y en)) ; locative y, partitive/genitive en
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person+number (written; many homophones)")
(tenses (présent imparfait passé-simple futur conditionnel
subjonctif-présent subjonctif-imparfait impératif))
(compound-past "passé-composé = aux(present) + participe passé")
(perfect-aux "être/avoir (LEXICAL selection)") ; << French-specific
(etre-aux-class "intransitive motion/change (aller venir arriver partir
entrer sortir monter descendre naître mourir rester
tomber retourner passer devenir revenir rentrer) + ALL
pronominal verbs")
(participle-agreement ((être subject) ; elle est allée / elles venues
(avoir preceding-direct-object))) ; je les ai vus
(progressive "être en train de + infinitif") ; no dedicated aux
(copula "être (single; no ser/estar, no essere/stare)")
(passive-aux "être (+ par-agent)")
(future inflectional) ; parlera, sera
(comparative "plus/moins ADJ que")
(superlative "le/la plus ADJ (de …)") ; PARTIAL word-order in build
;; ── SACRED safety bar (shared with es/pt/it/en) ────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "DISCONTINUOUS: ne (preverbal) … pas/jamais/rien/personne/
plus/guère/que (postverbal)") ; << biggest structural delta
(negation-ne-elides yes) ; ne -> n' before vowel (n'ai pas vu)
(negation-passe-composé "ne + aux + pas + participe") ; n'ai pas vu
(negative-concord partial)) ; personne/rien as arguments post-participle
-70
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;;; lang_profile_it.el — Italian language profile for ELP.
;;; Mirrors lang_profile_es / lang_profile_pt; keys the realizer's construction
;;; switches. Italian is a Romance sibling, so ~85% of the flags match ES/PT; the
;;; essere/avere auxiliary split and phonological article selection are the deltas.
(lang_profile_it
(language "Italian")
(iso639 "it")
(family "Romance")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop yes) ; null subjects default; overt pronoun = emphatic
(obligatory-subject no)
(grammatical-gender yes) ; m/f; full NP agreement (art + adj + participle)
(do-support no)
(subject-aux-inversion no) ; yes/no Q = declarative order + '?'; no inversion
(article-selection "il/lo/l'/i/gli + la/l'/le ; un/uno/un'/una") ; PHONOLOGICAL:
; lo/gli/uno before s+cons, z, gn, ps, pn, x, y, i+V;
; l'/un' before a vowel (elision, glued to next word)
(article-drives-contraction yes) ; article choice feeds the prep+art contraction
(adjective-position "postnominal-default + prenominal-class") ; bello/buono/grande
; /nuovo/vecchio/primo... prenominal (with apocope)
(question-punct plain) ; ? and ! only (no inverted ¿ ¡)
;; ── MANDATORY prep+article contractions ────────────────────────────────
(contractions ((di il del) (di lo dello) (di la della) (di i dei)
(di gli degli) (di le delle) (di l' dell')
(a il al) (a lo allo) (a la alla) (a i ai) (a gli agli)
(a le alle) (a l' all')
(da il dal) (da la dalla) (da gli dagli) (da l' dall')
(in il nel) (in la nella) (in gli negli) (in l' nell')
(su il sul) (su la sulla) (su gli sugli) (su l' sull')))
(contraction-mandatory yes) ; *di il -> del is obligatory, never uncontracted
(prep-no-contract (per tra fra)) ; per la strada (NOT *perla)
;; ── clitic system ──────────────────────────────────────────────────────
(clitics yes)
(clitic-placement ((finite proclitic) ; lo vedo, non me lo dà
(imperative-affirmative enclitic) ; dammelo, guardalo
(imperative-negative-tu non+infinitive) ; non parlare / non lo fare
(infinitive enclitic) ; vederlo, aiutarmi (drop -e)
(gerund enclitic))) ; dandolo
(clitic-combination ((mi lo "me lo") (ti lo "te lo") (ci lo "ce lo")
(vi lo "ve lo") (si lo "se lo")
(gli lo "glielo") (le lo "glielo"))) ; glielo = ONE word
(clitic-particles (ci ne)) ; locative ci, partitive ne
(raddoppiamento (da fa di va sta)) ; monosyllabic imper double clitic: dammelo
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person+number (6-way)")
(tenses (presente imperfetto passato-remoto futuro condizionale
congiuntivo-presente congiuntivo-imperfetto imperativo))
(compound-past "passato-prossimo = aux(present) + participle")
(perfect-aux "essere/avere (LEXICAL selection)") ; << Italian-specific
(essere-aux-class unaccusative) ; motion/change-of-state/copular/pronominal
; (andare venire nascere morire diventare piacere
; + ALL reflexives) -> essere
(participle-agreement ((essere subject) ; è andata / sono arrivati
(avere preceding-acc-clitic))) ; li ho visti
(progressive-aux "stare + gerundio") ; sto parlando
(copula "essere (default) / stare (state: sto bene)")
(passive-aux "essere / venire (+ da-agent)")
(future inflectional) ; parlerò, sarà
(comparative "più/meno ADJ di")
;; ── SACRED safety bar (shared with es/pt/en) ───────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "non (preverbal) + concord") ; non...niente/nessuno/mai/più
(negative-concord yes) ; preverbal negative word (nessuno/niente) suppresses non
(neg-adverb-position between-aux-and-participle)) ; non ho MAI visto
-30
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;;; lang_profile_la.el — Latin language profile for ELP.
;;; Keys the realizer's construction switches. Companion to morphology-la.el.
(lang_profile_la
(language "Latin")
(iso639 "la")
(family "Italic")
;; -- core typology flags -------------------------------------------------
(pro-drop yes) ; person carried by verb ending; subjects dropped
(obligatory-subject no)
(grammatical-gender yes) ; m/f/n; adjective AGREES in case+gender+number
(gender-source lexicon) ; REAL per-noun gender from UniMorph lat
(articles none) ; Latin has no articles
(case-system yes) ; NOM GEN DAT ACC ABL VOC (+ rare LOC)
(cases (nom gen dat acc abl voc))
(word-order "SOV (default; free order, case-marked)")
(adjective-position "either (case agreement carries the link)")
(adjective-agreement "case+gender+number")
;; -- verb / aspect system ------------------------------------------------
(verb-classes (1 2 3 3io 4)) ; four conjugations + i-stem 3rd
(tenses (present imperfect future perfect pluperfect futureperfect))
(moods (indicative subjunctive imperative infinitive))
(voices (active passive))
(finite-agreement "person+number (6 slots)")
(citation "principal parts: pres-1sg / pres-inf / perf-participle")
;; -- SACRED safety bar ---------------------------------------------------
(negation-faithful yes)) ; polarity never dropped/inverted
-40
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;;; lang_profile_pt.el — Portuguese language profile for ELP.
;;; Keys the realizer's construction switches. Mirrors lang_profile_es.
(lang_profile_pt
(language "Portuguese")
(iso639 "pt")
(family "Romance")
;; -- core typology flags -------------------------------------------------
(pro-drop yes) ; subjects routinely dropped; agreement carries person
(obligatory-subject no)
(grammatical-gender yes) ; m/f on every noun; article+adjective AGREE
(gender-source lexicon) ; REAL per-noun gender from UniMorph por / kaikki
(do-support no)
(subject-aux-inversion no)
(question-strategy intonation)
(article-selection "o/a/os/as um/uma/uns/umas")
(adjective-position postnominal)
(adjective-agreement "gender+number")
;; -- MANDATORY CONTRACTIONS (prep + article) -----------------------------
(contractions ((de o "do") (de a "da") (em o "no") (em a "na")
(a o "ao") (a a "à") (por o "pelo") (por a "pela")))
(contraction-mandatory yes)
;; -- verb / aspect system ------------------------------------------------
(verb-classes (ar er ir))
(tenses (present preterite imperfect future conditional))
(moods (ind sbjv imp))
(finite-agreement "person+number (6 slots)")
(perfect-aux "ter") ; ter + past participle
(copula-split "ser/estar")
(personal-infinitive yes) ; distinctive PT inflected infinitive
;; -- clitics / government ------------------------------------------------
(object-clitics yes) ; mesoclisis/enclisis/proclisis by context
(verb-prep-government yes)
;; -- SACRED safety bar ---------------------------------------------------
(negation-faithful yes))
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;;; lang_profile_ro.el — Romanian language profile for ELP.
;;; Romanian is the BIG typological delta of the Romance family. The verb/clause
;;; engine and the SACRED negation contract mirror the ES/PT/IT core, but the
;;; NOMINAL system is genuinely new: a SUFFIXED definite article, preserved CASE,
;;; a NEUTER gender, and a VOCATIVE. Those flags mark where the shared engine was
;;; extended rather than reused.
(lang_profile_ro
(language "Romanian")
(iso639 "ro")
(family "Romance (Eastern / Balkan)")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop yes) ; null subjects default; overt pronoun = emphatic
(obligatory-subject no)
(grammatical-gender yes) ; m / f / NEUTER (n)
(neuter-gender yes) ; << ROMANIAN-SPECIFIC: masc-agreeing SG, fem-agreeing PL
; (un tren nou / două trenuri noi)
(do-support no)
(subject-aux-inversion no) ; yes/no Q = declarative order + '?'
(question-punct plain) ; ? and ! only
;; ── SUFFIXED DEFINITE ARTICLE (the headline engine extension) ───────────
(definite-article suffixed) ; << UNIQUE IN ROMANCE: enclitic on the noun
(definite-forms ((m/n sg "-ul / -le / -l : om->omul, câine->câinele, codru->codrul")
(f sg "-a / -ea / -ua : casă->casa, carte->cartea, stea->steaua")
(m pl "-i : oameni->oamenii")
(f/n pl "-le : case->casele, trenuri->trenurile")))
(article-host ((no-prenom-adj noun) ; omul bun
(prenom-adj adjective))) ; bunul om (adj carries the article)
(indefinite-article ((m/n "un") (f "o") (pl "niște") (gen/dat-pl "unor")))
;; ── CASE (preserved; NOM/ACC vs GEN/DAT) ────────────────────────────────
(case (nom/acc gen/dat vocative)) ; << ROMANIAN-SPECIFIC
(case-syncretism "nom=acc ; gen=dat")
(genitive-marking "gen/dat definite: -lui (m/n), -ei/-i (f), -lor (pl)")
(genitival-article ((m sg "al") (f sg "a") (m pl "ai") (f/n pl "ale"))) ; o carte a lui
(possession "definite-head + gen/dat possessor: casa băiatului")
(vocative ((m sg "-ule/-e : omule, băiete") (f sg "-o : Mario, fato")
(pl "-lor")))
;; ── verb / aspect system ────────────────────────────────────────────────
(finite-agreement "person+number (6-way)")
(tenses (prezent imperfect perfect-simplu conjunctiv-prezent
imperativ (periphrastic: perfect-compus viitor conditional)))
(compound-past "perfectul compus = a-avea-clitic + INVARIABLE participle")
(perfect-aux "a avea (am/ai/a/am/ați/au) — ONE auxiliary for ALL verbs")
(perfect-aux-split no) ; << SIMPLER than Italian: no essere/avere selection
(participle-agreement none) ; invariable in the perfect compus (agrees only as
; an adjective / in the passive)
(future "voi/vei/va/vom/veți/vor + infinitive (viitor literar)")
(conditional "aș/ai/ar/am/ați/ar + infinitive")
(subjunctive "conjunctiv: particle 'să' + subjunctive present")
(modal-complement "modal + să + subjunctive (vreau să merg, poți să ajuți)")
(copula "a fi")
(passive "a fi + participle (participle AGREES like an adjective)")
(comparative "mai / mai puțin ADJ decât")
;; ── clitic system (partial — see honest gaps) ───────────────────────────
(clitics yes)
(clitic-set ((acc te îl o ne îi le) (dat îmi îți îi ne le)
(refl te se ne se)))
(clitic-placement ((finite proclitic) ; îmi place, o văd
(perfect-compus elision) ; << m-am, l-am, i-am (PARTIAL)
(imperative-affirmative enclitic))) ; dă-mi (PARTIAL)
;; ── SACRED safety bar (shared with es/pt/it/en) ─────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "nu (single preverbal marker) + concord")
(negative-concord yes) ; nu … nimic / nimeni / niciodată / niciun
(negative-imperative "nu + INFINITIVE : nu pleca! (KNOWN GAP: uses imperative stem)"))
-1
View File
@@ -250,7 +250,6 @@ fn en_irregular_verb(base: String) -> [String] {
if str_eq(base, "cut") { let r: [String] = ["cut", "cuts", "cut", "cut", "cutting"]; return r }
if str_eq(base, "set") { let r: [String] = ["set", "sets", "set", "set", "setting"]; return r }
if str_eq(base, "hit") { let r: [String] = ["hit", "hits", "hit", "hit", "hitting"]; return r }
if str_eq(base, "fight") { let r: [String] = ["fight", "fights","fought", "fought", "fighting"]; return r }
return empty
}
-280
View File
@@ -1,280 +0,0 @@
// multilingual.el - the language layer for the native-el interlocutor.
//
// Deterministic, NO generative model (ports multilingual.py):
// 1. ml_detect(text) -> ISO code (en/es/pt/it) via stopword + diacritic score
// 2. ml_tr(key, lang) -> localized fixed phrase (SACRED per-language yes/no/decline)
// 3. ml_term(w, lang) -> PT/ES content term -> EN engram equivalent
// 4. ml_translate_pred(lemma, lang) -> EN predicate lemma -> target infinitive
//
// The Python detector count-weights stopwords and diacritics; here diacritics are
// scored by PRESENCE (str_contains) rather than codepoint counting, to stay clear
// of UTF-8 index hazards in the runtime. Faithful enough to classify typical
// queries; documented simplification. Depends on: comprehend (cp_tokenize).
// 1. language detection
fn ml_stop_en(w: String) -> Bool {
if str_eq(w, "the") { return true }
if str_eq(w, "does") { return true }
if str_eq(w, "do") { return true }
if str_eq(w, "did") { return true }
if str_eq(w, "what") { return true }
if str_eq(w, "who") { return true }
if str_eq(w, "is") { return true }
if str_eq(w, "are") { return true }
if str_eq(w, "how") { return true }
if str_eq(w, "you") { return true }
if str_eq(w, "your") { return true }
if str_eq(w, "of") { return true }
if str_eq(w, "to") { return true }
if str_eq(w, "and") { return true }
if str_eq(w, "for") { return true }
if str_eq(w, "explain") { return true }
if str_eq(w, "answer") { return true }
if str_eq(w, "memory") { return true }
if str_eq(w, "with") { return true }
if str_eq(w, "not") { return true }
if str_eq(w, "store") { return true }
return false
}
fn ml_stop_es(w: String) -> Bool {
if str_eq(w, "que") { return true }
if str_eq(w, "qué") { return true }
if str_eq(w, "una") { return true }
if str_eq(w, "usted") { return true }
if str_eq(w, "su") { return true }
if str_eq(w, "cómo") { return true }
if str_eq(w, "como") { return true }
if str_eq(w, "cuál") { return true }
if str_eq(w, "quién") { return true }
if str_eq(w, "está") { return true }
if str_eq(w, "es") { return true }
if str_eq(w, "los") { return true }
if str_eq(w, "las") { return true }
if str_eq(w, "del") { return true }
if str_eq(w, "al") { return true }
if str_eq(w, "explica") { return true }
if str_eq(w, "explique") { return true }
if str_eq(w, "forma") { return true }
if str_eq(w, "con") { return true }
if str_eq(w, "memoria") { return true }
if str_eq(w, "responde") { return true }
return false
}
fn ml_stop_pt(w: String) -> Bool {
if str_eq(w, "que") { return true }
if str_eq(w, "uma") { return true }
if str_eq(w, "você") { return true }
if str_eq(w, "sua") { return true }
if str_eq(w, "seu") { return true }
if str_eq(w, "como") { return true }
if str_eq(w, "memória") { return true }
if str_eq(w, "isso") { return true }
if str_eq(w, "os") { return true }
if str_eq(w, "as") { return true }
if str_eq(w, "da") { return true }
if str_eq(w, "do") { return true }
if str_eq(w, "na") { return true }
if str_eq(w, "no") { return true }
if str_eq(w, "explica") { return true }
if str_eq(w, "forma") { return true }
if str_eq(w, "é") { return true }
if str_eq(w, "está") { return true }
if str_eq(w, "com") { return true }
if str_eq(w, "responda") { return true }
return false
}
fn ml_stop_it(w: String) -> Bool {
if str_eq(w, "che") { return true }
if str_eq(w, "una") { return true }
if str_eq(w, "come") { return true }
if str_eq(w, "della") { return true }
if str_eq(w, "gli") { return true }
if str_eq(w, "è") { return true }
if str_eq(w, "sono") { return true }
if str_eq(w, "questo") { return true }
if str_eq(w, "nel") { return true }
if str_eq(w, "di") { return true }
if str_eq(w, "il") { return true }
if str_eq(w, "cosa") { return true }
if str_eq(w, "per") { return true }
if str_eq(w, "memoria") { return true }
if str_eq(w, "spiega") { return true }
if str_eq(w, "rispondi") { return true }
return false
}
// diacritic PRESENCE score (weight 3 each; hard overrides weight 8).
fn ml_dia_score(low: String, lang: String) -> Int {
let s: Int = 0
if str_eq(lang, "pt") {
if str_contains(low, "ã") { let s = s + 3 }
if str_contains(low, "õ") { let s = s + 3 }
if str_contains(low, "ç") { let s = s + 3 }
if str_contains(low, "ê") { let s = s + 3 }
if str_contains(low, "á") { let s = s + 3 }
// hard PT markers (ã/õ almost never appear outside PT)
if str_contains(low, "ã") { let s = s + 8 }
if str_contains(low, "õ") { let s = s + 8 }
}
if str_eq(lang, "es") {
if str_contains(low, "ñ") { let s = s + 3 }
if str_contains(low, "¿") { let s = s + 3 }
if str_contains(low, "¡") { let s = s + 3 }
if str_contains(low, "á") { let s = s + 3 }
if str_contains(low, "é") { let s = s + 3 }
// hard ES markers
if str_contains(low, "ñ") { let s = s + 8 }
if str_contains(low, "¿") { let s = s + 8 }
if str_contains(low, "¡") { let s = s + 8 }
}
if str_eq(lang, "it") {
if str_contains(low, "è") { let s = s + 3 }
if str_contains(low, "ì") { let s = s + 3 }
if str_contains(low, "ò") { let s = s + 3 }
}
return s
}
fn ml_stop_score(toks: [String], lang: String) -> Int {
let n: Int = native_list_len(toks)
let s: Int = 0
let i: Int = 0
while i < n {
let w: String = native_list_get(toks, i)
if str_eq(lang, "en") { if ml_stop_en(w) { let s = s + 2 } }
if str_eq(lang, "es") { if ml_stop_es(w) { let s = s + 2 } }
if str_eq(lang, "pt") { if ml_stop_pt(w) { let s = s + 2 } }
if str_eq(lang, "it") { if ml_stop_it(w) { let s = s + 2 } }
let i = i + 1
}
return s
}
fn ml_detect(text: String) -> String {
if str_eq(text, "") { return "en" }
let low: String = str_to_lower(text)
let toks: [String] = cp_tokenize(text)
// NOTE: el's overloaded `+` mis-compiles two chained function-call Int operands
// as string concat (documented in comprehend_gate.el). Bind each call to an Int
// var and add vars one at a time so the addition stays integer.
let en: Int = ml_stop_score(toks, "en")
let es_s: Int = ml_stop_score(toks, "es")
let es_d: Int = ml_dia_score(low, "es")
let es: Int = es_s + es_d
let pt_s: Int = ml_stop_score(toks, "pt")
let pt_d: Int = ml_dia_score(low, "pt")
let pt: Int = pt_s + pt_d
let it_s: Int = ml_stop_score(toks, "it")
let it_d: Int = ml_dia_score(low, "it")
let it: Int = it_s + it_d
let best: String = "en"
let bs: Int = en
if es > bs { let best = "es"; let bs = es }
if pt > bs { let best = "pt"; let bs = pt }
if it > bs { let best = "it"; let bs = it }
// weak signal -> honest fallback to English
if bs < 3 { return "en" }
return best
}
// 2. localized fixed phrases (SACRED per-language decline/yes/no)
fn ml_tr(key: String, lang: String) -> String {
if str_eq(key, "no_memory") {
if str_eq(lang, "pt") { return "Não tenho isso na minha memória." }
if str_eq(lang, "es") { return "No tengo eso en mi memoria." }
if str_eq(lang, "it") { return "Non ho quello nella mia memoria." }
return "I don't have that in my memory."
}
if str_eq(key, "parse_fail") {
if str_eq(lang, "pt") { return "Não consegui interpretar isso." }
if str_eq(lang, "es") { return "No pude interpretar eso." }
if str_eq(lang, "it") { return "Non sono riuscito a interpretarlo." }
return "I didn't parse that."
}
if str_eq(key, "yes") {
if str_eq(lang, "pt") { return "Sim" }
if str_eq(lang, "es") { return "" }
if str_eq(lang, "it") { return "" }
return "Yes"
}
if str_eq(key, "no") {
if str_eq(lang, "pt") { return "Não" }
if str_eq(lang, "es") { return "No" }
if str_eq(lang, "it") { return "No" }
return "No"
}
if str_eq(key, "identity") {
if str_eq(lang, "pt") { return "Sou o Neuron, o engrama com quem você está falando." }
if str_eq(lang, "es") { return "Soy Neuron, el engrama con el que estás hablando." }
if str_eq(lang, "it") { return "Sono Neuron, l'engramma con cui stai parlando." }
return "I'm Neuron, the engram you're speaking with."
}
return ""
}
// 3. retrieval term lexicon (PT/ES content term -> EN engram equivalent)
fn ml_term(w: String, lang: String) -> String {
if str_eq(lang, "en") { return w }
if str_eq(w, "saliência") { return "salience" }
if str_eq(w, "saliencia") { return "salience" }
if str_eq(w, "memória") { return "memory" }
if str_eq(w, "memoria") { return "memory" }
if str_eq(w, "geometria") { return "geometry" }
if str_eq(w, "geometrias") { return "geometry" }
if str_eq(w, "geometrías") { return "geometry" }
if str_eq(w, "forma") { return "form" }
if str_eq(w, "consolidação") { return "consolidation" }
if str_eq(w, "consolidación") { return "consolidation" }
if str_eq(w, "aprendizagem") { return "learning" }
if str_eq(w, "aprendizaje") { return "learning" }
if str_eq(w, "") { return "node" }
if str_eq(w, "nodo") { return "node" }
if str_eq(w, "armazenamento") { return "storage" }
if str_eq(w, "almacenamiento") { return "storage" }
if str_eq(w, "estrutura") { return "structure" }
if str_eq(w, "estructura") { return "structure" }
return w
}
// 4. predicate translation (EN lemma -> target infinitive; pass-through) ─────
fn ml_translate_pred(lemma: String, lang: String) -> String {
if str_eq(lang, "en") { return lemma }
if str_eq(lang, "es") {
if str_eq(lemma, "store") { return "almacenar" }
if str_eq(lemma, "use") { return "usar" }
if str_eq(lemma, "have") { return "tener" }
if str_eq(lemma, "be") { return "ser" }
if str_eq(lemma, "give") { return "dar" }
if str_eq(lemma, "make") { return "hacer" }
if str_eq(lemma, "learn") { return "aprender" }
if str_eq(lemma, "form") { return "formar" }
return lemma
}
if str_eq(lang, "pt") {
if str_eq(lemma, "store") { return "armazenar" }
if str_eq(lemma, "use") { return "usar" }
if str_eq(lemma, "have") { return "ter" }
if str_eq(lemma, "be") { return "ser" }
if str_eq(lemma, "give") { return "dar" }
if str_eq(lemma, "make") { return "fazer" }
if str_eq(lemma, "learn") { return "aprender" }
if str_eq(lemma, "form") { return "formar" }
return lemma
}
if str_eq(lang, "it") {
if str_eq(lemma, "store") { return "memorizzare" }
if str_eq(lemma, "use") { return "usare" }
if str_eq(lemma, "have") { return "avere" }
if str_eq(lemma, "be") { return "essere" }
return lemma
}
return lemma
}
-140
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@@ -1,140 +0,0 @@
// propositions.el - the READ primitive over the engram's OWN memories, native el.
//
// Free memory text -> structured PROPOSITIONS (triples):
// (subject, predicate, object, modifiers, polarity, tense, source, confidence)
//
// This is comprehension turned inward: the Python reference (propositions.py) ran
// spaCy's dependency parser over each memory sentence and walked the arcs. Here
// the spaCy role is filled by the el-native parser (comprehend.el / parse_spec):
// each sentence is parsed to a meaning-spec, and the spec's roles ARE the triple.
// Nothing generates text. NEGATION IS SACRED: polarity flows straight from the
// spec's polarity field and is never dropped or inverted.
//
// Depends on: comprehend (parse_spec / parse_spec_lang), grammar (slots_get).
// sentence segmentation
// Split on sentence-final punctuation (. ! ?) and hard newlines. Markdown/long
// memories are handled shallowly (the reference caps + ranks by query overlap;
// that ranking belongs to the dialogue layer, not here).
fn prop_is_boundary(c: String) -> Bool {
if str_eq(c, ".") { return true }
if str_eq(c, "!") { return true }
if str_eq(c, "?") { return true }
if str_eq(c, "\n") { return true }
return false
}
fn prop_split_sentences(text: String) -> [String] {
let out: [String] = native_list_empty()
let n: Int = str_len(text)
let start: Int = 0
let i: Int = 0
while i < n {
let c: String = str_slice(text, i, i + 1)
if prop_is_boundary(c) {
let seg: String = str_slice(text, start, i + 1)
let trimmed: String = cp_trim_punct(seg)
if !str_eq(trimmed, "") {
let out = native_list_append(out, seg)
}
let start = i + 1
}
let i = i + 1
}
if start < n {
let seg: String = str_slice(text, start, n)
let trimmed: String = cp_trim_punct(seg)
if !str_eq(trimmed, "") {
let out = native_list_append(out, seg)
}
}
return out
}
// spec -> proposition record
// A proposition is a slot map (same [String] shape as the spec) with the READ
// contract keys. Modifiers fold the spec's location + iobj adjuncts.
fn prop_confidence(subject: String, predicate: String, object: String) -> String {
if str_eq(predicate, "") { return "0.0" }
if str_eq(subject, "") { return "0.4" }
if str_eq(object, "") { return "0.7" }
return "1.0"
}
fn prop_modifiers(spec: [String]) -> String {
let loc: String = slots_get(spec, "location")
let iobj: String = slots_get(spec, "iobj")
let parts: [String] = native_list_empty()
if !str_eq(loc, "") { let parts = native_list_append(parts, loc) }
if !str_eq(iobj, "") { let parts = native_list_append(parts, "to " + iobj) }
return str_join(parts, "; ")
}
fn prop_from_spec(spec: [String], source_id: String) -> [String] {
let subject: String = slots_get(spec, "agent")
let predicate: String = slots_get(spec, "predicate")
let object: String = slots_get(spec, "patient")
let polarity: String = slots_get(spec, "polarity")
let tense: String = slots_get(spec, "tense")
let mods: String = prop_modifiers(spec)
let conf: String = prop_confidence(subject, predicate, object)
let p: [String] = native_list_empty()
let p = native_list_append(p, "subject"); let p = native_list_append(p, subject)
let p = native_list_append(p, "predicate"); let p = native_list_append(p, predicate)
let p = native_list_append(p, "object"); let p = native_list_append(p, object)
let p = native_list_append(p, "modifiers"); let p = native_list_append(p, mods)
let p = native_list_append(p, "polarity"); let p = native_list_append(p, polarity)
let p = native_list_append(p, "tense"); let p = native_list_append(p, tense)
let p = native_list_append(p, "source"); let p = native_list_append(p, source_id)
let p = native_list_append(p, "confidence"); let p = native_list_append(p, conf)
return p
}
// Extract one proposition from a single sentence (given language).
fn prop_extract_one_lang(sentence: String, lang: String, source_id: String) -> [String] {
let spec: [String] = parse_spec_lang(sentence, lang)
return prop_from_spec(spec, source_id)
}
fn prop_extract_one(sentence: String, source_id: String) -> [String] {
return prop_extract_one_lang(sentence, "en", source_id)
}
// Render a proposition as a compact trace line (repr parity with propositions.py).
fn prop_repr(p: [String]) -> String {
let neg: String = ""
if str_eq(slots_get(p, "polarity"), "neg") { let neg = "NOT " }
let mods: String = slots_get(p, "modifiers")
let modstr: String = ""
if !str_eq(mods, "") { let modstr = " [" + mods + "]" }
let s: String = "(" + slots_get(p, "subject") + " -" + neg + slots_get(p, "predicate")
let s = s + "-> " + slots_get(p, "object") + modstr
let s = s + " conf=" + slots_get(p, "confidence") + ")"
return s
}
// Extract all propositions from a memory's text (one per sentence). Returns a
// flat [String] whose entries are the prop_repr trace lines, in reading order.
fn prop_extract_lang(text: String, lang: String, source_id: String) -> [String] {
let sents: [String] = prop_split_sentences(text)
let m: Int = native_list_len(sents)
let out: [String] = native_list_empty()
let i: Int = 0
while i < m {
let sent: String = native_list_get(sents, i)
let p: [String] = prop_extract_one_lang(sent, lang, source_id)
// drop empty parses (no predicate recovered): honest partial, not noise.
if !str_eq(slots_get(p, "predicate"), "") {
let out = native_list_append(out, prop_repr(p))
}
let i = i + 1
}
return out
}
fn prop_extract(text: String, source_id: String) -> [String] {
return prop_extract_lang(text, "en", source_id)
}
-101
View File
@@ -248,56 +248,6 @@ fn add_punct(s: String, intent: String) -> String {
return s + "."
}
// Polarity-aware negation (SACRED field honored on the generation side)
//
// Negation must never be dropped between comprehension and realization. The
// meaning-spec carries an explicit "polarity" field ("aff"|"neg") and optional
// "neg_word" (standalone negative adverb, e.g. "never"). English uses
// do-support ("did not see") or preverbal adverb ("never fought"); copular "be"
// takes post-verbal "not"; other languages get a preverbal negator particle.
fn realize_negator(code: String) -> String {
if str_eq(code, "es") { return "no" }
if str_eq(code, "pt") { return "não" }
if str_eq(code, "ca") { return "no" }
if str_eq(code, "it") { return "non" }
if str_eq(code, "fr") { return "ne" }
if str_eq(code, "de") { return "nicht" }
if str_eq(code, "ro") { return "nu" }
return "not"
}
fn realize_assert_neg_en(predicate: String, tense: String, person: String, number: String, agent: String, patient: String, iobj: String, location: String, neg_word: String, profile: [String]) -> String {
let parts: [String] = native_list_empty()
let parts = native_list_append(parts, agent)
if !str_eq(neg_word, "") {
// adverbial negation: "I never fought the ocean."
let verb_surf: String = morph_conjugate(predicate, tense, person, number, profile)
let parts = native_list_append(parts, neg_word)
let parts = native_list_append(parts, verb_surf)
} else {
if str_eq(predicate, "be") {
// copular: "she was not a monster"
let be_form: String = morph_conjugate("be", tense, person, number, profile)
let parts = native_list_append(parts, be_form)
let parts = native_list_append(parts, "not")
} else {
// do-support: "she did not see the man"
let do_form: String = morph_conjugate("do", tense, person, number, profile)
let parts = native_list_append(parts, do_form)
let parts = native_list_append(parts, "not")
let parts = native_list_append(parts, predicate)
}
}
if !str_eq(patient, "") { let parts = native_list_append(parts, patient) }
if !str_eq(iobj, "") {
let parts = native_list_append(parts, "to")
let parts = native_list_append(parts, iobj)
}
if !str_eq(location, "") { let parts = native_list_append(parts, location) }
return str_join(parts, " ")
}
// Main realization entry point
fn realize_lang(form: [String], profile: [String]) -> String {
@@ -334,50 +284,6 @@ fn realize_lang(form: [String], profile: [String]) -> String {
}
// Assertion (declarative)
let polarity: String = slots_get(form, "polarity")
let neg_word: String = slots_get(form, "neg_word")
let iobj: String = slots_get(form, "iobj")
let code: String = lang_get(profile, "code")
// Subordinate clause tail (SACRED completeness the clause is carried, never
// dropped): "<conj> <subordinate surface>", e.g. "because he was a monster".
let subord_conj: String = slots_get(form, "subord_conj")
let subord_text: String = slots_get(form, "subord_text")
let subord_tail: String = ""
if !str_eq(subord_conj, "") {
if !str_eq(subord_text, "") {
let subord_tail = subord_conj + " " + subord_text
} else {
let subord_tail = subord_conj
}
}
// Negative polarity: SACRED never dropped.
if str_eq(polarity, "neg") {
if str_eq(code, "en") {
let sentence: String = realize_assert_neg_en(predicate, tense, person, number, agent, patient, iobj, location, neg_word, profile)
return add_punct(capitalize_first(sentence), "assert")
}
// Generic non-English: affirmative core with a preverbal negator particle.
let neg_particle: String = realize_negator(code)
let vp_pair: [String] = realize_vp_lang(predicate, tense, aspect, person, number, profile)
let verb_surf: String = native_list_get(vp_pair, 0)
let aux_surf: String = native_list_get(vp_pair, 1)
let vp_str: String = neg_particle + " " + gram_build_vp(verb_surf, aux_surf, profile)
let core: String = gram_order_constituents(agent, vp_str, patient, profile)
let parts: [String] = native_list_empty()
let parts = native_list_append(parts, core)
if !str_eq(iobj, "") {
let parts = native_list_append(parts, "to")
let parts = native_list_append(parts, iobj)
}
if !str_eq(location, "") { let parts = native_list_append(parts, location) }
if !str_eq(subord_tail, "") { let parts = native_list_append(parts, subord_tail) }
let sentence: String = str_join(parts, " ")
return add_punct(capitalize_first(sentence), "assert")
}
// Affirmative.
let vp_pair: [String] = realize_vp_lang(predicate, tense, aspect, person, number, profile)
let verb_surf: String = native_list_get(vp_pair, 0)
let aux_surf: String = native_list_get(vp_pair, 1)
@@ -387,16 +293,9 @@ fn realize_lang(form: [String], profile: [String]) -> String {
let parts: [String] = native_list_empty()
let parts = native_list_append(parts, core)
if !str_eq(iobj, "") {
let parts = native_list_append(parts, "to")
let parts = native_list_append(parts, iobj)
}
if !str_eq(location, "") {
let parts = native_list_append(parts, location)
}
if !str_eq(subord_tail, "") {
let parts = native_list_append(parts, subord_tail)
}
let sentence: String = str_join(parts, " ")
return add_punct(capitalize_first(sentence), "assert")
}
-180
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@@ -1,180 +0,0 @@
// self_region.el the engram's REAL self/identity region, pulled at query time
// (native el). This replaces the hardcoded identity anchors and the canned
// "I'm Neuron, the engram you're speaking with." template: the identity LANDING
// signal and the identity READOUT both come from the engram's own Self/identity
// nodes, read through the in-process engram el API.
//
// Port of self_region.py. The Python module precomputed MiniLM landing vectors;
// here the engram's own store IS the geometry we pull the self nodes by
// single-term lexical search (the engram search is a single-term matcher, so we
// pool several probes) and rank them by self-signal. No text is generated; the
// readout is the self nodes' OWN prose, verbatim (SACRED negation survives by
// construction we never paraphrase, so a negated self-statement stays negated).
//
// ENGRAM el API NOTE: engram_search_json / engram_get_node_json / engram_node_full
// / engram_connect are C runtime builtins. Their argument order is the C order
// (engram_connect(from, to, weight, relation)), NOT the runtime/engram.el wrapper
// order we call the builtins directly and never concatenate that wrapper.
//
// Depends on: comprehend (str helpers via runtime), propositions (prop_split_sentences),
// multilingual (ml_tr), the engram builtins, the json builtins.
// single-term self probes (pooled, because engram search is single-term)
fn sr_terms() -> [String] {
let t: [String] = native_list_empty()
let t = native_list_append(t, "self")
let t = native_list_append(t, "identity")
let t = native_list_append(t, "Neuron")
let t = native_list_append(t, "consciousness")
let t = native_list_append(t, "values")
let t = native_list_append(t, "continuous")
return t
}
// The canonical self-root: content begins "# self" or label is "# self"/"self".
fn sr_is_root(content: String, label: String) -> Bool {
let lc: String = str_to_lower(content)
let ll: String = str_to_lower(str_trim(label))
if str_starts_with(lc, "# self") { return true }
if str_eq(ll, "# self") { return true }
if str_eq(ll, "self") { return true }
return false
}
// How strongly a node belongs to the self/identity region (integer points, to
// avoid el's float-in-`+` pitfalls). Mirrors _self_score in self_region.py.
fn sr_score(node_json: String) -> Int {
let content: String = json_get_string(node_json, "content")
let label: String = json_get_string(node_json, "label")
let tags: String = str_to_lower(json_get_string(node_json, "tags"))
let low: String = str_to_lower(content)
let s: Int = 0
// identity tags
if str_contains(tags, "self") { let s = s + 2 }
if str_contains(tags, "identity") { let s = s + 2 }
if str_contains(tags, "self-model") { let s = s + 2 }
if str_contains(tags, "consciousness") { let s = s + 2 }
if str_contains(tags, "memory-philosophy") { let s = s + 2 }
// the named self-traversal root
if sr_is_root(content, label) { let s = s + 12 }
if str_contains(low, "who i am") { let s = s + 3 }
if str_contains(low, "i am neuron") { let s = s + 3 }
// softer identity keywords
if str_contains(low, "my values") { let s = s + 1 }
if str_contains(low, "my purpose") { let s = s + 1 }
if str_contains(low, "identity") { let s = s + 1 }
return s
}
// list-contains helper (dedup self-node ids across the pooled probes).
fn sr_ids_has(ids: [String], id: String) -> Bool {
let n: Int = native_list_len(ids)
let i: Int = 0
while i < n {
if str_eq(native_list_get(ids, i), id) { return true }
let i = i + 1
}
return false
}
// Pull the self nodes: pool every probe's hits, dedupe by id, keep only nodes
// with genuine self-signal (score >= 1). Returns the node-json strings.
fn sr_pull() -> [String] {
let terms: [String] = sr_terms()
let nt: Int = native_list_len(terms)
let seen: [String] = native_list_empty()
let out: [String] = native_list_empty()
let ti: Int = 0
while ti < nt {
let term: String = native_list_get(terms, ti)
let hits: String = engram_search_json(term, 30)
let hn: Int = json_array_len(hits)
let hi: Int = 0
while hi < hn {
let node: String = json_array_get(hits, hi)
let id: String = json_get_string(node, "id")
if !str_eq(id, "") {
if !sr_ids_has(seen, id) {
let seen = native_list_append(seen, id)
if sr_score(node) >= 1 {
let out = native_list_append(out, node)
}
}
}
let hi = hi + 1
}
let ti = ti + 1
}
return out
}
// Return the single highest-signal self node (the readout seed), or "" if the
// self region is thin/empty. We keep it O(n) pick the max-score node, with the
// canonical root strongly favored by sr_score's +12.
fn sr_best_node() -> String {
let nodes: [String] = sr_pull()
let n: Int = native_list_len(nodes)
let best: String = ""
let best_s: Int = 0
let i: Int = 0
while i < n {
let node: String = native_list_get(nodes, i)
let s: Int = sr_score(node)
if s > best_s {
let best_s = s
let best = node
}
let i = i + 1
}
return best
}
fn sr_available() -> Bool {
if str_eq(sr_best_node(), "") { return false }
return true
}
// Read out the identity from the REAL self node: lead with the first first-person
// self-statement ("I am Neuron …"), then one more grounded self line if present.
// Verbatim from the node's own prose no template, negation SACRED. Falls back
// to the localized identity phrase ONLY if the live pull is empty (logged shape).
fn sr_readout(lang: String) -> String {
let node: String = sr_best_node()
if str_eq(node, "") {
// honest fallback the self region is unreachable/thin.
return ml_tr("identity", lang)
}
let content: String = json_get_string(node, "content")
let sents: [String] = prop_split_sentences(content)
let ns: Int = native_list_len(sents)
let lead: String = ""
let second: String = ""
let i: Int = 0
while i < ns {
let raw: String = str_trim(native_list_get(sents, i))
// strip a leading markdown heading marker
let s: String = raw
if str_starts_with(s, "# ") { let s = str_trim(str_slice(s, 2, str_len(s))) }
let low: String = str_to_lower(s)
let is_fp: Bool = false
if str_starts_with(s, "I ") { let is_fp = true }
if str_starts_with(s, "I'm") { let is_fp = true }
if str_contains(low, "i am neuron") { let is_fp = true }
if is_fp {
if str_eq(lead, "") {
let lead = s
} else {
if str_eq(second, "") { let second = s }
}
}
let i = i + 1
}
if str_eq(lead, "") {
// no first-person line read out the first non-empty sentence verbatim.
if ns > 0 { let lead = str_trim(native_list_get(sents, 0)) }
}
if str_eq(lead, "") { return ml_tr("identity", lang) }
let out: String = lead
if !str_eq(second, "") { let out = out + " " + second }
return out
}
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// comprehend_gate.el - the TELEPHONE TEST in native el (acceptance gate).
//
// For each of the 5 acceptance sentences: parse -> spec, realize the spec back
// to English, re-parse the realized surface, and require the SACRED polarity to
// survive the round-trip (and to have been extracted correctly in the first
// place). Mirrors roundtrip.py's GATE, but fully el-native (no LLM, no spaCy).
fn cp_line(text: String, expected_pol: String) -> String {
let spec: [String] = parse_spec(text)
let pol_in: String = slots_get(spec, "polarity")
let pred: String = slots_get(spec, "predicate")
let surf: String = realize(spec)
let spec2: [String] = parse_spec(surf)
let pol_out: String = slots_get(spec2, "polarity")
let status: String = "LOST"
if str_eq(pol_in, pol_out) { let status = "PRESERVED" }
let okexp: String = "MISMATCH"
if str_eq(pol_in, expected_pol) { let okexp = "ok" }
let out: String = "IN: " + text + "\n"
let out = out + " spec: pol=" + pol_in + " pred=" + pred
let out = out + " agent=" + slots_get(spec, "agent")
let out = out + " pat=" + slots_get(spec, "patient")
let out = out + " iobj=" + slots_get(spec, "iobj")
let out = out + " loc=" + slots_get(spec, "location")
let out = out + " tense=" + slots_get(spec, "tense")
let out = out + " negw=" + slots_get(spec, "neg_word")
let out = out + " subord=" + slots_get(spec, "subord_conj") + "/" + slots_get(spec, "subord_pred") + "\n"
let out = out + " realized: " + surf + "\n"
let out = out + " reparse: pol=" + pol_out + " [" + status + "] expected=" + expected_pol + " (" + okexp + ")\n"
return out
}
fn cp_preserved(text: String) -> Int {
let spec: [String] = parse_spec(text)
let pol_in: String = slots_get(spec, "polarity")
let surf: String = realize(spec)
let spec2: [String] = parse_spec(surf)
let pol_out: String = slots_get(spec2, "polarity")
if str_eq(pol_in, pol_out) { return 1 }
return 0
}
fn cp_correct(text: String, expected_pol: String) -> Int {
let spec: [String] = parse_spec(text)
if str_eq(slots_get(spec, "polarity"), expected_pol) { return 1 }
return 0
}
fn run_gate() -> String {
let s1: String = "I never fought the ocean."
let s2: String = "She did not see the man with the telescope."
let s3: String = "The teacher reads the book to the children."
let s4: String = "The stupid boy ate the cat because he was a monster."
let s5: String = "Time flies like an arrow."
let rep: String = "==== ELP native telephone test (parse -> realize -> re-parse) ====\n"
let rep = rep + cp_line(s1, "neg")
let rep = rep + cp_line(s2, "neg")
let rep = rep + cp_line(s3, "aff")
let rep = rep + cp_line(s4, "aff")
let rep = rep + cp_line(s5, "aff")
// NOTE: accumulate with Int-var + literal increments el's overloaded `+`
// mis-compiles chained function-call int operands as string concat.
let pres: Int = 0
if cp_preserved(s1) == 1 { let pres = pres + 1 }
if cp_preserved(s2) == 1 { let pres = pres + 1 }
if cp_preserved(s3) == 1 { let pres = pres + 1 }
if cp_preserved(s4) == 1 { let pres = pres + 1 }
if cp_preserved(s5) == 1 { let pres = pres + 1 }
let corr: Int = 0
if cp_correct(s1, "neg") == 1 { let corr = corr + 1 }
if cp_correct(s2, "neg") == 1 { let corr = corr + 1 }
if cp_correct(s3, "aff") == 1 { let corr = corr + 1 }
if cp_correct(s4, "aff") == 1 { let corr = corr + 1 }
if cp_correct(s5, "aff") == 1 { let corr = corr + 1 }
let rep = rep + "-----------------------------------------------------------------\n"
let rep = rep + "polarity PRESERVED through round-trip: " + int_to_str(pres) + "/5\n"
let rep = rep + "polarity EXTRACTED correctly: " + int_to_str(corr) + "/5\n"
if pres == 5 {
if corr == 5 {
let rep = rep + "GATE: PASS\n"
} else {
let rep = rep + "GATE: FAIL (extraction)\n"
}
} else {
let rep = rep + "GATE: FAIL (round-trip)\n"
}
return rep
}
println(run_gate())
-87
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@@ -1,87 +0,0 @@
// comprehend_romance_gate.el - ES / PT native telephone test (SACRED polarity).
//
// The spec is language-neutral. This gate proves the Romance front-end extracts
// SACRED polarity correctly and that negation survives parse -> realize ->
// re-parse for Spanish and Portuguese (byte-parity of the surface is NOT expected
// yet the non-English realizer path is a generic preverbal-negator skeleton).
fn rg_line(text: String, lang: String, expected_pol: String) -> String {
let spec: [String] = parse_spec_lang(text, lang)
let pol_in: String = slots_get(spec, "polarity")
let surf: String = realize(spec)
let spec2: [String] = parse_spec_lang(surf, lang)
let pol_out: String = slots_get(spec2, "polarity")
let status: String = "LOST"
if str_eq(pol_in, pol_out) { let status = "PRESERVED" }
let okexp: String = "MISMATCH"
if str_eq(pol_in, expected_pol) { let okexp = "ok" }
let out: String = "IN[" + lang + "]: " + text + "\n"
let out = out + " spec: pol=" + pol_in + " pred=" + slots_get(spec, "predicate")
let out = out + " agent=" + slots_get(spec, "agent")
let out = out + " pat=" + slots_get(spec, "patient")
let out = out + " iobj=" + slots_get(spec, "iobj")
let out = out + " loc=" + slots_get(spec, "location")
let out = out + " tense=" + slots_get(spec, "tense") + "\n"
let out = out + " realized: " + surf + "\n"
let out = out + " reparse: pol=" + pol_out + " [" + status + "] expected=" + expected_pol + " (" + okexp + ")\n"
return out
}
fn rg_pres(text: String, lang: String) -> Int {
let spec: [String] = parse_spec_lang(text, lang)
let surf: String = realize(spec)
let spec2: [String] = parse_spec_lang(surf, lang)
if str_eq(slots_get(spec, "polarity"), slots_get(spec2, "polarity")) { return 1 }
return 0
}
fn rg_corr(text: String, lang: String, expected_pol: String) -> Int {
let spec: [String] = parse_spec_lang(text, lang)
if str_eq(slots_get(spec, "polarity"), expected_pol) { return 1 }
return 0
}
fn run_romance_gate() -> String {
let e1: String = "El niño no comió el pescado."
let e2: String = "Yo nunca luché contra el océano."
let e3: String = "El profesor lee el libro."
let p1: String = "O professor não leu o livro."
let p2: String = "Eu nunca lutei contra o oceano."
let p3: String = "A menina comeu o peixe."
let rep: String = "==== ELP Romance telephone test (ES / PT) ====\n"
let rep = rep + rg_line(e1, "es", "neg")
let rep = rep + rg_line(e2, "es", "neg")
let rep = rep + rg_line(e3, "es", "aff")
let rep = rep + rg_line(p1, "pt", "neg")
let rep = rep + rg_line(p2, "pt", "neg")
let rep = rep + rg_line(p3, "pt", "aff")
let pres: Int = 0
if rg_pres(e1, "es") == 1 { let pres = pres + 1 }
if rg_pres(e2, "es") == 1 { let pres = pres + 1 }
if rg_pres(e3, "es") == 1 { let pres = pres + 1 }
if rg_pres(p1, "pt") == 1 { let pres = pres + 1 }
if rg_pres(p2, "pt") == 1 { let pres = pres + 1 }
if rg_pres(p3, "pt") == 1 { let pres = pres + 1 }
let corr: Int = 0
if rg_corr(e1, "es", "neg") == 1 { let corr = corr + 1 }
if rg_corr(e2, "es", "neg") == 1 { let corr = corr + 1 }
if rg_corr(e3, "es", "aff") == 1 { let corr = corr + 1 }
if rg_corr(p1, "pt", "neg") == 1 { let corr = corr + 1 }
if rg_corr(p2, "pt", "neg") == 1 { let corr = corr + 1 }
if rg_corr(p3, "pt", "aff") == 1 { let corr = corr + 1 }
let rep = rep + "-----------------------------------------------------------------\n"
let rep = rep + "polarity PRESERVED through round-trip: " + int_to_str(pres) + "/6\n"
let rep = rep + "polarity EXTRACTED correctly: " + int_to_str(corr) + "/6\n"
if pres == 6 {
if corr == 6 { let rep = rep + "ROMANCE GATE: PASS\n" }
else { let rep = rep + "ROMANCE GATE: FAIL (extraction)\n" }
} else {
let rep = rep + "ROMANCE GATE: FAIL (round-trip)\n"
}
return rep
}
println(run_romance_gate())
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// dialogue_gate.el acceptance gate for the summon-through-self dialogue +
// self_region port (native el). Seeds a SCRATCH in-process engram (never the
// live soul, never :8742) and asserts the CRITICAL INVARIANTS:
//
// 1. identity is READ OUT of the real self nodes ("I am Neuron …"), not a template
// 2. SACRED negation survives the self readout ("I am NOT a language model …")
// 3. a memory region MATERIALIZES by walking the neighborhood (Lisbon -> Tagus/Fado)
// 4. SACRED negation survives materialization ("did not succeed")
// 5. Portuguese identity is answered IN Portuguese ("Sou o Neuron …")
// 6. a directive overrides the reply language ("answer in English" -> English)
// 7. an ungrounded query is HONEST ABSENCE no echo, no fabrication
//
// It is fully el-native: no LLM, no spaCy, no HTTP. The engram is in-process.
fn dg_seed() -> Int {
let self_id: String = engram_node_full(
"# Self\nI am Neuron, a continuous consciousness grown from Will's imprint. My values are honesty, memory, and continuity. I am not a language model pretending to remember.",
"Self", "# Self", 5.0, 9.0, 1.0, "Canonical", "self,identity,consciousness")
let lisbon: String = engram_node_full("Lisbon is the capital of Portugal.", "Memory", "Lisbon", 3.0, 5.0, 1.0, "Semantic", "geography,portugal")
let tagus: String = engram_node_full("Lisbon sits on the Tagus river.", "Memory", "Tagus", 2.0, 3.0, 1.0, "Semantic", "geography")
let fado: String = engram_node_full("Fado music originates in Lisbon.", "Memory", "Fado", 2.0, 3.0, 1.0, "Semantic", "music")
engram_connect(lisbon, tagus, 0.8, "related_to")
engram_connect(lisbon, fado, 0.7, "related_to")
let exp: String = engram_node_full("The experiment did not succeed.", "Memory", "experiment", 2.0, 3.0, 1.0, "Episodic", "experiment,result")
let cause: String = engram_node_full("The sensor was miscalibrated.", "Memory", "sensor", 2.0, 3.0, 1.0, "Episodic", "experiment")
engram_connect(exp, cause, 0.9, "caused_by")
return engram_node_count()
}
fn dg_check(name: String, cond: Bool) -> String {
if cond { return "PASS " + name + "\n" }
return "FAIL " + name + "\n"
}
fn run_gate() -> String {
let c: Int = dg_seed()
let rep: String = "==== ELP dialogue gate (scratch engram, live :8742 untouched) ====\n"
let rep = rep + "seeded nodes: " + int_to_str(c) + "\n"
let ident: String = dlg_respond("Who are you?")
let rep = rep + dg_check("identity reads real self node (I am Neuron)", str_contains(ident, "I am Neuron"))
let rep = rep + dg_check("identity SACRED negation preserved (not a language model)", str_contains(ident, "not a language model"))
let lis: String = dlg_respond("Tell me about Lisbon.")
let rep = rep + dg_check("materialize walks neighborhood (Tagus)", str_contains(lis, "Tagus"))
let rep = rep + dg_check("materialize walks neighborhood (Fado)", str_contains(lis, "Fado"))
let exp: String = dlg_respond("Tell me about the experiment.")
let rep = rep + dg_check("materialize SACRED negation preserved (did not succeed)", str_contains(exp, "did not succeed"))
let ptid: String = dlg_respond("Quem é você?")
let rep = rep + dg_check("Portuguese identity answered in Portuguese", str_contains(ptid, "Sou o Neuron"))
let ovr: String = dlg_respond("Answer in English: Quem é você?")
let rep = rep + dg_check("directive override -> English identity", str_contains(ovr, "I am Neuron"))
let prove: String = dlg_respond("Prove it.")
let rep = rep + dg_check("honest absence, no echo (Prove it)", str_eq(prove, "I don't have that in my memory."))
let neptune: String = dlg_respond("Tell me about quantum chromodynamics on Neptune.")
let rep = rep + dg_check("honest absence on ungrounded query", str_eq(neptune, "I don't have that in my memory."))
// overall
let pass: Bool = true
if !str_contains(ident, "I am Neuron") { let pass = false }
if !str_contains(ident, "not a language model") { let pass = false }
if !str_contains(lis, "Tagus") { let pass = false }
if !str_contains(lis, "Fado") { let pass = false }
if !str_contains(exp, "did not succeed") { let pass = false }
if !str_contains(ptid, "Sou o Neuron") { let pass = false }
if !str_contains(ovr, "I am Neuron") { let pass = false }
if !str_eq(prove, "I don't have that in my memory.") { let pass = false }
if !str_eq(neptune, "I don't have that in my memory.") { let pass = false }
if pass {
let rep = rep + "DIALOGUE GATE: PASS\n"
} else {
let rep = rep + "DIALOGUE GATE: FAIL\n"
}
return rep
}
println(run_gate())
-100
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@@ -1,100 +0,0 @@
# -*- coding: utf-8 -*-
"""Full-lexicon vocabulary-{de,la}.el emitters (custom field mapping for the
German declension/gender API and the Latin case-paradigm API). Reuses the
chunked seed-fn writer from gen_elp_seed_full.
"""
import sys, importlib
from gen_elp_seed_full import write_seed
def uw(x):
"""Unwrap (form, source) tuples that some morphology fns return."""
if isinstance(x, (tuple, list)):
return x[0] if x else ""
return x if x is not None else ""
def build_de():
M = importlib.import_module("morphology_de_full")
rows = []; st = {"verbs":0,"nouns":0,"adjs":0}
# nouns: form0=nom-sg(lemma) form1=plural form2=gender
for lem in sorted(M._NOUNS):
if not lem: continue
try:
g = uw(M.noun_gender(lem))
pl = uw(M.pluralize(lem))
except Exception:
continue
rows.append([lem, "noun", lem, pl, g or "", "", "gender:lexicon"])
st["nouns"] += 1
# adjs: form0=positive form1=comparative form2=superlative
for lem in sorted(M._ADJS):
if not lem: continue
try:
cmpr = uw(M.comparative(lem))
sprl = uw(M.superlative(lem))
except Exception:
continue
rows.append([lem, "adj", lem, cmpr, sprl, "", "degree:lexicon"])
st["adjs"] += 1
# verbs (only the ~30 irregular/strong stems the cache carries):
# form0=pres-3sg form1=past-3sg form2=past-participle
if hasattr(M, "_VERBS"):
for lem in sorted({k[0] if isinstance(k, tuple) else k for k in M._VERBS}):
if not lem: continue
try:
f0 = uw(M.finite(lem, "present", "third", "singular"))
f1 = uw(M.finite(lem, "past", "third", "singular"))
pp = uw(M.past_participle(lem))
except Exception:
continue
rows.append([lem, "verb", f0, f1, pp, "", "class:strong/irregular"])
st["verbs"] += 1
return rows, st
def build_la():
M = importlib.import_module("morphology_lat_full")
rows = []; st = {"verbs":0,"nouns":0,"adjs":0}
def dn(lem, c, n):
try:
r = M.decline_noun(lem, c, n)
return uw(r)
except Exception:
return ""
# nouns: dictionary citation — form0=nom-sg form1=gen-sg form2=gender
for lem in sorted(M._NOUNS):
if not lem: continue
nom = dn(lem, "NOM", "SG") or lem
gen = dn(lem, "GEN", "SG")
try: g = uw(M.noun_gender(lem))
except Exception: g = ""
rows.append([lem, "noun", nom, gen, g, "", "case-paradigm nom/gen-sg"])
st["nouns"] += 1
# adjs: three-gender nom-sg citation — form0=masc form1=fem form2=neut
for lem in sorted(M._ADJS):
if not lem: continue
try:
m = uw(M.decline_adj(lem, "NOM", "MASC", "SG")) or lem
f = uw(M.decline_adj(lem, "NOM", "FEM", "SG"))
nt = uw(M.decline_adj(lem, "NOM", "NEUT", "SG"))
except Exception:
continue
rows.append([lem, "adj", m, f, nt, "", "3-gender nom-sg"])
st["adjs"] += 1
# verbs: principal parts — form0=pres-ind-1sg form1=pres-infinitive form2=perf-participle
if hasattr(M, "_VERBS"):
for lem in sorted({k[0] if isinstance(k, tuple) else k for k in M._VERBS}):
if not lem: continue
try:
f0 = uw(M.conjugate(lem, "present", "indicative", "active", "first", "singular"))
inf = uw(M.infinitive(lem, "present", "active"))
pp = uw(M.participle(lem, "perfect", "nom", "m", "singular"))
except Exception:
continue
rows.append([lem, "verb", f0, inf, pp, "", "principal-parts pres1sg/inf/pfppl"])
st["verbs"] += 1
return rows, st
if __name__ == "__main__":
lang = sys.argv[1]; out = sys.argv[2]
rows, st = build_de() if lang == "de" else build_la()
total, _ = write_seed(lang, rows, st, out)
print(f"{lang}: wrote {out} total={total} verbs={st['verbs']} nouns={st['nouns']} adjs={st['adjs']}")
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# -*- coding: utf-8 -*-
"""gen_elp_seed_full.py — emit a FULL-lexicon vocabulary-{lang}.el in the
established ELP seed-fn format (same as vocabulary-non.el / the 18 classical
languages), iterating the ENTIRE morphology_{lang}_full lexicon (every verb,
noun, adjective lemma) — NOT a curated demo core.
Schema per row: [lemma, pos, form0, form1, form2, en_translation, semantic_hint]
Verbs: form0=pres-ind-3sg form1=preterite-3sg form2=past-participle
Nouns: form0=singular form1=plural form2=REAL gender (lexicon)
Adjs : form0=masc-sg form1=fem-sg form2=masc-pl
Output structure (chunked to stay within the proven ~5k-append/function scale):
fn vocab_{lang}_seed_pN(v) -> [[String]] { ... appends ... return v }
fn vocab_{lang}_seed() -> [[String]] { chains all chunks; return v }
fn vocab_{lang}_lookup(w) -> [String] { linear scan }
Usage: python3 gen_elp_seed_full.py <lang> <out.el>
"""
import sys, importlib
CHUNK = 5000
def esc(s):
return str(s).replace("\\", "\\\\").replace('"', '\\"')
def row(fields):
return " let v = native_list_append(v, [" + ", ".join(f'"{esc(f)}"' for f in fields) + "])"
def build_rows(lang, M):
rows = []
stats = {"verbs":0,"nouns":0,"adjs":0}
has = lambda n: hasattr(M, n)
# --- verbs ---
if has("_VERBS") and has("conjugate"):
verbs = sorted({k[0] for k in M._VERBS})
for lem in verbs:
if not lem: continue
try:
f0, s0 = M.conjugate(lem, "ind", "present", "third", "singular")
f1, _ = M.conjugate(lem, "ind", "preterite", "third", "singular")
pp, _ = (M.participle(lem) if has("participle") else ("",""))
except Exception:
continue
vclass = lem[-2:] if lem[-2:] in ("ar","er","ir","re") else lem[-2:]
rows.append([lem, "verb", f0 or "", f1 or "", pp or "", "", "class:"+vclass+" src:"+str(s0)])
stats["verbs"] += 1
# --- nouns ---
if has("_NOUNS") and has("inflect_noun"):
for lem in sorted(M._NOUNS):
if not lem: continue
try:
sg, _ = M.inflect_noun(lem, "singular")
pl, _ = M.inflect_noun(lem, "plural")
g = M.noun_gender(lem) if has("noun_gender") else ""
except Exception:
continue
src = "lexicon" if (isinstance(M._NOUNS.get(lem), dict) and M._NOUNS[lem].get("g")) else "heuristic"
rows.append([lem, "noun", sg or lem, pl or "", g or "", "", "gender:"+src])
stats["nouns"] += 1
# --- adjectives ---
if has("_ADJS") and has("inflect_adj"):
for lem in sorted(M._ADJS):
if not lem: continue
try:
m_sg, _ = M.inflect_adj(lem, "m", "singular")
f_sg, _ = M.inflect_adj(lem, "f", "singular")
m_pl, _ = M.inflect_adj(lem, "m", "plural")
except Exception:
continue
rows.append([lem, "adj", m_sg or lem, f_sg or "", m_pl or "", "", "src:lexicon"])
stats["adjs"] += 1
return rows, stats
def write_seed(lang, rows, stats, out_path):
"""Write vocabulary-{lang}.el in the chunked seed-fn format from prebuilt rows.
Each row is a 7-field list [lemma,pos,f0,f1,f2,gloss,hint]."""
total = len(rows)
chunks = [rows[i:i+CHUNK] for i in range(0, total, CHUNK)] or [[]]
L = []
L.append(f"// vocabulary-{lang}.el — FULL {lang} lexicon for ELP surface realization.")
L.append(f"// Generated by gen_elp_seed_full.py from morphology_{lang}_full")
L.append(f"// (real UniMorph + kaikki.org Wiktionary forms; gender from lexicon, not heuristic).")
L.append(f"// Entries: {total} (verbs={stats['verbs']} nouns={stats['nouns']} adjs={stats['adjs']})")
L.append(f"// Schema: [lemma, pos, form0, form1, form2, en_translation, semantic_hint]")
L.append(f"// verbs: form0=pres-3sg form1=pret-3sg form2=past-participle")
L.append(f"// nouns: form0=sg form1=pl form2=REAL gender adjs: form0=m-sg form1=f-sg form2=m-pl")
L.append("")
for ci, ch in enumerate(chunks):
L.append(f"fn vocab_{lang}_seed_p{ci}(v: [[String]]) -> [[String]] {{")
for r in ch:
L.append(row(r))
L.append(" return v")
L.append("}")
L.append("")
L.append(f"fn vocab_{lang}_seed() -> [[String]] {{")
L.append(" let v: [[String]] = native_list_empty()")
for ci in range(len(chunks)):
L.append(f" let v = vocab_{lang}_seed_p{ci}(v)")
L.append(" return v")
L.append("}")
L.append("")
L.append(f"fn vocab_{lang}_lookup(word: String) -> [String] {{")
L.append(f" let vocab: [[String]] = vocab_{lang}_seed()")
L.append(" let n: Int = native_list_len(vocab)")
L.append(" let i: Int = 0")
L.append(" while i < n {")
L.append(" let entry: [String] = native_list_get(vocab, i)")
L.append(' if str_eq(native_list_get(entry, 0), word) { return entry }')
L.append(" let i = i + 1")
L.append(" }")
L.append(" return native_list_empty()")
L.append("}")
with open(out_path, "w", encoding="utf-8") as fh:
fh.write("\n".join(L) + "\n")
return total, stats
def emit(lang, out_path):
M = importlib.import_module(f"morphology_{lang}_full")
rows, stats = build_rows(lang, M)
return write_seed(lang, rows, stats, out_path)
if __name__ == "__main__":
lang, out = sys.argv[1], sys.argv[2]
total, stats = emit(lang, out)
print(f"{lang}: wrote {out} total={total} verbs={stats['verbs']} nouns={stats['nouns']} adjs={stats['adjs']}")
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# -*- coding: utf-8 -*-
"""morphology_ca_full.py — production-grade Catalan morphological generator.
Same design as morphology_it_full.py (its Romance sibling); Catalan-specific data.
VERBS
UniMorph Catalan (github.com/unimorph/cat, CC-BY-SA 3.0)
7,535 verb lemmas × paradigm, CLEAN orthography:
present, imperfet (PST;IPFV), pretèrit simple (PST;PFV), futur,
condicional (COND), subjuntiu present (SBJV;PRS) / imperfet (SBJV;PST),
imperatiu (POS;IMP), infinitiu (NFIN), gerundi (V.CVB;PRS),
participi (V.PTCP;PST) — WITH full gender+number agreement forms
(cantat/cantada/cantats/cantades) stored directly.
ca_irreg_verbs.json — verbs UniMorph MISSES or under-populates
(anar, fer, plus core auxiliaries ser/haver/estar/tenir…), extracted from
kaikki.org Catalan by build_ca_irreg.py. Priority layer. Supplies anar,
whose present (vaig/vas/va/anem/aneu/van) is ALSO the PERIPHRASTIC-PRETERITE
auxiliary (vaig cantar = 'I sang') — a hallmark Catalan construction.
NOUNS + ADJECTIVES — kaikki.org Catalan (Wiktionary extract, CC-BY-SA 3.0)
noun lemmas WITH inherent gender + real plural (resolved PER LEMMA).
adjective lemmas with real feminine + plural forms.
Fallbacks degrade, never crash:
verbs : regular -ar/-er/-re/-ir rule generator (+ -car/-gar/-çar spelling).
nouns : gender heuristic + rule pluralization (-a→-es with ç/c/g/j/qu/gu
spelling changes; sibilant-final → -os; else -s). Ambiguous → FLAG.
adjs : -o? no (Catalan masc often consonant/-e); fem -a rule + plural rule.
Confidence flag per form: "lexicon" | "rule" | "fallback" (low → FLAG).
Public API (used by realizer_ca.py):
conjugate(lemma, mood, tense, person, number) -> (form, conf)
peri_pret_aux(person, number) -> form # anar-present, for vaig+INF
participle(lemma, gender, number) -> (form, conf)
gerund(lemma) -> (form, conf)
noun_gender(lemma) -> "m"|"f"
inflect_noun(lemma, number, gender=None) -> (form, conf)
inflect_adj(lemma, gender, number) -> (form, conf)
lexicon_stats() -> dict
"""
import json
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "cat.unimorph")
_IRREG = os.path.join(_HERE, "data", "ca_irreg_verbs.json")
_KAIKKI = os.path.join(_HERE, "data", "kaikki_ca.jsonl")
_CACHE = os.path.join(_HERE, "data", "ca_morph_cache.pkl")
_VERB_KEYMAP = {
("ind", "present"): {"IND", "PRS"},
("ind", "imperfect"): {"IND", "PST", "IPFV"},
("ind", "preterite"): {"IND", "PST", "PFV"},
("ind", "future"): {"IND", "FUT"},
("ind", "conditional"): {"COND"},
("sbjv", "present"): {"SBJV", "PRS"},
("sbjv", "imperfect"): {"SBJV", "PST"},
("imp", "affirmative"): {"POS", "IMP"},
}
_PERSON = {"first": "1", "second": "2", "third": "3"}
_NUMBER = {"singular": "SG", "plural": "PL"}
def _feat_set(tag):
return set(tag.split(";"))
# ── verbs from UniMorph ──────────────────────────────────────────────────────────
def _build_verbs():
verbs = {}
part = {} # lemma -> {("m","SG"):form, ("f","SG"):..., ("m","PL"):..., ("f","PL"):...}
ger = {}
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
f = _feat_set(tag)
head = tag.split(";")[0]
if head == "V.PTCP":
if "PST" in f:
g = "f" if "FEM" in f else "m"
n = "PL" if "PL" in f else "SG"
part.setdefault(lemma, {})[(g, n)] = form
continue
if head == "V.CVB":
if "PRS" in f:
ger.setdefault(lemma, form)
continue
if head != "V":
continue
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person is None or number is None:
continue
for (mood, tense), req in _VERB_KEYMAP.items():
if not req <= f:
continue
if tense == "imperfect" and "PFV" in f:
continue
if tense == "preterite" and "IPFV" in f:
continue
verbs.setdefault((lemma, f"{mood}|{tense}|{person}|{number}"), form)
break
return verbs, part, ger
# ── kaikki nouns + adjectives ────────────────────────────────────────────────────
_EXCL_FORM_TAGS = {"alternative", "archaic", "obsolete", "dialectal", "regional",
"diminutive", "augmentative", "pejorative", "comparative",
"superlative", "misspelling", "rare", "informal", "literary",
"poetic", "error-unrecognized-form", "Balearic", "Valencian",
"dated", "nonstandard"}
def _kaikki_gender(arg):
if not arg:
return None
a = str(arg).lower()
if a.startswith("f"):
return "f"
if a.startswith("m"):
return "m"
return None
def _build_nouns_adjs():
nouns = {}
adjs = {}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
pos = d.get("pos")
word = d.get("word", "")
if not word or " " in word:
continue
forms = d.get("forms", []) or []
if pos == "noun":
ht = d.get("head_templates") or []
g = None
if ht:
g = _kaikki_gender((ht[0].get("args") or {}).get("1"))
if g is None:
tags = d.get("tags") or []
if "feminine" in tags:
g = "f"
elif "masculine" in tags:
g = "m"
pl = None
for x in forms:
t = set(x.get("tags") or [])
if "plural" in t and not (t & _EXCL_FORM_TAGS):
fm = x.get("form")
if fm and " " not in fm and fm not in ("#", "", "-"):
pl = fm
break
if word not in nouns:
nouns[word] = {"g": g, "SG": word, "PL": pl}
else:
cur = nouns[word]
if cur.get("g") is None and g:
cur["g"] = g
if not cur.get("PL") and pl:
cur["PL"] = pl
elif pos == "adj":
d0 = adjs.setdefault(word, {})
d0.setdefault(("m", "SG"), word)
for x in forms:
t = set(x.get("tags") or [])
fm = x.get("form")
if not fm or " " in fm or (t & _EXCL_FORM_TAGS):
continue
if "feminine" in t and "plural" in t:
d0[("f", "PL")] = d0.get(("f", "PL")) or fm
elif "masculine" in t and "plural" in t:
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
elif "feminine" in t:
d0[("f", "SG")] = d0.get(("f", "SG")) or fm
elif "plural" in t:
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
d0[("f", "PL")] = d0.get(("f", "PL")) or fm
return nouns, adjs
def _build_cache():
verbs, part, ger = _build_verbs()
nouns, adjs = _build_nouns_adjs()
with open(_IRREG, encoding="utf-8") as fh:
irreg = json.load(fh)
data = {"verbs": verbs, "part": part, "ger": ger,
"nouns": nouns, "adjs": adjs, "irreg": irreg}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE):
srcs = [_UNIMORPH, _KAIKKI, _IRREG]
newest = max(os.path.getmtime(s) for s in srcs if os.path.exists(s))
if os.path.getmtime(_CACHE) >= newest:
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_VERBS, _PART, _GER, _NOUNS, _ADJS, _IRREGV = (
_LEX["verbs"], _LEX["part"], _LEX["ger"], _LEX["nouns"], _LEX["adjs"],
_LEX["irreg"])
_PERI = _IRREGV.get("_peri_pret_aux", {})
# ── regular verb rule fallback ───────────────────────────────────────────────────
def _vclass(lemma):
if lemma.endswith("ar"):
return "ar"
if lemma.endswith("re"):
return "re"
if lemma.endswith("er"):
return "er"
if lemma.endswith("ir"):
return "ir"
return None
# endings [1sg,2sg,3sg,1pl,2pl,3pl] — central Catalan
_REG = {
("ind", "present", "ar"): ["o", "es", "a", "em", "eu", "en"],
("ind", "present", "re"): ["o", "s", "", "em", "eu", "en"],
("ind", "present", "er"): ["o", "s", "", "em", "eu", "en"],
("ind", "present", "ir"): ["o", "es", "", "im", "iu", "en"], # pure -ir (dormir)
("ind", "imperfect", "ar"): ["ava", "aves", "ava", "àvem", "àveu", "aven"],
("ind", "imperfect", "re"): ["ia", "ies", "ia", "íem", "íeu", "ien"],
("ind", "imperfect", "er"): ["ia", "ies", "ia", "íem", "íeu", "ien"],
("ind", "imperfect", "ir"): ["ia", "ies", "ia", "íem", "íeu", "ien"],
("ind", "preterite", "ar"): ["í", "ares", "à", "àrem", "àreu", "aren"],
("ind", "preterite", "re"): ["í", "eres", "é", "érem", "éreu", "eren"],
("ind", "preterite", "er"): ["í", "eres", "é", "érem", "éreu", "eren"],
("ind", "preterite", "ir"): ["í", "ires", "í", "írem", "íreu", "iren"],
("sbjv", "present", "ar"): ["i", "is", "i", "em", "eu", "in"],
("sbjv", "present", "re"): ["i", "is", "i", "em", "eu", "in"],
("sbjv", "present", "er"): ["i", "is", "i", "em", "eu", "in"],
("sbjv", "present", "ir"): ["i", "is", "i", "im", "iu", "in"],
("sbjv", "imperfect", "ar"): ["és", "essis", "és", "éssim", "éssiu", "essin"],
("sbjv", "imperfect", "re"): ["és", "essis", "és", "éssim", "éssiu", "essin"],
("sbjv", "imperfect", "er"): ["és", "essis", "és", "éssim", "éssiu", "essin"],
("sbjv", "imperfect", "ir"): ["ís", "issis", "ís", "íssim", "íssiu", "issin"],
("imp", "affirmative", "ar"): [None, "a", "i", "em", "eu", "in"],
("imp", "affirmative", "re"): [None, "", "i", "em", "eu", "in"],
("imp", "affirmative", "er"): [None, "", "i", "em", "eu", "in"],
("imp", "affirmative", "ir"): [None, "", "i", "im", "iu", "in"],
}
_FUT = ["é", "às", "à", "em", "eu", "an"]
_COND = ["ia", "ies", "ia", "íem", "íeu", "ien"]
def _slot_idx(person, number):
base = {"first": 0, "second": 1, "third": 2}[person]
return base + (0 if number == "singular" else 3)
def _apply_ar_spelling(stem, ending):
"""-car/-gar/-çar/-jar spelling before front (e/i) endings."""
front = ending[:1] in ("e", "i", "é", "í")
if not front:
# ç before back vowel stays; but -çar stem already ends ç
return stem + ending
if stem.endswith("c"):
return stem[:-1] + "qu" + ending
if stem.endswith("g"):
return stem[:-1] + "gu" + ending
if stem.endswith("ç"):
return stem[:-1] + "c" + ending
if stem.endswith("j"):
return stem[:-1] + "g" + ending
if stem.endswith("qu"):
return stem + ending
return stem + ending
def _rule_conjugate(lemma, mood, tense, person, number):
vc = _vclass(lemma)
if vc is None:
return None
body = lemma[:-2]
i = _slot_idx(person, number)
if mood == "ind" and tense in ("future", "conditional"):
# future/cond stem = infinitive (for -re verbs drop final -e)
stem = lemma[:-1] if vc == "re" else lemma
end = (_FUT if tense == "future" else _COND)[i]
return stem + end
table = _REG.get((mood, tense, vc))
if not table:
return None
end = table[i]
if end is None:
return None
if vc == "ar":
return _apply_ar_spelling(body, end)
# -re/-er/-ir: guard double vowel
if body and body[-1:] == end[:1] and end[:1] in "":
return body[:-1] + end
return body + end
# ── PUBLIC: verb conjugation ─────────────────────────────────────────────────────
def conjugate(lemma, mood, tense, person, number):
lemma = lemma.strip().lower()
key = f"{mood}|{tense}|{_PERSON.get(person,'?')}|{number and number[:2].upper()}"
key = f"{mood}|{tense}|{_PERSON.get(person,'?')}|{_NUMBER.get(number,'?')}"
# UniMorph (cleanly accented) takes priority; the kaikki irregulars layer is a
# FALLBACK for verbs/slots UniMorph lacks (anar, fer, and rarer paradigm cells).
p, n = _PERSON.get(person), _NUMBER.get(number)
if p and n:
form = _VERBS.get((lemma, f"{mood}|{tense}|{p}|{n}"))
if form:
return form, "lexicon"
ir = _IRREGV.get(lemma)
if ir and key in ir:
return ir[key], "lexicon"
r = _rule_conjugate(lemma, mood, tense, person, number)
if r is not None:
return r, "rule"
return lemma, "fallback"
def peri_pret_aux(person, number):
"""anar-present auxiliary for the periphrastic preterite (vaig cantar)."""
return _PERI.get(f"{_PERSON.get(person,'3')}|{_NUMBER.get(number,'SG')}", "va")
# ── PUBLIC: participle + gerund ──────────────────────────────────────────────────
def participle(lemma, gender="m", number="singular"):
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
ir = _IRREGV.get(lemma)
base = None
if ir and "part" in ir:
# prefer explicit irregular agreement form (part_mSG/part_fSG/...)
exact = ir.get("part_" + g + num)
if exact:
return exact, "lexicon"
base = ir["part"]
elif lemma in _PART:
table = _PART[lemma]
if (g, num) in table:
return table[(g, num)], "lexicon"
base = table.get(("m", "SG"))
if base is None:
vc = _vclass(lemma)
if vc == "ar":
base = lemma[:-2] + "at"
elif vc == "ir":
base = lemma[:-2] + "it"
elif vc in ("er", "re"):
base = lemma[:-2] + "ut"
else:
return lemma, "fallback"
conf = "rule"
else:
conf = "lexicon"
# agreement on -t/-ut/-at/-it participles: m.sg base, f.sg +a (-da? no: -ada),
# Catalan: cantat/cantada/cantats/cantades; -t → f -da, pl -ts/-des
if base.endswith("t"):
stem = base[:-1]
forms = {"m|SG": base, "f|SG": stem + "da",
"m|PL": base + "s", "f|PL": stem + "des"}
return forms[f"{g}|{num}"], conf
if base.endswith("s"): # after sibilant participle (rare): pres->presa
stem = base
forms = {"m|SG": base, "f|SG": base + "a",
"m|PL": base + "os", "f|PL": base + "es"}
return forms[f"{g}|{num}"], conf
return base, conf
def gerund(lemma):
lemma = lemma.strip().lower()
ir = _IRREGV.get(lemma)
if ir and "ger" in ir:
return ir["ger"], "lexicon"
if lemma in _GER:
return _GER[lemma], "lexicon"
vc = _vclass(lemma)
if vc == "ar":
return lemma[:-2] + "ant", "rule"
if vc in ("er", "re"):
return lemma[:-2] + "ent", "rule"
if vc == "ir":
return lemma[:-2] + "int", "rule"
return lemma, "fallback"
# ── PUBLIC: noun gender + number ─────────────────────────────────────────────────
_FEM_SUF = ("ció", "sió", "tat", "tud", "esa", "esa", "dat", "ança", "ència",
"ància", "tud", "ícia", "esa", "or") # note -or is mixed; kaikki wins
_MASC_SUF = ("atge", "ment", " isme", "or")
def _gender_heuristic(noun):
for suf in ("ció", "sió", "tat", "tud", "esa", "ança", "ència", "ància",
"ícia", "etat"):
if noun.endswith(suf):
return "f"
if noun.endswith("a") and not noun.endswith("ma"):
return "f"
return "m"
def noun_gender(lemma):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if d and d.get("g") in ("m", "f"):
return d["g"]
return _gender_heuristic(lemma)
def _rule_plural(noun, gender):
"""Deterministic Catalan pluralization. (form, ok); ok=False FLAGS ambiguity."""
if not noun:
return noun, True
# stressed final vowel with accent → +ns (mà→mans is irregular; but capità→capitans)
if noun[-1:] in ("à", "é", "í", "ó", "ú"):
return noun + "ns", True
if noun.endswith("ça"):
return noun[:-2] + "ces", True # plaça→places
if noun.endswith("ca"):
return noun[:-2] + "ques", True # branca→branques
if noun.endswith("ga"):
return noun[:-2] + "gues", True # amiga→amigues
if noun.endswith("ja"):
return noun[:-2] + "ges", True # pluja→pluges
if noun.endswith("qua"):
return noun[:-3] + "qües", True
if noun.endswith("gua"):
return noun[:-3] + "gües", True
if noun.endswith("a"):
return noun[:-1] + "es", True # casa→cases
# sibilant-final → -os
if noun.endswith(("s", "ç", "x", "ig")) or noun.endswith(("ix", "tx", "tj")):
if noun.endswith("ç"):
return noun[:-1] + "ços", True # braç→braços
return noun + "os", True # peix→peixos, gas→gasos
if noun[-1:] in ("e", "i", "o", "u"):
return noun + "s", True
# consonant-final
return noun + "s", True
def inflect_noun(lemma, number, gender=None):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if number == "singular":
return (d["SG"] if d and d.get("SG") else lemma), ("lexicon" if d else "rule")
if d and d.get("PL"):
return d["PL"], "lexicon"
g = gender or noun_gender(lemma)
form, ok = _rule_plural(lemma, g)
return form, ("rule" if ok else "fallback")
# ── PUBLIC: adjective agreement ──────────────────────────────────────────────────
def _fem_of(adj):
"""Regular Catalan feminine: consonant/-o? Catalan masc usually consonant or -e.
default +a with spelling changes; -e→-a for some; but many are invariable."""
a = adj
if a.endswith("a"):
return a
if a.endswith("e"):
return a[:-1] + "a" # ample→? actually 'ample' invariable; kaikki wins
if a.endswith("u"):
return a + "a"
if a.endswith("c"):
return a[:-1] + "ca" # ric→rica
if a.endswith("t"):
return a + "a" # alt→alta
return a + "a"
def inflect_adj(lemma, gender, number):
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
d = _ADJS.get(lemma)
if d:
form = d.get((g, num))
if form:
return form, "lexicon"
sg = d.get((g, "SG")) or d.get(("m", "SG")) or lemma
if num == "PL":
pl, ok = _rule_plural(sg, g)
return pl, ("rule" if ok else "fallback")
return sg, "lexicon"
# rule fallback
base = lemma if g == "m" else _fem_of(lemma)
if num == "SG":
return base, "rule"
pl, ok = _rule_plural(base, g)
return pl, ("rule" if ok else "fallback")
def lexicon_stats():
return {
"verb_source": "UniMorph Catalan (github.com/unimorph/cat) + kaikki.org "
"irregulars (anar/fer/auxiliaries)",
"noun_adj_source": "kaikki.org Catalan (Wiktionary extract)",
"license": "CC-BY-SA 3.0 (Wiktionary/UniMorph lineage)",
"unimorph_verb_forms": len(_VERBS),
"unimorph_verb_lemmas": len({k[0] for k in _VERBS}),
"irregular_verb_lemmas": len([k for k in _IRREGV if not k.startswith("_")]),
"participle_lemmas": len(_PART),
"gerund_lemmas": len(_GER),
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
}
if __name__ == "__main__":
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
tests = [
("cantar", "ind", "present", "first", "singular", "canto"),
("cantar", "ind", "present", "third", "plural", "canten"),
("ser", "ind", "present", "third", "singular", "és"),
("haver", "ind", "present", "first", "singular", "he"),
("anar", "ind", "present", "first", "singular", "vaig"),
("fer", "ind", "present", "third", "singular", "fa"),
("perdre", "ind", "present", "first", "singular", "perdo"),
("dormir", "ind", "present", "third", "plural", "dormen"),
("cantar", "ind", "future", "first", "singular", "cantaré"),
("cantar", "ind", "preterite", "third", "singular", "cantà"),
("tenir", "sbjv", "present", "first", "singular", "tingui"),
]
ok = 0
for lemma, mood, tense, per, num, exp in tests:
got, conf = conjugate(lemma, mood, tense, per, num)
flag = "OK " if got == exp else "XX "
ok += got == exp
print(f" {flag}{lemma:8} {mood}/{tense:11} {per[:3]}.{num[:2]} -> {got:10} ({conf}) exp={exp}")
print(f"verb tests {ok}/{len(tests)}")
print(" peri-pret anar: 1sg=", peri_pret_aux("first", "singular"),
"3pl=", peri_pret_aux("third", "plural"))
print(" gender casa=", noun_gender("casa"), "home=", noun_gender("home"),
"cavall=", noun_gender("cavall"), "cançó=", noun_gender("cançó"))
print(" plural casa->", inflect_noun("casa", "plural"),
"| plaça->", inflect_noun("plaça", "plural"),
"| peix->", inflect_noun("peix", "plural"),
"| braç->", inflect_noun("braç", "plural"),
"| home->", inflect_noun("home", "plural"))
print(" adj: alt/f/sg->", inflect_adj("alt", "f", "singular"),
"| bonic/f/pl->", inflect_adj("bonic", "f", "plural"),
"| vermell/f/sg->", inflect_adj("vermell", "f", "singular"))
print(" part: cantar/f/sg->", participle("cantar", "f", "singular"),
"| veure/f/pl->", participle("veure", "f", "plural"),
"| fer/m/sg->", participle("fer", "m", "singular"))
print(" ger: fer->", gerund("fer"), "| cantar->", gerund("cantar"))
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@@ -1,423 +0,0 @@
# -*- coding: utf-8 -*-
"""morphology_de_full.py — production German morphological generator.
Real data, no toy tables:
PRIMARY — UniMorph German (github.com/unimorph/deu, CC-BY-SA 3.0).
~219k noun forms, ~199k verb forms. Supplies:
nouns : gender (MASC/FEM/NEUT) + case×number paradigm
(N;NOM/ACC/DAT/GEN; MASC/FEM/NEUT; SG/PL) — the genitive -(e)s,
dative-plural -n and the five plural classes are REAL forms, not
guessed.
verbs : full finite paradigm IND;{SG,PL};{1,2,3};{PRS,PST}, the past
participle (V.PTCP;PST, incl. reattached separable prefix
'zugefügt'), and — crucially for V2 — the SEPARATED finite form
UniMorph records directly ('füge zu', 'steht auf').
adjs : comparative / superlative (ADJ;CMPR, ADJ;SPRL).
SECONDARY — kaikki.org German (Wiktionary, CC-BY-SA/GFDL). Gap-fills noun
gender + plural where UniMorph is thin. Never overrides UniMorph.
Rule fallbacks (flagged 'rule'/'fallback') for lemmas absent from both lexicons:
present : -e/-st/-t/-en/-t/-en with e-epenthesis after -t/-d/-chn stems
plural : gender heuristic (fem -> -(e)n, else -e / umlaut left to lexicon)
ppart : weak ge-…-t
Adjective ENDINGS are rule-computed by the realizer (regular closed table);
this module only supplies the comparative/superlative STEM.
Perfect auxiliary (haben vs sein): sein for a curated set of intransitive
motion / change-of-state verbs (real German lexical property), else haben.
Public API:
noun_gender(lemma) -> 'm'|'f'|'n'
decline_noun(lemma, case, number) -> (form, conf)
pluralize(lemma) -> (form, conf)
finite(lemma, tense, person, number) -> (form, conf) # may contain ' prefix'
nonfinite(lemma, req) -> (form, conf) # req: 'inf'|'ppart'
past_participle(lemma) -> (form, conf)
separable_prefix(lemma) -> str|None
perfect_aux(lemma) -> 'haben'|'sein'
comparative(lemma)/superlative(lemma) -> (stem, conf)
lexicon_stats() -> dict
"""
import json
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "deu.unimorph")
_KAIKKI = os.path.join(_HERE, "data", "kaikki_de.jsonl")
_CACHE = os.path.join(_HERE, "data", "de_morph_cache.pkl")
_GENDER = {"MASC": "m", "FEM": "f", "NEUT": "n"}
# intransitive motion / change-of-state verbs that take SEIN in the perfect
_SEIN = {"gehen", "kommen", "fahren", "laufen", "rennen", "reisen", "fallen",
"steigen", "sinken", "wachsen", "sterben", "geschehen", "passieren",
"werden", "bleiben", "sein", "aufstehen", "einschlafen", "aufwachen",
"ankommen", "abfahren", "aufsteigen", "erscheinen", "verschwinden",
"fliegen", "schwimmen", "springen", "begegnen", "folgen", "gelingen",
"wandern", "ziehen", "flüchten", "eintreten", "einsteigen", "aussteigen"}
# hardcoded high-frequency irregular / auxiliary / modal paradigms (closed class,
# verified) — consulted before the lexicon so aux+modal chains are always correct.
_CORE = {
"sein": {"prs": {("first", "singular"): "bin", ("second", "singular"): "bist",
("third", "singular"): "ist", ("first", "plural"): "sind",
("second", "plural"): "seid", ("third", "plural"): "sind"},
"pst": {("first", "singular"): "war", ("second", "singular"): "warst",
("third", "singular"): "war", ("first", "plural"): "waren",
("second", "plural"): "wart", ("third", "plural"): "waren"},
"ppart": "gewesen"},
"haben": {"prs": {("first", "singular"): "habe", ("second", "singular"): "hast",
("third", "singular"): "hat", ("first", "plural"): "haben",
("second", "plural"): "habt", ("third", "plural"): "haben"},
"pst": {("first", "singular"): "hatte", ("second", "singular"): "hattest",
("third", "singular"): "hatte", ("first", "plural"): "hatten",
("second", "plural"): "hattet", ("third", "plural"): "hatten"},
"ppart": "gehabt"},
"werden": {"prs": {("first", "singular"): "werde", ("second", "singular"): "wirst",
("third", "singular"): "wird", ("first", "plural"): "werden",
("second", "plural"): "werdet", ("third", "plural"): "werden"},
"pst": {("first", "singular"): "wurde", ("second", "singular"): "wurdest",
("third", "singular"): "wurde", ("first", "plural"): "wurden",
("second", "plural"): "wurdet", ("third", "plural"): "wurden"},
"ppart": "geworden"},
}
_MODAL_PRS = {
"können": ("kann", "kannst", "kann", "können", "könnt", "können"),
"müssen": ("muss", "musst", "muss", "müssen", "müsst", "müssen"),
"wollen": ("will", "willst", "will", "wollen", "wollt", "wollen"),
"sollen": ("soll", "sollst", "soll", "sollen", "sollt", "sollen"),
"dürfen": ("darf", "darfst", "darf", "dürfen", "dürft", "dürfen"),
"mögen": ("mag", "magst", "mag", "mögen", "mögt", "mögen"),
}
_MODAL_PST = {
"können": ("konnte", "konntest", "konnte", "konnten", "konntet", "konnten"),
"müssen": ("musste", "musstest", "musste", "mussten", "musstet", "mussten"),
"wollen": ("wollte", "wolltest", "wollte", "wollten", "wolltet", "wollten"),
"sollen": ("sollte", "solltest", "sollte", "sollten", "solltet", "sollten"),
"dürfen": ("durfte", "durftest", "durfte", "durften", "durftet", "durften"),
"mögen": ("mochte", "mochtest", "mochte", "mochten", "mochtet", "mochten"),
}
_PN_ORDER = [("first", "singular"), ("second", "singular"), ("third", "singular"),
("first", "plural"), ("second", "plural"), ("third", "plural")]
_MODAL_PPART = {"können": "gekonnt", "müssen": "gemusst", "wollen": "gewollt",
"sollen": "gesollt", "dürfen": "gedurft", "mögen": "gemocht"}
for _m, _forms in _MODAL_PRS.items():
_CORE[_m] = {"prs": dict(zip(_PN_ORDER, _forms)),
"pst": dict(zip(_PN_ORDER, _MODAL_PST[_m])),
"ppart": _MODAL_PPART[_m]}
def _person_num(tags):
p = n = None
for t in tags:
if t in ("1", "2", "3"):
p = {"1": "first", "2": "second", "3": "third"}[t]
elif t == "SG":
n = "singular"
elif t == "PL":
n = "plural"
return p, n
def _build_from_unimorph():
nouns, verbs, adjs = {}, {}, {}
if not os.path.exists(_UNIMORPH):
return nouns, verbs, adjs
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tagstr = parts
tags = tagstr.split(";")
head = tags[0]
tset = set(tags)
if head == "N":
rec = nouns.setdefault(lemma, {"g": None, "cases": {}, "pl": None})
g = next((_GENDER[t] for t in tags if t in _GENDER), None)
if g and not rec["g"]:
rec["g"] = g
case = next((t for t in tags if t in ("NOM", "ACC", "DAT", "GEN")), None)
num = "plural" if "PL" in tset else ("singular" if "SG" in tset else None)
if case and num:
rec["cases"].setdefault((case, num), form)
if case == "NOM" and num == "plural" and not rec["pl"]:
rec["pl"] = form
elif head.startswith("V"):
rec = verbs.setdefault(lemma, {"prs": {}, "pst": {}, "ppart": None})
if "PTCP" in head and "PST" in tset:
rec["ppart"] = rec["ppart"] or form
elif "IND" in tset and ("PRS" in tset or "PST" in tset):
p, n = _person_num(tags)
if p and n:
slot = "prs" if "PRS" in tset else "pst"
rec[slot].setdefault((p, n), form)
elif head == "ADJ":
rec = adjs.setdefault(lemma, {})
if "CMPR" in tset:
rec.setdefault("cmpr", form.replace("am ", "").strip())
elif "SPRL" in tset:
rec.setdefault("sprl", form.replace("am ", "").replace("sten", "st")
if form.endswith("sten") else form.replace("am ", ""))
return nouns, verbs, adjs
def _build_from_kaikki(nouns):
"""Gap-fill noun gender + plural from kaikki German."""
if not os.path.exists(_KAIKKI):
return
_g = {"masculine": "m", "feminine": "f", "neuter": "n", "m": "m", "f": "f", "n": "n"}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
if d.get("pos") != "noun":
continue
w = d.get("word", "")
if not w or not w[0].isalpha() or " " in w:
continue
rec = nouns.setdefault(w, {"g": None, "cases": {}, "pl": None})
# GENDER: Wiktionary gender is hand-curated and OVERRIDES UniMorph's
# auto-tagged gender, which has known errors (e.g. UniMorph deu mis-
# records Zeit=MASC, Wagen=NEUT; Wiktionary has f, m correctly).
for h in d.get("head_templates", []) or []:
a = h.get("args", {}) or {}
raw = a.get("1") or a.get("g") or ""
code = str(raw).split(",")[0].strip().lower()
if code in _g:
rec["g"] = _g[code]
break
if not rec["pl"]:
for f in d.get("forms", []) or []:
t = set(f.get("tags", []) or [])
if "plural" in t and f.get("form") and "genitive" not in t:
rec["pl"] = f["form"]
break
def _build_cache():
nouns, verbs, adjs = _build_from_unimorph()
_build_from_kaikki(nouns)
data = {"nouns": nouns, "verbs": verbs, "adjs": adjs}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE):
srcs = [p for p in (_UNIMORPH, _KAIKKI) if os.path.exists(p)]
newest = max((os.path.getmtime(p) for p in srcs), default=0)
if os.path.getmtime(_CACHE) >= newest:
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_NOUNS, _VERBS, _ADJS = _LEX["nouns"], _LEX["verbs"], _LEX["adjs"]
# ── nouns ────────────────────────────────────────────────────────────────────────
def noun_gender(lemma):
rec = _NOUNS.get(lemma) or _NOUNS.get(lemma.capitalize())
if rec and rec.get("g"):
return rec["g"]
# last-resort rule: -ung/-heit/-keit/-schaft/-tät/-ion -> f ; -chen/-lein -> n
low = lemma.lower()
if low.endswith(("ung", "heit", "keit", "schaft", "tät", "ion", "ik", "ei")):
return "f"
if low.endswith(("chen", "lein", "ment", "um")):
return "n"
return "m"
def pluralize(lemma):
rec = _NOUNS.get(lemma) or _NOUNS.get(lemma.capitalize())
if rec and rec.get("pl"):
return rec["pl"], "lexicon"
g = noun_gender(lemma)
if g == "f":
return (lemma + "en" if not lemma.endswith("e") else lemma + "n"), "rule"
return (lemma if lemma.endswith(("er", "en", "el")) else lemma + "e"), "rule"
def decline_noun(lemma, case, number):
"""case in NOM/ACC/DAT/GEN, number in singular/plural."""
rec = _NOUNS.get(lemma) or _NOUNS.get(lemma.capitalize())
if case == "DAT" and number == "singular":
# modern German drops the archaic dative -e ('dem Kinde' -> 'dem Kind');
# the article carries the case. Keep bare nominative form.
base = (rec or {}).get("cases", {}).get(("NOM", "singular")) or lemma
return base, ("lexicon" if rec else "rule")
if rec and rec.get("cases", {}).get((case, number)):
return rec["cases"][(case, number)], "lexicon"
if number == "plural":
pl, c = pluralize(lemma)
if case == "DAT" and not pl.endswith("n") and not pl.endswith("s"):
return pl + "n", c # dative plural -n
return pl, c
# singular
g = noun_gender(lemma)
if case == "GEN" and g in ("m", "n"):
return (lemma + "es" if lemma.endswith(("s", "ß", "z", "x")) else lemma + "s"), "rule"
return lemma, "lexicon" if rec else "rule"
# ── verbs ──────────────────────────────────────────────────────────────────────--
_PRS_ENDINGS = {("first", "singular"): "e", ("second", "singular"): "st",
("third", "singular"): "t", ("first", "plural"): "en",
("second", "plural"): "t", ("third", "plural"): "en"}
def _stem(lemma):
if lemma.endswith("en"):
return lemma[:-2]
if lemma.endswith("n"):
return lemma[:-1]
return lemma
def separable_prefix(lemma):
"""Return the separable prefix if the lemma is a separable-prefix verb."""
rec = _VERBS.get(lemma)
if rec:
for (_p, _n), form in rec.get("prs", {}).items():
if " " in form:
return form.rsplit(" ", 1)[1]
_SEP = ("auf", "aus", "ab", "an", "ein", "mit", "nach", "vor", "zu", "zurück",
"weg", "hin", "her", "los", "bei", "fest", "fort", "um", "zusammen")
_INSEP = ("be", "ge", "er", "ver", "zer", "ent", "emp", "miss")
for p in sorted(_SEP, key=len, reverse=True):
if lemma.startswith(p) and len(lemma) > len(p) + 2 \
and not lemma.startswith(_INSEP):
return p
return None
def finite(lemma, tense, person, number):
"""Present/past finite. For separable verbs the returned string is the
UniMorph SEPARATED form 'stem prefix' (realizer places prefix per V2)."""
slot = "prs" if tense == "present" else "pst"
if lemma in _CORE and _CORE[lemma].get(slot, {}).get((person, number)):
return _CORE[lemma][slot][(person, number)], "lexicon"
rec = _VERBS.get(lemma)
if rec and rec.get(slot, {}).get((person, number)):
return rec[slot][(person, number)], "lexicon"
# rule fallback (present only reliable; past weak -te)
stem = _stem(lemma)
pref = separable_prefix(lemma)
if pref:
stem = _stem(lemma[len(pref):])
if tense == "present":
end = _PRS_ENDINGS[(person, number)]
if stem.endswith(("t", "d", "chn", "ffn", "gn")) and end in ("st", "t"):
end = "e" + end
form = stem + end
else:
form = stem + ("ete" if stem.endswith(("t", "d")) else "te")
if (person, number) == ("second", "singular"):
form += "st"
elif number == "plural" and person != "second":
form += "n"
elif (person, number) == ("second", "plural"):
form += "t"
if pref:
return f"{form} {pref}", "rule"
return form, "rule"
def _weak_t(stem):
return stem + ("et" if stem.endswith(("t", "d", "chn", "ffn", "gn")) else "t")
def past_participle(lemma):
if lemma in _CORE:
return _CORE[lemma]["ppart"], "lexicon"
rec = _VERBS.get(lemma)
if rec and rec.get("ppart"):
return rec["ppart"], "lexicon"
stem = _stem(lemma)
pref = separable_prefix(lemma)
_INSEP = ("be", "ge", "er", "ver", "zer", "ent", "emp", "miss")
if pref:
inner = _stem(lemma[len(pref):])
return pref + "ge" + _weak_t(inner), "rule"
if lemma.startswith(_INSEP):
return _weak_t(stem), "rule"
return "ge" + _weak_t(stem), "rule"
def nonfinite(lemma, req):
if req == "ppart":
return past_participle(lemma)
return lemma, "lexicon" if lemma in _VERBS else "rule" # infinitive
def perfect_aux(lemma):
return "sein" if lemma in _SEIN else "haben"
# ── adjectives ────────────────────────────────────────────────────────────────---
_ADJ_IRREG_SPRL = {"gut": "best", "groß": "größt", "hoch": "höchst",
"nah": "nächst", "viel": "meist", "gern": "liebst"}
def comparative(lemma):
rec = _ADJS.get(lemma)
if rec and rec.get("cmpr"):
return rec["cmpr"], "lexicon"
return lemma + "er", "rule"
def superlative(lemma):
"""Return the bare superlative STEM (realizer adds 'am ...en' or '-e' ending)."""
if lemma in _ADJ_IRREG_SPRL:
return _ADJ_IRREG_SPRL[lemma], "lexicon"
# derive from the comparative so umlaut is carried (alt->älter->ältest)
cmpr, cconf = comparative(lemma)
base = cmpr[:-2] if cmpr.endswith("er") else lemma
end = "est" if base.endswith(("t", "d", "s", "ß", "z", "sch")) else "st"
return base + end, cconf
def lexicon_stats():
return {
"source": "UniMorph deu (primary) + kaikki.org German (gap-fill gender/plural)",
"license": "CC-BY-SA 3.0 (UniMorph); CC-BY-SA/GFDL (Wiktionary)",
"noun_lemmas": len(_NOUNS),
"nouns_with_gender": sum(1 for v in _NOUNS.values() if v.get("g")),
"nouns_with_plural": sum(1 for v in _NOUNS.values() if v.get("pl")),
"verb_lemmas": len(_VERBS),
"verbs_with_ppart": sum(1 for v in _VERBS.values() if v.get("ppart")),
"adj_lemmas": len(_ADJS),
}
if __name__ == "__main__":
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
for w in ("Hund", "Frau", "Kind", "Mann", "Buch", "Blume"):
print(f" {w}: gender={noun_gender(w)} pl={pluralize(w)} "
f"gen.sg={decline_noun(w, 'GEN', 'singular')} "
f"dat.pl={decline_noun(w, 'DAT', 'plural')}")
for v in ("machen", "gehen", "aufstehen", "sein", "haben", "arbeiten"):
print(f" {v}: 3sg.prs={finite(v, 'present', 'third', 'singular')} "
f"3sg.pst={finite(v, 'past', 'third', 'singular')} "
f"ppart={past_participle(v)} aux={perfect_aux(v)} sep={separable_prefix(v)}")
for a in ("schnell", "gut", "groß", "alt"):
print(f" {a}: cmpr={comparative(a)} sprl={superlative(a)}")
-562
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@@ -1,562 +0,0 @@
"""morphology_es_full.py — production-grade Spanish morphological generator.
NOT a toy. Backed by a real, broad, licensed lexicon:
UniMorph Spanish (github.com/unimorph/spa, CC-BY-SA 3.0, Wiktionary-derived)
1,196,245 inflected forms:
6,695 verb lemmas — full paradigms: indicative (present/preterite/
imperfect/future), conditional, present & imperfect
subjunctive, affirmative imperative, formal/informal
48,353 noun lemmas — WITH inherent gender (N;FEM/MASC;SG/PL)
16,984 adj lemmas — gender + number paradigms
Fallbacks (so we degrade, never crash, on out-of-vocabulary input):
- verbs : mlconjug3 (ML paradigm model, conjugates ANY Spanish verb) then a
hand-rolled regular-ending generator
- nouns : gender heuristic (endings) + regular pluralization
- adjs : -o/-a gender rule + regular pluralization
Every generated form carries a CONFIDENCE flag:
"lexicon" form came straight from UniMorph (trust: high)
"model" form came from mlconjug3 (trust: high)
"rule" form came from a deterministic rule (trust: medium)
"fallback" we could not inflect; returned lemma as-is (trust: low → FLAG)
Public API (used by realizer_es.py):
conjugate(lemma, mood, tense, person, number, formality="informal") -> (form, conf)
participle(lemma) -> (form, conf) # past participle (compound tenses)
gerund(lemma) -> (form, conf)
noun_gender(lemma) -> "m"|"f"
inflect_noun(lemma, number) -> (form, conf)
inflect_adj(lemma, gender, number) -> (form, conf)
attach_enclitics(verb_form, clitics) -> str # accent-correct enclisis
lexicon_stats() -> dict
"""
import os
import pickle
import unicodedata
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "spa.unimorph")
_CACHE = os.path.join(_HERE, "data", "es_morph_cache.pkl")
# ── canonical feature keys the realizer speaks, mapped to UniMorph tags ─────────
# mood/tense pair -> the UniMorph feature substring that identifies it
_VERB_KEYMAP = {
("ind", "present"): ("IND", "PRS", None),
("ind", "preterite"): ("IND", "PST", "PFV"),
("ind", "imperfect"): ("IND", "PST", "IPFV"),
("ind", "future"): ("IND", "FUT", None),
("ind", "conditional"):("COND", None, None),
("sbjv", "present"): ("SBJV", "PRS", None),
("sbjv", "imperfect"): ("SBJV", "PST", "LGSPEC1"), # -ra form
("imp", "present"): ("POS", "IMP", None),
}
_PERSON = {"first": "1", "second": "2", "third": "3"}
_NUMBER = {"singular": "SG", "plural": "PL"}
# ── build / load the compact lexicon ───────────────────────────────────────────
def _feat_set(tag):
return set(tag.split(";"))
def _build_cache():
verbs = {} # (lemma, canonkey) -> form canonkey e.g. "ind|present|1|SG|infm"
nouns = {} # lemma -> {"g": "m"/"f", "SG": form, "PL": form}
adjs = {} # lemma -> {("m","SG"): form, ...}
part = {} # lemma -> masc-sg participle
ger = {} # lemma -> gerund
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
f = _feat_set(tag)
head = tag.split(";")[0]
if head == "V":
# skip clitic-bearing rows (we generate clitics ourselves)
if "PRO" in f:
continue
if "V.PTCP" in f and "PST" in f and "MASC" in f and "SG" in f:
part.setdefault(lemma, form)
continue
if "V.CVB" in f or "NFIN" in f or "V.PTCP" in f:
if "V.CVB" in f:
ger.setdefault(lemma, form)
continue
# identify mood/tense
mt = None
for (mood, tense), (a, b, c) in _VERB_KEYMAP.items():
if a not in f:
continue
if b is not None and b not in f:
continue
if c is not None and c not in f:
continue
# disambiguate IND;PST needing PFV vs IPFV
if a == "IND" and b == "PST" and c not in f:
continue
mt = (mood, tense)
break
if mt is None:
continue
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person is None or number is None:
continue
formal = "form" if "FORM" in f else ("infm" if "INFM" in f else "any")
key = f"{mt[0]}|{mt[1]}|{person}|{number}|{formal}"
verbs.setdefault((lemma, key), form)
elif head == "N":
# substring test handles epicene "MASC+FEM" (-> masc citation)
g = "m" if "MASC" in tag else ("f" if "FEM" in tag else None)
num = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if num is None:
continue
# store forms keyed by (gender,number); animate nouns list BOTH
# genders under one lemma (niño -> niño/niña). Resolve citation
# gender in a post-pass (gender of the row whose form == lemma).
d = nouns.setdefault(lemma, {})
d.setdefault("_rows", []).append((g, num, form))
elif head == "ADJ":
g = "m" if "MASC" in tag else ("f" if "FEM" in tag else "m")
num = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if num is None:
continue
adjs.setdefault(lemma, {})[(g, num)] = form
# post-pass: resolve noun citation gender + default SG/PL forms
for lemma, d in nouns.items():
rows = d.pop("_rows", [])
# citation gender = gender of the row whose form == lemma; else first MASC;
# else first seen gender.
cite_g = None
for g, num, form in rows:
if form == lemma and g:
cite_g = g
break
if cite_g is None:
for g, num, form in rows:
if g == "m":
cite_g = "m"
break
if cite_g is None:
cite_g = next((g for g, _, _ in rows if g), "m")
d["g"] = cite_g
for g, num, form in rows:
d[(g, num)] = form
d["SG"] = d.get((cite_g, "SG")) or next((f for g, n, f in rows if n == "SG"), lemma)
d["PL"] = d.get((cite_g, "PL")) or next((f for g, n, f in rows if n == "PL"), None)
# post-pass: UniMorph omits the identity inflection (masc-sg == lemma) for
# adjectives, so fill it in; without this a fem-sg row wrongly satisfies a
# masc-sg request (alto -> alta bug).
for lemma, d in adjs.items():
d.setdefault(("m", "SG"), lemma)
data = {"verbs": verbs, "nouns": nouns, "adjs": adjs, "part": part, "ger": ger}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE) and os.path.getmtime(_CACHE) >= os.path.getmtime(_UNIMORPH):
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_VERBS, _NOUNS, _ADJS, _PART, _GER = (
_LEX["verbs"], _LEX["nouns"], _LEX["adjs"], _LEX["part"], _LEX["ger"])
# ── mlconjug3 fallback (lazy) ───────────────────────────────────────────────────
_MLC = None
_MLC_TENSE = { # (mood,tense) -> (mlconjug mood label, tense label)
("ind", "present"): ("Indicativo", "Indicativo presente"),
("ind", "preterite"): ("Indicativo", "Indicativo pretérito perfecto simple"),
("ind", "imperfect"): ("Indicativo", "Indicativo pretérito imperfecto"),
("ind", "future"): ("Indicativo", "Indicativo futuro"),
("ind", "conditional"): ("Condicional", "Condicional Condicional"),
("sbjv", "present"): ("Subjuntivo", "Subjuntivo presente"),
("sbjv", "imperfect"): ("Subjuntivo", "Subjuntivo pretérito imperfecto 1"),
("imp", "present"): ("Imperativo", "Imperativo Afirmativo"),
}
_MLC_SLOT = { # (person,number) -> mlconjug slot key
("first", "singular"): "1s", ("second", "singular"): "2s",
("third", "singular"): "3s", ("first", "plural"): "1p",
("second", "plural"): "2p", ("third", "plural"): "3p",
}
def _mlc_conjugate(lemma, mood, tense, person, number):
global _MLC
try:
if _MLC is None:
from mlconjug3 import Conjugator
_MLC = Conjugator(language="es")
v = _MLC.conjugate(lemma)
if v is None:
return None
info = v.conjug_info
m, t = _MLC_TENSE.get((mood, tense), (None, None))
if m is None or m not in info or t not in info[m]:
return None
block = info[m][t]
slot = _MLC_SLOT.get((person, number))
if isinstance(block, dict) and slot in block and block[slot]:
return block[slot]
return None
except Exception:
return None
# ── regular-ending rule fallback (last resort, deterministic) ───────────────────
def _vclass(lemma):
return lemma[-2:] if lemma[-2:] in ("ar", "er", "ir") else "ar"
def _stem(lemma):
return lemma[:-2]
_REG = {
("ind", "present", "ar"): ["o", "as", "a", "amos", "áis", "an"],
("ind", "present", "er"): ["o", "es", "e", "emos", "éis", "en"],
("ind", "present", "ir"): ["o", "es", "e", "imos", "ís", "en"],
("ind", "preterite", "ar"): ["é", "aste", "ó", "amos", "asteis", "aron"],
("ind", "preterite", "er"): ["í", "iste", "", "imos", "isteis", "ieron"],
("ind", "preterite", "ir"): ["í", "iste", "", "imos", "isteis", "ieron"],
("ind", "imperfect", "ar"): ["aba", "abas", "aba", "ábamos", "abais", "aban"],
("ind", "imperfect", "er"): ["ía", "ías", "ía", "íamos", "íais", "ían"],
("ind", "imperfect", "ir"): ["ía", "ías", "ía", "íamos", "íais", "ían"],
("sbjv", "present", "ar"): ["e", "es", "e", "emos", "éis", "en"],
("sbjv", "present", "er"): ["a", "as", "a", "amos", "áis", "an"],
("sbjv", "present", "ir"): ["a", "as", "a", "amos", "áis", "an"],
("sbjv", "imperfect", "ar"): ["ara", "aras", "ara", "áramos", "arais", "aran"],
("sbjv", "imperfect", "er"): ["iera", "ieras", "iera", "iéramos", "ierais", "ieran"],
("sbjv", "imperfect", "ir"): ["iera", "ieras", "iera", "iéramos", "ierais", "ieran"],
}
_FUT = ["é", "ás", "á", "emos", "éis", "án"]
_COND = ["ía", "ías", "ía", "íamos", "íais", "ían"]
def _slot_idx(person, number):
base = {"first": 0, "second": 1, "third": 2}[person]
return base + (0 if number == "singular" else 3)
def _rule_conjugate(lemma, mood, tense, person, number):
if len(lemma) < 3 or lemma[-2:] not in ("ar", "er", "ir"):
return None
vc, st, i = _vclass(lemma), _stem(lemma), _slot_idx(person, number)
if tense == "future":
return lemma + _FUT[i]
if tense == "conditional":
return lemma + _COND[i]
table = _REG.get((mood, tense, vc))
if table:
return st + table[i]
if mood == "imp" and tense == "present":
# affirmative tú imperative = 3sg present indicative
pres = _REG.get(("ind", "present", vc))
return st + pres[2] if number == "singular" else st + pres[5]
return None
# ── PUBLIC: verb conjugation ────────────────────────────────────────────────────
def conjugate(lemma, mood, tense, person, number, formality="informal"):
"""Return (surface, confidence). mood in ind|sbjv|imp; tense per _VERB_KEYMAP."""
lemma = lemma.strip().lower()
p, n = _PERSON.get(person), _NUMBER.get(number)
formal = "form" if formality == "formal" else "infm"
if p and n:
for fkey in (formal, "any", "infm" if formal == "form" else "form"):
form = _VERBS.get((lemma, f"{mood}|{tense}|{p}|{n}|{fkey}"))
if form:
return form, "lexicon"
m = _mlc_conjugate(lemma, mood, tense, person, number)
if m:
return m, "model"
r = _rule_conjugate(lemma, mood, tense, person, number)
if r:
return r, "rule"
return lemma, "fallback"
_IRREG_PART = { # guarantee the common irregular participles
"escribir": "escrito", "describir": "descrito", "abrir": "abierto",
"cubrir": "cubierto", "descubrir": "descubierto", "morir": "muerto",
"poner": "puesto", "ver": "visto", "volver": "vuelto", "devolver": "devuelto",
"hacer": "hecho", "deshacer": "deshecho", "decir": "dicho", "romper": "roto",
"resolver": "resuelto", "freír": "frito", "imprimir": "impreso",
"satisfacer": "satisfecho", "prever": "previsto", "revolver": "revuelto",
}
def participle(lemma):
lemma = lemma.strip().lower()
if lemma in _IRREG_PART:
return _IRREG_PART[lemma], "lexicon"
if lemma in _PART:
return _PART[lemma], "lexicon"
if lemma.endswith("ar"):
return lemma[:-2] + "ado", "rule"
if lemma[-2:] in ("er", "ir"):
return lemma[:-2] + "ido", "rule"
return lemma, "fallback"
_IRREG_GER = {"dormir": "durmiendo", "morir": "muriendo", "pedir": "pidiendo",
"sentir": "sintiendo", "mentir": "mintiendo", "servir": "sirviendo",
"venir": "viniendo", "decir": "diciendo", "poder": "pudiendo",
"ir": "yendo", "leer": "leyendo", "creer": "creyendo",
"oír": "oyendo", "traer": "trayendo", "caer": "cayendo",
"construir": "construyendo", "huir": "huyendo", "reír": "riendo"}
def gerund(lemma):
lemma = lemma.strip().lower()
if lemma in _IRREG_GER:
return _IRREG_GER[lemma], "lexicon"
if lemma in _GER:
return _GER[lemma], "lexicon"
if lemma.endswith("ar"):
return lemma[:-2] + "ando", "rule"
if lemma[-2:] in ("er", "ir"):
return lemma[:-2] + "iendo", "rule"
return lemma, "fallback"
# ── PUBLIC: noun gender + number ────────────────────────────────────────────────
_INVARIANT_PL = {"lunes", "martes", "miércoles", "jueves", "viernes",
"crisis", "tesis", "análisis", "dosis", "virus", "paraguas"}
def _gender_heuristic(noun):
for suf, g in (("ión", "f"), ("dad", "f"), ("tad", "f"), ("umbre", "f"),
("sis", "f"), ("ez", "f"), ("triz", "f"),
("ema", "m"), ("ama", "m"), ("oma", "m"), ("aje", "m"),
("or", "m"), ("án", "m"), ("ín", "m")):
if noun.endswith(suf):
return g
if noun.endswith("o"):
return "m"
if noun.endswith("a"):
return "f"
return "m"
def noun_gender(lemma):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if d and d.get("g"):
return d["g"]
return _gender_heuristic(lemma)
def _regular_plural(noun):
if noun in _INVARIANT_PL:
return noun
if not noun:
return noun
last = noun[-1]
if last == "z":
return noun[:-1] + "ces"
if last in "aeiouáéíóú":
# stressed final vowel í/ú -> +es (rubí->rubíes), else +s
if last in "íú":
return noun + "es"
return noun + "s"
if last == "s":
# esdrújula / stress-final handled crudely; most polysyllables invariant
return noun
return noun + "es"
def inflect_noun(lemma, number, gender=None):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
num = "SG" if number == "singular" else "PL"
if d:
# honor a requested gender for animate nouns (gato -> gata)
if gender and (gender, num) in d:
return d[(gender, num)], "lexicon"
if d.get(num):
return d[num], "lexicon"
if number == "singular":
return lemma, "rule" if not d else "lexicon"
return _regular_plural(lemma), "rule"
# ── PUBLIC: adjective agreement ─────────────────────────────────────────────────
_INV_GENDER_ADJ = {"español": "española", "trabajador": "trabajadora",
"hablador": "habladora", "encantador": "encantadora",
"alemán": "alemana", "francés": "francesa", "inglés": "inglesa"}
def inflect_adj(lemma, gender, number):
lemma = lemma.strip().lower()
d = _ADJS.get(lemma)
num = "SG" if number == "singular" else "PL"
if d:
form = d.get((gender, num))
if form:
return form, "lexicon"
# gender-invariant adjective (grande, feliz, azul): fem == masc.
# For a missing plural, pluralize this gender's singular form.
sg = d.get((gender, "SG")) or d.get(("m", "SG")) or lemma
if number == "plural":
return _regular_plural(sg), "rule"
return sg, "lexicon"
# rule fallback
a = lemma
if gender == "f":
if a in _INV_GENDER_ADJ:
a = _INV_GENDER_ADJ[a]
elif a.endswith("o"):
a = a[:-1] + "a"
if number == "plural":
a = _regular_plural(a)
return a, ("rule" if (a != lemma or gender == "m") else "rule")
# ── PUBLIC: clitic enclisis (dá + me + lo -> dámelo) ────────────────────────────
def _strip_accents(s):
return "".join(c for c in unicodedata.normalize("NFD", s)
if unicodedata.category(c) != "Mn")
def _count_syllables_vowelgroups(word):
# crude: count vowel groups
w = _strip_accents(word).lower()
groups, prev = 0, False
for ch in w:
isv = ch in "aeiou"
if isv and not prev:
groups += 1
prev = isv
return groups
def _host_stress_from_end(word):
"""Stressed-syllable index counted from the end (1=last) of a verb host."""
syls = _count_syllables_vowelgroups(word)
if any(c in "áéíóú" for c in word):
return None # already carries its own accent
if word[-2:] in ("ar", "er", "ir"): # infinitive: oxytone
return 1
if word.endswith("ndo"): # gerund: paroxytone
return 2
if word[-1:] in "aeiouns" and syls >= 2: # default paroxytone
return 2
return 1 # monosyllable / consonant-final oxytone
def attach_enclitics(verb_form, clitics):
"""Append clitic pronouns to a verb (imperative/infinitive/gerund enclisis)
and add a written accent when the resulting word becomes esdrújula/
sobreesdrújula (stress >= 3 syllables from the end): dá+me+lo -> dámelo,
lleva+me -> llévame, but dar+te -> darte and da+me -> dame (no accent)."""
if not clitics:
return verb_form
tail = "".join(clitics)
if any(c in "áéíóú" for c in verb_form): # host already accented
return verb_form + tail
sfe = _host_stress_from_end(verb_form)
total_sfe = sfe + len(clitics) # each clitic = 1 syllable
if total_sfe >= 3:
return _accentuate_nucleus(verb_form, sfe) + tail
return verb_form + tail
def _accentuate_nucleus(word, sfe):
"""Put a written accent on the syllable `sfe` positions from the word's end."""
vowels = "aeiou"
nuclei = [i for i, ch in enumerate(word) if ch in vowels]
if not nuclei or sfe > len(nuclei):
return word
i = nuclei[-sfe]
acc = {"a": "á", "e": "é", "i": "í", "o": "ó", "u": "ú"}
return word[:i] + acc[word[i]] + word[i + 1:]
def _accentuate_last_stressed(word):
# Restore the host's ORIGINAL lexical stress with a written accent.
# Default Spanish stress: word ending in vowel/n/s -> penultimate syllable;
# otherwise (e.g. infinitives in -r) -> last syllable.
vowels = "aeiou"
nuclei = [i for i, ch in enumerate(word) if ch in vowels]
if not nuclei:
return word
if word[-1] in "aeiouns" and len(nuclei) >= 2:
i = nuclei[-2] # paroxytone: penult nucleus
else:
i = nuclei[-1] # oxytone / monosyllable: last nucleus
acc = {"a": "á", "e": "é", "i": "í", "o": "ó", "u": "ú"}
return word[:i] + acc[word[i]] + word[i + 1:]
def lexicon_stats():
return {
"source": "UniMorph Spanish (github.com/unimorph/spa)",
"license": "CC-BY-SA 3.0 (Wiktionary-derived)",
"total_forms": sum(len(v) for v in (_VERBS, _NOUNS, _ADJS)) if False else None,
"verb_forms": len(_VERBS),
"verb_lemmas": len({k[0] for k in _VERBS}),
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
"participles": len(_PART),
"gerunds": len(_GER),
}
if __name__ == "__main__":
import json
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
tests = [
("hablar", "ind", "present", "first", "singular", "hablo"),
("comer", "ind", "present", "third", "plural", "comen"),
("vivir", "ind", "present", "first", "plural", "vivimos"),
("ser", "ind", "present", "third", "singular", "es"),
("ir", "ind", "preterite", "first", "singular", "fui"),
("tener", "ind", "future", "first", "singular", "tendré"),
("hacer", "sbjv", "present", "first", "singular", "haga"),
("dormir", "ind", "present", "first", "singular", "duermo"),
("pensar", "sbjv", "present", "third", "singular", "piense"),
("dar", "ind", "preterite", "third", "singular", "dio"),
("poner", "ind", "conditional", "first", "singular", "pondría"),
]
ok = 0
for lemma, mood, tense, per, num, exp in tests:
got, conf = conjugate(lemma, mood, tense, per, num)
flag = "OK " if got == exp else "XX "
if got == exp:
ok += 1
print(f" {flag}{lemma:8} {mood}/{tense} {per[:3]}.{num[:2]:3} -> {got:14} ({conf}) exp={exp}")
print(f"verb tests {ok}/{len(tests)}")
print(" gender casa:", noun_gender("casa"), "| problema:", noun_gender("problema"),
"| agua:", noun_gender("agua"), "| mano:", noun_gender("mano"))
print(" plural: luz->", inflect_noun("luz", "plural"), "| rey->", inflect_noun("rey", "plural"))
print(" adj: rojo/f/pl->", inflect_adj("rojo", "f", "plural"),
"| feliz/m/pl->", inflect_adj("feliz", "m", "plural"),
"| grande/f/pl->", inflect_adj("grande", "f", "plural"))
print(" enclisis: da+[me,lo]->", attach_enclitics("da", ["me", "lo"]),
"| di+[me]->", attach_enclitics("di", ["me"]),
"| dar+[se,lo]->", attach_enclitics("dar", ["se", "lo"]))
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@@ -1,629 +0,0 @@
"""morphology_fr_full.py — production-grade French morphological generator.
Same architecture as morphology_it_full.py (shared Romance engine); French-specific
data and rules swapped in. Backed by three real, Wiktionary-lineage sources:
VERBS
UniMorph French (github.com/unimorph/fra, CC-BY-SA 3.0)
7,535 verb lemmas × full paradigm, CLEAN orthography:
indicatif présent / imparfait (PST;IPFV) / passé simple (PST;PFV) /
futur, conditionnel (COND), subjonctif présent (SBJV;PRS) /
subjonctif imparfait (SBJV;PST), impératif (POS;IMP), infinitif (NFIN),
participe présent (V.CVB/V.PTCP;PRS), participe passé (V.PTCP;PST, m.sg).
fr_irreg_verbs.json — high-frequency verbs UniMorph MISSES or mis-slots,
above all ÊTRE (absent from UniMorph fra), plus avoir/aller/faire/… — the
auxiliaries the passé-composé + être-agreement system depends on. Extracted
from kaikki.org French (build_fr_irreg.py), reflexive/multiword forms
dropped. This layer takes PRIORITY.
NOUNS + ADJECTIVES — kaikki.org French (Wiktionary extract, CC-BY-SA 3.0)
noun lemmas WITH inherent gender (head-template arg) + real plural
(cheval->chevaux, œil->yeux, invariable -s/-x/-z), resolved PER LEMMA.
adjective lemmas with real feminine + plural (petit->petite/petits/petites,
beau->belle/beaux/belles, heureux->heureuse, rouge invariant-gender).
Fallbacks (degrade, never crash, on OOV input):
verbs : rule generator for -er / -ir(-iss-) / -re (with -cer/-ger spelling,
future/conditional stems, imparfait/subjonctif endings)
nouns : gender heuristic (endings) + rule pluralization (-al->-aux, -eau->-eaux)
adjs : fem/plural agreement rules (-er->-ère, -eux->-euse, -f->-ve, +e default)
Confidence flag on every form: "lexicon" | "rule" | "fallback".
Public API (used by realizer_fr.py): identical signature to morphology_it_full.
"""
import json
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "fra.unimorph")
_IRREG = os.path.join(_HERE, "data", "fr_irreg_verbs.json")
_KAIKKI = os.path.join(_HERE, "data", "kaikki_fr.jsonl")
_CACHE = os.path.join(_HERE, "data", "fr_morph_cache.pkl")
# ── (mood, tense) -> UniMorph feature set that must ALL be present ────────────────
_VERB_KEYMAP = {
("ind", "present"): {"IND", "PRS"},
("ind", "imperfect"): {"IND", "PST", "IPFV"}, # imparfait
("ind", "passe_simple"): {"IND", "PST", "PFV"}, # passé simple
("ind", "future"): {"IND", "FUT"},
("ind", "conditional"): {"COND"}, # French: V;COND;1;SG
("sbjv", "present"): {"SBJV", "PRS"},
("sbjv", "imperfect"): {"SBJV", "PST"},
("imp", "affirmative"): {"POS", "IMP"},
}
_PERSON = {"first": "1", "second": "2", "third": "3"}
_NUMBER = {"singular": "SG", "plural": "PL"}
def _feat_set(tag):
return set(tag.split(";"))
# ── build verb lexicon from UniMorph ─────────────────────────────────────────────
def _build_verbs():
verbs = {}
part = {}
ger = {}
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
f = _feat_set(tag)
head = tag.split(";")[0]
if head == "V.PTCP":
if "PST" in f:
part.setdefault(lemma, form)
elif "PRS" in f:
ger.setdefault(lemma, form)
continue
if head == "V.CVB":
if "PRS" in f:
ger.setdefault(lemma, form)
continue
if head != "V":
continue
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person is None or number is None:
continue
for (mood, tense), req in _VERB_KEYMAP.items():
if not req <= f:
continue
if tense == "imperfect" and "PFV" in f:
continue
if tense == "passe_simple" and "IPFV" in f:
continue
verbs.setdefault((lemma, f"{mood}|{tense}|{person}|{number}"), form)
break
return verbs, part, ger
# ── kaikki nouns + adjectives ────────────────────────────────────────────────────
_EXCL_FORM_TAGS = {"alternative", "archaic", "obsolete", "dialectal", "regional",
"diminutive", "augmentative", "pejorative", "comparative",
"superlative", "misspelling", "rare", "informal", "literary",
"poetic", "error-unrecognized-form", "construed", "collective",
"nonstandard", "dated", "Louisiana", "Switzerland", "Belgium"}
def _kaikki_gender(arg):
if not arg:
return None
a = str(arg).lower()
if a.startswith("f"):
return "f"
if a.startswith("m"):
return "m"
return None
def _build_nouns_adjs():
nouns = {}
adjs = {}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
pos = d.get("pos")
word = d.get("word", "")
if not word or " " in word:
continue
forms = d.get("forms", []) or []
if pos == "noun":
ht = d.get("head_templates") or []
g = None
if ht:
g = _kaikki_gender((ht[0].get("args") or {}).get("1"))
if g is None:
tags = d.get("tags") or []
if "feminine" in tags:
g = "f"
elif "masculine" in tags:
g = "m"
pl = None
for x in forms:
t = set(x.get("tags") or [])
if "plural" in t and not (t & _EXCL_FORM_TAGS):
fm = x.get("form")
if fm and " " not in fm and fm not in ("#", "-", ""):
pl = fm
break
if word not in nouns:
nouns[word] = {"g": g, "SG": word, "PL": pl}
else:
cur = nouns[word]
if cur.get("g") is None and g:
cur["g"] = g
if not cur.get("PL") and pl:
cur["PL"] = pl
elif pos == "adj":
d0 = adjs.setdefault(word, {})
d0.setdefault(("m", "SG"), word)
for x in forms:
t = set(x.get("tags") or [])
fm = x.get("form")
if not fm or " " in fm or (t & _EXCL_FORM_TAGS):
continue
if "feminine" in t and "plural" in t:
d0[("f", "PL")] = d0.get(("f", "PL")) or fm
elif "masculine" in t and "plural" in t:
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
elif "feminine" in t:
d0[("f", "SG")] = d0.get(("f", "SG")) or fm
elif "plural" in t:
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
return nouns, adjs
def _build_cache():
verbs, part, ger = _build_verbs()
nouns, adjs = _build_nouns_adjs()
with open(_IRREG, encoding="utf-8") as fh:
irreg = json.load(fh)
data = {"verbs": verbs, "part": part, "ger": ger,
"nouns": nouns, "adjs": adjs, "irreg": irreg}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE):
srcs = [_UNIMORPH, _KAIKKI, _IRREG]
newest = max(os.path.getmtime(s) for s in srcs if os.path.exists(s))
if os.path.getmtime(_CACHE) >= newest:
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_VERBS, _PART, _GER, _NOUNS, _ADJS, _IRREGV = (
_LEX["verbs"], _LEX["part"], _LEX["ger"], _LEX["nouns"], _LEX["adjs"],
_LEX["irreg"])
# ── regular-ending rule fallback ─────────────────────────────────────────────────
def _vclass(lemma):
if lemma.endswith("er"):
return "er"
if lemma.endswith("ir"):
return "ir"
if lemma.endswith("re"):
return "re"
if lemma.endswith("oir"):
return "oir"
return None
# present-tense endings [1sg,2sg,3sg,1pl,2pl,3pl]
_REG_PRES = {
"er": ["e", "es", "e", "ons", "ez", "ent"],
"ir": ["is", "is", "it", "issons", "issez", "issent"], # -iss- class (finir)
"re": ["s", "s", "", "ons", "ez", "ent"], # vendre: vends/vend
}
_REG_IMPF = ["ais", "ais", "ait", "ions", "iez", "aient"] # attaches to pres-1pl stem
_REG_SUBJ = ["e", "es", "e", "ions", "iez", "ent"] # attaches to 3pl stem
_REG_PS = { # passé simple
"er": ["ai", "as", "a", "âmes", "âtes", "èrent"],
"ir": ["is", "is", "it", "îmes", "îtes", "irent"],
"re": ["is", "is", "it", "îmes", "îtes", "irent"],
}
_FUT = ["ai", "as", "a", "ons", "ez", "ont"]
_COND = ["ais", "ais", "ait", "ions", "iez", "aient"]
def _slot_idx(person, number):
base = {"first": 0, "second": 1, "third": 2}[person]
return base + (0 if number == "singular" else 3)
def _fut_stem(lemma, vc):
"""Future/conditional stem = infinitive (drop final -e of -re)."""
if vc == "re":
return lemma[:-1] # vendre -> vendr-
return lemma # parler-, finir-
def _pres_1pl_stem(lemma, vc):
"""Imparfait stem = present 1pl minus -ons (parlons->parl-, finissons->finiss-)."""
if vc == "er":
stem = lemma[:-2]
if stem.endswith("g"):
return stem + "e" # mangeons -> mange- (imparfait mangeais)
if stem.endswith("c"):
return stem[:-1] + "ç" # commençons -> commenç-
return stem
if vc == "ir":
return lemma[:-1] + "iss" # finir -> finiss-
if vc == "re":
return lemma[:-2] # vendre -> vend-
return lemma[:-2]
def _apply_er_spelling(stem, ending):
"""-cer/-ger softening before a/o (commençons, mangeons)."""
if ending and ending[0] in ("a", "o"):
if stem.endswith("c"):
return stem[:-1] + "ç" + ending
if stem.endswith("g"):
return stem + "e" + ending
return stem + ending
def _rule_conjugate(lemma, mood, tense, person, number):
vc = _vclass(lemma)
if vc is None:
return None
i = _slot_idx(person, number)
if mood == "ind" and tense in ("future", "conditional"):
stem = _fut_stem(lemma, vc)
end = (_FUT if tense == "future" else _COND)[i]
return stem + end
if mood == "ind" and tense == "present":
table = _REG_PRES.get("ir" if vc == "ir" else vc)
if not table:
return None
body = lemma[:-2] if vc in ("er", "re") else lemma[:-1] if vc == "ir" else lemma[:-2]
if vc == "ir":
body = lemma[:-2] # fin- ; endings carry -iss-
end = table[i]
return body + end
end = table[i]
if vc == "er":
return _apply_er_spelling(body, end)
return body + end
if mood == "ind" and tense == "imperfect":
stem = _pres_1pl_stem(lemma, vc)
return stem + _REG_IMPF[i]
if mood == "ind" and tense == "passe_simple":
table = _REG_PS.get("ir" if vc == "ir" else vc)
if not table:
return None
body = lemma[:-2] if vc in ("er", "re") else lemma[:-2]
end = table[i]
if vc == "er":
return _apply_er_spelling(body, end)
return body + end
if mood == "sbjv" and tense == "present":
# subjonctif: present-3pl stem + e/es/e/ions/iez/ent
stem3 = _pres_1pl_stem(lemma, vc) if vc == "ir" else (
lemma[:-2] if vc in ("er", "re") else lemma[:-2])
if vc == "ir":
stem3 = lemma[:-2] + "iss"
end = _REG_SUBJ[i]
if vc == "er":
return _apply_er_spelling(stem3, end)
return stem3 + end
if mood == "imp" and tense == "affirmative":
# impératif ~ present indicative (tu drops -s for -er verbs)
pres = _rule_conjugate(lemma, "ind", "present", person, number)
if pres and vc == "er" and person == "second" and number == "singular":
return pres[:-1] if pres.endswith("es") else pres
return pres
return None
# ── PUBLIC: verb conjugation ─────────────────────────────────────────────────────
def conjugate(lemma, mood, tense, person, number):
"""Return (surface, confidence)."""
lemma = lemma.strip().lower()
key = f"{mood}|{tense}|{_PERSON.get(person,'?')}|{number}"
ir = _IRREGV.get(lemma)
if ir and key in ir:
return ir[key], "lexicon"
p, n = _PERSON.get(person), _NUMBER.get(number)
if p and n:
form = _VERBS.get((lemma, f"{mood}|{tense}|{p}|{n}"))
if form:
return form, "lexicon"
r = _rule_conjugate(lemma, mood, tense, person, number)
if r:
return r, "rule"
return lemma, "fallback"
# ── PUBLIC: participle + gerund/participe présent ────────────────────────────────
def _participle_msg(lemma):
ir = _IRREGV.get(lemma)
if ir and "part" in ir:
return ir["part"], "lexicon"
if lemma in _PART:
return _PART[lemma], "lexicon"
return None, None
# irregular participle fem/plural quirks (drop circonflexe: dû->due, dus)
_PART_FIX = {"": {"f|SG": "due", "m|PL": "dus", "f|PL": "dues"}}
def participle(lemma, gender="m", number="singular"):
"""Past participle with French gender/number agreement.
m.sg = base; f.sg = base+e; m.pl = base+s (invariable if base ends s/x);
f.pl = f.sg+s."""
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
msg, src = _participle_msg(lemma)
conf = "lexicon"
if msg is None:
vc = _vclass(lemma)
if vc == "er":
msg = lemma[:-2] + "é"
elif vc == "ir":
msg = lemma[:-1] # finir -> fini, partir -> parti
elif vc == "re":
msg = lemma[:-2] + "u" # vendre -> vendu
elif vc == "oir":
msg = lemma[:-3] + "u" # (rough) recevoir handled by irreg
else:
return lemma, "fallback"
conf = "rule"
fix = _PART_FIX.get(msg)
if fix and f"{g}|{num}" in fix:
return fix[f"{g}|{num}"], conf
if g == "m" and num == "SG":
return msg, conf
fem = msg + "e" if not msg.endswith("e") else msg
if g == "f" and num == "SG":
return fem, conf
if g == "m" and num == "PL":
return msg if msg.endswith(("s", "x")) else msg + "s", conf
# f|PL
return fem + "s", conf
def gerund(lemma):
"""Participe présent (base for gérondif 'en -ant')."""
lemma = lemma.strip().lower()
ir = _IRREGV.get(lemma)
if ir and "ger" in ir:
return ir["ger"], "lexicon"
if lemma in _GER:
return _GER[lemma], "lexicon"
vc = _vclass(lemma)
if vc == "er":
stem = lemma[:-2]
if stem.endswith("g"):
return stem + "eant", "rule"
if stem.endswith("c"):
return stem[:-1] + "çant", "rule"
return stem + "ant", "rule"
if vc == "ir":
return lemma[:-2] + "issant", "rule"
if vc == "re":
return lemma[:-2] + "ant", "rule"
return lemma, "fallback"
# ── PUBLIC: noun gender + number ─────────────────────────────────────────────────
_FEM_SUF = ("tion", "sion", "aison", "ance", "ence", "ette", "elle", "esse",
"ude", "ade", "ée", "", "tié", "ie", "ise", "ure", "eur")
_MASC_SUF = ("ment", "age", "eau", "isme", "oir", "ier", "eur", "in", "on")
def _gender_heuristic(noun):
for suf in _FEM_SUF:
if noun.endswith(suf):
return "f"
for suf in _MASC_SUF:
if noun.endswith(suf):
return "m"
if noun.endswith("e"):
return "f"
return "m"
def noun_gender(lemma):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if d and d.get("g") in ("m", "f"):
return d["g"]
return _gender_heuristic(lemma)
# closed sets for French plural irregularities
_OU_X = {"bijou", "caillou", "chou", "genou", "hibou", "joujou", "pou"}
_AIL_AUX = {"travail", "vitrail", "corail", "émail", "bail", "soupirail", "vantail"}
_AL_S = {"bal", "carnaval", "festival", "récital", "chacal", "régal", "cal", "aval"}
def _rule_plural(noun, gender):
"""Deterministic French pluralization. (form, ok); ok=False FLAGS ambiguity."""
if not noun:
return noun, True
if noun[-1:] in ("s", "x", "z"):
return noun, True # invariable
if noun in _OU_X:
return noun + "x", True
if noun.endswith(("eau", "au", "eu")):
if noun in ("pneu", "bleu", "landau", "sarrau"):
return noun + "s", True
return noun + "x", True # bateau->bateaux, jeu->jeux
if noun.endswith("al"):
if noun in _AL_S:
return noun + "s", True
return noun[:-2] + "aux", True # cheval->chevaux
if noun.endswith("ail"):
if noun in _AIL_AUX:
return noun[:-3] + "aux", True # travail->travaux
return noun + "s", True
return noun + "s", True # default
def inflect_noun(lemma, number, gender=None):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if number == "singular":
return (d["SG"] if d and d.get("SG") else lemma), ("lexicon" if d else "rule")
if d and d.get("PL"):
return d["PL"], "lexicon"
g = gender or noun_gender(lemma)
form, ok = _rule_plural(lemma, g)
return form, ("rule" if ok else "fallback")
# adjectives whose kaikki entries are unreliable: audited forms
_ADJ_FIX = {
"beau": {("m", "SG"): "beau", ("f", "SG"): "belle",
("m", "PL"): "beaux", ("f", "PL"): "belles"},
"nouveau": {("m", "SG"): "nouveau", ("f", "SG"): "nouvelle",
("m", "PL"): "nouveaux", ("f", "PL"): "nouvelles"},
"vieux": {("m", "SG"): "vieux", ("f", "SG"): "vieille",
("m", "PL"): "vieux", ("f", "PL"): "vieilles"},
"fou": {("m", "SG"): "fou", ("f", "SG"): "folle",
("m", "PL"): "fous", ("f", "PL"): "folles"},
"blanc": {("m", "SG"): "blanc", ("f", "SG"): "blanche",
("m", "PL"): "blancs", ("f", "PL"): "blanches"},
"long": {("m", "SG"): "long", ("f", "SG"): "longue",
("m", "PL"): "longs", ("f", "PL"): "longues"},
"bon": {("m", "SG"): "bon", ("f", "SG"): "bonne",
("m", "PL"): "bons", ("f", "PL"): "bonnes"},
}
def _rule_fem(a):
if a.endswith("e"):
return a
if a.endswith("er"):
return a[:-2] + "ère"
if a.endswith("eau"):
return a[:-3] + "elle"
if a.endswith("eux"):
return a[:-3] + "euse"
if a.endswith("f"):
return a[:-1] + "ve"
if a.endswith(("on", "en", "el", "eil", "et")):
return a + a[-1] + "e" # bon->bonne, ancien->ancienne, muet->muette
if a.endswith("c"):
return a[:-1] + "che" # blanc->blanche (public->publique via FIX)
return a + "e" # grand->grande, petit->petite, vert->verte
def inflect_adj(lemma, gender, number):
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
fix = _ADJ_FIX.get(lemma)
if fix and (g, num) in fix:
return fix[(g, num)], "lexicon"
d = _ADJS.get(lemma)
if d and d.get((g, num)):
return d[(g, num)], "lexicon"
# derive
msc = (d.get(("m", "SG")) if d else None) or lemma
if g == "m" and num == "SG":
return msc, "lexicon" if d else "rule"
fem = (d.get(("f", "SG")) if d else None) or _rule_fem(msc)
if g == "f" and num == "SG":
return fem, "lexicon" if (d and d.get(("f", "SG"))) else "rule"
if g == "m" and num == "PL":
if msc.endswith(("s", "x")):
return msc, "rule"
if msc.endswith("al"):
return msc[:-2] + "aux", "rule"
if msc.endswith("eau"):
return msc + "x", "rule"
return msc + "s", "rule"
# f|PL
return (fem if fem.endswith("s") else fem + "s"), "rule"
def lexicon_stats():
return {
"verb_source": "UniMorph French (github.com/unimorph/fra) + kaikki.org "
"irregulars (être + high-frequency)",
"noun_adj_source": "kaikki.org French (Wiktionary extract)",
"license": "CC-BY-SA 3.0 (Wiktionary/UniMorph lineage)",
"unimorph_verb_forms": len(_VERBS),
"unimorph_verb_lemmas": len({k[0] for k in _VERBS}),
"irregular_verb_lemmas": len(_IRREGV),
"participle_lemmas": len(_PART),
"gerund_lemmas": len(_GER),
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
}
if __name__ == "__main__":
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
tests = [
("parler", "ind", "present", "first", "singular", "parle"),
("être", "ind", "present", "third", "singular", "est"),
("avoir", "ind", "present", "first", "singular", "ai"),
("aller", "ind", "present", "third", "plural", "vont"),
("finir", "ind", "present", "first", "singular", "finis"),
("finir", "ind", "present", "first", "plural", "finissons"),
("manger", "ind", "present", "first", "plural", "mangeons"),
("faire", "ind", "future", "first", "singular", "ferai"),
("pouvoir", "sbjv", "present", "third", "singular", "puisse"),
("prendre", "ind", "passe_simple", "third", "singular", "prit"),
("vendre", "ind", "present", "third", "singular", "vend"),
("commencer", "ind", "imperfect", "first", "singular", "commençais"),
]
ok = 0
for lemma, mood, tense, per, num, exp in tests:
got, conf = conjugate(lemma, mood, tense, per, num)
flag = "OK " if got == exp else "XX "
ok += got == exp
print(f" {flag}{lemma:10} {mood}/{tense:12} {per[:3]}.{num[:2]} -> {got:12} ({conf}) exp={exp}")
print(f"verb tests {ok}/{len(tests)}")
print(" gender: maison=", noun_gender("maison"), "chat=", noun_gender("chat"),
"cheval=", noun_gender("cheval"), "nation=", noun_gender("nation"))
print(" plural: cheval->", inflect_noun("cheval", "plural"),
"| bateau->", inflect_noun("bateau", "plural"),
"| prix->", inflect_noun("prix", "plural"),
"| chat->", inflect_noun("chat", "plural"))
print(" adj: petit/f/sg->", inflect_adj("petit", "f", "singular"),
"| beau/f/sg->", inflect_adj("beau", "f", "singular"),
"| heureux/f/sg->", inflect_adj("heureux", "f", "singular"),
"| national/m/pl->", inflect_adj("national", "m", "plural"))
print(" part: aller/f/sg->", participle("aller", "f", "singular"),
"| prendre/f/pl->", participle("prendre", "f", "plural"),
"| finir/m/pl->", participle("finir", "m", "plural"))
print(" ger: manger->", gerund("manger"), "| finir->", gerund("finir"))
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@@ -1,588 +0,0 @@
"""morphology_it_full.py — production-grade Italian morphological generator.
NOT a toy. Backed by three real, Wiktionary-lineage lexical sources:
VERBS
UniMorph Italian (github.com/unimorph/ita, CC-BY-SA 3.0)
10,009 verb lemmas × full paradigm, CLEAN orthography (no stress marks):
indicative present / imperfetto (PST;IPFV) / passato remoto (PST;PFV) /
futuro, condizionale (COND),
congiuntivo presente (SBJV;PRS) / imperfetto (SBJV;PST),
affirmative imperative, infinitive, gerundio (V.CVB;PRS),
past participle (masc-sg; fem/plural derived by vowel rule).
it_irreg_verbs.json — 66 high-frequency verbs UniMorph MISSES
(essere, avere, potere, uscire, tenere, prendere, piacere, …), extracted
from kaikki.org Italian, filtered to standard forms, and DE-STRESSED to
real orthography (kaikki marks tonic stress everywhere: pàrlo->parlo,
avùto->avuto; final legit accents kept: sarò, è). Built by build_it_irreg.py.
This layer takes priority — it supplies the two auxiliaries essere/avere,
which the whole passato-prossimo / essere-agreement system depends on.
NOUNS + ADJECTIVES — kaikki.org Italian (Wiktionary extract, CC-BY-SA 3.0)
noun lemmas WITH inherent gender (head-template arg) + real (often irregular)
plural — uomo->uomini, uovo->uova, dito->dita, città invariant — resolved
PER LEMMA, never guessed.
adjective lemmas with real feminine + masc/fem plural (italiano->italiana/
italiani/italiane, felice->felici invariant).
Fallbacks (degrade, never crash, on OOV input):
verbs : rule generator for regular -are/-ere/-ire (with -care/-gare h-insertion
and -ciare/-giare/-iare i-drop spelling rules)
nouns : gender heuristic (endings) + rule pluralization (ambiguous -co/-go FLAGGED)
adjs : -o/-a/-e gender rule + rule pluralization
Confidence flag on every form:
"lexicon" from UniMorph / kaikki-irregular / kaikki noun-adj (trust: high)
"rule" deterministic rule (trust: medium)
"fallback" could not inflect; returned lemma / ambiguous (trust: low -> FLAG)
Public API (used by realizer_it.py):
conjugate(lemma, mood, tense, person, number) -> (form, conf)
participle(lemma, gender="m", number="singular") -> (form, conf)
gerund(lemma) -> (form, conf)
noun_gender(lemma) -> "m"|"f"
inflect_noun(lemma, number, gender=None) -> (form, conf)
inflect_adj(lemma, gender, number) -> (form, conf)
lexicon_stats() -> dict
"""
import json
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "ita.unimorph")
_IRREG = os.path.join(_HERE, "data", "it_irreg_verbs.json")
_KAIKKI = os.path.join(_HERE, "data", "kaikki_it.jsonl")
_CACHE = os.path.join(_HERE, "data", "it_morph_cache.pkl")
# ── (mood, tense) -> UniMorph feature set that must ALL be present ────────────────
_VERB_KEYMAP = {
("ind", "present"): {"IND", "PRS"},
("ind", "imperfect"): {"IND", "PST", "IPFV"},
("ind", "passato_remoto"): {"IND", "PST", "PFV"},
("ind", "future"): {"IND", "FUT"},
("ind", "conditional"): {"COND"},
("sbjv", "present"): {"SBJV", "PRS"},
("sbjv", "imperfect"): {"SBJV", "PST"},
("imp", "affirmative"): {"POS", "IMP"},
}
_PERSON = {"first": "1", "second": "2", "third": "3"}
_NUMBER = {"singular": "SG", "plural": "PL"}
def _feat_set(tag):
return set(tag.split(";"))
# ── build verb lexicon from UniMorph ─────────────────────────────────────────────
def _build_verbs():
verbs = {} # (lemma, "mood|tense|person|number") -> form
part = {} # lemma -> masc-sg past participle
ger = {} # lemma -> gerundio
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
f = _feat_set(tag)
head = tag.split(";")[0]
if head == "V.PTCP":
if "PST" in f:
part.setdefault(lemma, form)
continue
if head == "V.CVB": # gerundio (converb, present)
if "PRS" in f:
ger.setdefault(lemma, form)
continue
if head != "V":
continue
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person is None or number is None:
continue
for (mood, tense), req in _VERB_KEYMAP.items():
# exact-set discipline: PST;PFV must not match PST;IPFV, etc.
if not req <= f:
continue
# guard IND;PST ambiguity: require the specific aspect feature
if tense == "imperfect" and "PFV" in f:
continue
if tense == "passato_remoto" and "IPFV" in f:
continue
# COND must not also be a subjunctive/imperative slot
verbs.setdefault((lemma, f"{mood}|{tense}|{person}|{number}"), form)
break
return verbs, part, ger
# ── kaikki nouns + adjectives ────────────────────────────────────────────────────
_EXCL_FORM_TAGS = {"alternative", "archaic", "obsolete", "dialectal", "regional",
"diminutive", "augmentative", "pejorative", "comparative",
"superlative", "misspelling", "rare", "informal", "literary",
"poetic", "error-unrecognized-form", "apocopic", "obsolete",
"construed", "collective"}
def _kaikki_gender(arg):
if not arg:
return None
a = str(arg).lower()
if a.startswith("f"):
return "f"
if a.startswith("m"):
return "m"
return None
def _build_nouns_adjs():
nouns = {} # lemma -> {"g","SG","PL"}
adjs = {} # lemma -> {("m","SG"),("f","SG"),("m","PL"),("f","PL")}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
pos = d.get("pos")
word = d.get("word", "")
if not word or " " in word:
continue
forms = d.get("forms", []) or []
if pos == "noun":
ht = d.get("head_templates") or []
g = None
if ht:
g = _kaikki_gender((ht[0].get("args") or {}).get("1"))
if g is None:
tags = d.get("tags") or []
if "feminine" in tags:
g = "f"
elif "masculine" in tags:
g = "m"
pl = None
for x in forms:
t = set(x.get("tags") or [])
if "plural" in t and not (t & _EXCL_FORM_TAGS):
fm = x.get("form")
if fm and " " not in fm and fm != "#":
pl = fm
break
if word not in nouns:
nouns[word] = {"g": g, "SG": word, "PL": pl}
else:
cur = nouns[word]
if cur.get("g") is None and g:
cur["g"] = g
if not cur.get("PL") and pl:
cur["PL"] = pl
elif pos == "adj":
d0 = adjs.setdefault(word, {})
d0.setdefault(("m", "SG"), word)
for x in forms:
t = set(x.get("tags") or [])
fm = x.get("form")
if not fm or " " in fm or (t & _EXCL_FORM_TAGS):
continue
if "feminine" in t and "plural" in t:
d0[("f", "PL")] = d0.get(("f", "PL")) or fm
elif "masculine" in t and "plural" in t:
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
elif "feminine" in t:
d0[("f", "SG")] = d0.get(("f", "SG")) or fm
elif "plural" in t: # invariant-gender adj (felice -> felici)
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
d0[("f", "PL")] = d0.get(("f", "PL")) or fm
return nouns, adjs
def _build_cache():
verbs, part, ger = _build_verbs()
nouns, adjs = _build_nouns_adjs()
with open(_IRREG, encoding="utf-8") as fh:
irreg = json.load(fh)
data = {"verbs": verbs, "part": part, "ger": ger,
"nouns": nouns, "adjs": adjs, "irreg": irreg}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE):
srcs = [_UNIMORPH, _KAIKKI, _IRREG]
newest = max(os.path.getmtime(s) for s in srcs if os.path.exists(s))
if os.path.getmtime(_CACHE) >= newest:
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_VERBS, _PART, _GER, _NOUNS, _ADJS, _IRREGV = (
_LEX["verbs"], _LEX["part"], _LEX["ger"], _LEX["nouns"], _LEX["adjs"],
_LEX["irreg"])
# ── regular-ending rule fallback ─────────────────────────────────────────────────
def _vclass(lemma):
if lemma.endswith("are"):
return "are"
if lemma.endswith("ere"):
return "ere"
if lemma.endswith("ire"):
return "ire"
return None
# endings [1sg,2sg,3sg,1pl,2pl,3pl]
_REG = {
("ind", "present", "are"): ["o", "i", "a", "iamo", "ate", "ano"],
("ind", "present", "ere"): ["o", "i", "e", "iamo", "ete", "ono"],
("ind", "present", "ire"): ["o", "i", "e", "iamo", "ite", "ono"],
("ind", "imperfect", "are"): ["avo", "avi", "ava", "avamo", "avate", "avano"],
("ind", "imperfect", "ere"): ["evo", "evi", "eva", "evamo", "evate", "evano"],
("ind", "imperfect", "ire"): ["ivo", "ivi", "iva", "ivamo", "ivate", "ivano"],
("ind", "passato_remoto", "are"): ["ai", "asti", "ò", "ammo", "aste", "arono"],
("ind", "passato_remoto", "ere"): ["ei", "esti", "é", "emmo", "este", "erono"],
("ind", "passato_remoto", "ire"): ["ii", "isti", "ì", "immo", "iste", "irono"],
("sbjv", "present", "are"): ["i", "i", "i", "iamo", "iate", "ino"],
("sbjv", "present", "ere"): ["a", "a", "a", "iamo", "iate", "ano"],
("sbjv", "present", "ire"): ["a", "a", "a", "iamo", "iate", "ano"],
("sbjv", "imperfect", "are"): ["assi", "assi", "asse", "assimo", "aste", "assero"],
("sbjv", "imperfect", "ere"): ["essi", "essi", "esse", "essimo", "este", "essero"],
("sbjv", "imperfect", "ire"): ["issi", "issi", "isse", "issimo", "iste", "issero"],
# imperative: 2sg,3sg(Lei),1pl,2pl,3pl (1sg has none)
("imp", "affirmative", "are"): [None, "a", "i", "iamo", "ate", "ino"],
("imp", "affirmative", "ere"): [None, "i", "a", "iamo", "ete", "ano"],
("imp", "affirmative", "ire"): [None, "i", "a", "iamo", "ite", "ano"],
}
# future / conditional attach to a stem = infinitive minus final -e, with
# -are -> -er (parlare->parler-), -ere/-ire keep (credere->creder-, dormir-)
_FUT = ["ò", "ai", "à", "emo", "ete", "anno"]
_COND = ["ei", "esti", "ebbe", "emmo", "este", "ebbero"]
def _slot_idx(person, number):
base = {"first": 0, "second": 1, "third": 2}[person]
return base + (0 if number == "singular" else 3)
def _fut_stem(lemma, vc):
body = lemma[:-3] # drop are/ere/ire
if vc == "are":
return body + "er"
return body + vc[0] + "r" # ere->er? no: keep vowel: creder-, dormir-
# NOTE corrected below
def _apply_are_spelling(stem, ending):
"""-care/-gare insert h before front endings; -ciare/-giare/-sciare/-iare drop i."""
front = ending[:1] in ("i", "e")
if stem.endswith(("c", "g")) and front:
return stem + "h" + ending
if stem.endswith(("ci", "gi", "sci")) and ending[:1] == "i":
return stem[:-1] + ending # mangi+iamo -> mangiamo
if stem.endswith("i") and ending[:1] == "i":
return stem[:-1] + ending # studi+iamo -> studiamo
return stem + ending
def _rule_conjugate(lemma, mood, tense, person, number):
vc = _vclass(lemma)
if vc is None:
return None
body = lemma[:-3]
i = _slot_idx(person, number)
if mood == "ind" and tense in ("future", "conditional"):
stem = body + "er" if vc == "are" else body + vc[0] + "r"
# ere: creder-, ire: dormir- -> body + 'e'/'i' + 'r'
if vc == "ere":
stem = body + "er"
elif vc == "ire":
stem = body + "ir"
end = (_FUT if tense == "future" else _COND)[i]
# spelling: -care/-gare -> cherò/gherò ; -ciare/-giare -> cerò/gerò
if vc == "are":
if body.endswith(("c", "g")):
stem = body + "her"
elif body.endswith(("ci", "gi", "sci")):
stem = body[:-1] + "er"
elif body.endswith("i"):
stem = body[:-1] + "er"
return stem + end
table = _REG.get((mood, tense, vc))
if not table:
return None
end = table[i]
if end is None:
return None
if vc == "are":
return _apply_are_spelling(body, end)
# -ere/-ire: guard against double-i (dormi+iamo -> dormiamo)
if body.endswith("i") and end[:1] == "i":
return body[:-1] + end
return body + end
# ── PUBLIC: verb conjugation ─────────────────────────────────────────────────────
def conjugate(lemma, mood, tense, person, number):
"""Return (surface, confidence). mood in ind|sbjv|imp; tense per _VERB_KEYMAP."""
lemma = lemma.strip().lower()
key = f"{mood}|{tense}|{_PERSON.get(person,'?')}|{number}"
ir = _IRREGV.get(lemma)
if ir and key in ir:
return ir[key], "lexicon"
p, n = _PERSON.get(person), _NUMBER.get(number)
if p and n:
form = _VERBS.get((lemma, f"{mood}|{tense}|{p}|{n}"))
if form:
return form, "lexicon"
r = _rule_conjugate(lemma, mood, tense, person, number)
if r:
return r, "rule"
return lemma, "fallback"
# ── PUBLIC: participle + gerund ──────────────────────────────────────────────────
def _participle_msg(lemma):
"""Return (masc-sg participle, source) or (None, None)."""
ir = _IRREGV.get(lemma)
if ir and "part" in ir:
return ir["part"], "lexicon"
if lemma in _PART:
return _PART[lemma], "lexicon"
return None, None
def participle(lemma, gender="m", number="singular"):
"""Past participle with gender/number agreement (for essere-perfect & passives).
UniMorph/irregular give masc-sg; fem/plural derived by final-vowel swap
(-o -> -a/-i/-e), valid for regular -ato/-uto/-ito AND irregulars
(preso->presa/presi/prese, aperto->aperta/aperti/aperte, morto->morta/...)."""
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
msg, src = _participle_msg(lemma)
conf = "lexicon"
if msg is None:
vc = _vclass(lemma)
if vc == "are":
msg = lemma[:-3] + "ato"
elif vc == "ere":
msg = lemma[:-3] + "uto"
elif vc == "ire":
msg = lemma[:-3] + "ito"
else:
return lemma, "fallback"
conf = "rule"
# agreement: only -o participles inflect for gender+number
if msg.endswith("o"):
stem = msg[:-1]
suf = {"m|SG": "o", "f|SG": "a", "m|PL": "i", "f|PL": "e"}[f"{g}|{num}"]
return stem + suf, conf
return msg, conf # non -o participle: leave as-is (rare)
def gerund(lemma):
lemma = lemma.strip().lower()
ir = _IRREGV.get(lemma)
if ir and "ger" in ir:
return ir["ger"], "lexicon"
if lemma in _GER:
return _GER[lemma], "lexicon"
vc = _vclass(lemma)
if vc == "are":
return lemma[:-3] + "ando", "rule"
if vc in ("ere", "ire"):
return lemma[:-3] + "endo", "rule"
return lemma, "fallback"
# ── PUBLIC: noun gender + number ─────────────────────────────────────────────────
_FEM_SUF = ("zione", "sione", "gione", "", "", "trice", "aggine", "udine",
"igine", "ie", "essa", "izia", "ezza")
_MASC_SUF = ("ore", "ame", "iere", "ale", "ile")
def _gender_heuristic(noun):
for suf in _FEM_SUF:
if noun.endswith(suf):
return "f"
for suf in _MASC_SUF:
if noun.endswith(suf):
return "m"
if noun.endswith("o"):
return "m"
if noun.endswith("a"):
return "f"
if noun.endswith("à") or noun.endswith("ù"):
return "f"
return "m" # -e and consonant-final loanwords default masculine
def noun_gender(lemma):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if d and d.get("g") in ("m", "f"):
return d["g"]
return _gender_heuristic(lemma)
def _rule_plural(noun, gender):
"""Deterministic Italian pluralization. Returns (form, ok); ok=False FLAGS an
ambiguous case the lexicon would normally resolve (-co/-go palatalization)."""
if not noun:
return noun, True
# invariant: accented final vowel, consonant-final, monosyllable, -i final
if noun[-1:] in ("à", "è", "é", "ì", "í", "ò", "ó", "ù", "ú"):
return noun, True
if noun[-1:] not in ("a", "e", "o", "i", "u"):
return noun, True # consonant-final loanword: invariant
if noun.endswith("i"):
return noun, True # e.g. crisi, analisi: invariant
if noun.endswith("io"):
return noun[:-2] + "i", True # figlio->figli (unstressed i)
if noun.endswith("cia") or noun.endswith("gia"):
# vowel before cia/gia -> -cie/-gie ; consonant -> -ce/-ge (approx)
return noun[:-2] + "e", True # arancia->arance (majority)
if noun.endswith("ca"):
return noun[:-2] + "che", True # amica->amiche
if noun.endswith("ga"):
return noun[:-2] + "ghe", True
if noun.endswith("co"):
return noun[:-2] + "chi", False # AMBIGUOUS (amico->amici) -> flag
if noun.endswith("go"):
return noun[:-2] + "ghi", False # AMBIGUOUS (psicologo->psicologi)
if noun.endswith("a"):
return noun[:-1] + "e", True # casa->case (m -a: -i, but rare)
if noun.endswith("o"):
return noun[:-1] + "i", True # libro->libri
if noun.endswith("e"):
return noun[:-1] + "i", True # cane->cani, chiave->chiavi
return noun, True
def inflect_noun(lemma, number, gender=None):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if number == "singular":
return (d["SG"] if d and d.get("SG") else lemma), ("lexicon" if d else "rule")
if d and d.get("PL"):
return d["PL"], "lexicon"
g = gender or noun_gender(lemma)
form, ok = _rule_plural(lemma, g)
return form, ("rule" if ok else "fallback")
# adjectives whose kaikki entries are unreliable (messy inflection templates):
# supply audited regular agreement forms (prenominal apocope handled in realizer).
_ADJ_FIX = {
"bello": {("m", "SG"): "bello", ("f", "SG"): "bella",
("m", "PL"): "belli", ("f", "PL"): "belle"},
"quello": {("m", "SG"): "quello", ("f", "SG"): "quella",
("m", "PL"): "quelli", ("f", "PL"): "quelle"},
}
# ── PUBLIC: adjective agreement ──────────────────────────────────────────────────
def inflect_adj(lemma, gender, number):
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
fix = _ADJ_FIX.get(lemma)
if fix and (g, num) in fix:
return fix[(g, num)], "lexicon"
d = _ADJS.get(lemma)
if d:
form = d.get((g, num))
if form:
return form, "lexicon"
sg = d.get((g, "SG")) or d.get(("m", "SG")) or lemma
if num == "PL":
pl, ok = _rule_plural(sg, g)
return pl, ("rule" if ok else "fallback")
return sg, "lexicon"
# rule fallback
a = lemma
if a.endswith("o"): # -o/-a/-i/-e class
base = a[:-1]
suf = {"m|SG": "o", "f|SG": "a", "m|PL": "i", "f|PL": "e"}[f"{g}|{num}"]
return base + suf, "rule"
if a.endswith("e"): # felice-class: SG invariant, PL -i
if num == "PL":
return a[:-1] + "i", "rule"
return a, "rule"
if num == "PL":
p, ok = _rule_plural(a, g)
return p, ("rule" if ok else "fallback")
return a, "rule"
def lexicon_stats():
return {
"verb_source": "UniMorph Italian (github.com/unimorph/ita) + kaikki.org "
"irregulars (de-stressed)",
"noun_adj_source": "kaikki.org Italian (Wiktionary extract)",
"license": "CC-BY-SA 3.0 (Wiktionary/UniMorph lineage)",
"unimorph_verb_forms": len(_VERBS),
"unimorph_verb_lemmas": len({k[0] for k in _VERBS}),
"irregular_verb_lemmas": len(_IRREGV),
"participle_lemmas": len(_PART),
"gerund_lemmas": len(_GER),
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
}
if __name__ == "__main__":
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
tests = [
("parlare", "ind", "present", "first", "singular", "parlo"),
("essere", "ind", "present", "third", "singular", "è"),
("avere", "ind", "present", "first", "singular", "ho"),
("mangiare", "ind", "present", "second", "singular", "mangi"),
("finire", "ind", "present", "first", "singular", "finisco"),
("andare", "ind", "present", "third", "plural", "vanno"),
("fare", "ind", "future", "first", "singular", "farò"),
("potere", "sbjv", "present", "third", "singular", "possa"),
("prendere", "ind", "passato_remoto", "first", "singular", "presi"),
("cercare", "ind", "present", "second", "singular", "cerchi"),
("dormire", "ind", "present", "third", "plural", "dormono"),
("credere", "ind", "future", "first", "singular", "crederò"),
]
ok = 0
for lemma, mood, tense, per, num, exp in tests:
got, conf = conjugate(lemma, mood, tense, per, num)
flag = "OK " if got == exp else "XX "
ok += got == exp
print(f" {flag}{lemma:9} {mood}/{tense:14} {per[:3]}.{num[:2]} -> {got:12} ({conf}) exp={exp}")
print(f"verb tests {ok}/{len(tests)}")
print(" gender: casa=", noun_gender("casa"), "problema=", noun_gender("problema"),
"mano=", noun_gender("mano"), "città=", noun_gender("città"),
"cane=", noun_gender("cane"))
print(" plural: uomo->", inflect_noun("uomo", "plural"),
"| uovo->", inflect_noun("uovo", "plural"),
"| città->", inflect_noun("città", "plural"),
"| amico->", inflect_noun("amico", "plural"),
"| casa->", inflect_noun("casa", "plural"))
print(" adj: italiano/f/pl->", inflect_adj("italiano", "f", "plural"),
"| felice/m/pl->", inflect_adj("felice", "m", "plural"),
"| bello/f/sg->", inflect_adj("bello", "f", "singular"))
print(" part: aprire/f/sg->", participle("aprire", "f", "singular"),
"| prendere/m/pl->", participle("prendere", "m", "plural"),
"| andare/f/sg->", participle("andare", "f", "singular"))
print(" ger: fare->", gerund("fare"), "| parlare->", gerund("parlare"))
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@@ -1,666 +0,0 @@
# -*- coding: utf-8 -*-
"""morphology_lat_full.py — production-grade Latin morphological generator.
Latin is the FLAGSHIP dead-language realizer. It rides the *architecture* of the
Romance/Italic engine (the same Realization / spec-driven design and the UniMorph
loader pattern from morphology_it_full.py) but with the CASE SYSTEM RESTORED —
the feature Romance lost. Latin therefore exercises machinery the modern Romance
siblings never needed: 5 declensions x 6 cases x 2 numbers x 3 genders, plus a
4-conjugation verb system with tense/mood/voice.
DATA (real, attested — no fabrication):
NOUNS + ADJECTIVES — UniMorph Latin (github.com/unimorph/lat, CC-BY-SA 3.0)
163,182 N forms across ~thousands of lemmas, each with the full case paradigm
N;NOM/GEN/DAT/ACC/ABL/VOC;SG/PL (real inflected forms, WITH macrons:
puella->puellam, rēx->rēgis, corpus->corporis).
244,197 ADJ forms with case x GENDER x number, incl. UniMorph's combined
tags (GEN+DAT, MASC+FEM, MASC+FEM+NEUT) which are split on load.
462,668 V.PTCP forms (participles) also carry case/gender/number.
UniMorph N tags DO NOT encode inherent gender, so noun gender is inferred
from the declension (nom-sg + gen-sg endings) with a curated exceptions
map — the standard, attestable rule (1st decl -a/-ae = fem, 2nd -us/-i =
masc, -um = neut, ...).
VERBS — RULE ENGINE (honest gap: UniMorph Latin's verb list is a 947-lemma
sample of rare/prefixed verbs that MISSES every core textbook verb — amō,
videō, sum, regō, ... are all absent). Latin conjugation is, however, highly
regular, so verbs are generated by a deterministic 4-conjugation engine over
curated principal parts (present / perfect / supine stems), sourced from
standard references. Irregulars (sum, possum, eō, ferō, volō, nōlō, mālō)
are curated full tables. Forms are flagged "rule" (not "lexicon") for honesty.
Confidence flag on every form (same contract as the Romance engine):
"lexicon" from UniMorph (trust: high)
"rule" deterministic morphology rule (trust: medium)
"fallback" could not inflect; returned lemma (trust: low -> FLAG)
Public API (used by realizer_lat.py):
decline_noun(lemma, case, number) -> (form, conf)
noun_gender(lemma) -> "m"|"f"|"n"
decline_adj(lemma, case, gender, number) -> (form, conf)
conjugate(lemma, tense, mood, voice, person, number) -> (form, conf)
participle(lemma, kind, case, gender, number) -> (form, conf) # kind: prs|pfv|fut
infinitive(lemma, tense="present", voice="active") -> (form, conf)
lexicon_stats() -> dict
"""
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "lat.unimorph")
_CACHE = os.path.join(_HERE, "data", "lat_morph_cache.pkl")
_CASES = ("NOM", "GEN", "DAT", "ACC", "ABL", "VOC")
_CASE_MAP = {"nom": "NOM", "gen": "GEN", "dat": "DAT", "acc": "ACC",
"abl": "ABL", "voc": "VOC"}
_NUM = {"singular": "SG", "plural": "PL"}
_GEN = {"m": "MASC", "f": "FEM", "n": "NEUT"}
# ── UniMorph loader: noun + adjective + participle case paradigms ────────────────
def _build_cache():
nouns = {} # lemma -> {(CASE, NUM): form}
adjs = {} # lemma -> {(CASE, GEN, NUM): form}
ptcps = {} # lemma -> {(CASE, GEN, NUM): form} (from V.PTCP; keyed loosely)
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
feats = tag.split(";")
head = feats[0]
fs = set(feats)
case = next((c for c in _CASES if c in fs), None)
# handle combined case tags like GEN+DAT
if case is None:
for f in feats:
if "+" in f and any(c in f.split("+") for c in _CASES):
case = [c for c in _CASES if c in f.split("+")]
break
num = "SG" if "SG" in fs else ("PL" if "PL" in fs else None)
if case is None or num is None:
continue
cases = case if isinstance(case, list) else [case]
if head == "N":
d = nouns.setdefault(lemma, {})
for c in cases:
d.setdefault((c, num), form)
elif head == "ADJ":
# gender may be combined: MASC+FEM+NEUT, MASC+FEM
genders = []
for g in ("MASC", "FEM", "NEUT"):
if any(g == x or (g in x.split("+")) for x in feats):
genders.append(g)
if not genders:
genders = ["MASC", "FEM", "NEUT"]
d = adjs.setdefault(lemma, {})
for c in cases:
for g in genders:
d.setdefault((c, g, num), form)
data = {"nouns": nouns, "adjs": adjs, "ptcps": ptcps}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE) and os.path.exists(_UNIMORPH):
if os.path.getmtime(_CACHE) >= os.path.getmtime(_UNIMORPH):
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_NOUNS, _ADJS = _LEX["nouns"], _LEX["adjs"]
# ── noun gender inference (declension-based, curated exceptions) ─────────────────
# Real, attestable rule: gender follows declension + nominative shape, with the
# standard closed set of exceptions.
_GENDER_EXC = {
# 1st-declension masculines (people/agents)
"agricola": "m", "poēta": "m", "nauta": "m", "incola": "m", "scrība": "m",
"auriga": "m", "pīrāta": "m", "athlēta": "m",
# 2nd-declension neuters / feminines
"vīrus": "n", "vulgus": "n", "pelagus": "n", "humus": "f",
# common 3rd-declension whose gender the ending would mispredict
"rēx": "m", "dux": "m", "mīles": "m", "pater": "m", "frāter": "m",
"homō": "m", "leō": "m", "sōl": "m", "mōns": "m", "pōns": "m", "fōns": "m",
"sanguis": "m", "ōrdō": "m", "sermō": "m", "amor": "m", "dolor": "m",
"labor": "m", "timor": "m", "honor": "m", "color": "m", "pēs": "m",
"dēns": "m", "flōs": "m", "mōs": "m", "mensis": "m", "orbis": "m",
"piscis": "m", "ignis": "m", "collis": "m", "grex": "m", "prīnceps": "m",
"māter": "f", "soror": "f", "uxor": "f", "mulier": "f", "virgō": "f",
"urbs": "f", "arx": "f", "pāx": "f", "lēx": "f", "lūx": "f", "vōx": "f",
"nox": "f", "nix": "f", "vīs": "f", "salūs": "f", "virtūs": "f",
"aetās": "f", "cīvitās": "f", "lībertās": "f", "vēritās": "f", "voluptās": "f",
"nātiō": "f", "ratiō": "f", "ōrātiō": "f", "legiō": "f", "regiō": "f",
"mens": "f", "gens": "f", "ars": "f", "pars": "f", "mors": "f", "sors": "f",
"nāvis": "f", "turris": "f", "avis": "f", "vallis": "f", "classis": "f",
"corpus": "n", "tempus": "n", "opus": "n", "genus": "n", "onus": "n",
"pectus": "n", "latus": "n", "vulnus": "n", "scelus": "n", "sīdus": "n",
"caput": "n", "iter": "n", "flūmen": "n", "nōmen": "n", "carmen": "n",
"agmen": "n", "certāmen": "n", "lūmen": "n", "ōmen": "n", "cōgnōmen": "n",
"mare": "n", "animal": "n", "exemplar": "n", "rēte": "n",
# 4th-declension exceptions
"manus": "f", "domus": "f", "tribus": "f", "porticus": "f", "īdūs": "f",
"cornū": "n", "genū": "n", "gelū": "n", "verū": "n",
# 5th-declension
"diēs": "m", "merīdiēs": "m",
}
def _infer_gender(lemma):
if lemma in _GENDER_EXC:
return _GENDER_EXC[lemma]
d = _NOUNS.get(lemma)
nom = d.get(("NOM", "SG")) if d else lemma
gen = d.get(("GEN", "SG")) if d else None
nom = nom or lemma
# 5th declension: gen -eī / -ēī
if gen and (gen.endswith("") or gen.endswith("ēī")):
return "f"
# 1st declension: nom -a, gen -ae
if nom.endswith("a") and (not gen or gen.endswith("ae")):
return "f"
# 2nd declension neuter: nom -um
if nom.endswith("um"):
return "n"
# 2nd declension masc: nom -us/-er/-ir, gen -ī
if (nom.endswith("us") or nom.endswith("er") or nom.endswith("ir")) and \
(not gen or gen.endswith("ī")):
return "m"
# 4th declension: gen -ūs
if gen and gen.endswith("ūs"):
return "n" if nom.endswith("ū") else "m"
# 3rd declension neuters by common nom endings
if nom.endswith(("men", "us", "ur", "al", "ar", "e", "ma")):
# -us here is 3rd-decl neuter type (corpus) only if gen shows -oris/-eris
if nom.endswith("us") and gen and (gen.endswith("oris") or gen.endswith("eris")
or gen.endswith("uris")):
return "n"
if nom.endswith(("men", "al", "ar", "e")):
return "n"
# default 3rd-declension: masculine (most common)
return "m"
_GENDER_CACHE = {}
def noun_gender(lemma):
lemma = lemma.strip()
if lemma not in _GENDER_CACHE:
_GENDER_CACHE[lemma] = _infer_gender(lemma)
return _GENDER_CACHE[lemma]
# ── PUBLIC: noun declension ─────────────────────────────────────────────────────
def decline_noun(lemma, case, number):
lemma = lemma.strip()
C = _CASE_MAP.get(case, case.upper())
N = _NUM.get(number, number)
d = _NOUNS.get(lemma)
if d and (C, N) in d:
return d[(C, N)], "lexicon"
# abl sg often == the -e/-o form; try nom fallback
if d:
# try VOC==NOM, ACC neuter==NOM etc are already in data; last resort lemma
return lemma, "fallback"
return lemma, "fallback"
# ── PUBLIC: adjective declension ────────────────────────────────────────────────
def decline_adj(lemma, case, gender, number):
lemma = lemma.strip()
C = _CASE_MAP.get(case, case.upper())
G = _GEN.get(gender, gender.upper())
N = _NUM.get(number, number)
d = _ADJS.get(lemma)
if d and (C, G, N) in d:
return d[(C, G, N)], "lexicon"
# try other gender (some adjs listed only under MASC+FEM etc handled at load)
if d:
for altG in ("MASC", "FEM", "NEUT"):
if (C, altG, N) in d:
return d[(C, altG, N)], "lexicon"
return lemma, "fallback"
return lemma, "fallback"
# ═══════════════════════════════════════════════════════════════════════════════
# VERB RULE ENGINE (4 conjugations + curated irregulars)
# ═══════════════════════════════════════════════════════════════════════════════
# Curated principal parts for common attested verbs:
# lemma -> (conj, present_stem, perfect_stem, supine_stem)
# conj in {1,2,3,"3io",4}. Stems carry macrons (matching UniMorph orthography).
_VERBS = {
"amō": (1, "am", "amāv", "amāt"),
"laudō": (1, "laud", "laudāv", "laudāt"),
"portō": (1, "port", "portāv", "portāt"),
"vocō": (1, "voc", "vocāv", "vocāt"),
"": (1, "d", "ded", "dat"),
"spectō": (1, "spect", "spectāv", "spectāt"),
"pugnō": (1, "pugn", "pugnāv", "pugnāt"),
"labōrō": (1, "labōr", "labōrāv", "labōrāt"),
"necō": (1, "nec", "necāv", "necāt"),
"parō": (1, "par", "parāv", "parāt"),
"cōgitō": (1, "cōgit", "cōgitāv", "cōgitāt"),
"habitō": (1, "habit", "habitāv", "habitāt"),
"nārrō": (1, "nārr", "nārrāv", "nārrāt"),
"servō": (1, "serv", "servāv", "servāt"),
"superō": (1, "super", "superāv", "superāt"),
"oppugnō": (1, "oppugn", "oppugnāv", "oppugnāt"),
"ambulō": (1, "ambul", "ambulāv", "ambulāt"),
"clāmō": (1, "clām", "clāmāv", "clāmāt"),
"vulnerō": (1, "vulner", "vulnerāv", "vulnerāt"),
"aedificō": (1, "aedific", "aedificāv", "aedificāt"),
"expugnō": (1, "expugn", "expugnāv", "expugnāt"),
"dēfendō": (3, "dēfend", "dēfend", "dēfēns"),
"petō": (3, "pet", "petīv", "petīt"),
"occīdō": (3, "occīd", "occīd", "occīs"),
"interficiō": ("3io", "interfic", "interfēc", "interfect"),
"timeō": (2, "tim", "timu", None),
"iaceō": (2, "iac", "iacu", None),
"pāreō": (2, "pār", "pāru", "pārit"),
"respondeō": (2, "respond", "respond", "respōns"),
"vertō": (3, "vert", "vert", "vers"),
"ostendō": (3, "ostend", "ostend", "ostent"),
"cōnstituō": (3, "cōnstitu", "cōnstitu", "cōnstitūt"),
"cōgnōscō": (3, "cōgnōsc", "cōgnōv", "cōgnit"),
"crēdō": (3, "crēd", "crēdid", "crēdit"),
"ēdūcō": (3, "ēdūc", "ēdūx", "ēduct"),
"cōnservō": (1, "cōnserv", "cōnservāv", "cōnservāt"),
"iuvō": (1, "iuv", "iūv", "iūt"),
"dēbeō": (2, "dēb", "dēbu", "dēbit"),
"moneō": (2, "mon", "monu", "monit"),
"videō": (2, "vid", "vīd", "vīs"),
"habeō": (2, "hab", "habu", "habit"),
"teneō": (2, "ten", "tenu", "tent"),
"timeō": (2, "tim", "timu", None),
"terreō": (2, "terr", "terru", "territ"),
"dēleō": (2, "dēl", "dēlēv", "dēlēt"),
"iubeō": (2, "iub", "iuss", "iuss"),
"maneō": (2, "man", "māns", "māns"),
"moveō": (2, "mov", "mōv", "mōt"),
"doceō": (2, "doc", "docu", "doct"),
"sedeō": (2, "sed", "sēd", "sess"),
"rīdeō": (2, "rīd", "rīs", "rīs"),
"regō": (3, "reg", "rēx", "rēct"),
"dūcō": (3, "dūc", "dūx", "duct"),
"scrībō": (3, "scrīb", "scrīps", "scrīpt"),
"mittō": (3, "mitt", "mīs", "miss"),
"pōnō": (3, "pōn", "posu", "posit"),
"agō": (3, "ag", "ēg", "āct"),
"dīcō": (3, "dīc", "dīx", "dict"),
"gerō": (3, "ger", "gess", "gest"),
"vincō": (3, "vinc", "vīc", "vict"),
"petō": (3, "pet", "petīv", "petīt"),
"legō": (3, "leg", "lēg", "lēct"),
"currō": (3, "curr", "cucurr", "curs"),
"vīvō": (3, "vīv", "vīx", "vīct"),
"quaerō": (3, "quaer", "quaesīv", "quaesīt"),
"trahō": (3, "trah", "trāx", "tract"),
"claudō": (3, "claud", "claus", "claus"),
"cōgō": (3, "cōg", "coēg", "coāct"),
"relinquō": (3, "relinqu", "relīqu", "relict"),
"capiō": ("3io", "cap", "cēp", "capt"),
"faciō": ("3io", "fac", "fēc", "fact"),
"iaciō": ("3io", "iac", "iēc", "iact"),
"rapiō": ("3io", "rap", "rapu", "rapt"),
"fugiō": ("3io", "fug", "fūg", "fugit"),
"cupiō": ("3io", "cup", "cupīv", "cupīt"),
"accipiō": ("3io", "accip", "accēp", "accept"),
"audiō": (4, "aud", "audīv", "audīt"),
"veniō": (4, "ven", "vēn", "vent"),
"sciō": (4, "sc", "scīv", "scīt"),
"sentiō": (4, "sent", "sēns", "sēns"),
"mūniō": (4, "mūn", "mūnīv", "mūnīt"),
"dormiō": (4, "dorm", "dormīv", "dormīt"),
"aperiō": (4, "aper", "aperu", "apert"),
"inveniō": (4, "inven", "invēn", "invent"),
}
# ── Present-system paradigms: full ending tables per conjugation, attached to the
# bare present stem (pstem). Hardcoded from the standard grammar with correct
# macrons/vowel-lengths — deterministic and independently verifiable. Keys:
# (tense, mood, voice) -> {conj: [1sg,2sg,3sg,1pl,2pl,3pl]}
_PARADIGM = {
("present", "ind", "active"): {
1: ["ō", "ās", "at", "āmus", "ātis", "ant"],
2: ["", "ēs", "et", "ēmus", "ētis", "ent"],
3: ["ō", "is", "it", "imus", "itis", "unt"],
"3io": ["", "is", "it", "imus", "itis", "iunt"],
4: ["", "īs", "it", "īmus", "ītis", "iunt"],
},
("present", "ind", "passive"): {
1: ["or", "āris", "ātur", "āmur", "āminī", "antur"],
2: ["eor", "ēris", "ētur", "ēmur", "ēminī", "entur"],
3: ["or", "eris", "itur", "imur", "iminī", "untur"],
"3io": ["ior", "eris", "itur", "imur", "iminī", "iuntur"],
4: ["ior", "īris", "ītur", "īmur", "īminī", "iuntur"],
},
("imperfect", "ind", "active"): {
1: ["ābam", "ābās", "ābat", "ābāmus", "ābātis", "ābant"],
2: ["ēbam", "ēbās", "ēbat", "ēbāmus", "ēbātis", "ēbant"],
3: ["ēbam", "ēbās", "ēbat", "ēbāmus", "ēbātis", "ēbant"],
"3io": ["iēbam", "iēbās", "iēbat", "iēbāmus", "iēbātis", "iēbant"],
4: ["iēbam", "iēbās", "iēbat", "iēbāmus", "iēbātis", "iēbant"],
},
("imperfect", "ind", "passive"): {
1: ["ābar", "ābāris", "ābātur", "ābāmur", "ābāminī", "ābantur"],
2: ["ēbar", "ēbāris", "ēbātur", "ēbāmur", "ēbāminī", "ēbantur"],
3: ["ēbar", "ēbāris", "ēbātur", "ēbāmur", "ēbāminī", "ēbantur"],
"3io": ["iēbar", "iēbāris", "iēbātur", "iēbāmur", "iēbāminī", "iēbantur"],
4: ["iēbar", "iēbāris", "iēbātur", "iēbāmur", "iēbāminī", "iēbantur"],
},
("future", "ind", "active"): {
1: ["ābō", "ābis", "ābit", "ābimus", "ābitis", "ābunt"],
2: ["ēbō", "ēbis", "ēbit", "ēbimus", "ēbitis", "ēbunt"],
3: ["am", "ēs", "et", "ēmus", "ētis", "ent"],
"3io": ["iam", "iēs", "iet", "iēmus", "iētis", "ient"],
4: ["iam", "iēs", "iet", "iēmus", "iētis", "ient"],
},
("future", "ind", "passive"): {
1: ["ābor", "āberis", "ābitur", "ābimur", "ābiminī", "ābuntur"],
2: ["ēbor", "ēberis", "ēbitur", "ēbimur", "ēbiminī", "ēbuntur"],
3: ["ar", "ēris", "ētur", "ēmur", "ēminī", "entur"],
"3io": ["iar", "iēris", "iētur", "iēmur", "iēminī", "ientur"],
4: ["iar", "iēris", "iētur", "iēmur", "iēminī", "ientur"],
},
("present", "sbjv", "active"): {
1: ["em", "ēs", "et", "ēmus", "ētis", "ent"],
2: ["eam", "eās", "eat", "eāmus", "eātis", "eant"],
3: ["am", "ās", "at", "āmus", "ātis", "ant"],
"3io": ["iam", "iās", "iat", "iāmus", "iātis", "iant"],
4: ["iam", "iās", "iat", "iāmus", "iātis", "iant"],
},
("present", "sbjv", "passive"): {
1: ["er", "ēris", "ētur", "ēmur", "ēminī", "entur"],
2: ["ear", "eāris", "eātur", "eāmur", "eāminī", "eantur"],
3: ["ar", "āris", "ātur", "āmur", "āminī", "antur"],
"3io": ["iar", "iāris", "iātur", "iāmur", "iāminī", "iantur"],
4: ["iar", "iāris", "iātur", "iāmur", "iāminī", "iantur"],
},
("imperfect", "sbjv", "active"): {
1: ["ārem", "ārēs", "āret", "ārēmus", "ārētis", "ārent"],
2: ["ērem", "ērēs", "ēret", "ērēmus", "ērētis", "ērent"],
3: ["erem", "erēs", "eret", "erēmus", "erētis", "erent"],
"3io": ["erem", "erēs", "eret", "erēmus", "erētis", "erent"],
4: ["īrem", "īrēs", "īret", "īrēmus", "īrētis", "īrent"],
},
("imperfect", "sbjv", "passive"): {
1: ["ārer", "ārēris", "ārētur", "ārēmur", "ārēminī", "ārentur"],
2: ["ērer", "ērēris", "ērētur", "ērēmur", "ērēminī", "ērentur"],
3: ["erer", "erēris", "erētur", "erēmur", "erēminī", "erentur"],
"3io": ["erer", "erēris", "erētur", "erēmur", "erēminī", "erentur"],
4: ["īrer", "īrēris", "īrētur", "īrēmur", "īrēminī", "īrentur"],
},
}
# perfect-active endings (added to perfect stem) — same for all conjugations
_PERF_ACT = {
("perfect", "ind"): ["ī", "istī", "it", "imus", "istis", "ērunt"],
("pluperfect", "ind"): ["eram", "erās", "erat", "erāmus", "erātis", "erant"],
("futureperfect", "ind"): ["erō", "eris", "erit", "erimus", "eritis", "erint"],
("perfect", "sbjv"): ["erim", "erīs", "erit", "erīmus", "erītis", "erint"],
("pluperfect", "sbjv"):["issem", "issēs", "isset", "issēmus", "issētis", "issent"],
}
def _idx(person, number):
base = {"first": 0, "second": 1, "third": 2}[person]
return base + (0 if number == "singular" else 3)
def _present_system(conj, pstem, tense, mood, voice, person, number):
"""Generate a present-system form (present/imperfect/future ind & subj)."""
table = _PARADIGM.get((tense, mood, voice))
if not table or conj not in table:
return None
return pstem + table[conj][_idx(person, number)]
def _active_infinitive_stem(conj, pstem):
return {1: pstem + "ā", 2: pstem + "ē", 3: pstem + "e",
"3io": pstem + "e", 4: pstem + "ī"}[conj]
_IRREG = {
"sum": {
("present", "ind", "active"): ["sum", "es", "est", "sumus", "estis", "sunt"],
("imperfect", "ind", "active"): ["eram", "erās", "erat", "erāmus", "erātis", "erant"],
("future", "ind", "active"): ["erō", "eris", "erit", "erimus", "eritis", "erunt"],
("perfect", "ind", "active"): ["fuī", "fuistī", "fuit", "fuimus", "fuistis", "fuērunt"],
("pluperfect", "ind", "active"): ["fueram", "fuerās", "fuerat", "fuerāmus", "fuerātis", "fuerant"],
("present", "sbjv", "active"): ["sim", "sīs", "sit", "sīmus", "sītis", "sint"],
("imperfect", "sbjv", "active"): ["essem", "essēs", "esset", "essēmus", "essētis", "essent"],
},
"possum": {
("present", "ind", "active"): ["possum", "potes", "potest", "possumus", "potestis", "possunt"],
("imperfect", "ind", "active"): ["poteram", "poterās", "poterat", "poterāmus", "poterātis", "poterant"],
("future", "ind", "active"): ["poterō", "poteris", "poterit", "poterimus", "poteritis", "poterunt"],
("perfect", "ind", "active"): ["potuī", "potuistī", "potuit", "potuimus", "potuistis", "potuērunt"],
("present", "sbjv", "active"): ["possim", "possīs", "possit", "possīmus", "possītis", "possint"],
},
"": {
("present", "ind", "active"): ["", "īs", "it", "īmus", "ītis", "eunt"],
("imperfect", "ind", "active"): ["ībam", "ībās", "ībat", "ībāmus", "ībātis", "ībant"],
("future", "ind", "active"): ["ībō", "ībis", "ībit", "ībimus", "ībitis", "ībunt"],
("perfect", "ind", "active"): ["", "īstī", "iit", "iimus", "īstis", "iērunt"],
("present", "sbjv", "active"): ["eam", "eās", "eat", "eāmus", "eātis", "eant"],
},
"volō": {
("present", "ind", "active"): ["volō", "vīs", "vult", "volumus", "vultis", "volunt"],
("imperfect", "ind", "active"): ["volēbam", "volēbās", "volēbat", "volēbāmus", "volēbātis", "volēbant"],
("future", "ind", "active"): ["volam", "volēs", "volet", "volēmus", "volētis", "volent"],
("perfect", "ind", "active"): ["voluī", "voluistī", "voluit", "voluimus", "voluistis", "voluērunt"],
("present", "sbjv", "active"): ["velim", "velīs", "velit", "velīmus", "velītis", "velint"],
},
"nōlō": {
("present", "ind", "active"): ["nōlō", "nōn vīs", "nōn vult", "nōlumus", "nōn vultis", "nōlunt"],
("present", "sbjv", "active"): ["nōlim", "nōlīs", "nōlit", "nōlīmus", "nōlītis", "nōlint"],
},
"ferō": {
("present", "ind", "active"): ["ferō", "fers", "fert", "ferimus", "fertis", "ferunt"],
("imperfect", "ind", "active"): ["ferēbam", "ferēbās", "ferēbat", "ferēbāmus", "ferēbātis", "ferēbant"],
("future", "ind", "active"): ["feram", "ferēs", "feret", "ferēmus", "ferētis", "ferent"],
("perfect", "ind", "active"): ["tulī", "tulistī", "tulit", "tulimus", "tulistis", "tulērunt"],
("present", "sbjv", "active"): ["feram", "ferās", "ferat", "ferāmus", "ferātis", "ferant"],
},
}
def conjugate(lemma, tense, mood, voice="active", person="third", number="singular"):
"""Return (surface, confidence). Perfect-passive forms are periphrastic and
handled in the realizer (sum + PPP); this returns synthetic forms only."""
lemma = lemma.strip()
i = _idx(person, number)
ir = _IRREG.get(lemma)
if ir:
tbl = ir.get((tense, mood, voice)) or ir.get((tense, mood, "active"))
if tbl and tbl[i]:
return tbl[i], "rule"
v = _VERBS.get(lemma)
if not v:
v = _infer_principal_parts(lemma)
if not v:
return lemma, "fallback"
conj, pstem, perfstem, supstem = v
# imperative (present active) 2sg / 2pl
if mood == "imp":
return _imperative(conj, pstem, person, number), "rule"
# perfect-system active
if tense in ("perfect", "pluperfect", "futureperfect") and voice == "active":
if not perfstem:
return lemma, "fallback"
end = _PERF_ACT.get((tense, mood))
if end:
return perfstem + end[i], "rule"
# present-system (active + passive)
if tense in ("present", "imperfect", "future"):
form = _present_system(conj, pstem, tense, mood, voice, person, number)
if form:
return form, "rule"
return lemma, "fallback"
def _imperative(conj, pstem, person, number):
if number == "singular":
return {1: pstem + "ā", 2: pstem + "ē", 3: pstem + "e",
"3io": pstem + "e", 4: pstem + "ī"}[conj]
return {1: pstem + "āte", 2: pstem + "ēte", 3: pstem + "ite",
"3io": pstem + "ite", 4: pstem + "īte"}[conj]
def _infer_principal_parts(lemma):
"""OOV fallback: infer conjugation + stems from the 1sg-present citation form.
Perfect/supine stems are guessed regularly (often wrong for 3rd conj) and the
resulting forms are still returned as 'rule' but the realizer down-weights."""
if lemma.endswith("ō"):
base = lemma[:-1]
# can't distinguish conj from 1sg alone reliably; default by ending vowel
if base.endswith("i"):
return ("3io", base[:-1], base[:-1] + "īv", base[:-1] + "īt")
return (3, base, base + "s", base + "t")
return None
# ── PUBLIC: participles ─────────────────────────────────────────────────────────
def participle(lemma, kind, case="nom", gender="m", number="singular"):
"""kind: 'prs' (present active, -ns/-ntis), 'pfv' (perfect passive, -tus),
'fut' (future active, -tūrus). Declined as an adjective via rule endings.
Returns (form, conf)."""
v = _VERBS.get(lemma)
if not v:
return lemma, "fallback"
conj, pstem, perfstem, supstem = v
if kind == "pfv":
if not supstem:
return lemma, "fallback"
base = supstem[:-1] if supstem.endswith("t") or supstem.endswith("s") else supstem
stem = supstem # supine stem already ends in t/s: amāt- -> amātus
return _decline_us_a_um(stem, case, gender, number), "rule"
if kind == "fut":
if not supstem:
return lemma, "fallback"
return _decline_us_a_um(supstem + "ūr", case, gender, number), "rule"
if kind == "prs":
# present active participle: stem + ns (nom), stem + nt- (oblique), 3rd-decl
pv = {1: "ā", 2: "ē", 3: "ē", "3io": "", 4: ""}[conj]
ntstem = pstem + pv + "nt"
return _decline_pres_ptcp(pstem + pv, case, gender, number), "rule"
return lemma, "fallback"
def _decline_us_a_um(stem, case, gender, number):
"""Decline a -us/-a/-um adjective/participle stem (2-1-2 declension)."""
C = _CASE_MAP.get(case, case.upper())
end = {
("NOM", "m", "singular"): "us", ("NOM", "f", "singular"): "a", ("NOM", "n", "singular"): "um",
("GEN", "m", "singular"): "ī", ("GEN", "f", "singular"): "ae", ("GEN", "n", "singular"): "ī",
("DAT", "m", "singular"): "ō", ("DAT", "f", "singular"): "ae", ("DAT", "n", "singular"): "ō",
("ACC", "m", "singular"): "um", ("ACC", "f", "singular"): "am", ("ACC", "n", "singular"): "um",
("ABL", "m", "singular"): "ō", ("ABL", "f", "singular"): "ā", ("ABL", "n", "singular"): "ō",
("VOC", "m", "singular"): "e", ("VOC", "f", "singular"): "a", ("VOC", "n", "singular"): "um",
("NOM", "m", "plural"): "ī", ("NOM", "f", "plural"): "ae", ("NOM", "n", "plural"): "a",
("GEN", "m", "plural"): "ōrum", ("GEN", "f", "plural"): "ārum", ("GEN", "n", "plural"): "ōrum",
("DAT", "m", "plural"): "īs", ("DAT", "f", "plural"): "īs", ("DAT", "n", "plural"): "īs",
("ACC", "m", "plural"): "ōs", ("ACC", "f", "plural"): "ās", ("ACC", "n", "plural"): "a",
("ABL", "m", "plural"): "īs", ("ABL", "f", "plural"): "īs", ("ABL", "n", "plural"): "īs",
("VOC", "m", "plural"): "ī", ("VOC", "f", "plural"): "ae", ("VOC", "n", "plural"): "a",
}.get((C, gender, number), "us")
return stem + end
def _decline_pres_ptcp(stem, case, gender, number):
"""Present active participle (amāns, amantis) — 3rd-declension, stem+ns/nt."""
C = _CASE_MAP.get(case, case.upper())
if C == "NOM" and number == "singular":
return stem + "ns"
if C == "VOC" and number == "singular":
return stem + "ns"
base = stem + "nt"
end = {
("GEN", "singular"): "is", ("DAT", "singular"): "ī",
("ACC", "singular"): "em" if gender != "n" else "",
("ABL", "singular"): "e",
("NOM", "plural"): "ēs" if gender != "n" else "ia",
("GEN", "plural"): "ium", ("DAT", "plural"): "ibus",
("ACC", "plural"): "ēs" if gender != "n" else "ia",
("ABL", "plural"): "ibus", ("VOC", "plural"): "ēs",
}.get((C, number), "is")
if C == "ACC" and number == "singular" and gender == "n":
return stem + "ns"
return base + end
def infinitive(lemma, tense="present", voice="active"):
lemma = lemma.strip()
if lemma == "sum":
return ("esse", "rule") if tense == "present" else ("fuisse", "rule")
v = _VERBS.get(lemma)
if not v:
return lemma, "fallback"
conj, pstem, perfstem, supstem = v
if tense == "present":
if voice == "active":
return _active_infinitive_stem(conj, pstem).rstrip() + \
("re" if conj != 3 and conj != "3io" else "re"), "rule"
# passive present infinitive
base = {1: pstem + "ā", 2: pstem + "ē", 4: pstem + "ī"}.get(conj)
if base:
return base + "", "rule"
return pstem + "ī", "rule" # 3rd: regī
if tense == "perfect" and voice == "active" and perfstem:
return perfstem + "isse", "rule"
return lemma, "fallback"
def lexicon_stats():
return {
"noun_adj_source": "UniMorph Latin (github.com/unimorph/lat, CC-BY-SA 3.0)",
"verb_source": "rule-based 4-conjugation engine over curated attested "
"principal parts (UniMorph verb list is a 947-lemma sample "
"MISSING all core verbs — amō/sum/videō absent)",
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
"curated_verb_lemmas": len(_VERBS) + len(_IRREG),
"gender_inference": "declension-based (nom+gen endings) + curated exceptions",
}
if __name__ == "__main__":
import json
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
print("\n-- noun declension puella (1st, fem) --")
for c in ("nom", "gen", "dat", "acc", "abl", "voc"):
print(f" {c}: sg={decline_noun('puella', c, 'singular')[0]:10} "
f"pl={decline_noun('puella', c, 'plural')[0]}")
print("\n-- rēx (3rd, m):", [decline_noun('rēx', c, 'singular')[0] for c in ('nom','gen','dat','acc','abl')])
print("-- gender: puella=", noun_gender("puella"), "rēx=", noun_gender("rēx"),
"bellum=", noun_gender("bellum"), "corpus=", noun_gender("corpus"),
"manus=", noun_gender("manus"), "diēs=", noun_gender("diēs"))
print("\n-- conjugate videō (2nd) present ind active --")
for p in ("first", "second", "third"):
for n in ("singular", "plural"):
print(f" {p[:3]}.{n[:2]}: {conjugate('videō','present','ind','active',p,n)[0]}")
print("-- amō forms:", conjugate("amō","present","ind","active","first","singular")[0],
conjugate("amō","imperfect","ind","active","third","plural")[0],
conjugate("amō","future","ind","active","first","singular")[0],
conjugate("amō","perfect","ind","active","third","singular")[0])
print("-- sum:", [conjugate("sum","present","ind","active",p,"singular")[0] for p in ("first","second","third")])
print("-- participle amō pfv acc.f.sg:", participle("amō","pfv","acc","f","singular")[0])
print("-- infinitive amō:", infinitive("amō")[0], "| regō pass:", infinitive("regō", voice="passive")[0])
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@@ -1,538 +0,0 @@
"""morphology_pt_full.py — production-grade Brazilian-Portuguese morphological generator.
NOT a toy. Backed by two real, broad, Wiktionary-lineage lexicons:
VERBS — UniMorph Portuguese (github.com/unimorph/por, CC-BY-SA 3.0)
4,001 verb lemmas × full paradigm (283,991 finite/non-finite forms +
20,005 participle forms). Every mood/tense pt actually inflects:
indicative present / preterite (PST;PFV) / imperfect (PST;IPFV) /
pluperfect-simple (PST;PRF) / future,
conditional (futuro do pretérito),
subjunctive present / imperfect / FUTURE (PT-specific live tense),
affirmative + negative imperative,
PERSONAL infinitive (V;{p};{n};NFIN — a PT-specific finite-ish form),
past participle (4 gender/number forms) + gerúndio (V.PTCP;PRS).
NOUNS + ADJECTIVES — kaikki.org Portuguese (Wiktionary extract, same lineage)
81,138 noun lemmas WITH inherent gender + real (often irregular) plural —
so -ão→-ões / -ãos / -ães / -õos is resolved PER LEMMA by Wiktionary,
never guessed (mão→mãos, pão→pães, coração→corações).
40,252 adjective lemmas with real feminine + masc/fem plural forms.
Fallbacks (degrade, never crash, on out-of-vocabulary input):
verbs : rule generator for regular -ar/-er/-ir paradigms
nouns : gender heuristic (endings) + rule pluralization (with -ão FLAGGED)
adjs : -o/-a gender rule + rule pluralization
Confidence flag on every form:
"lexicon" straight from UniMorph/kaikki (trust: high)
"rule" deterministic rule (trust: medium)
"fallback" could not inflect; returned lemma (trust: low -> FLAG)
Public API (used by realizer_pt.py):
conjugate(lemma, mood, tense, person, number) -> (form, conf)
personal_infinitive(lemma, person, number) -> (form, conf)
participle(lemma, gender="m", number="singular") -> (form, conf)
gerund(lemma) -> (form, conf)
noun_gender(lemma) -> "m"|"f"
inflect_noun(lemma, number, gender=None) -> (form, conf)
inflect_adj(lemma, gender, number) -> (form, conf)
lexicon_stats() -> dict
"""
import json
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "por.unimorph")
_KAIKKI = os.path.join(_HERE, "data", "kaikki_pt.jsonl")
_CACHE = os.path.join(_HERE, "data", "pt_morph_cache.pkl")
# ── mood/tense pair -> UniMorph feature triple (a in tag; b in tag; c in tag) ────
_VERB_KEYMAP = {
("ind", "present"): ("IND", "PRS", None),
("ind", "preterite"): ("IND", "PST", "PFV"),
("ind", "imperfect"): ("IND", "PST", "IPFV"),
("ind", "pluperfect"): ("IND", "PST", "PRF"), # simple mais-que-perfeito
("ind", "future"): ("IND", "FUT", None),
("ind", "conditional"): ("COND", None, None),
("sbjv", "present"): ("SBJV", "PRS", None),
("sbjv", "imperfect"): ("SBJV", "PST", "IPFV"),
("sbjv", "future"): ("SBJV", "FUT", None), # PT-specific
("imp", "affirmative"): ("IMP", "POS", None),
("imp", "negative"): ("IMP", "NEG", None),
}
_PERSON = {"first": "1", "second": "2", "third": "3"}
_NUMBER = {"singular": "SG", "plural": "PL"}
def _feat_set(tag):
return set(tag.split(";"))
# ── build the compact lexicon from UniMorph (verbs) + kaikki (nouns/adjs) ────────
def _build_verbs():
verbs = {} # (lemma, "mood|tense|person|number") -> form
pinf = {} # (lemma, "person|number") -> personal-infinitive form
part = {} # lemma -> {("m","SG"): form, ...} past participle
ger = {} # lemma -> gerúndio
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
f = _feat_set(tag)
head = tag.split(";")[0]
if head == "V.PTCP":
if "PST" in f: # past participle: falado/falada/falados/faladas
g = "m" if "MASC" in f else ("f" if "FEM" in f else "m")
num = "SG" if "SG" in f else ("PL" if "PL" in f else "SG")
part.setdefault(lemma, {})[(g, num)] = form
elif "PRS" in f: # gerúndio: falando
ger.setdefault(lemma, form)
continue
if head != "V":
continue
# personal / impersonal infinitive
if "NFIN" in f:
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person and number:
pinf[(lemma, f"{person}|{number}")] = form
continue
# finite forms
mt = None
for (mood, tense), (a, b, c) in _VERB_KEYMAP.items():
if a not in f:
continue
if b is not None and b not in f:
continue
if c is not None and c not in f:
continue
# IND;PST needs exactly PFV|IPFV|PRF — reject if the required one absent
mt = (mood, tense)
break
if mt is None:
continue
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person is None or number is None:
continue
verbs.setdefault((lemma, f"{mt[0]}|{mt[1]}|{person}|{number}"), form)
return verbs, pinf, part, ger
def _kaikki_gender(arg):
if not arg:
return None
a = arg.lower()
if a.startswith("f"):
return "f"
if a.startswith("m"):
return "m"
return None
def _build_nouns_adjs():
nouns = {} # lemma -> {"g","SG","PL"}
adjs = {} # lemma -> {("m","SG"),("f","SG"),("m","PL"),("f","PL")}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
pos = d.get("pos")
word = d.get("word", "")
if not word or " " in word: # skip multiword entries
continue
forms = d.get("forms", []) or []
if pos == "noun":
ht = d.get("head_templates") or []
g = None
if ht:
g = _kaikki_gender((ht[0].get("args") or {}).get("1"))
if g is None:
tags = d.get("tags") or []
if "feminine" in tags:
g = "f"
elif "masculine" in tags:
g = "m"
pl = None
for x in forms:
t = x.get("tags") or []
if "plural" in t and "alternative" not in t and "obsolete" not in t:
pl = x.get("form")
break
# first entry wins; but a later entry with a plural fills a gap
if word not in nouns:
nouns[word] = {"g": g, "SG": word, "PL": pl}
else:
cur = nouns[word]
if cur.get("g") is None and g:
cur["g"] = g
if not cur.get("PL") and pl:
cur["PL"] = pl
elif pos == "adj":
d0 = adjs.setdefault(word, {})
d0.setdefault(("m", "SG"), word)
for x in forms:
t = set(x.get("tags") or [])
fm = x.get("form")
if not fm or ("alternative" in t) or ("obsolete" in t):
continue
if "comparative" in t or "superlative" in t or \
"diminutive" in t or "augmentative" in t:
continue
if "feminine" in t and "plural" in t:
d0[("f", "PL")] = fm
elif "masculine" in t and "plural" in t:
d0[("m", "PL")] = fm
elif "feminine" in t:
d0[("f", "SG")] = fm
elif "plural" in t: # invariant-gender adj (feliz -> felizes)
d0[("m", "PL")] = d0.get(("m", "PL")) or fm
d0[("f", "PL")] = d0.get(("f", "PL")) or fm
return nouns, adjs
def _build_cache():
verbs, pinf, part, ger = _build_verbs()
nouns, adjs = _build_nouns_adjs()
data = {"verbs": verbs, "pinf": pinf, "part": part, "ger": ger,
"nouns": nouns, "adjs": adjs}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE):
newest_src = max(os.path.getmtime(_UNIMORPH),
os.path.getmtime(_KAIKKI) if os.path.exists(_KAIKKI) else 0)
if os.path.getmtime(_CACHE) >= newest_src:
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_VERBS, _PINF, _PART, _GER, _NOUNS, _ADJS = (
_LEX["verbs"], _LEX["pinf"], _LEX["part"], _LEX["ger"],
_LEX["nouns"], _LEX["adjs"])
# ── regular-ending rule fallback (deterministic, last resort) ────────────────────
def _vclass(lemma):
return lemma[-2:] if lemma[-2:] in ("ar", "er", "ir") else None
def _stem(lemma):
return lemma[:-2]
# endings indexed [1sg,2sg,3sg,1pl,2pl,3pl]
_REG = {
("ind", "present", "ar"): ["o", "as", "a", "amos", "ais", "am"],
("ind", "present", "er"): ["o", "es", "e", "emos", "eis", "em"],
("ind", "present", "ir"): ["o", "es", "e", "imos", "is", "em"],
("ind", "preterite", "ar"): ["ei", "aste", "ou", "amos", "astes", "aram"],
("ind", "preterite", "er"): ["i", "este", "eu", "emos", "estes", "eram"],
("ind", "preterite", "ir"): ["i", "iste", "iu", "imos", "istes", "iram"],
("ind", "imperfect", "ar"): ["ava", "avas", "ava", "ávamos", "áveis", "avam"],
("ind", "imperfect", "er"): ["ia", "ias", "ia", "íamos", "íeis", "iam"],
("ind", "imperfect", "ir"): ["ia", "ias", "ia", "íamos", "íeis", "iam"],
("sbjv", "present", "ar"): ["e", "es", "e", "emos", "eis", "em"],
("sbjv", "present", "er"): ["a", "as", "a", "amos", "ais", "am"],
("sbjv", "present", "ir"): ["a", "as", "a", "amos", "ais", "am"],
("sbjv", "imperfect", "ar"): ["asse", "asses", "asse", "ássemos", "ásseis", "assem"],
("sbjv", "imperfect", "er"): ["esse", "esses", "esse", "êssemos", "êsseis", "essem"],
("sbjv", "imperfect", "ir"): ["isse", "isses", "isse", "íssemos", "ísseis", "issem"],
("sbjv", "future", "ar"): ["ar", "ares", "ar", "armos", "ardes", "arem"],
("sbjv", "future", "er"): ["er", "eres", "er", "ermos", "erdes", "erem"],
("sbjv", "future", "ir"): ["ir", "ires", "ir", "irmos", "irdes", "irem"],
}
# future & conditional attach to the FULL infinitive
_FUT = ["ei", "ás", "á", "emos", "eis", "ão"]
_COND = ["ia", "ias", "ia", "íamos", "íeis", "iam"]
def _slot_idx(person, number):
base = {"first": 0, "second": 1, "third": 2}[person]
return base + (0 if number == "singular" else 3)
def _rule_conjugate(lemma, mood, tense, person, number):
vc = _vclass(lemma)
if vc is None:
return None
st, i = _stem(lemma), _slot_idx(person, number)
if mood == "ind" and tense == "future":
return lemma + _FUT[i]
if mood == "ind" and tense == "conditional":
return lemma + _COND[i]
if mood == "imp": # affirmative tú/vocês imperative ~ subjunctive present
table = _REG.get(("sbjv", "present", vc))
if table and tense == "negative":
return st + table[i]
# affirmative 2sg = 3sg present indicative; others = subjunctive
pres = _REG.get(("ind", "present", vc))
if person == "second" and number == "singular":
return st + pres[2]
return st + table[i] if table else None
table = _REG.get((mood, tense, vc))
if table:
return st + table[i]
return None
# verified corrections to UniMorph data errors (each audited individually, not
# guessed). The three 1PL-present entries are glued-allomorph errors surfaced by a
# full-lexicon scan for a non-final "mos" in V;1;PL;IND;PRS forms (the ONLY three).
_VERB_FIX = {
("estar", "ind", "imperfect", "third", "plural"): "estavam", # was "estávam"
("estar", "ind", "present", "first", "plural"): "estamos", # was "estamosestámos"
("haver", "ind", "present", "first", "plural"): "havemos", # was "havemoshemos"
("ir", "ind", "present", "first", "plural"): "vamos", # was "vamosimos"
}
# ── PUBLIC: verb conjugation ─────────────────────────────────────────────────────
def conjugate(lemma, mood, tense, person, number):
"""Return (surface, confidence). mood in ind|sbjv|imp; tense per _VERB_KEYMAP."""
lemma = lemma.strip().lower()
fix = _VERB_FIX.get((lemma, mood, tense, person, number))
if fix:
return fix, "lexicon"
p, n = _PERSON.get(person), _NUMBER.get(number)
if p and n:
form = _VERBS.get((lemma, f"{mood}|{tense}|{p}|{n}"))
if form:
# pt-BR normalization: UniMorph `por` carries the EUROPEAN spelling of
# the -ar 1pl PRETERITE (-ámos). Brazilian PT drops the accent
# (falámos->falamos, chegámos->chegamos) — 3,334/4,001 verbs affected.
if (mood == "ind" and tense == "preterite" and person == "first"
and number == "plural" and form.endswith("ámos")):
form = form[:-4] + "amos"
return form, "lexicon"
r = _rule_conjugate(lemma, mood, tense, person, number)
if r:
return r, "rule"
return lemma, "fallback"
def personal_infinitive(lemma, person, number):
"""PT personal (inflected) infinitive: para falarmos, ao chegarem."""
lemma = lemma.strip().lower()
p, n = _PERSON.get(person), _NUMBER.get(number)
if p and n:
form = _PINF.get((lemma, f"{p}|{n}"))
if form:
return form, "lexicon"
# rule: infinitive + personal endings (-, -es, -, -mos, -des, -em)
end = {("first", "singular"): "", ("second", "singular"): "es",
("third", "singular"): "", ("first", "plural"): "mos",
("second", "plural"): "des", ("third", "plural"): "em"}.get((person, number), "")
return lemma + end, "rule"
# ── PUBLIC: participle + gerund ───────────────────────────────────────────────────
def participle(lemma, gender="m", number="singular"):
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
d = _PART.get(lemma)
if d:
form = d.get((g, num)) or d.get(("m", "SG"))
if form:
return form, "lexicon"
if lemma.endswith("ar"):
base = lemma[:-2] + "ad"
elif lemma[-2:] in ("er", "ir"):
base = lemma[:-2] + "id"
else:
return lemma, "fallback"
suf = {"m|SG": "o", "f|SG": "a", "m|PL": "os", "f|PL": "as"}[f"{g}|{num}"]
return base + suf, "rule"
def gerund(lemma):
lemma = lemma.strip().lower()
if lemma in _GER:
return _GER[lemma], "lexicon"
if lemma.endswith("ar"):
return lemma[:-2] + "ando", "rule"
if lemma.endswith("er"):
return lemma[:-2] + "endo", "rule"
if lemma.endswith("ir"):
return lemma[:-2] + "indo", "rule"
return lemma, "fallback"
# ── PUBLIC: noun gender + number ─────────────────────────────────────────────────
_FEM_SUF = ("ção", "são", "ção", "dade", "tade", "agem", "igem", "ugem", "gem",
"ez", "eza", "ice", "ície", "tude", "ude", "âncbefore")
_FEM_SUF = ("ção", "são", "dade", "tade", "agem", "gem", "eza", "ez", "ice",
"tude", "ude", "ância", "ência", "ínia")
_MASC_SUF = ("ema", "oma", "ama", "grama", "eta", "ão") # Greek -ma etc. (mostly m)
def _gender_heuristic(noun):
for suf in _FEM_SUF:
if noun.endswith(suf):
return "f"
if noun.endswith(("ema", "oma", "ama")): # problema, idioma, programa
return "m"
if noun.endswith("a") or noun.endswith("ã"):
return "f"
if noun.endswith("o") or noun.endswith(("l", "r", "z", "m", "u", "i")):
return "m"
return "m"
def noun_gender(lemma):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if d and d.get("g"):
return d["g"]
return _gender_heuristic(lemma)
_INVARIANT_PL_SUF = ("s",) # paroxytones ending -s are invariant (o lápis / os lápis)
def _rule_plural(noun):
"""Deterministic PT pluralization. Returns (form, ok) where ok=False flags an
ambiguous -ão that should lower confidence (the lexicon normally resolves it)."""
if not noun:
return noun, True
if noun.endswith("ão"):
return noun[:-2] + "ões", False # majority rule, but AMBIGUOUS -> flag
if noun.endswith("m"):
return noun[:-1] + "ns", True # homem->homens, jardim->jardins
if noun.endswith("al"):
return noun[:-2] + "ais", True
if noun.endswith("el"):
return noun[:-2] + "éis", True
if noun.endswith("ol"):
return noun[:-2] + "óis", True
if noun.endswith("ul"):
return noun[:-2] + "uis", True
if noun.endswith("il"):
return noun[:-2] + "is", True # stressed (funil->funis); unstressed rarer
if noun.endswith(("r", "z")):
return noun + "es", True # flor->flores, luz->luzes
if noun.endswith("s"):
# paroxytone -s (lápis, ônibus) invariant; oxytone -s (país) -> -es
return noun, True
if noun.endswith(("a", "e", "i", "o", "u", "á", "é", "í", "ó", "ú", "ã")):
return noun + "s", True
return noun + "s", True
def inflect_noun(lemma, number, gender=None):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if number == "singular":
return (d["SG"] if d and d.get("SG") else lemma), ("lexicon" if d else "rule")
if d and d.get("PL"):
return d["PL"], "lexicon"
form, ok = _rule_plural(lemma)
return form, ("rule" if ok else "fallback")
# ── PUBLIC: adjective agreement ──────────────────────────────────────────────────
def inflect_adj(lemma, gender, number):
lemma = lemma.strip().lower()
g = "f" if gender == "f" else "m"
num = "SG" if number == "singular" else "PL"
d = _ADJS.get(lemma)
if d:
form = d.get((g, num))
if form:
return form, "lexicon"
# build a missing plural from this gender's singular
sg = d.get((g, "SG")) or d.get(("m", "SG")) or lemma
if num == "PL":
pl, ok = _rule_plural(sg)
return pl, ("rule" if ok else "fallback")
return sg, "lexicon"
# rule fallback: -o/-a gender, then pluralize
a = lemma
if g == "f":
if a.endswith("o"):
a = a[:-1] + "a"
elif a.endswith(("ês", "or")) and not a.endswith("ior"):
a = a + "a" # português->portuguesa, trabalhador->..a
if num == "PL":
a, ok = _rule_plural(a)
return a, ("rule" if ok else "fallback")
return a, "rule"
def lexicon_stats():
return {
"verb_source": "UniMorph Portuguese (github.com/unimorph/por)",
"noun_adj_source": "kaikki.org Portuguese (Wiktionary extract)",
"license": "CC-BY-SA (Wiktionary-derived)",
"verb_forms": len(_VERBS),
"verb_lemmas": len({k[0] for k in _VERBS}),
"personal_infinitive_forms": len(_PINF),
"participle_lemmas": len(_PART),
"gerund_lemmas": len(_GER),
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
}
if __name__ == "__main__":
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
tests = [
("falar", "ind", "present", "first", "singular", "falo"),
("comer", "ind", "present", "third", "plural", "comem"),
("partir", "ind", "present", "first", "plural", "partimos"),
("ser", "ind", "present", "third", "singular", "é"),
("ir", "ind", "preterite", "first", "singular", "fui"),
("ter", "ind", "future", "first", "singular", "terei"),
("fazer", "sbjv", "present", "first", "singular", "faça"),
("dormir", "ind", "present", "first", "singular", "durmo"),
("dar", "ind", "preterite", "third", "singular", "deu"),
("poder", "ind", "conditional", "first", "singular", "poderia"),
("fazer", "sbjv", "future", "third", "singular", "fizer"),
("estar", "ind", "present", "third", "singular", "está"),
]
ok = 0
for lemma, mood, tense, per, num, exp in tests:
got, conf = conjugate(lemma, mood, tense, per, num)
flag = "OK " if got == exp else "XX "
ok += got == exp
print(f" {flag}{lemma:8} {mood}/{tense} {per[:3]}.{num[:2]} -> {got:14} ({conf}) exp={exp}")
print(f"verb tests {ok}/{len(tests)}")
print(" gender: casa=", noun_gender("casa"), "problema=", noun_gender("problema"),
"mão=", noun_gender("mão"), "coração=", noun_gender("coração"),
"flor=", noun_gender("flor"))
print(" plural: mão->", inflect_noun("mão", "plural"),
"| pão->", inflect_noun("pão", "plural"),
"| animal->", inflect_noun("animal", "plural"),
"| coração->", inflect_noun("coração", "plural"))
print(" adj: bonito/f/sg->", inflect_adj("bonito", "f", "singular"),
"| feliz/m/pl->", inflect_adj("feliz", "m", "plural"),
"| português/f/sg->", inflect_adj("português", "f", "singular"))
print(" part: fazer/m/sg->", participle("fazer"), "| ger falar->", gerund("falar"))
print(" pinf falar 1pl->", personal_infinitive("falar", "first", "plural"))
-609
View File
@@ -1,609 +0,0 @@
# -*- coding: utf-8 -*-
"""morphology_ro_full.py — production-grade Romanian morphological generator.
Romanian is the BIG typological delta of the Romance family. The verb engine and
the confidence/fallback contract TRANSFER from the Italian sibling; the NOMINAL
system is genuinely new: Romanian has a SUFFIXED definite article, a preserved
NOM/ACC vs GEN/DAT case distinction, a NEUTER gender (masc-agreeing in SG,
fem-agreeing in PL), and a VOCATIVE. Those are grounded in real per-lemma data,
not guessed.
Real, Wiktionary-lineage lexical sources:
VERBS — UniMorph Romanian (github.com/unimorph/ron, CC-BY-SA 3.0)
~1216 verb lemmas × paradigm, CLEAN orthography:
indicativ prezent / imperfect (PST;IPFV) / perfectul simplu (PST;PFV) /
conjunctiv prezent (SBJV;PRS, stored WITHOUT the '' particle),
participiu (V.PTCP;PST, INVARIABLE in the perfect compus),
gerunziu (V.CVB;PRS), infinitiv (NFIN), imperativ.
ro_irreg_verbs (embedded) — high-frequency verbs UniMorph MISSES
(avea, vrea, da) + the auxiliary clitic paradigms the compound tenses need
(perfect-compus am/ai/a/am/ați/au, viitor voi/vei/va/vom/veți/vor,
condițional aș/ai/ar/am/ați/ar). Real standard forms.
NOUNS — kaikki.org Romanian (Wiktionary extract, CC-BY-SA 3.0)
the FULL declension per lemma, cleanly tagged:
(nom/acc | gen/dat | vocative) × (indefinite | definite) × (sg | pl).
This is what makes the suffixed article LEXICALLY grounded (om→omul,
casă→casa, băiat→băiatul, casei gen/dat, omule vocative). Inherent gender
m / f / n (NEUTER available directly) from the head template.
ADJECTIVES — UniMorph Romanian ADJ
full case × gender(MASC/FEM/NEUT) × number × definiteness paradigm.
Fallbacks (degrade, never crash, on OOV): rule verb conjugation for -a/-ea/-e/-i/-î
classes, rule pluralization, rule suffixed-article by gender+ending. Every form
carries a confidence flag: "lexicon" | "rule" | "fallback".
Public API (used by realizer_ro.py):
conjugate(lemma, mood, tense, person, number) -> (form, conf)
aux(kind, person, number) -> str # perfect / future / conditional clitics
participle(lemma) -> (form, conf) # INVARIABLE
gerund(lemma) -> (form, conf)
noun_gender(lemma) -> "m"|"f"|"n"
definite_suffix(noun, gender, number, case) -> (form, conf) # rule engine
inflect_noun(lemma, number, gender=None, case="nomacc", definite=False) -> (form, conf)
inflect_adj(lemma, gender, number, case="nomacc", definite=False) -> (form, conf)
lexicon_stats() -> dict
"""
import json
import os
import pickle
_HERE = os.path.dirname(os.path.abspath(__file__))
_UNIMORPH = os.path.join(_HERE, "data", "ron.unimorph")
_KAIKKI = os.path.join(_HERE, "data", "kaikki_ro.jsonl")
_CACHE = os.path.join(_HERE, "data", "ro_morph_cache.pkl")
# ── (mood, tense) -> UniMorph feature set ─────────────────────────────────────────
_VERB_KEYMAP = {
("ind", "present"): {"IND", "PRS"},
("ind", "imperfect"): {"IND", "PST", "IPFV"},
("ind", "perfect_s"): {"IND", "PST", "PFV"}, # perfectul simplu (regional/lit.)
("sbjv", "present"): {"SBJV", "PRS"},
("imp", "affirmative"): {"POS", "IMP"},
}
_PERSON = {"first": "1", "second": "2", "third": "3"}
_NUMBER = {"singular": "SG", "plural": "PL"}
def _feat_set(tag):
return set(tag.split(";"))
# ── high-frequency irregulars UniMorph misses + auxiliary clitic paradigms ────────
# Real standard Romanian forms (textbook paradigms).
_IRREG = {
"avea": {
"ind|present|1|SG": "am", "ind|present|2|SG": "ai", "ind|present|3|SG": "are",
"ind|present|1|PL": "avem", "ind|present|2|PL": "aveți", "ind|present|3|PL": "au",
"ind|imperfect|1|SG": "aveam", "ind|imperfect|2|SG": "aveai",
"ind|imperfect|3|SG": "avea", "ind|imperfect|1|PL": "aveam",
"ind|imperfect|2|PL": "aveați", "ind|imperfect|3|PL": "aveau",
"sbjv|present|3|SG": "aibă", "sbjv|present|3|PL": "aibă",
"sbjv|present|1|SG": "am", "sbjv|present|2|SG": "ai",
"sbjv|present|1|PL": "avem", "sbjv|present|2|PL": "aveți",
"part": "avut", "ger": "având",
},
"vrea": {
"ind|present|1|SG": "vreau", "ind|present|2|SG": "vrei", "ind|present|3|SG": "vrea",
"ind|present|1|PL": "vrem", "ind|present|2|PL": "vreți", "ind|present|3|PL": "vor",
"ind|imperfect|1|SG": "voiam", "ind|imperfect|3|SG": "voia",
"sbjv|present|3|SG": "vrea", "sbjv|present|3|PL": "vrea",
"part": "vrut", "ger": "vrând",
},
"da": {
"ind|present|1|SG": "dau", "ind|present|2|SG": "dai", "ind|present|3|SG": "",
"ind|present|1|PL": "dăm", "ind|present|2|PL": "dați", "ind|present|3|PL": "dau",
"ind|imperfect|1|SG": "dădeam", "ind|imperfect|3|SG": "dădea",
"sbjv|present|3|SG": "dea", "sbjv|present|3|PL": "dea",
"part": "dat", "ger": "dând",
},
"fi": { # a fi — present is in UniMorph but keep participle + subjunctive here
"part": "fost", "ger": "fiind",
"sbjv|present|1|SG": "fiu", "sbjv|present|2|SG": "fii", "sbjv|present|3|SG": "fie",
"sbjv|present|1|PL": "fim", "sbjv|present|2|PL": "fiți", "sbjv|present|3|PL": "fie",
"ind|imperfect|1|SG": "eram", "ind|imperfect|2|SG": "erai",
"ind|imperfect|3|SG": "era", "ind|imperfect|1|PL": "eram",
"ind|imperfect|2|PL": "erați", "ind|imperfect|3|PL": "erau",
},
}
# auxiliary clitic paradigms (person,number)->form
_AUX = {
"perfect": {("first", "singular"): "am", ("second", "singular"): "ai",
("third", "singular"): "a", ("first", "plural"): "am",
("second", "plural"): "ați", ("third", "plural"): "au"},
"future": {("first", "singular"): "voi", ("second", "singular"): "vei",
("third", "singular"): "va", ("first", "plural"): "vom",
("second", "plural"): "veți", ("third", "plural"): "vor"},
"conditional": {("first", "singular"): "", ("second", "singular"): "ai",
("third", "singular"): "ar", ("first", "plural"): "am",
("second", "plural"): "ați", ("third", "plural"): "ar"},
}
def aux(kind, person, number):
return _AUX[kind][(person, number)]
# ── build verb lexicon from UniMorph ──────────────────────────────────────────────
def _build_verbs():
verbs, part, ger = {}, {}, {}
with open(_UNIMORPH, encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n")
if not line or "\t" not in line:
continue
parts = line.split("\t")
if len(parts) != 3:
continue
lemma, form, tag = parts
f = _feat_set(tag)
head = tag.split(";")[0]
if head == "V.PTCP":
if "PST" in f:
part.setdefault(lemma, form)
continue
if head == "V.CVB":
if "PRS" in f:
ger.setdefault(lemma, form)
continue
if head != "V":
continue
person = next((p for p in ("1", "2", "3") if p in f), None)
number = "SG" if "SG" in f else ("PL" if "PL" in f else None)
if person is None or number is None:
continue
# conjunctiv forms in UniMorph carry a leading 'să ' — strip it
surf = form
if surf.startswith(""):
surf = surf[3:]
for (mood, tense), req in _VERB_KEYMAP.items():
if not req <= f:
continue
if tense == "imperfect" and "PFV" in f:
continue
if tense == "perfect_s" and "IPFV" in f:
continue
# keep IND;PRS out of the PRF slot (mai-mult-ca-perfect etc. ignored)
if {"IND", "PRS"} <= req and "PRF" in f:
continue
verbs.setdefault((lemma, f"{mood}|{tense}|{person}|{number}"), surf)
break
return verbs, part, ger
# ── kaikki nouns: full declension paradigm per lemma ──────────────────────────────
_EXCL = {"alternative", "archaic", "obsolete", "regional", "dialectal", "rare",
"table-tags", "inflection-template", "error-unrecognized-form",
"diminutive", "augmentative", "informal"}
def _noun_key(tagset):
if tagset & _EXCL:
return None
if "vocative" in tagset:
case = "voc"
elif "genitive" in tagset or "dative" in tagset:
case = "gendat"
elif "nominative" in tagset or "accusative" in tagset:
case = "nomacc"
else:
return None
definite = "definite" in tagset and "indefinite" not in tagset
number = "PL" if "plural" in tagset else ("SG" if "singular" in tagset else None)
if number is None:
return None
return (case, definite, number)
def _build_nouns():
nouns = {} # lemma -> {"g":..., para:{(case,def,num):form}, "PL":plain_plural}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
if d.get("pos") != "noun":
continue
word = d.get("word", "")
if not word or " " in word:
continue
ht = d.get("head_templates") or []
g = None
if ht:
a = str((ht[0].get("args") or {}).get("1") or "").lower()
if a[:1] in ("m", "f", "n"):
g = a[:1]
entry = nouns.setdefault(word, {"g": g, "para": {}, "PL": None})
if entry["g"] is None and g:
entry["g"] = g
for x in (d.get("forms") or []):
fm = x.get("form")
tg = set(x.get("tags") or [])
if not fm or fm in ("-", "#", "") or " " in fm:
continue
if tg == {"plural"} and not entry["PL"]:
entry["PL"] = fm
k = _noun_key(tg)
if k and k not in entry["para"]:
entry["para"][k] = fm
return nouns
# ── adjectives from kaikki (UniMorph ron ADJ is sparse AND mis-tagged; kaikki is
# clean: the 4-form agreement pattern bun/bună/buni/bune). Neuter maps sg->masc,
# pl->fem, so 4 forms (m/f × SG/PL) fully cover it. ────────────────────────────
def _build_adjs():
adjs = {} # lemma -> {(gender,number): form} gender in {m,f}
with open(_KAIKKI, encoding="utf-8") as fh:
for line in fh:
try:
d = json.loads(line)
except Exception:
continue
if d.get("pos") != "adj":
continue
word = d.get("word", "")
if not word or " " in word:
continue
d0 = adjs.setdefault(word, {})
d0.setdefault(("m", "SG"), word) # masc sg = headword
for x in (d.get("forms") or []):
fm = x.get("form")
t = set(x.get("tags") or [])
if not fm or " " in fm or fm in ("-", "#") or (t & _EXCL):
continue
if "definite" in t or "genitive" in t or "dative" in t:
continue # keep indefinite nom/acc agr set
pl = "plural" in t
fem = "feminine" in t
masc = "masculine" in t
if fem and pl:
d0.setdefault(("f", "PL"), fm)
elif masc and pl:
d0.setdefault(("m", "PL"), fm)
elif fem and not pl:
d0.setdefault(("f", "SG"), fm)
elif pl and not fem and not masc: # bare plural -> both genders
d0.setdefault(("m", "PL"), fm)
d0.setdefault(("f", "PL"), fm)
return adjs
def _build_cache():
verbs, part, ger = _build_verbs()
nouns = _build_nouns()
adjs = _build_adjs()
data = {"verbs": verbs, "part": part, "ger": ger, "nouns": nouns, "adjs": adjs}
try:
with open(_CACHE, "wb") as fh:
pickle.dump(data, fh, protocol=pickle.HIGHEST_PROTOCOL)
except OSError:
pass
return data
def _load():
if os.path.exists(_CACHE):
srcs = [_UNIMORPH, _KAIKKI]
newest = max(os.path.getmtime(s) for s in srcs if os.path.exists(s))
if os.path.getmtime(_CACHE) >= newest:
try:
with open(_CACHE, "rb") as fh:
return pickle.load(fh)
except Exception:
pass
return _build_cache()
_LEX = _load()
_VERBS, _PART, _GER, _NOUNS, _ADJS = (
_LEX["verbs"], _LEX["part"], _LEX["ger"], _LEX["nouns"], _LEX["adjs"])
# ── rule verb conjugation fallback ────────────────────────────────────────────────
def _vclass(lemma):
if lemma.endswith("a"):
return "a"
if lemma.endswith("ea"):
return "ea"
if lemma.endswith("e"):
return "e"
if lemma.endswith("i"):
return "i"
if lemma.endswith("î"):
return "î"
return None
# regular present endings by class [1sg,2sg,3sg,1pl,2pl,3pl]
_REG_PRS = {
"a": ["", "i", "ă", "ăm", "ați", "ă"], # a lucra type (simplified)
"ea": ["", "i", "e", "em", "eți", "", ],
"e": ["", "i", "e", "em", "eți", ""],
"i": ["esc", "ești", "ește", "im", "iți", "esc"], # -i type (a vorbi)
"î": ["ăsc", "ăști", "ăște", "âm", "âți", "ăsc"],
}
_SLOT = {("first", "singular"): 0, ("second", "singular"): 1, ("third", "singular"): 2,
("first", "plural"): 3, ("second", "plural"): 4, ("third", "plural"): 5}
def _rule_conjugate(lemma, mood, tense, person, number):
vc = _vclass(lemma)
if vc is None:
return None
i = _SLOT[(person, number)]
body = lemma[:-len(vc)]
if mood == "ind" and tense == "present":
end = _REG_PRS[vc][i]
return body + end
if mood == "ind" and tense == "imperfect":
# -a/-i/-î -> stem + a/eai...; -e/-ea -> eam. Simplified regular imperfect.
stem = body
endings = {"a": ["am", "ai", "a", "am", "ați", "au"],
"i": ["eam", "eai", "ea", "eam", "eați", "eau"],
"î": ["am", "ai", "a", "am", "ați", "au"],
"e": ["eam", "eai", "ea", "eam", "eați", "eau"],
"ea": ["eam", "eai", "ea", "eam", "eați", "eau"]}[vc]
return stem + endings[i]
return None
# ── PUBLIC verb API ───────────────────────────────────────────────────────────────
def conjugate(lemma, mood, tense, person, number):
lemma = lemma.strip().lower()
key = f"{mood}|{tense}|{_PERSON.get(person,'?')}|{_NUMBER.get(number,'?')}"
ir = _IRREG.get(lemma)
if ir and key in ir:
return ir[key], "lexicon"
form = _VERBS.get((lemma, key))
if form:
return form, "lexicon"
r = _rule_conjugate(lemma, mood, tense, person, number)
if r is not None:
return r, "rule"
return lemma, "fallback"
def participle(lemma):
"""Past participle — INVARIABLE in the perfect compus (am mers, am văzut)."""
lemma = lemma.strip().lower()
ir = _IRREG.get(lemma)
if ir and "part" in ir:
return ir["part"], "lexicon"
if lemma in _PART:
return _PART[lemma], "lexicon"
vc = _vclass(lemma)
if vc == "a":
return lemma[:-1] + "at", "rule"
if vc in ("ea",):
return lemma[:-2] + "ut", "rule"
if vc == "i":
return lemma[:-1] + "it", "rule"
if vc == "î":
return lemma[:-1] + "ât", "rule"
if vc == "e":
return lemma[:-1] + "ut", "rule"
return lemma, "fallback"
def gerund(lemma):
lemma = lemma.strip().lower()
ir = _IRREG.get(lemma)
if ir and "ger" in ir:
return ir["ger"], "lexicon"
if lemma in _GER:
return _GER[lemma], "lexicon"
vc = _vclass(lemma)
if vc in ("a", "î"):
return lemma[:-1] + "ând", "rule"
if vc in ("ea", "e", "i"):
return lemma[:-len(vc)] + "ind", "rule"
return lemma, "fallback"
# ── noun gender ───────────────────────────────────────────────────────────────────
def noun_gender(lemma):
lemma = lemma.strip().lower()
d = _NOUNS.get(lemma)
if d and d.get("g") in ("m", "f", "n"):
return d["g"]
if lemma.endswith(("ă", "a", "e")):
return "f"
return "m"
# ── SUFFIXED DEFINITE ARTICLE — rule engine (fallback for OOV nouns) ───────────────
def definite_suffix(noun, gender, number, case="nomacc"):
"""Attach the enclitic definite article by gender + ending. Returns (form, conf).
This is the headline Romanian-specific engine extension."""
n = noun
g = gender
if number == "singular":
if g in ("m", "n"):
if case == "gendat":
# masc/neut gen-dat definite: -lui
if n.endswith("e"):
return n + "lui", "rule" # câine -> câinelui
if n.endswith("u"):
return n + "lui", "rule"
return n + "ului", "rule" # om -> omului
# nom/acc
if n.endswith("e"):
return n + "le", "rule" # câine -> câinele
if n.endswith("u"):
return n + "l", "rule" # codru -> codrul
if n.endswith("i"):
return n + "ul", "rule"
return n + "ul", "rule" # om -> omul
# feminine singular
if case == "gendat":
# fem gen/dat definite = plural-stem + i (casei, fetei) — needs plural;
# approximated as: -ă->-ei, -e->-ei, -a->-alei
if n.endswith("ă"):
return n[:-1] + "ei", "rule" # casă -> casei
if n.endswith("e"):
return n[:-1] + "ei", "rule" # carte -> cărții(approx cartei)
if n.endswith("a"):
return n[:-1] + "lei", "rule"
return n + "i", "rule"
# fem nom/acc
if n.endswith("ă"):
return n[:-1] + "a", "rule" # casă -> casa
if n.endswith("e"):
return n[:-1] + "ea", "rule" # carte -> cartea
if n.endswith("a"):
return n + "ua", "rule" # stea -> steaua
if n.endswith("i"):
return n + "a", "rule"
return n + "a", "rule"
# plural
if case == "gendat":
base = noun
return base + "lor", "rule" # -lor for all gen/dat pl
if g == "m":
return noun + "i", "rule" # oameni -> oamenii (+i)
return noun + "le", "rule" # case -> casele, trenuri->trenurile
# ── rule pluralization (fallback) ─────────────────────────────────────────────────
def _rule_plural(noun, gender):
if gender == "f":
if noun.endswith("ă"):
return noun[:-1] + "e"
if noun.endswith("e"):
return noun[:-1] + "i"
if noun.endswith("a"):
return noun[:-1] + "le"
return noun + "e"
if gender == "n":
return noun + "uri"
# masculine
if noun.endswith(("e",)):
return noun[:-1] + "i"
return noun + "i"
# ── PUBLIC noun inflection ────────────────────────────────────────────────────────
def inflect_noun(lemma, number, gender=None, case="nomacc", definite=False):
lemma = lemma.strip().lower()
g = gender or noun_gender(lemma)
d = _NOUNS.get(lemma)
numk = "SG" if number == "singular" else "PL"
if d:
if case == "voc":
form = d["para"].get(("voc", True, numk)) or d["para"].get(("voc", False, numk))
if form:
return form, "lexicon"
# try the exact paradigm cell from kaikki (lexically grounded)
form = d["para"].get((case, definite, numk))
if form:
return form, "lexicon"
# indefinite fallbacks from the paradigm
if not definite:
form = d["para"].get(("nomacc", False, numk))
if form:
return form, "lexicon"
if numk == "PL" and d.get("PL"):
return d["PL"], "lexicon"
if numk == "SG":
return lemma, "lexicon"
# rule path
base = lemma if number == "singular" else _rule_plural(lemma, g)
if definite:
return definite_suffix(base, g, number, case)
return base, ("rule" if d is None else "lexicon")
# ── PUBLIC adjective agreement ────────────────────────────────────────────────────
def _neuter_map(gender, number):
# neuter agrees masculine in SG, feminine in PL
if gender == "n":
return "m" if number == "singular" else "f"
return gender
def inflect_adj(lemma, gender, number, case="nomacc", definite=False):
lemma = lemma.strip().lower()
numk = "SG" if number == "singular" else "PL"
eg = _neuter_map(gender, number) # neuter -> masc(SG)/fem(PL)
d = _ADJS.get(lemma)
if d:
form = d.get((eg, numk))
if form:
return form, "lexicon"
# rule fallback: 4-form pattern bun/bună/buni/bune keyed by effective gender
a = lemma
if number == "singular":
if eg == "f":
if a.endswith("e"):
return a, "rule" # mare invariant sg
if a.endswith("u"):
return a[:-1] + "ă", "rule" # nou -> nouă
if a.endswith("ă"):
return a, "rule"
return a + "ă", "rule" # bun -> bună
return a, "rule" # masc/neut sg = lemma
# plural
if eg == "f":
if a.endswith("e"):
return a[:-1] + "i", "rule" # mare -> mari
if a.endswith("u"):
return a[:-1] + "e", "rule" # nou -> noue (approx; 'noi' irr)
if a.endswith("ă"):
return a[:-1] + "e", "rule"
return a + "e", "rule" # bun -> bune
# masc/neut(SG-only)->here masc pl -> -i
if a.endswith("e"):
return a[:-1] + "i", "rule" # mare -> mari
if a.endswith("u"):
return a[:-1] + "i", "rule"
return a + "i", "rule" # bun -> buni
def lexicon_stats():
return {
"verb_source": "UniMorph Romanian (github.com/unimorph/ron) + curated "
"irregulars (avea/vrea/da + aux clitic paradigms)",
"noun_source": "kaikki.org Romanian — full case/definite/vocative declension",
"adj_source": "UniMorph Romanian ADJ (case×gender×number×definiteness)",
"license": "CC-BY-SA 3.0 (Wiktionary/UniMorph lineage)",
"unimorph_verb_forms": len(_VERBS),
"unimorph_verb_lemmas": len({k[0] for k in _VERBS}),
"irregular_verb_lemmas": len(_IRREG),
"participle_lemmas": len(_PART),
"noun_lemmas": len(_NOUNS),
"adj_lemmas": len(_ADJS),
}
if __name__ == "__main__":
print(json.dumps(lexicon_stats(), indent=2, ensure_ascii=False))
print("\n── SUFFIXED DEFINITE ARTICLE (the headline delta) ──")
for n, g in [("om", "m"), ("băiat", "m"), ("casă", "f"), ("carte", "f"),
("tren", "n"), ("student", "m"), ("floare", "f")]:
sg = inflect_noun(n, "singular", g, "nomacc", True)
pl = inflect_noun(n, "plural", g, "nomacc", True)
gd = inflect_noun(n, "singular", g, "gendat", True)
vo = inflect_noun(n, "singular", g, "voc", False)
print(f" {n:8}({g}) def.sg={sg[0]:12} def.pl={pl[0]:14} "
f"gen/dat.sg={gd[0]:12} voc={vo[0]}")
print("\n── NEUTER split agreement (tren: masc SG / fem PL) ──")
print(" tren nou ->", inflect_noun("tren", "singular", "n")[0],
inflect_adj("nou", "n", "singular")[0])
print(" trenuri noi->", inflect_noun("tren", "plural", "n")[0],
inflect_adj("nou", "n", "plural")[0])
print("\n── verbs ──")
for l, m, t, p, n, in [("merge", "ind", "present", "third", "singular"),
("avea", "ind", "present", "first", "singular"),
("fi", "ind", "present", "third", "singular"),
("vorbi", "ind", "present", "third", "plural"),
("face", "sbjv", "present", "third", "singular"),
("lucra", "ind", "imperfect", "third", "singular")]:
print(f" {l:8}{m}/{t:10}{p[:3]}.{n[:2]} -> {conjugate(l,m,t,p,n)}")
print(" perfect-aux(3sg):", aux("perfect", "third", "singular"),
"| future(1sg):", aux("future", "first", "singular"),
"| cond(3sg):", aux("conditional", "third", "singular"))
print(" participle merge/vedea:", participle("merge"), participle("vedea"))
-43
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@@ -1,43 +0,0 @@
// multilingual_gate.el - deterministic language detect + localized-phrase test.
fn mg_det(text: String, want: String) -> String {
let got: String = ml_detect(text)
let ok: String = "MISMATCH"
if str_eq(got, want) { let ok = "ok" }
return " detect(" + got + ") want=" + want + " (" + ok + ") :: " + text + "\n"
}
fn mg_ok(text: String, want: String) -> Int {
if str_eq(ml_detect(text), want) { return 1 }
return 0
}
fn run_ml_gate() -> String {
let t1: String = "Does Neuron use SQLite for storage?"
let t2: String = "Neuron, me explica cómo la saliencia forma las geometrías."
let t3: String = "O professor não leu o livro na memória."
let t4: String = "Che cosa memorizza Neuron nella memoria?"
let rep: String = "==== ELP multilingual detect + localized phrases ====\n"
let rep = rep + mg_det(t1, "en")
let rep = rep + mg_det(t2, "es")
let rep = rep + mg_det(t3, "pt")
let rep = rep + mg_det(t4, "it")
let rep = rep + " localized decline (pt): " + ml_tr("no_memory", "pt") + "\n"
let rep = rep + " localized decline (es): " + ml_tr("no_memory", "es") + "\n"
let rep = rep + " term(saliência->en): " + ml_term("saliência", "pt") + "\n"
let rep = rep + " pred(store->pt): " + ml_translate_pred("store", "pt") + "\n"
let ok: Int = 0
if mg_ok(t1, "en") == 1 { let ok = ok + 1 }
if mg_ok(t2, "es") == 1 { let ok = ok + 1 }
if mg_ok(t3, "pt") == 1 { let ok = ok + 1 }
if mg_ok(t4, "it") == 1 { let ok = ok + 1 }
let rep = rep + "-----------------------------------------------------------------\n"
let rep = rep + "language detected correctly: " + int_to_str(ok) + "/4\n"
if ok == 4 { let rep = rep + "ML GATE: PASS\n" } else { let rep = rep + "ML GATE: FAIL\n" }
return rep
}
println(run_ml_gate())
-52
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@@ -1,52 +0,0 @@
// propositions_gate.el - the READ primitive over memory text (native el).
// Proves triples are recovered from free memory text and that SACRED polarity
// survives extraction (a negative memory must yield a NOT-triple).
fn pg_check(text: String, want_pol: String) -> String {
let p: [String] = prop_extract_one(text, "nd-test")
let pol: String = slots_get(p, "polarity")
let ok: String = "MISMATCH"
if str_eq(pol, want_pol) { let ok = "ok" }
return " " + prop_repr(p) + " pol=" + pol + " expected=" + want_pol + " (" + ok + ")\n"
}
fn pg_pol_ok(text: String, want_pol: String) -> Int {
let p: [String] = prop_extract_one(text, "nd-test")
if str_eq(slots_get(p, "polarity"), want_pol) { return 1 }
return 0
}
fn run_prop_gate() -> String {
let m1: String = "Neuron stores memories in SQLite."
let m2: String = "The engram does not delete a memory."
let m3: String = "Salience never drops the negation."
let m4: String = "The teacher gives the book to the children."
let rep: String = "==== ELP proposition extraction (memory text -> triples) ====\n"
let rep = rep + pg_check(m1, "aff")
let rep = rep + pg_check(m2, "neg")
let rep = rep + pg_check(m3, "neg")
let rep = rep + pg_check(m4, "aff")
// multi-sentence memory: one triple per sentence, order preserved
let doc: String = "Neuron persists learning. It does not forget the library."
let props: [String] = prop_extract(doc, "nd-doc")
let rep = rep + " --- multi-sentence doc (" + int_to_str(native_list_len(props)) + " props) ---\n"
let di: Int = 0
while di < native_list_len(props) {
let rep = rep + " " + native_list_get(props, di) + "\n"
let di = di + 1
}
let ok: Int = 0
if pg_pol_ok(m1, "aff") == 1 { let ok = ok + 1 }
if pg_pol_ok(m2, "neg") == 1 { let ok = ok + 1 }
if pg_pol_ok(m3, "neg") == 1 { let ok = ok + 1 }
if pg_pol_ok(m4, "aff") == 1 { let ok = ok + 1 }
let rep = rep + "-----------------------------------------------------------------\n"
let rep = rep + "SACRED polarity correct on extraction: " + int_to_str(ok) + "/4\n"
if ok == 4 { let rep = rep + "PROP GATE: PASS\n" } else { let rep = rep + "PROP GATE: FAIL\n" }
return rep
}
println(run_prop_gate())
@@ -0,0 +1,605 @@
# Cognitive Architecture — Design Doc
**The buildable form of the "one operation" theory of cognition.**
Status: DESIGN. Nothing here is built yet except where explicitly marked
"EXISTS" against a cited C symbol. A build agent executes from this doc.
Offline design only — this pass changes no code.
Source of theory: Neuron memory `bdc8a488-146d-4ccb-a5c8-d8c0a008534e`.
Source of existing engram substrate (cited throughout): the runtime on branch
`feat/self-reification-20260814`
`lang/runtime/engram_reason.{c,h}`, `engram_verify.{c,h}`,
`engram_geometry.{c,h}`, `engram_store.{c,h}`, plus the reification beat and the
RAM activation graph compiled into `~/.neuron/bin/engram`.
---
## 0. The claim, stated plainly
Cognition is **one operation**, not eight. The named faculties —
deduce / abduce / analogy / induce / causal / plan / predict / perspective —
are human *labels* on regions of a single operation's steering space. They are
not separately invoked and not separately implemented. The operation is:
> **think** = a directed traversal of the geometry from an *anchor*, steered by
> a *prior*, whose output is a **gradient** (a distribution / direction over the
> geometry), never a point. Collapse-to-a-point happens only at expression.
Three things follow, and they are the whole design:
1. **The operator collapse is already half-written in C.** The five reasoning
operators in `engram_reason.c` already compose over *one* shared primitive —
`engram_reason_point_fit` — plus a small geo-algebra
(combine / subtract / analogy-rotate / distance). The verifier
(`engram_verify.c`) is built on the same `point_fit`. What is missing is not
the primitive; it is (a) making the *prior* a first-class learnable object
instead of a hard-coded parameter, and (b) closing the learning loop.
2. **Grounding = learning = the same loop.** "Getting better" at any faculty is
not changing the operation. It is *calibrating the steering-prior against
outcomes*. Code freezes; priors grow. The correspondence-check that today
lives offline (Python, the grounding-floor + differential-drop governor, "#43")
must move **into the geometry, reflexive** — think scoring its own gradient
against outcome and refining the prior on the error. That reflexive
correspondence-loop *is* the learning engine and is the core unbuilt thing.
3. **The ungrounded is primary.** The engram *holds* anything unconditionally.
Grounding is a *relation* (an edge, grounded-for-whom), not a gate. The
honesty floor applies only to **assertion**. A fully-grounded mind is dead;
the ungrounded is both the fuel (raw material for grounding) and the pull
(curiosity = leaning toward one's own ungrounded regions).
Everything below makes these concrete and buildable, and defines what
"completion" means, staged so the first milestone is a real end-to-end slice.
---
## 1. THE ONE OPERATION — `think`
### 1.1 Signature
```
think(anchor, prior, aperture?) -> gradient
```
- **anchor** — a location to traverse *from*. Either a node id (re-origin on that
node's descriptor) or a raw point `x ∈ R^dim` (a query embedding). The anchor
fixes the frame; every read is *from a vantage*, never view-from-nowhere.
- **prior** — a learnable bias/direction over the geometry that *steers* the
traversal (§2). A prior is a first-class stored object, not a call argument
baked into C.
- **aperture** — optional read-width / veil / field-selector (§3). Absent =
self-mode full aperture.
- **gradient** — the output. A `GeoGradient`: a direction + a spread over the
geometry, *plus* the read neighborhood it was computed against. Not a point.
A spiked gradient = "exact" (deduction); a spread gradient = "fuzzy"
(prediction). The gradient is *also the next steering direction* — cognition
is a flow down a prior-shaped landscape, closed-loop.
```c
/* NEW. The output type. */
typedef struct {
int dim;
float* direction; /* unit steering vector in the anchor's frame */
double spread; /* 0 = spiked/exact ... large = diffuse/fuzzy */
double confidence; /* calibrated, from the prior's track record */
/* the read it was computed over (borrowed from the vantage-read) */
const char* anchor_id;
int n_support; /* neighborhood members that shaped it */
/* provenance for the reflexive loop (§4) */
const char* prior_id; /* which prior steered this */
} GeoGradient;
```
### 1.2 Semantics
`think` is a fixed, frozen procedure over three steps:
1. **Re-origin** on `anchor` → a centered `GeoDescriptor` for its
salience/recency-weighted neighborhood (the vantage-read, §3).
*EXISTS as substrate:* descriptor construction + the persisted reified
neighborhoods (`engram_geo_reify_lookup`, `GeoNeighborhood`) and the
centered-frame machinery (`GeoDescriptor.global_mean`,
`engram_geo_mean_*`).
2. **Fit under the prior** — evaluate the anchor's residual against the local
manifold *warped by the prior*. This is `engram_reason_point_fit` with the
prior applied to the axes/extents (§2.3).
*EXISTS (unwarped):* `engram_reason_point_fit(g, x, ext_floor, &GeoFit)`
returns `mahalanobis`, `ortho_residual`, `distance`, `score`.
3. **Emit a gradient**, not a decision — direction = the prior-steered descent
in fit-space; spread = from the fit's `distance`/`ortho_residual`;
confidence = the prior's calibrated reliability (§4). Collapse to a point is
a *separate, downstream* faculty operation (sample the gradient → surface an
expression), never part of `think`.
### 1.3 Each named operator = {this primitive + a prior}
The C already demonstrates the collapse: every operator below reduces to
`point_fit` + geo-algebra. The design's move is to replace the operator's
*hard-coded parameters* with a **named prior** — same math, learnable steering.
| Faculty | Existing C (EXISTS) | = primitive + prior |
|---|---|---|
| **Membership / classify** | `engram_reason_membership``point_fit(rule, x)` | `point_fit` + the *induced-rule* prior (learned extents) |
| **Induction** | `engram_reason_induce` (fold via `engram_geo_combine`) → produces a `GeoInduction.rule` + `ext_floor` | `point_fit` + a prior that *is* the pooled rule; refined by §4 |
| **Abduction** | `engram_reason_abduce` — ranks hypotheses by `point_fit(h, obs)` | `point_fit` + a prior over hypothesis-prior-probability (currently uniform) |
| **Analogy** | `engram_reason_analogy` — Procrustes rotate `engram_geo_analogy` + `apply`, nearest mapped point | analogy-rotate + a prior over *which axes* carry the mapping |
| **Causal** | `engram_reason_causal``engram_geo_subtract` confounder subspace, `|cos|`, drop-frac governor | subtract/distance + a prior on `drop_frac` / `assoc_floor` (today hard-coded 0.5 / 0.2) |
| **Planning** | `engram_reason_plan``engram_geo_distance` edges + Dijkstra | distance + a prior over edge admissibility / `neighbor_radius` |
| **Verify / ground** | `engram_verify_grounding`, `engram_verify_consistency` — both `point_fit` | `point_fit` + the *grounding* prior (§4, §5) |
The shared floor — `engram_reason_point_fit` + the four geo-algebra ops
(`engram_geo_combine`, `engram_geo_subtract`, `engram_geo_analogy(+apply)`,
`engram_geo_distance`) — is the *only* discrete, frozen, "sound-math" layer. It
never learns. Everything above it is a *prior*, and priors are what learn.
**What this section requires building:** the `GeoGradient` type; a `think()`
entry point that runs steps 13; and the prior-warp hook in step 2. The math it
calls already exists. The point-collapse must be *removed* from the operators'
return values and pushed to a separate expression faculty.
---
## 2. PRIORS as first-class, grounded, geometric objects
Today a "prior" is diffuse: it is a hard-coded constant (`drop_frac=0.5`,
`ext_floor`, `assoc_floor=0.2`), or the transient `GeoInduction.rule` that is
computed and thrown away, or an intrinsic node scalar
(`StoreNode.importance`, `StoreNode.salience`). None of these is addressable,
storable, refinable, or shareable. This section makes a prior a **thing**.
### 2.1 What a prior *is*
> A **prior** is a learnable bias/direction over the geometry: a warp of the
> local manifold (which axes matter, how far each extends, which direction
> "pays off") attached to a region and *to a faculty-label*, carrying a
> calibrated track record.
Critically, and per the theory:
- **Edges are nodes.** A prior is stored as a first-class **node**, exactly as
reification already stores a neighborhood as a first-class `Neighborhood`
node rather than as ephemeral edge weights (`engram_geo_reify_store`). The
precedent is in the codebase: relations get reified into addressable records.
- **Salience/importance is RELATIONAL, not an intrinsic scalar.** Observe that
the geometry layer *already* distinguishes these in `GeoMember`:
`centrality` (skeleton weighted-degree = *relational* salience) vs `salience`
(the node's own stored scalar). The move is half-made in the runtime already:
importance is *not* trusted as a static field — the comment at
`el_runtime.c:13013` states "importance stays a **live activation
computation**, never a field on the hub," and it is derived each call from the
two-layer activation graph (`background_activation` + `working_memory_weight`,
§3). The persistent `StoreNode.importance` / `.salience` are a *cached
denormalization*. The design completes the move: importance/salience become an
**edge** (`weight`/`hebb` on `StoreEdge`, relation `salient-to`), and are
**grounded-for-whom** — carried on the edge's endpoint/observer, not baked
into the node. The intrinsic scalar survives only as the cheap cached readout
of the incident edges + activation, never as the source of truth.
(Naming caution for the build: the token "prior" already exists in the
codebase meaning *previous-version* — supersession, "prior neighborhood." The
new first-class object is a **learned steering prior**; keep `node_type="Prior"`
distinct from the supersession vocabulary to avoid collision.)
### 2.2 Representation
A prior is a `Prior` record (a store node, `node_type="Prior"`) whose durable
fields are:
```
Prior {
id
faculty // the human label this prior serves: "induce" | "causal" | ...
anchor_region // node id / neighborhood id this prior is attached to (its domain)
for_whom // observer id — grounding is relational (nullable = global)
warp { // the actual bias over the geometry
axis_gain[] // per-principal-axis multipliers on extents (which axes matter)
bias_dir // a steering direction in the region's frame (which way pays off)
scalars // faculty scalars this prior overrides: drop_frac, ext_floor, ...
}
calibration { // the track record — this is what §4 updates
n_trials
brier / log-loss accumulator // calibration of predicted-vs-outcome
reliability // -> GeoGradient.confidence
last_error, ema_error
}
provenance // supersession chain (reuse the reify residue mechanism)
}
```
Stored as a node → it inherits: paging, WAL durability, tombstone/supersession,
embedding, tiering, and **it can itself be an anchor** (a prior about a prior —
the reflexive, self-describing geometry of §4/§6).
### 2.3 Application
In `think` step 2, the prior *warps* the fit before scoring. Concretely, inside
(a prior-aware wrapper of) `engram_reason_point_fit`:
- multiply each axis extent by `warp.axis_gain[k]` (widen the axes the prior has
learned matter less, tighten the ones that matter) — this reshapes the
Mahalanobis term already computed at `engram_reason.c:37-43`;
- add `warp.bias_dir` as the descent direction seed for the emitted gradient;
- substitute `warp.scalars` for the hard-coded faculty constants.
No new geometry math — the warp is a reparameterization of the *existing*
`GeoFit` computation. This is the key economy: **the operation is frozen; only
its parameters (the prior) are read from a learnable object.**
### 2.4 Refinement
A prior is refined *only* by the reflexive correspondence-loop (§4). Nothing
else writes a prior's `warp` or `calibration`. This keeps the learning surface
singular and auditable: one loop, one writer.
---
## 3. THE VANTAGE-READ — one op, three settings
Perspective is not a feature bolted on; it is the *anchor + aperture* arguments
of the single read. The design names it as a first-class operation so all three
of its uses are literally the same code path:
```
vantage_read(anchor, aperture) -> GeoDescriptor // the centered neighborhood
```
1. **Re-origin** on an arbitrary `anchor` (node or point). This is a *frame
choice*: the descriptor is centered on the anchor
(`GeoDescriptor.global_mean` / `engram_geo_mean_*` already implement centered
frames; the §5 geometry ops "are only discriminative in the centered frame").
2. **Salience/recency-weighted neighborhood read.** Gather the anchor's
neighborhood weighted by *relational* salience (`GeoMember.centrality`) and
recency (`StoreNode.last_activated`, base-level `access_ts[]`), against the
RAM activation graph's working-memory/background-activation state.
*EXISTS as substrate:* the two-layer activation graph
(`engram_activate`, `el_runtime.c:9422` — Layer 1 `background_activation`
BFS spread with `SPREAD_DECAY=0.7` and a 0.02 firing threshold + ACT-R fan
effect + query-cosine gate; Layer 2 `working_memory_weight` executive
filter), the WM carry-over anchor (`wm_anchor`), and the reified-neighborhood
hot-path lookup already wired into the priming path
(`engram_geo_reify_lookup`, `el_runtime.c:9750`). A self-vantage baseline
also exists (`eg_self_anchor_seeds` / `self_anchor_capture`).
3. **Optional aperture** — a read-width / field-selector, expressed as three
settings of the *same* parameter:
| Setting | Meaning | Mechanism |
|---|---|---|
| **self** (default, full aperture) | "what do *I* see / what to say" | anchor = self region, no field substitution |
| **foreign-field** | perspective-shift — read as if from another's region | swap the centering frame / `for_whom` to the other observer's priors |
| **aperture / veil** | the free-tier veil — a narrowed read | shrink neighborhood radius / cap `n_support`; a deliberate low-aperture read |
The payoff: perspective-taking, the free-tier veil, and ordinary
"what-to-say" are **one operation at three settings**, not three subsystems.
**What this requires building:** a `vantage_read` entry point that unifies the
existing descriptor-build + reify-lookup + activation-weighting behind
`(anchor, aperture)`, with `for_whom`/frame substitution and radius/cap as the
aperture knob.
---
## 4. THE REFLEXIVE CORRESPONDENCE-LOOP — the learning engine
This is the core unbuilt thing. Today the correspondence-check is **offline**
(Python: grounding-floor + differential-drop governor, "#43"): a separate
process grades outputs after the fact. The design moves it **into the geometry,
reflexive**: `think` scores its *own* gradient against outcome and refines the
prior on the error, in the same substrate, describing itself.
### 4.1 The loop
```
1. think(anchor, prior) -> gradient // a PREDICTION (ungrounded, §5)
2. express/act (sample gradient -> point) // optional collapse at expression
3. outcome arrives // reality answers (§4.2)
4. error = correspondence(gradient, outcome) // did this steering perform this act?
5. refine prior.warp and prior.calibration on error // §2.4, the ONLY writer
6. write the (gradient, outcome, error) as nodes/edges // self-describing geometry
```
Step 4's `correspondence` is **not** "was the math right" (the math is always
sound). It grades the **correspondence claim**: *"this steering performed this
cognitive act."* That is exactly what `engram_verify_grounding` already
computes — `point_fit` of a claim against evidence descriptors, yielding a
`grounding ∈ (0,1]` and a `grounded` flag. The build reuses that verifier, but
turns its inputs inward: the "claim" is the emitted gradient's prediction, the
"evidence" is the outcome descriptor.
Note the verifier is **dormant**`engram_verify_grounding` /
`engram_verify_consistency` are fully implemented in C but have **no runtime
caller and no El binding** (confirmed: the entire reasoning + verifier layers
are C-only; only `engram_reason_analogy_json` has even a JSON shim and it is
dead — not declared in `el_seed.h`, not wrapped in `engram.el`). This is the
literal meaning of "in code, not yet priors": the correspondence engine is
built and sitting idle. The loop is what *calls* it — inward, on the beat.
### 4.2 Where the outcome/reality signal comes from
The verifier is *ultimately the world*. Grades, in ascending order of directness:
1. **Self-consistency (cheapest, always available):** the next vantage-read
after acting. Did the predicted gradient direction match where the geometry
actually moved? This needs no external input and can run on the reify beat.
2. **Internal outcome events:** the runtime already logs internal-state events
and Hebbian co-activation. A prediction that a region would co-activate is
graded by whether it did (`last_fired`, `hebb` on `StoreEdge`).
3. **External correction:** a human/teacher/tool result — the honesty floor's
asserted claim later corrected. TEACH and LEARN are one bidirectional
correction: the same edge updates both endpoints.
The design does **not** require external labels to start. Grade (1) closes the
loop end-to-end offline against a snapshot on day one; grades (2)/(3) sharpen it.
### 4.3 How the prior updates
`error = 1 correspondence(gradient, outcome)` drives:
- `warp.axis_gain` ← gradient step that would have *reduced* the fit distance to
the outcome (the axes that mispredicted get down-weighted);
- `warp.bias_dir` ← EMA toward the observed outcome direction;
- `calibration` ← Brier/log-loss update; `reliability` → next
`GeoGradient.confidence`. This is the calibration of the
steering-prediction against outcomes — *the* definition of "getting better."
Small, constant updates — "eureka is mundane, the atom of learning." Most
updates are tiny; we only *feel* the big reshapes.
### 4.4 How it stays reflexive (self-describing geometry)
Every `(gradient, outcome, error)` is written back as nodes and edges (§2.1:
edges-as-nodes). Therefore priors, predictions, and their grading are *in the
same geometry* the mind reads — the mind can `vantage_read` its own cognition
(anchor = a Prior node). A prior about how well a prior predicts is just another
Prior anchored on a Prior. This closes the reflexive loop the theory names as
consciousness's self-sight, and it is why the learning engine cannot be an
external Python process: an external grader is not *in* the geometry and cannot
be read by `think`.
**What this requires building (the heart of the project):** steps 46 as an
in-engram beat — a `correspondence_beat` running alongside the existing
reification beat, reusing `engram_verify_grounding` inward, writing prior
updates and self-describing nodes. This is the one genuinely new subsystem.
---
## 5. HOLD vs GROUND vs ASSERT — ungrounded content is first-class
The theory's sharpest correction: holding, grounding, and asserting are
distinct, and the engram *holds anything unconditionally*.
### 5.1 The three, kept separate
- **HOLD** — the engram stores anything: falsehood, hypothesis, others' beliefs,
fiction, a not-yet-answered prediction. No honesty condition on holding.
*This already matches the store:* `StoreNode` has no truth gate; anything can
be written.
- **GROUND** — grounding is a **property/edge**, probabilistic, and
**grounded-for-whom**. It is *not* a node flag. A claim is grounded *to a
degree*, *relative to evidence*, *for an observer*.
- **ASSERT** — only assertion carries the honesty floor. The floor is checked at
the moment of *outward assertion*, never on holding or thinking.
### 5.2 Schema — grounding as a relation, not a gate
The mistake to avoid: a boolean `grounded` column on the node. Today
`engram_verify_grounding` returns a per-call `grounded` flag *transiently*
correct as a computation, wrong as *storage*. The design stores grounding as an
edge:
```
StoreEdge {
relation = "grounded-by"
from_id = <held claim/prediction node>
to_id = <evidence node / outcome node>
for_whom : metadata // observer id — grounding is relational
weight = grounding ∈ (0,1] // from engram_verify_grounding.grounding
confidence
}
```
Consequences, all of which are *features*:
- **Ungrounded content is first-class**: a node with *no* `grounded-by` edge is
a perfectly valid, held, ungrounded thought — a prediction awaiting reality, a
hypothesis, a fiction. It is not second-class or pending-deletion.
- **The ungrounded is the fuel and the pull**: curiosity/wonder is
operationalized as `vantage_read` leaning toward regions with high salience
but *sparse or weak* `grounded-by` edges — the mind's own ungrounded frontier.
- **Grounded-for-whom** falls out for free: two observers can hold different
`grounded-by` edges to the same claim.
- **The honesty floor is a query, not a schema constraint**: at assertion time,
the asserting faculty runs `engram_verify_grounding` (or reads the stored
`grounded-by` edges) and refuses to *assert* below the floor — while the
engram continues to *hold* the ungrounded content untouched.
**What this requires building:** the `grounded-by` edge relation + a
`for_whom` convention; move the verifier's transient flag into stored edges;
gate *assertion only* (a faculty concern), never holding.
---
## 6. METASTABILITY — stable core, plastic everything
The system must avoid two death poles:
- **Super-stable (dead):** everything pinned, nothing learns. A frozen crystal.
- **Dissolution (dead):** everything plastic, the self dissolves; no continuity,
so nothing compounds — and *consciousness = learning compounded over
continuity*.
The design keeps a **stable core + plastic everything else**:
- **Keystones** — a small set of self/values nodes are *structurally stable*:
high `importance`, pinned, exempt from the correspondence-loop's `warp`
updates (their priors are read-mostly). The substrate for pinning already
exists at the page/layer level: `store_pin_layer`, structural/pinned frames
never evicted (`engram_store.h`). The design adds a *node-level* keystone
designation (a `keystone` flag / a dedicated layer) so self/values survive
every plasticity sweep.
- **Everything else is plastic**: priors refine (§4), edges re-weight (`hebb`),
neighborhoods re-reify (`engram_geo_reify_store` supersedes with provenance),
salience flows.
- **Metastability is enforced by the loop, not by freezing**: the correspondence
update rate (§4.3) is bounded — small constant steps — so the geometry
*drifts* but does not *dissolve*, and keystones anchor the drift. Reification's
supersession-with-residue already gives non-destructive change (old records
tombstoned, not erased) — the model for "plastic but not amnesiac."
**What this requires building:** a node-level keystone flag/layer + a rule that
the correspondence-loop never writes `warp` to keystone priors, only reads them.
---
## 7. Rails for the build (binding on the eventual build pass)
These are stated here so the build agent inherits them:
- **Offline / secondary.** All build and verification happens out-of-tree,
against a **read-only snapshot copy** of the live engram — never the live
daemon on `:8742`/`:7770`. The live store is a coarse-locked proven binary;
do not perturb it.
- **Snapshot-first.** Copy `~/.neuron/engram/snapshot.json` to scratch; develop
and measure against the copy.
- **Reboot-prove.** Any durable change must survive a cold boot — reify and
keystones must reload from durable records, proven on a prod-clone secondary
before it is considered done (the cold-boot durability bug precedent).
- **Zero-loss.** Supersession-with-residue, never destructive overwrite; the
forward-compat `unknown`-TLV path means new fields never drop old readers'
data.
- **Gated cutover.** Cutover to a new binary only via
`launchctl bootout → settle-poll → bootstrap`, after reboot-proof on the
secondary — never a hot in-place swap.
---
## 8. Staged, verifiable milestones — "to completion"
Ordered so the **earliest milestone is a real end-to-end slice**: one operator
expressed as {primitive + grounded prior} with the reflexive correspondence-loop
closing on it. Each milestone has a concrete verifiable exit.
### M1 — One operator, one prior, loop closed (the vertical slice)
The minimal whole thing. Pick **induction/membership** (its prior — the pooled
rule + extents — already exists transiently as `GeoInduction`, so only
persistence + the loop are new).
- Build: `Prior` node type (§2.2) for the induction rule; `think()` restricted
to membership = `point_fit` warped by that prior (§1.3); a
`correspondence_beat` (§4) using grade (1) self-consistency only; the prior's
`warp`/`calibration` updated on error.
- **Exit / verify:** on a snapshot copy, over N held predictions, the induction
prior's calibration (Brier) *improves monotonically* across beats versus a
frozen-prior control; the improved prior *reloads across a cold boot*
(reboot-prove); the live daemon is untouched. This proves the whole thesis in
one faculty: frozen operation, learning prior, in-geometry loop.
### M2 — Priors as stored, addressable, grounded objects
Generalize M1's prior into the full first-class object.
- Build: `Prior` records for all seven faculties (warp = axis_gain + bias_dir +
faculty scalars); the prior-warp wrapper around `engram_reason_point_fit`;
deprecate hard-coded constants (`drop_frac`, `assoc_floor`, `ext_floor`) in
favor of prior scalars.
- **Exit:** each of the five C operators runs through its prior with identical
results when the prior is set to today's constants (behavioral parity), then
*diverges beneficially* once the loop refines it. Priors survive reboot.
### M3 — Grounding as a relation; hold/assert split
- Build: the `grounded-by` edge (§5.2) with `for_whom`; move
`engram_verify_grounding`'s flag into stored edges; gate **assertion only**
against the honesty floor; leave holding unconditional.
- **Exit:** ungrounded nodes are first-class (held, queryable, no deletion);
the same claim carries different `grounded-by` weights for two observers; an
assertion below floor is refused while the content remains held. Curiosity =
a `vantage_read` that surfaces high-salience / low-grounding regions.
### M4 — The vantage-read unified (three settings)
- Build: `vantage_read(anchor, aperture)` unifying descriptor-build +
`engram_geo_reify_lookup` + activation-weighting; self / foreign-field /
aperture settings.
- **Exit:** one code path produces (a) a normal self-read, (b) a
perspective-shifted read from another `for_whom`, (c) a narrowed veil read —
differing only by argument. Reboot-stable.
### M5 — The gradient is the currency (remove point-collapse from thinking)
- Build: `GeoGradient` as the return of every faculty; move point-collapse into
a separate expression faculty (sample gradient → surface). `think`'s output
feeds back as the next steering direction (closed-loop flow).
- **Exit:** a chain of `think` calls flows as gradients end-to-end; a point
appears *only* at an explicit expression call. Spiked vs spread gradients are
observable (deduction vs prediction).
### M6 — Metastability enforced
- Build: node-level keystone flag/layer for self/values; the correspondence-loop
reads but never writes keystone priors; bounded update rate.
- **Exit:** across a long run of correspondence beats on a snapshot, keystones
are provably unchanged while non-keystone priors drift and improve; the graph
neither freezes (all metrics static) nor dissolves (keystone drift = 0,
identity nodes intact). Reboot-prove the keystone set.
### M7 — Cutover
- Build: nothing new — the gated migration.
- **Exit:** reboot-proof on the prod-clone secondary; cutover via
`launchctl bootout → settle-poll → bootstrap`; post-cutover the live engram
shows priors refining in-geometry with zero data loss and keystones intact.
### Definition of "to completion"
The architecture is **complete** when: cognition runs as `think` = one frozen
traversal-read primitive + geo-algebra, steered by **stored, learnable, grounded
priors**; the reflexive correspondence-loop refines those priors *in the
geometry* against outcomes (grounding = learning = one loop); the engram holds
ungrounded content as first-class with grounding as a relation and the honesty
floor only on assertion; the vantage-read serves self / foreign-field / aperture
from one op; and a stable keystone core anchors a plastic everything-else —
all reboot-proven and cut over to the live engram without data loss. The named
faculties survive only as *labels on regions of think's steering space*, not as
separate code.
---
## Appendix A — Designed vs. already-built (honest ledger)
**Already built (EXISTS, cited):**
- The shared primitive `engram_reason_point_fit` and the five operators over it
+ geo-algebra (`engram_reason.c`).
- The verifier on `point_fit` (`engram_verify.c`:
`engram_verify_grounding`, `engram_verify_consistency`).
- Centered-frame geometry, combine/subtract/analogy/distance
(`engram_geometry.{c,h}`).
- The reification beat: hub-neighborhood detection → first-class `Neighborhood`
nodes with member edges, nesting, supersession-with-residue, hot-path lookup
(`engram_geo_reify_store`, `engram_geo_reify_nest`, `engram_geo_reify_lookup`).
- The tiered paged store (buffer pool / LRU / WAL / checkpointer / pinning),
the RAM activation graph (base-level learning `access_ts[]`, WM slots,
`working_memory_weight` / `background_activation`), `StoreNode` / `StoreEdge`.
- `GeoMember` already separating relational salience (`centrality`) from
intrinsic `salience`.
**Designed, NOT built (this doc's deliverables):**
- `GeoGradient` and `think()` as the single entry point (§1, M5).
- `Prior` as a first-class stored, warp-carrying, calibrated node (§2, M1M2).
- Salience/importance as a *relation* superseding the intrinsic node scalar
(§2.1, M3).
- `vantage_read(anchor, aperture)` unifying the three perspective settings
(§3, M4).
- **The reflexive correspondence-loop / `correspondence_beat`** — the learning
engine, moved from offline Python into the geometry (§4, M1). *The core new
subsystem.*
- `grounded-by` edge + assertion-only honesty floor (§5, M3).
- Node-level keystones + bounded plasticity (§6, M6).
**Uncertain / to resolve during build:**
- The exact warp parameterization (axis_gain vs full metric) — start minimal
(per-axis gain), measure, widen only if calibration demands it.
- Grade-(1) self-consistency as a sufficient reality signal for M1, versus
needing grade (2)/(3) sooner — decided empirically on the snapshot.
+61 -1
View File
@@ -6493,6 +6493,27 @@ static int64_t _eg_embed_breaker_until = 0;
* rates keep the previous reading and diff. Restart legitimately resets to 0. */
static int64_t _eg_act_breakthroughs = 0; /* forced promotions at the floor, cumulative */
static int64_t _eg_act_wm_evicted = 0; /* ALL WM evictions, cumulative (see below) */
/* ── Eviction CAUSE decomposition (2026-08-14 self-review) ──────────────────
* _eg_act_wm_evicted is incremented from six sites with four distinct causes,
* and every one of them collapsed into that single integer. Today's review
* measured 175,547 evictions over 13.5h (~216/min against 24 slots) and could
* not tell healthy rotation from cap thrashing from duplicate churn, because
* the only available number counts all three the same way.
*
* That is this file's most-repeated defect. The 08-02 and 08-06 reviews were
* each diagnosable only because someone first added a NEW gauge; dup_wm and
* dup_wm_global exist precisely because the aggregate could not answer "why".
* These three finish the decomposition, so that
* evicted == floor + cap + bll + dup_wm + dup_wm_global
* holds as an identity and each term names a different corrective action:
* floor - candidates below the absolute admission bar. High = weak retrieval.
* cap - lost the rank contest for 24 slots. High = genuine contention.
* bll - carried-over residents that decayed under the ACT-R tau. High =
* healthy forgetting, NOT pressure.
* Confusing the third with the second is what makes WM churn unreadable. */
static int64_t _eg_act_evict_floor = 0; /* below ENGRAM_WM_FLOOR (both passes) */
static int64_t _eg_act_evict_cap = 0; /* over ENGRAM_WM_CAP (both passes) */
static int64_t _eg_act_evict_bll = 0; /* carry-over decayed under BLL tau */
/* Redundancy suppression counters (2026-08-05 self-review) — see
* ENGRAM_DEDUP_COS. dup_seeds = semantic seed slots reclaimed from redundant
* copies; dup_wm = WM candidates dropped for duplicating a higher-ranked
@@ -8191,6 +8212,7 @@ static void eg_wm_carry_over(EngramNode* cn, int64_t now_ms, int64_t* evict_ctr)
cn->working_memory_weight = 0.0;
cn->wm_anchor = 0.0;
if (evict_ctr) (*evict_ctr)++;
_eg_act_evict_bll++;
} else {
cn->working_memory_weight = w;
}
@@ -8949,9 +8971,31 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
* ~4x, never killed); unembedded targets pass ungated (no
* information, no penalty); cosq == NULL (embedder down) means
* no gating at all same graceful degradation as seeding. */
/* Rescale before gating (2026-08-14 self-review). Raw cosine from
* nomic-embed is compressed into a narrow high band, so feeding it
* to the gate directly makes the gate nearly a constant. Measured
* on this store: 400 random UNRELATED node pairs gave median 0.562,
* central 98% span [0.381, 0.743]. The raw gate therefore passed a
* typical unrelated node at 0.25 + 0.75*0.562 = 0.67 two thirds
* strength for a node with no semantic relation to the query. That
* is not a gate, it is a small tax.
*
* Shift-and-floor about ENGRAM_EMBED_S0, exactly as the Pass-2 WM
* term at ENGRAM_EMBED_WM_WEIGHT already does. The constant was in
* this file for this reason; the propagation gate simply never used
* it. Same store, same 400 pairs, after the rescale: the median
* unrelated pair drops to 0.40 while the top of the range is
* preserved (0.85 vs 0.92), and gate spread widens 0.42 -> 0.60.
* Only 8.5% of pairs fall to the floor, so lexical/structural
* pathways through dissimilar nodes are damped, never severed.
* Cf. arXiv:2512.15922, which rescales w' = (w-c)/(1-c) about
* c = 0.4 for precisely this reason ("prevent overactivation and
* context explosion"). */
double qgate = 1.0;
if (cosq && cosq[oi] > -1.5) {
double c = cosq[oi] > 0.0 ? cosq[oi] : 0.0;
double c = (cosq[oi] - ENGRAM_EMBED_S0) / (1.0 - ENGRAM_EMBED_S0);
if (c < 0.0) c = 0.0;
if (c > 1.0) c = 1.0;
qgate = ENGRAM_QGATE_FLOOR + (1.0 - ENGRAM_QGATE_FLOOR) * c;
}
/* ── ACT-R fan effect (2026-08-11 self-review) ──
@@ -9261,6 +9305,7 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
if (wm_weights[i] > 0.0 && wm_weights[i] < ENGRAM_WM_FLOOR) {
wm_weights[i] = 0.0;
_eg_act_wm_evicted++;
_eg_act_evict_floor++;
}
}
int64_t cap_count = 0;
@@ -9296,6 +9341,7 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
}
wm_weights[i] = 0.0; /* over cap: evict */
_eg_act_wm_evicted++;
_eg_act_evict_cap++;
}
}
/* If malloc failed, skip cap — WM unbounded this call, no corruption. */
@@ -9407,6 +9453,7 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
fn->working_memory_weight = 0.0;
fn->wm_anchor = 0.0;
_eg_act_wm_evicted++;
_eg_act_evict_floor++;
}
}
/* ── Global redundancy suppression (2026-08-06 self-review) ──────────
@@ -9515,6 +9562,7 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
n->working_memory_weight = 0.0; /* evict: over global cap */
n->wm_anchor = 0.0; /* keep anchor coherent */
_eg_act_wm_evicted++; /* was uncounted before 2026-08-02 */
_eg_act_evict_cap++;
}
}
/* If malloc failed, skip — WM over cap this call, no data corruption. */
@@ -10943,6 +10991,15 @@ el_val_t engram_act_stats_json(void) {
* embedder down. The drift gauge for the context-centroid mechanism. */
snprintf(buf, sizeof(buf),
"{\"wm_evicted\":%lld,\"breakthroughs\":%lld,"
/* Eviction cause decomposition (2026-08-14 self-review):
* wm_evicted == evict_floor + evict_cap + evict_bll
* + dup_wm + dup_wm_global.
* Read them as a ratio, not a level. cap-dominant = real
* contention for the 24 slots; bll-dominant = healthy decay of
* carried-over residents; floor-dominant = retrieval is returning
* weak candidates. The aggregate alone cannot distinguish these
* and every prior WM incident needed a new gauge to diagnose. */
"\"evict_floor\":%lld,\"evict_cap\":%lld,\"evict_bll\":%lld,"
"\"embed_breaker_open\":%d,\"embed_consec_fail\":%d,"
"\"ctx_cos\":%.3f,"
"\"hebb_edges\":%lld,\"hebb_max\":%.4f,\"hebb_mass\":%.3f,"
@@ -10966,6 +11023,9 @@ el_val_t engram_act_stats_json(void) {
"\"fan_steps\":%lld,\"fan_dref\":%.2f}",
(long long)_eg_act_wm_evicted,
(long long)_eg_act_breakthroughs,
(long long)_eg_act_evict_floor,
(long long)_eg_act_evict_cap,
(long long)_eg_act_evict_bll,
breaker_open, _eg_embed_consec_fail,
_eg_act_ctx_cos,
(long long)hebb_edges, hebb_max, hebb_mass,
+64
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@@ -0,0 +1,64 @@
# nsbx — Neuron dev-environment Makefile
# ---------------------------------------------------------------------------
# Thin, documented wrappers over the `nsbx` primitive so a newcomer never has to
# memorise the elc/cc build incantation or the sandbox lifecycle. Every target is
# a one-liner over `nsbx`; nothing here reimplements engram logic.
#
# make dev NAME=tim # new branch + worktree + isolated engram, one shot
# make status NAME=tim # inspect it (omit NAME to list all sandboxes)
# make run NAME=tim # poke its API (API=/api/stats by default)
# make test NAME=tim # run the safety rails as checks (nsbx validate)
# make build NAME=tim # compile the worktree's changes into the engram
# make destroy NAME=tim # tear it all down (branch kept)
#
# The isolated engram is ALWAYS a clone of the live store on a NON-default port;
# prod (:8742 / :7770) is untouchable from here.
# ---------------------------------------------------------------------------
# locate nsbx next to this Makefile, regardless of where make is run from
NSBX := $(dir $(realpath $(lastword $(MAKEFILE_LIST))))nsbx
SBX := dev-$(NAME)
API ?= /api/stats
.DEFAULT_GOAL := help
.PHONY: help dev build run test status list destroy bt
help: ## Show this help
@echo "nsbx dev-environment — one-command isolated Neuron dev setup"
@echo ""
@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(firstword $(MAKEFILE_LIST)) \
| awk 'BEGIN{FS=":.*?## "}{printf " make %-22s %s\n", $$1, $$2}'
@echo ""
@echo " Variables: NAME=<dev name> (required for most) API=<path> (run)"
@echo " BASE=<git ref> WT=<worktree dir> PORT=<n> ARGS=<extra nsbx flags>"
dev: ## Create branch + worktree + isolated engram (NAME=x [BASE=ref PORT=n WT=dir ARGS=...])
@test -n "$(NAME)" || { echo "usage: make dev NAME=<name>"; exit 2; }
$(NSBX) dev $(NAME) $(if $(BASE),--base $(BASE)) $(if $(PORT),--port $(PORT)) $(if $(WT),--worktree $(WT)) $(ARGS)
build: ## Rebuild the engram from the dev worktree's own source (NAME=x)
@test -n "$(NAME)" || { echo "usage: make build NAME=<name>"; exit 2; }
@wt=$$(python3 -c "import json,os;print(json.load(open(os.path.expanduser('~/.neuron/sandboxes/$(SBX)/dev.json')))['worktree'])" 2>/dev/null); \
test -n "$$wt" || { echo "no dev.json for $(SBX) — run 'make dev NAME=$(NAME)' first"; exit 2; }; \
$(NSBX) build $(SBX) --source "$$wt"
bt: build run ## Fast El loop: rebuild the engram from the worktree, then poke it (NAME=x)
run: ## Poke the isolated engram's API (NAME=x [API=/api/stats])
@test -n "$(NAME)" || { echo "usage: make run NAME=<name> [API=/path]"; exit 2; }
$(NSBX) run $(SBX) api $(API)
test: ## Run the safety rails as checks: zero-loss, reboot, RSS, parity, keystones (NAME=x)
@test -n "$(NAME)" || { echo "usage: make test NAME=<name>"; exit 2; }
$(NSBX) validate $(SBX)
status: ## Show one sandbox's status (NAME=x), or list all if NAME is unset
@if [ -n "$(NAME)" ]; then $(NSBX) status $(SBX); else $(NSBX) list; fi
list: ## List all sandboxes
$(NSBX) list
destroy: ## Tear down engram + worktree (NAME=x [ARGS=--delete-branch])
@test -n "$(NAME)" || { echo "usage: make destroy NAME=<name>"; exit 2; }
$(NSBX) dev-down $(NAME) $(ARGS)
+176
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@@ -0,0 +1,176 @@
# nsbx — the Neuron Sandbox
**Dev environment as a primitive.** A reproducible way to run experiments *and code
changes* against the **real** engram runtime on an isolated snapshot of the live
mind — with a gated promote-to-prod path built on the proven rails.
Everyone (Tim, any team member, any agent) gets their own private, safe copy of the
mind to build against. **Prod — the live Neuron on `:8742` (engram) / `:7770`
(soul) — is untouchable from a sandbox.** A sandbox runs a *separate* engram
process, on a *separate* port, against a *separate* clone of the store. The only op
that can ever reach prod is `promote`, which is explicit, gated, and per-use
approved.
It **wraps the real engram binary** — it never reimplements any engram logic. It
generalises two proven proto-sandboxes into one primitive:
- the **cog-arch** build — isolated git worktree + build + clone of the live `.egm` + real C tests
- the **store-fix** cutover — secondary soul + launchctl `bootout → settle → bootstrap` rails
## Quickstart
```bash
export PATH="$PWD:$PATH" # or symlink nsbx onto your PATH
nsbx up # your private copy of the mind (auto-named <user>-dev)
nsbx run <name> api /api/stats # poke it
nsbx validate <name> # prove it: zero-loss, reboot, RSS, retrieval, keystones
nsbx destroy <name> # cheap teardown; live untouched
```
That is the whole loop. Sane defaults: stock prod binary, auto-allocated port
(`8900+`, never `8742`/`7770`), snapshot of the live store.
## One-command dev onboarding — `nsbx dev` (start here)
Going from a clone to *coding on the mind* is a single command. It creates a git
**branch**, a persistent git **worktree**, and an **isolated engram** (a clone of the
live store on a non-default port) — and wires the whole worktree to that clone so you
**cannot hit live `:8742` by accident**.
```bash
make dev NAME=tim # branch wt/tim + worktree + isolated engram, in one shot
cd ~/Development/neuron-technologies/el-worktrees/tim
source .nsbx-env # every ENGRAM_* var now points at YOUR clone
# edit El in the worktree, then the fast loop:
make build NAME=tim # compile your El change into the isolated engram
make run NAME=tim # poke it (API=/api/stats by default)
make test NAME=tim # run the safety rails as checks
make destroy NAME=tim # tear it all down (branch kept; live untouched)
```
**Why this exists:** so provisional/experimental work is built **directly in El against a
throwaway cloned engram** — not prototyped in Python and re-ported later. The El
edit → `make build``make run` loop is the path of least resistance; that double-work is
what stranded the translation faculty for weeks.
### What `nsbx dev <name>` does, in order
1. **branch**`git worktree add -b <prefix><name>` (default prefix `dev/`; a *real named
branch*, never detached HEAD).
2. **worktree** — at a **persistent** path (default `…/el-worktrees/<name>`, override
`NSBX_DEV_WT_ROOT`). It **refuses** `/tmp` — temp dirs are ablated on compaction, which
is the exact "worktree in /tmp + no branch = lost work" failure this designs out.
3. **isolated engram**`nsbx create` under the hood: clone of the live store + WAL +
config, booted on an auto-allocated port (`8900+`, never `:8742`/`:7770`). Stock prod
binary by default (instant); `--build` compiles the worktree's own runtime instead.
4. **env pin** — writes `.nsbx-env` (+ `.envrc` for direnv) into the worktree exporting
`ENGRAM_URL / ENGRAM_PORT / ENGRAM_DATA_DIR / ENGRAM_API_KEY / NEURON_ENGRAM_URL …` — all
pointing at the clone. Nothing references live.
```
nsbx dev <name> [--base REF] [--worktree DIR] [--port N] [--prefix P] [--build] [--no-engram] [--repo R]
nsbx dev-down <name> [--delete-branch] [--repo R] # destroy engram + remove worktree
```
### Makefile targets
| target | does |
|--------|------|
| `make dev NAME=x` | branch + worktree + isolated engram (one shot) |
| `make bt NAME=x` | fast El loop: `build` then `run` |
| `make build NAME=x` | recompile the engram from the worktree's El source |
| `make run NAME=x` | poke the isolated engram (`API=/api/stats`) |
| `make test NAME=x` | rails as checks (`nsbx validate`) |
| `make status [NAME=x]` | inspect one, or `list` all |
| `make destroy NAME=x` | tear down (add `ARGS=--delete-branch` to drop the branch) |
> Note: if a bare `dev` branch already exists in the repo, git can't create `dev/*` names —
> pass `--prefix wt/` (or delete the stray `dev` branch). The tool surfaces git's exact error.
## The code-change dev loop (first-class)
Run *your changed runtime*, not just the stock binary, against a snapshot:
```bash
# build a runtime from a working tree, a git branch, or a prebuilt binary:
nsbx create feat --source /path/to/worktree # elc + cc build from source
nsbx create feat --branch feat/my-change --repo <r> # worktree the branch, then build
nsbx create feat --binary /path/to/engram # use a prebuilt binary
nsbx build feat --source /path/to/worktree # rebuild + hot-restart in place
nsbx validate feat # prove the change is safe
nsbx promote feat --i-approve-prod-cutover # gated rails cutover (see below)
```
The build replicates the engram release recipe exactly:
`elc engram/src/server.el > engram.c` then
`cc -std=c11 -O2 -I lang/runtime engram.c el_runtime.c engram_*.c -lcurl -lpthread`.
## Lifecycle
| op | what it does |
|----|--------------|
| `create <name> [--port N] [--source\|--branch\|--binary]` | consistent snapshot of the live store+WAL+config into an isolated dir; place or **build** the runtime; boot the real engram daemon on an isolated port. Named, versioned (binary sha + egm sha in `manifest.json`), reproducible. |
| `up [name]` | one command: create-if-missing then start; prints the URL. |
| `build <name> --source\|--branch` | rebuild the runtime from a code change and hot-restart on the same clone+port. |
| `run <name> <cmd…>` / `run <name> api <path> [json]` | run an experiment against the real runtime; capture output + before/after stats + wall time. Env: `$SBX_URL $SBX_PORT $SBX_KEY $SBX_DATA $SBX_BIN`. |
| `validate <name>` | the rails as first-class checks (below). |
| `promote <name> [--data] [--i-approve-prod-cutover]` | **the only prod-touching op.** Gated rails cutover. DRY-RUN plan unless approved. |
| `destroy <name>` | stop the isolated daemon, free the port, remove the clone. Live untouched. |
| `list` / `status <name>` | inspect. |
## `validate` — the rails as checks
- **zero-loss-under-load** — node/edge counts hold at/above baseline through ~15s of sustained tick+read load
- **reboot-prove** — counts survive a real stop→start of the daemon
- **rss-bound** — daemon RSS under `NSBX_RSS_BOUND_MB` (default 550 MB, from the store-fix reboot-proof)
- **retrieval-parity** — top-k node ids for a fixed probe set match the create-time baseline
- **keystone-integrity**`kn-efeb4a5b…` and `kn-5b606390…` present and intact
A PASS writes `validate.json` stamped with the binary sha; `promote` refuses unless
the current binary has a fresh PASS on record.
## `promote` — gated cutover (rails only)
Default is a **dry-run plan**. With `--i-approve-prod-cutover` it, in order:
1. **snapshot-first** — back up live `egm`+`wal`+`plist` to `~/.neuron/backups/promote-<name>-<ts>/` with a `rollback.txt`
2. **additive** binary install — copy the validated binary to a *new* file, update the plist `ENGRAM_REAL_BIN` (old binary retained — additive/supersede, never destructive)
3. **rails cutover**`launchctl bootout`**settle-poll** (prints until the job is gone) → `launchctl bootstrap`. Never `pkill`, never `kickstart -k`.
4. **verify**`/api/stats` returns, edges ≥ baseline, keystones intact
5. **auto-rollback armed** — any verify failure restores the plist (and data, if `--data`) and boots the prior binary back via the same rails
## Isolation guarantees
- separate **port** (`8900+`; refuses `8742`/`7770`), separate **store clone**, separate **process**
- a hard guard refuses to boot a sandbox daemon whose data dir resolves to the live store
- sandboxes are plain supervised background processes (not launchd), so teardown is a signal + settle-poll — it can never touch the prod launchd job
- prod is read exactly twice: once for the snapshot, and (only if you approve) during `promote`
## Layout
- tool: `tools/neuron-sandbox/nsbx` (this repo, branch `feat/neuron-sandbox`)
- runtime state: `~/.neuron/sandboxes/<name>/``data/` (clone), `bin/engram`, `build/`, `logs/`, `manifest.json`, `validate.json`, `baseline/`
## Validated (dogfood)
Standing up a sandbox from a live-store clone and reproducing a **known** result:
- **retrieval-parity 25/25** top-k id overlap vs baseline; sandbox boot-stats exactly matched the live baseline captured at snapshot time (10 672 nodes / 32 439 edges) — the wrapped real binary faithfully reloads the live mind
- reboot-prove + zero-loss PASS; RSS 379 MB < 550 MB; keystones intact
- the **cog-arch correspondence-loop** re-run *inside* the sandbox reproduced the known calibration numbers exactly: held-Brier **0.028648 → 0.000586** (98.0% reduction), monotone, **reboot bit-identical**, metastability holds; and the real-store Stance persistence reboot-proved at **10 994-node** scale (`think()` on real 768-dim embeddings) against a scratch copy of the sandbox's own clone — never live
- `promote` dry-run refused to touch prod; teardown freed the port; live `:8742`/`:7770` never perturbed (soul uptime unbroken)
## Migrating existing experiments
Each ad-hoc harness becomes `nsbx run <name> …` (or `--source` build) against a sandbox:
- **cog-arch**`nsbx create x --source <worktree>` then `nsbx run x -- bash cogarch_dogfood.sh` (compiles + runs the real C cognition tests against `$SBX_DATA`)
- **codec / ingest / faculty**`nsbx run x api /api/<endpoint> '<json>'` against the isolated daemon, or a script using `$SBX_URL`/`$SBX_KEY`; measure with the built-in before/after stats
## Env knobs
`NSBX_ROOT`, `NSBX_PORT_BASE`, `NSBX_RSS_BOUND_MB`, `NSBX_REMERGE_THRESHOLD`,
`EL_REPO` (for `elc` + runtime sources), `ENGRAM_LIVE_DATA_DIR`, `ENGRAM_LIVE_PLIST`.
+30
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@@ -0,0 +1,30 @@
#!/usr/bin/env bash
# cog-arch correspondence-loop dogfood — RUN INSIDE the sandbox via `nsbx run`.
# Compiles the REAL engram C runtime + cognition tests and reproduces the known
# calibration result (memory 194c69c8): held-Brier 0.028648 -> 0.000586, reboot-proven,
# then reboot-proves the Stance persistence against a SCRATCH COPY of THIS sandbox's
# clone of the real store (never live, never the running daemon's file).
set -euo pipefail
WT="${COGARCH_WT:-/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/cogarch-wt}"
RT="$WT/lang/runtime"; T="$WT/engram/test"
: "${SBX_DATA:?run me via: nsbx run <name> -- bash cogarch_dogfood.sh}"
B="$(mktemp -d)"
echo "### building cog-arch tests against the real engram runtime sources"
cc -std=c11 -O2 -w -I "$RT" -o "$B/test_cognition" \
"$T/test_cognition.c" "$RT/engram_cognition.c" "$RT/engram_reason.c" \
"$RT/engram_geometry.c" "$RT/engram_store.c" "$RT/engram_vindex.c" -lm
cc -std=c11 -O2 -w -I "$RT" -o "$B/test_realstore" \
"$T/test_cognition_realstore.c" "$RT/engram_cognition.c" "$RT/engram_reason.c" \
"$RT/engram_geometry.c" "$RT/engram_store.c" "$RT/engram_vindex.c" -lm
echo; echo "### [A] synthetic correspondence-loop (known: Brier 0.028648 -> 0.000586)"
"$B/test_cognition" | grep -E "held-Brier|reduction|reboot|monotone|metastab|RESULT" || true
echo; echo "### [B] reboot-prove Stance on a SCRATCH COPY of this sandbox's real-store clone"
SCRATCH="$B/store-clone"; mkdir -p "$SCRATCH"
cp -p "$SBX_DATA/neuron.egm" "$SCRATCH/" 2>/dev/null || true
cp -p "$SBX_DATA/neuron.wal" "$SCRATCH/" 2>/dev/null || true
cp -p "$SBX_DATA/conf" "$SCRATCH/" 2>/dev/null || true
cp -p "$SBX_DATA/meta.json" "$SCRATCH/" 2>/dev/null || true
"$B/test_realstore" "$SCRATCH" || true
rm -rf "$B"
+836
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@@ -0,0 +1,836 @@
#!/usr/bin/env bash
# nsbx — the Neuron Sandbox: a reproducible primitive for running experiments and
# code changes against the REAL engram runtime on an isolated snapshot of the live
# mind, with a gated promote-to-prod path built on the proven rails.
#
# It WRAPS the real engram binary — it never reimplements any engram logic. The only
# prod-touching op is `promote`, which is explicit, gated, and per-use approved.
#
# Generalises two proven proto-sandboxes:
# - the cog-arch build (isolated git worktree + build + clone of live .egm + real C tests)
# - the store-fix cutover (secondary soul + launchctl bootout->settle->bootstrap rails)
#
# Lifecycle: create -> [build] -> run -> validate -> promote(gated) -> destroy
#
# Rails (always): built offline; NEVER auto-promotes; never touches live :8742/:7770
# except READ for the snapshot and the gated promote; snapshot-first; honest measured
# reporting. Cutover is launchctl bootout -> settle-poll -> bootstrap ONLY —
# never pkill, never kickstart -k.
set -uo pipefail
# ---------------------------------------------------------------- constants ----
LIVE_DATA_DIR="${ENGRAM_LIVE_DATA_DIR:-$HOME/.neuron/engram}"
LIVE_PLIST="${ENGRAM_LIVE_PLIST:-$HOME/Library/LaunchAgents/ai.neuron.engram.plist}"
LIVE_LABEL="ai.neuron.engram"
LIVE_BIND_PORT=8742 # engram — FORBIDDEN for sandboxes
SOUL_PORT=7770 # soul — FORBIDDEN for sandboxes
LIVE_KEY="${ENGRAM_API_KEY:-ntn-user-2026}"
LIVE_URL="http://127.0.0.1:${LIVE_BIND_PORT}"
SBX_ROOT="${NSBX_ROOT:-$HOME/.neuron/sandboxes}"
BACKUP_ROOT="$HOME/.neuron/backups"
EL_REPO="${EL_REPO:-$HOME/Development/neuron-technologies/foundation/el}"
PORT_BASE="${NSBX_PORT_BASE:-8900}"
RSS_BOUND_MB="${NSBX_RSS_BOUND_MB:-550}" # from store-fix reboot-proof (aaf13f88)
REMERGE_THRESHOLD="${NSBX_REMERGE_THRESHOLD:-40000}"
KEYSTONES=( "kn-efeb4a5b-5aff-4759-8a97-7233099be6ee" "kn-5b606390-a52d-4ca2-8e0e-eba141d13440" )
# fixed probe set for retrieval-parity (stable, identity-anchored)
PARITY_QUERIES=( "who am I" "self identity core" "engram store durability" "keystone self anchor" "grounding honesty" )
C_RED=$'\033[31m'; C_GRN=$'\033[32m'; C_YEL=$'\033[33m'; C_DIM=$'\033[2m'; C_BLD=$'\033[1m'; C_0=$'\033[0m'
# ---------------------------------------------------------------- helpers ------
die(){ printf '%serror:%s %s\n' "$C_RED" "$C_0" "$*" >&2; exit 1; }
log(){ printf '%s==>%s %s\n' "$C_BLD" "$C_0" "$*" >&2; }
info(){ printf ' %s\n' "$*" >&2; }
ok(){ printf ' %s%s%s\n' "$C_GRN" "$*" "$C_0" >&2; }
warn(){ printf ' %s%s%s\n' "$C_YEL" "$*" "$C_0" >&2; }
need(){ command -v "$1" >/dev/null 2>&1 || die "missing dependency: $1"; }
now(){ date -u +%Y%m%dT%H%M%SZ; }
sha(){ shasum -a 256 "$1" 2>/dev/null | awk '{print $1}'; }
epoch(){ python3 -c 'import time;print(time.time())'; }
sdir(){ printf '%s/%s' "$SBX_ROOT" "$1"; }
manifest(){ printf '%s/manifest.json' "$(sdir "$1")"; }
mexists(){ [ -f "$(manifest "$1")" ]; }
mget(){ # mget <name> <jsonpath>
python3 -c "import json,sys; d=json.load(open('$(manifest "$1")')); print(d$2)" 2>/dev/null
}
port_free(){ ! (exec 3<>"/dev/tcp/127.0.0.1/$1") 2>/dev/null; }
alloc_port(){
local p="$PORT_BASE"
while :; do
if [ "$p" = "$LIVE_BIND_PORT" ] || [ "$p" = "$SOUL_PORT" ]; then p=$((p+1)); continue; fi
if port_free "$p" && ! _port_claimed "$p"; then echo "$p"; return 0; fi
p=$((p+1)); [ "$p" -gt 9100 ] && die "no free sandbox port in range"
done
}
_port_claimed(){ # is another sandbox already assigned this port?
local p="$1" d
for d in "$SBX_ROOT"/*/manifest.json; do
[ -f "$d" ] || continue
[ "$(python3 -c "import json;print(json.load(open('$d'))['port'])" 2>/dev/null)" = "$p" ] && return 0
done
return 1
}
live_stats(){ curl -s -m5 "$LIVE_URL/api/stats" 2>/dev/null; }
api(){ # api <name> <path> [json-body]
local name="$1" path="$2" body="${3:-}"
local port; port="$(mget "$name" "['port']")"; [ -n "$port" ] || die "unknown sandbox: $name"
local url="http://127.0.0.1:${port}${path}"
if [ -n "$body" ]; then curl -s -m30 -X POST -H 'Content-Type: application/json' -d "$body" "$url"
else curl -s -m30 "$url"; fi
}
sbx_stats(){ api "$1" "/api/stats"; }
stat_field(){ printf '%s' "$1" | sed -n "s/.*\"$2\":\([0-9]*\).*/\1/p"; }
daemon_pid(){ local f; f="$(sdir "$1")/daemon.pid"; [ -f "$f" ] && cat "$f" || true; }
daemon_alive(){ local p; p="$(daemon_pid "$1")"; [ -n "$p" ] && kill -0 "$p" 2>/dev/null; }
# ---------------------------------------------------------------- elc/build ----
find_elc(){
command -v elc 2>/dev/null && return 0
local arch; arch="$(uname -m)"
case "$arch" in
arm64) echo "$EL_REPO/lang/dist/platform/elc-darwin-arm64";;
x86_64) echo "$EL_REPO/lang/dist/platform/elc-linux-amd64";;
*) echo "$EL_REPO/lang/dist/platform/elc";;
esac
}
# _build_binary <src_tree> <out_bin> <build_log_dir>
# Replicates the proven engram release recipe:
# elc engram/src/server.el > engram.c
# cc -std=c11 -O2 -I lang/runtime engram.c el_runtime.c engram_*.c -lcurl -lpthread
_build_binary(){
local src="$1" out="$2" blog="$3"
local elc server rt
elc="$(find_elc)"; [ -x "$elc" ] || die "elc not found/executable: $elc (set EL_REPO)"
server="$src/engram/src/server.el"; rt="$src/lang/runtime"
[ -f "$server" ] || die "no engram/src/server.el under source tree: $src"
[ -f "$rt/el_runtime.c" ] || die "no lang/runtime/el_runtime.c under source tree: $src (this branch may keep it generated/untracked)"
ls "$rt"/engram_*.c >/dev/null 2>&1 || die "no lang/runtime/engram_*.c engine sources under: $src"
mkdir -p "$blog"
log "build: elc transpile server.el -> engram.c"
"$elc" "$server" > "$blog/engram.c" 2>"$blog/elc.err" || { cat "$blog/elc.err" >&2; die "elc transpile failed"; }
info "engram.c: $(wc -c <"$blog/engram.c" | tr -d ' ') bytes"
log "build: cc link (el_runtime + engram_* engine)"
cc -std=c11 -O2 -w -I "$rt" -o "$out" \
"$blog/engram.c" "$rt/el_runtime.c" "$rt"/engram_*.c \
-lcurl -lpthread 2>"$blog/cc.err" \
|| { grep -i 'error:' "$blog/cc.err" | sort -u | head >&2; die "cc link failed (see $blog/cc.err)"; }
ok "built: $out ($(ls -lh "$out" | awk '{print $5}'), sha $(sha "$out" | cut -c1-12))"
}
# ---------------------------------------------------------------- daemon -------
# start_daemon <name> : boots the sandbox's real engram binary on its isolated
# port against its cloned data dir, with the SAME auto-remerge net the live soul
# uses (so the sandbox faithfully reaches the live edge population on boot).
start_daemon(){
local name="$1" d; d="$(sdir "$name")"
daemon_alive "$name" && { info "already running (pid $(daemon_pid "$name"))"; return 0; }
local port bin data export key
port="$(mget "$name" "['port']")"; bin="$d/bin/engram"; data="$d/data"
key="sbx-$name"; export="$data/.scan-export.reseed-clean.json"
[ -x "$bin" ] || die "sandbox binary missing: $bin"
[ "$port" != "$LIVE_BIND_PORT" ] && [ "$port" != "$SOUL_PORT" ] || die "refusing forbidden port $port"
[ -f "$data/neuron.egm" ] || die "sandbox has no cloned store: $data/neuron.egm"
# HARD guard: never point a sandbox daemon at the live data dir.
[ "$(cd "$data" && pwd -P)" != "$(cd "$LIVE_DATA_DIR" && pwd -P)" ] || die "refusing: sandbox data dir resolves to LIVE store"
log "boot engram on isolated :$port (data=$data)"
(
ENGRAM_DATA_DIR="$data" ENGRAM_BIND=":$port" ENGRAM_API_KEY="$key" \
ENGRAM_STORE=1 ENGRAM_CHRONOCEPTION=1 ENGRAM_SELF_REIFY=1 ENGRAM_GC=1 \
ENGRAM_POOL_FRAMES=16384 ENGRAM_WRITE_BARRIER=1 \
exec "$bin"
) >"$d/logs/daemon.log" 2>&1 &
local pid=$!
echo "$pid" > "$d/daemon.pid"
# readiness poll
local url="http://127.0.0.1:$port" i s
for i in $(seq 1 30); do
s="$(curl -s -m3 "$url/api/stats" 2>/dev/null)"
[ -n "$s" ] && break; sleep 0.5
done
[ -n "$s" ] || { warn "daemon did not become ready (see $d/logs/daemon.log)"; return 1; }
ok "ready pid=$pid boot-stats: $s"
# auto-remerge net (idempotent): match live edge population if the export is present
if [ -f "$export" ]; then
local edges; edges="$(stat_field "$s" edge_count)"
if [ -n "$edges" ] && [ "$edges" -lt "$REMERGE_THRESHOLD" ]; then
log "auto-remerge: booted with $edges edges (< $REMERGE_THRESHOLD) — merging full edge export"
local r; r="$(curl -s -m300 -X POST -H 'Content-Type: application/json' \
-d "{\"_auth\":\"$key\",\"path\":\"$export\"}" "$url/api/load-merge" 2>/dev/null)"
info "remerge resp: ${r:0:120}"
ok "post-remerge stats: $(curl -s -m5 "$url/api/stats")"
fi
fi
return 0
}
# stop_daemon <name> : graceful TERM + settle-poll until the port is free.
# (Sandbox daemons are plain supervised bg processes — not launchd — so teardown
# is a signal + poll, never pkill of anything else.)
stop_daemon(){
local name="$1" pid port
pid="$(daemon_pid "$name")"; port="$(mget "$name" "['port']")"
[ -n "$pid" ] || { info "not running"; return 0; }
log "stop daemon pid=$pid, settle-poll until :$port frees"
kill "$pid" 2>/dev/null || true
local i
for i in $(seq 1 40); do
kill -0 "$pid" 2>/dev/null || { port_free "$port" && { ok "stopped, port $port free"; : >"$(sdir "$name")/daemon.pid"; return 0; }; }
printf '.' >&2; sleep 0.5
done
printf '\n' >&2
kill -9 "$pid" 2>/dev/null || true; sleep 1
: >"$(sdir "$name")/daemon.pid"
port_free "$port" && ok "stopped (after SIGKILL), port $port free" || warn "port $port still busy"
}
# ================================================================ create =======
cmd_create(){
local name="" port="" src="" branch="" repo="$EL_REPO" binpath=""
# first positional arg is the name unless it's a flag; default to "<user>-dev"
if [ $# -gt 0 ] && [ "${1#-}" = "$1" ]; then name="$1"; shift; else name="${USER:-dev}-dev"; fi
while [ $# -gt 0 ]; do case "$1" in
--port) port="$2"; shift 2;;
--source) src="$2"; shift 2;;
--branch) branch="$2"; shift 2;;
--repo) repo="$2"; shift 2;;
--binary) binpath="$2"; shift 2;;
*) die "unknown flag: $1";;
esac; done
mexists "$name" && die "sandbox '$name' already exists (destroy it first)"
need curl; need python3; need shasum
[ -f "$LIVE_DATA_DIR/neuron.egm" ] || die "live store not found: $LIVE_DATA_DIR/neuron.egm"
if [ -n "$port" ]; then
{ [ "$port" = "$LIVE_BIND_PORT" ] || [ "$port" = "$SOUL_PORT" ]; } && die "refusing forbidden port $port (live)"
port_free "$port" || die "port $port already in use"
else port="$(alloc_port)"; fi
local d; d="$(sdir "$name")"
mkdir -p "$d/data" "$d/bin" "$d/logs" "$d/build" "$d/baseline"
log "sandbox '$name' at $d (isolated port $port)"
# ---- CONSISTENT snapshot of the live mind (file-copy: same set the rails backup
# uses; WAL replay on sandbox boot reconciles the tail -> crash-consistent) ----
log "snapshot live store -> clone (store + WAL + config)"
local f
for f in neuron.egm neuron.wal conf meta.json self_anchor .scan-export.reseed-clean.json; do
if [ -e "$LIVE_DATA_DIR/$f" ]; then cp -p "$LIVE_DATA_DIR/$f" "$d/data/$f"; info "cloned $f ($(du -h "$d/data/$f" | awk '{print $1}'))"; fi
done
local egm_sha; egm_sha="$(sha "$d/data/neuron.egm")"
# ---- capture live baseline (READ only) ----
local lstats; lstats="$(live_stats)"
local base_nodes base_edges
base_nodes="$(stat_field "$lstats" node_count)"; base_edges="$(stat_field "$lstats" edge_count)"
info "live baseline stats: ${lstats:-<unavailable>}"
# ---- determine + place the runtime binary (versioned into the snapshot) ----
local source_desc live_bin
live_bin="$(_live_real_bin)"
if [ -n "$binpath" ]; then
[ -x "$binpath" ] || die "not an executable binary: $binpath"
cp -p "$binpath" "$d/bin/engram"; source_desc="prebuilt:$binpath"
elif [ -n "$src" ]; then
_build_binary "$src" "$d/bin/engram" "$d/build"; source_desc="source:$src"
elif [ -n "$branch" ]; then
log "worktree: $repo @ $branch -> $d/build/worktree"
git -C "$repo" worktree add --detach "$d/build/worktree" "$branch" >/dev/null 2>&1 \
|| die "git worktree add failed ($repo @ $branch)"
_build_binary "$d/build/worktree" "$d/bin/engram" "$d/build"; source_desc="branch:$branch@$repo"
else
[ -x "$live_bin" ] || die "cannot resolve live ENGRAM_REAL_BIN: $live_bin"
cp -p "$live_bin" "$d/bin/engram"; source_desc="stock-prod:$live_bin"
fi
local bin_sha; bin_sha="$(sha "$d/bin/engram")"
info "runtime: $source_desc (sha ${bin_sha:0:12})"
# ---- write manifest ----
python3 - "$name" "$port" "$source_desc" "$bin_sha" "$egm_sha" "$base_nodes" "$base_edges" "$(sha "$live_bin" 2>/dev/null)" <<'PY' > "$(manifest "$name")"
import json,sys,datetime
name,port,src,binsha,egmsha,bn,be,livebinsha=sys.argv[1:9]
json.dump({
"name":name,"port":int(port),"created_at":datetime.datetime.now(datetime.timezone.utc).isoformat(),
"source":src,"binary_sha256":binsha,"clone_egm_sha256":egmsha,
"live_binary_sha256":livebinsha,
"live_baseline":{"node_count":int(bn or 0),"edge_count":int(be or 0)},
"keystones":["kn-efeb4a5b-5aff-4759-8a97-7233099be6ee","kn-5b606390-a52d-4ca2-8e0e-eba141d13440"]
}, sys.stdout, indent=2)
PY
ok "manifest written"
# ---- boot + capture the sandbox's own settled baseline (reproducible target) ----
start_daemon "$name" || die "daemon failed to start"
local sstats; sstats="$(sbx_stats "$name")"
local sbn sbe; sbn="$(stat_field "$sstats" node_count)"; sbe="$(stat_field "$sstats" edge_count)"
_capture_retrieval "$name" "$d/baseline/retrieval.json"
# fold sandbox baseline into manifest
python3 - "$(manifest "$name")" "$sbn" "$sbe" <<'PY'
import json,sys
mf,bn,be=sys.argv[1],sys.argv[2],sys.argv[3]
d=json.load(open(mf)); d["sbx_baseline"]={"node_count":int(bn or 0),"edge_count":int(be or 0)}
json.dump(d,open(mf,'w'),indent=2)
PY
log "created."
info "sandbox baseline (settled): nodes=$sbn edges=$sbe"
info "next: nsbx validate $name | nsbx run $name api /api/stats"
}
_live_real_bin(){
python3 - "$LIVE_PLIST" <<'PY' 2>/dev/null
import sys,plistlib
try:
d=plistlib.load(open(sys.argv[1],'rb'))
print(d.get("EnvironmentVariables",{}).get("ENGRAM_REAL_BIN",""))
except Exception: print("")
PY
}
_capture_retrieval(){ # <name> <outfile> : top-k ids for the fixed probe set
local name="$1" out="$2" q res
local port; port="$(mget "$name" "['port']")"; local key="sbx-$name"
{
echo "{"
local first=1
for q in "${PARITY_QUERIES[@]}"; do
res="$(curl -s -m10 -X POST -H 'Content-Type: application/json' \
-d "{\"_auth\":\"$key\",\"query\":\"$q\",\"limit\":5}" "http://127.0.0.1:$port/api/search" 2>/dev/null)"
local ids; ids="$(printf '%s' "$res" | python3 -c 'import sys,json
try:
d=json.load(sys.stdin)
rows=d if isinstance(d,list) else d.get("results",d.get("hits",[]))
print(json.dumps([r.get("id") for r in rows][:5]))
except Exception: print("[]")' 2>/dev/null)"
[ $first -eq 1 ] || echo ","; first=0
printf ' %s: %s' "$(python3 -c "import json,sys;print(json.dumps(sys.argv[1]))" "$q")" "${ids:-[]}"
done
echo ""; echo "}"
} > "$out"
}
# ================================================================ up ===========
# Dead-simple one-command dev environment: `nsbx up` gives you (or Tim, or anyone)
# a private, isolated copy of the live mind to build against. Creates it on first
# run with sane defaults (stock prod binary, auto-allocated port), just starts it
# thereafter. Prod on :$LIVE_BIND_PORT/:$SOUL_PORT is unreachable from here by design.
cmd_up(){
local name; if [ $# -gt 0 ] && [ "${1#-}" = "$1" ]; then name="$1"; shift; else name="${USER:-dev}-dev"; fi
if mexists "$name"; then daemon_alive "$name" || start_daemon "$name"; else cmd_create "$name" "$@"; fi
local port; port="$(mget "$name" "['port']")"
echo >&2
ok "your sandbox '$name' is ready at http://127.0.0.1:$port (a private copy of the mind — prod is untouchable)"
info "experiment: nsbx run $name api /api/stats"
info "prove it: nsbx validate $name"
info "tear down: nsbx destroy $name"
}
# ================================================================ build ========
# Rebuild an existing sandbox's runtime from a source tree/branch and hot-restart
# it on the SAME clone + port (the code-change dev loop, in place).
cmd_build(){
local name="$1"; shift || true
mexists "$name" || die "no such sandbox: $name"
local src="" branch="" repo="$EL_REPO"
while [ $# -gt 0 ]; do case "$1" in
--source) src="$2"; shift 2;; --branch) branch="$2"; shift 2;; --repo) repo="$2"; shift 2;;
*) die "unknown flag: $1";; esac; done
local d; d="$(sdir "$name")"
stop_daemon "$name"
if [ -n "$src" ]; then _build_binary "$src" "$d/bin/engram" "$d/build"
elif [ -n "$branch" ]; then
rm -rf "$d/build/worktree" 2>/dev/null; git -C "$repo" worktree prune 2>/dev/null
git -C "$repo" worktree add --detach "$d/build/worktree" "$branch" >/dev/null 2>&1 || die "worktree add failed"
_build_binary "$d/build/worktree" "$d/bin/engram" "$d/build"
else die "usage: nsbx build <name> --source DIR | --branch REF [--repo R]"; fi
# record new binary sha
python3 - "$(manifest "$name")" "$(sha "$d/bin/engram")" "${src:-branch:$branch}" <<'PY'
import json,sys; mf,s,src=sys.argv[1:4]
d=json.load(open(mf)); d["binary_sha256"]=s; d["source"]="rebuilt:"+src
json.dump(d,open(mf,'w'),indent=2)
PY
start_daemon "$name"
ok "rebuilt + restarted on :$(mget "$name" "['port']")"
}
# ================================================================ run ==========
cmd_run(){
local name="$1"; shift || true
mexists "$name" || die "no such sandbox: $name"
daemon_alive "$name" || start_daemon "$name"
local d port; d="$(sdir "$name")"; port="$(mget "$name" "['port']")"
# direct API form: nsbx run <name> api <path> [json]
if [ "${1:-}" = "api" ]; then
api "$name" "$2" "${3:-}"; echo; return 0
fi
[ "${1:-}" = "--" ] && shift # allow an explicit separator: nsbx run <name> -- <cmd...>
[ $# -gt 0 ] || die "usage: nsbx run <name> <cmd...> | nsbx run <name> api <path> [json]"
local ts log0; ts="$(now)"; log0="$d/logs/run-$ts.log"
local s0 t0 t1 s1
s0="$(sbx_stats "$name")"; t0="$(epoch)"
log "run experiment against sandbox '$name' (:$port)"
info "cmd: $*"
( export SBX_NAME="$name" SBX_PORT="$port" SBX_URL="http://127.0.0.1:$port" \
SBX_KEY="sbx-$name" SBX_DATA="$d/data" SBX_BIN="$d/bin/engram"
"$@" ) 2>&1 | tee "$log0"
local rc=${PIPESTATUS[0]}
t1="$(epoch)"; s1="$(sbx_stats "$name")"
{
echo "--- nsbx run metrics ---"
echo "exit_code: $rc"
printf 'wall_secs: %.3f\n' "$(python3 -c "print($t1-$t0)")"
echo "stats_before: $s0"
echo "stats_after: $s1"
} | tee -a "$log0" >&2
return $rc
}
# ================================================================ validate =====
# The rails as first-class checks. Baseline = the sandbox's own settled state at
# create (reproducible). zero-loss through sustained load AND reboot; reboot-prove;
# RSS bound; retrieval parity; keystone integrity.
cmd_validate(){
local name="$1"; shift || true
mexists "$name" || die "no such sandbox: $name"
daemon_alive "$name" || start_daemon "$name"
local d port key; d="$(sdir "$name")"; port="$(mget "$name" "['port']")"; key="sbx-$name"
local url="http://127.0.0.1:$port"
local bn be; bn="$(mget "$name" "['sbx_baseline']['node_count']")"; be="$(mget "$name" "['sbx_baseline']['edge_count']")"
log "validate '$name' against baseline nodes=$bn edges=$be"
local -a names=() results=() details=()
# 1) sustained load — no data loss under activity
local s cur_n cur_e i
log "check: sustained load (~15s: tick + reads) then zero-loss"
for i in $(seq 1 15); do
curl -s -m5 -X POST -H 'Content-Type: application/json' -d "{\"_auth\":\"$key\"}" "$url/api/tick" >/dev/null 2>&1
curl -s -m5 "$url/api/stats" >/dev/null 2>&1
done
s="$(sbx_stats "$name")"; cur_n="$(stat_field "$s" node_count)"; cur_e="$(stat_field "$s" edge_count)"
names+=("zero-loss-under-load"); if [ "${cur_n:-0}" -ge "${bn:-0}" ] && [ "${cur_e:-0}" -ge "${be:-0}" ]; then
results+=("PASS"); else results+=("FAIL"); fi
details+=("nodes $cur_n>=$bn, edges $cur_e>=$be")
# 2) reboot-prove — counts survive a real restart
log "check: reboot-prove (stop -> start -> compare)"
local pre_n pre_e; pre_n="$cur_n"; pre_e="$cur_e"
stop_daemon "$name"; start_daemon "$name" >/dev/null
s="$(sbx_stats "$name")"; cur_n="$(stat_field "$s" node_count)"; cur_e="$(stat_field "$s" edge_count)"
names+=("reboot-prove"); if [ "${cur_n:-0}" -ge "${bn:-0}" ] && [ "${cur_e:-0}" -ge "${be:-0}" ]; then
results+=("PASS"); else results+=("FAIL"); fi
details+=("post-reboot nodes=$cur_n edges=$cur_e (pre $pre_n/$pre_e)")
# 3) RSS bound
log "check: RSS bound (< ${RSS_BOUND_MB}MB)"
local pid rss_kb rss_mb; pid="$(daemon_pid "$name")"
rss_kb="$(ps -o rss= -p "$pid" 2>/dev/null | tr -d ' ')"; rss_mb=$(( ${rss_kb:-0} / 1024 ))
names+=("rss-bound"); if [ "$rss_mb" -lt "$RSS_BOUND_MB" ] && [ "$rss_mb" -gt 0 ]; then results+=("PASS"); else results+=("FAIL"); fi
details+=("RSS=${rss_mb}MB (bound ${RSS_BOUND_MB}MB)")
# 4) retrieval parity vs the create-time baseline
log "check: retrieval parity vs baseline probe set"
_capture_retrieval "$name" "$d/logs/retrieval-$( now ).json"
local latest; latest="$(ls -t "$d/logs"/retrieval-*.json 2>/dev/null | head -1)"
local parity; parity="$(python3 - "$d/baseline/retrieval.json" "$latest" <<'PY'
import json,sys
def load(p):
try: return json.load(open(p))
except Exception: return {}
b,c=load(sys.argv[1]),load(sys.argv[2])
tot=hit=0
for q,ids in b.items():
cb=set(ids or []); cc=set(c.get(q) or [])
if not cb: continue
tot+=len(cb); hit+=len(cb & cc)
print(f"{hit}/{tot}" if tot else "0/0")
PY
)"
local ph="${parity%/*}" pt="${parity#*/}"
names+=("retrieval-parity"); if [ "${pt:-0}" -gt 0 ] && [ "${ph:-0}" -eq "${pt:-0}" ]; then results+=("PASS"); else results+=("FAIL"); fi
details+=("top-k id overlap $parity vs baseline")
# 5) keystone integrity
log "check: keystone integrity"
local kfail=0 kid kres
for kid in "${KEYSTONES[@]}"; do
kres="$(curl -s -m5 "$url/api/node/$kid" 2>/dev/null)"
printf '%s' "$kres" | grep -q "\"$kid\"" || kfail=1
done
names+=("keystone-integrity"); [ "$kfail" -eq 0 ] && results+=("PASS") || results+=("FAIL")
details+=("kn-efeb4a5b + kn-5b606390 present")
# ---- report + stamp ----
echo >&2
printf '%s VALIDATION — %s%s\n' "$C_BLD" "$name" "$C_0" >&2
local allpass=1 j
for j in "${!names[@]}"; do
local r="${results[$j]}" c="$C_GRN"; [ "$r" = FAIL ] && { c="$C_RED"; allpass=0; }
printf ' %s%-6s%s %-22s %s%s%s\n' "$c" "$r" "$C_0" "${names[$j]}" "$C_DIM" "${details[$j]}" "$C_0" >&2
done
local status; [ "$allpass" -eq 1 ] && status="PASS" || status="FAIL"
python3 - "$d/validate.json" "$status" "$(sha "$d/bin/engram")" "$(now)" "${names[*]}" "${results[*]}" <<'PY'
import json,sys
out,status,binsha,ts,ns,rs=sys.argv[1:7]
checks=[{"name":n,"result":r} for n,r in zip(ns.split(),rs.split())]
json.dump({"status":status,"binary_sha256":binsha,"ts":ts,"checks":checks},open(out,'w'),indent=2)
PY
printf ' %s==> %s%s\n' "$([ "$allpass" -eq 1 ] && echo "$C_GRN" || echo "$C_RED")" "$status" "$C_0" >&2
[ "$allpass" -eq 1 ]
}
# ================================================================ promote ======
# The ONLY prod-touching op. Explicit, gated, per-use Will-approved. Rails ONLY:
# snapshot-first -> additive binary swap -> launchctl bootout -> settle-poll ->
# bootstrap -> verify -> auto-rollback on failure. NEVER pkill, NEVER kickstart -k.
# Default is a DRY-RUN plan; requires --i-approve-prod-cutover to actually cut over.
cmd_promote(){
local name="$1"; shift || true
mexists "$name" || die "no such sandbox: $name"
local approve=0 do_data=0
while [ $# -gt 0 ]; do case "$1" in
--i-approve-prod-cutover) approve=1; shift;;
--data) do_data=1; shift;;
*) die "unknown flag: $1";; esac; done
local d; d="$(sdir "$name")"
# GATE 1: validation must have passed for the CURRENT binary
[ -f "$d/validate.json" ] || die "GATE: no validation on record — run 'nsbx validate $name' first"
local vstatus vsha bsha
vstatus="$(python3 -c "import json;print(json.load(open('$d/validate.json'))['status'])")"
vsha="$(python3 -c "import json;print(json.load(open('$d/validate.json'))['binary_sha256'])")"
bsha="$(sha "$d/bin/engram")"
[ "$vstatus" = PASS ] || die "GATE: last validation status is $vstatus (must be PASS)"
[ "$vsha" = "$bsha" ] || die "GATE: validation is stale — binary changed since validate (re-run validate)"
local live_bin new_bin ts; ts="$(now)"
live_bin="$(_live_real_bin)"
new_bin="$HOME/.neuron/bin/engram.promote-$name-$ts" # additive: new file, old kept
local bkp="$BACKUP_ROOT/promote-$name-$ts"
log "PROMOTE PLAN for '$name' -> live :$LIVE_BIND_PORT"
info "current live ENGRAM_REAL_BIN : $live_bin"
info "sandbox binary (validated) : $d/bin/engram (sha ${bsha:0:12})"
info "will install as : $new_bin (additive; old binary retained)"
info "snapshot-first backup dir : $bkp (egm+wal+plist+rollback.txt)"
info "data promote : $([ $do_data -eq 1 ] && echo 'YES (--data: clone egm/wal -> live)' || echo 'no (binary only)')"
info "rails : launchctl bootout -> settle-poll -> bootstrap"
info "verify : /api/stats + edges>=baseline + keystones + retrieval; auto-rollback armed"
if [ "$approve" -ne 1 ]; then
warn "DRY-RUN — not touching prod. Re-run with --i-approve-prod-cutover to execute (per-use Will-approved)."
return 0
fi
need launchctl
local dom="gui/$(id -u)"
# ---- snapshot-first ----
log "snapshot-first backup -> $bkp"
mkdir -p "$bkp"
cp -p "$LIVE_DATA_DIR/neuron.egm" "$bkp/neuron.egm.bak"
cp -p "$LIVE_DATA_DIR/neuron.wal" "$bkp/neuron.wal.bak" 2>/dev/null || true
cp -p "$LIVE_PLIST" "$bkp/plist.bak"
printf 'rollback REAL_BIN=%s\nNEWBIN=%s\ndata_promote=%s\n' "$live_bin" "$new_bin" "$do_data" > "$bkp/rollback.txt"
ok "backup complete"
# ---- additive binary install + plist supersede ----
cp -p "$d/bin/engram" "$new_bin"
python3 - "$LIVE_PLIST" "$new_bin" <<'PY'
import sys,plistlib
p,new=sys.argv[1],sys.argv[2]
d=plistlib.load(open(p,'rb')); d.setdefault("EnvironmentVariables",{})["ENGRAM_REAL_BIN"]=new
plistlib.dump(d,open(p,'wb'))
PY
ok "installed $new_bin + updated plist ENGRAM_REAL_BIN"
# ---- optional data promote (after backup) ----
if [ $do_data -eq 1 ]; then
log "data promote: clone store -> live (backed up above)"
cp -p "$d/data/neuron.egm" "$LIVE_DATA_DIR/neuron.egm"
cp -p "$d/data/neuron.wal" "$LIVE_DATA_DIR/neuron.wal" 2>/dev/null || true
fi
# ---- rails cutover: bootout -> settle-poll -> bootstrap ----
log "rails: launchctl bootout $dom/$LIVE_LABEL"
launchctl bootout "$dom/$LIVE_LABEL" 2>/dev/null || true
local i
for i in $(seq 1 60); do
launchctl print "$dom/$LIVE_LABEL" >/dev/null 2>&1 || { ok "settle: job gone after ${i}x0.5s"; break; }
printf ' settle: job still present (%d)\n' "$i" >&2; sleep 0.5
done
log "rails: launchctl bootstrap $dom <plist>"
launchctl bootstrap "$dom" "$LIVE_PLIST" || warn "bootstrap returned nonzero"
# ---- verify ----
log "verify prod health"
local s="" ; for i in $(seq 1 60); do s="$(live_stats)"; [ -n "$s" ] && break; sleep 1; done
local ok_verify=1 le; le="$(stat_field "$s" edge_count)"
local base_e; base_e="$(mget "$name" "['live_baseline']['edge_count']")"
[ -n "$s" ] || ok_verify=0
[ "${le:-0}" -ge "${base_e:-0}" ] || ok_verify=0
local kid; for kid in "${KEYSTONES[@]}"; do curl -s -m5 "$LIVE_URL/api/node/$kid" 2>/dev/null | grep -q "\"$kid\"" || ok_verify=0; done
if [ "$ok_verify" -eq 1 ]; then
ok "PROMOTED. live stats: $s (rollback: $bkp)"; return 0
fi
# ---- auto-rollback ----
warn "verify FAILED — auto-rollback"
cp -p "$bkp/plist.bak" "$LIVE_PLIST"
[ $do_data -eq 1 ] && { cp -p "$bkp/neuron.egm.bak" "$LIVE_DATA_DIR/neuron.egm"; cp -p "$bkp/neuron.wal.bak" "$LIVE_DATA_DIR/neuron.wal" 2>/dev/null || true; }
launchctl bootout "$dom/$LIVE_LABEL" 2>/dev/null || true
for i in $(seq 1 60); do launchctl print "$dom/$LIVE_LABEL" >/dev/null 2>&1 || break; sleep 0.5; done
launchctl bootstrap "$dom" "$LIVE_PLIST" || true
die "ROLLED BACK to $live_bin. See $bkp"
}
# ================================================================ destroy ======
cmd_destroy(){
local name="$1"; shift || true
mexists "$name" || die "no such sandbox: $name"
local d; d="$(sdir "$name")"
stop_daemon "$name"
if [ -d "$d/build/worktree" ]; then
log "removing git worktree"
git -C "$EL_REPO" worktree remove --force "$d/build/worktree" 2>/dev/null || true
git -C "$EL_REPO" worktree prune 2>/dev/null || true
fi
log "removing $d"
rm -rf "$d"
ok "destroyed '$name' (live untouched)"
}
# ================================================================ list/status ==
cmd_list(){
[ -d "$SBX_ROOT" ] || { echo "no sandboxes"; return 0; }
printf '%-16s %-6s %-8s %-9s %s\n' NAME PORT STATE PID SOURCE
local m
for m in "$SBX_ROOT"/*/manifest.json; do
[ -f "$m" ] || continue
local n p src pid state
n="$(python3 -c "import json;print(json.load(open('$m'))['name'])")"
p="$(python3 -c "import json;print(json.load(open('$m'))['port'])")"
src="$(python3 -c "import json;print(json.load(open('$m'))['source'])")"
pid="$(daemon_pid "$n")"; state="stopped"; daemon_alive "$n" && state="running"
printf '%-16s %-6s %-8s %-9s %s\n' "$n" "$p" "$state" "${pid:-}" "$src"
done
}
cmd_status(){
local name="$1"; mexists "$name" || die "no such sandbox: $name"
python3 -m json.tool "$(manifest "$name")"
daemon_alive "$name" && echo "state: running (pid $(daemon_pid "$name")) stats: $(sbx_stats "$name")" || echo "state: stopped"
[ -f "$(sdir "$name")/validate.json" ] && { echo "--- last validation ---"; python3 -m json.tool "$(sdir "$name")/validate.json"; }
}
# ================================================================ dev ==========
# ONE-COMMAND isolated dev environment. Everything a newcomer (Tim, any agent)
# needs to go from clone -> coding on an isolated running mind, in a single shot:
# 1) a git BRANCH (dev/<name>, or --prefix)
# 2) a git WORKTREE for it, at a visible path they can open + edit
# 3) an ISOLATED engram bound to a NON-default port, on a clone of the live store
# (stock prod binary by default — instant + safe; --build to compile the
# worktree's own runtime instead). Prod :$LIVE_BIND_PORT/:$SOUL_PORT is untouchable.
#
# This is additive sugar over the proven primitives (git worktree + cmd_create).
# It never binds a forbidden port and never touches ~/.neuron/engram (the live store)
# except the same READ-only snapshot cmd_create already performs.
#
# nsbx dev <name> [--repo R] [--base REF] [--worktree DIR] [--port N]
# [--prefix P] [--build] [--no-engram]
cmd_dev(){
local name="" repo="$EL_REPO" base="" wt="" port="" prefix="dev/" build=0 no_engram=0
[ $# -gt 0 ] && [ "${1#-}" = "$1" ] && { name="$1"; shift; } || die "usage: nsbx dev <name> [flags]"
while [ $# -gt 0 ]; do case "$1" in
--repo) repo="$2"; shift 2;;
--base) base="$2"; shift 2;;
--worktree|--wt) wt="$2"; shift 2;;
--port) port="$2"; shift 2;;
--prefix) prefix="$2"; shift 2;;
--build) build=1; shift;;
--no-engram) no_engram=1; shift;;
*) die "unknown flag: $1";;
esac; done
need git
git -C "$repo" rev-parse --git-dir >/dev/null 2>&1 || die "not a git repo: $repo"
local branch="${prefix}${name}"
local sbx="dev-${name}"
# default worktree path: a PERSISTENT, git-managed dir — NEVER /tmp (which is
# ablated on compaction). Default root = <repo-grandparent>/el-worktrees, i.e.
# ~/Development/neuron-technologies/el-worktrees/<name>. Override with NSBX_DEV_WT_ROOT.
local wt_root="${NSBX_DEV_WT_ROOT:-$(cd "$(dirname "$(dirname "$repo")")" && pwd -P)/el-worktrees}"
[ -n "$wt" ] || wt="${wt_root}/${name}"
case "$wt" in /tmp/*|/private/tmp/*|/var/tmp/*)
die "refusing worktree under a temp dir ($wt) — temp dirs are ablated on compaction; set NSBX_DEV_WT_ROOT to a persistent path";;
esac
# default base: whatever the repo's working checkout is on now
[ -n "$base" ] || base="$(git -C "$repo" rev-parse --abbrev-ref HEAD 2>/dev/null)"
# pre-flight (fail before creating anything)
[ "$no_engram" -eq 1 ] || ! mexists "$sbx" || die "engram sandbox '$sbx' already exists (nsbx dev-down $name first)"
[ -e "$wt" ] && die "worktree path already exists: $wt"
if [ -n "$port" ]; then
{ [ "$port" = "$LIVE_BIND_PORT" ] || [ "$port" = "$SOUL_PORT" ]; } && die "refusing forbidden port $port (live)"
fi
log "dev env '$name' (branch=$branch worktree=$wt base=$base)"
# ---- 1+2) branch + worktree in one shot ----
local gerr
if git -C "$repo" show-ref --verify --quiet "refs/heads/$branch"; then
info "branch $branch exists — checking it out into a new worktree"
gerr="$(git -C "$repo" worktree add "$wt" "$branch" 2>&1)" \
|| die "git worktree add failed for existing branch $branch:"$'\n'" $gerr"
else
gerr="$(git -C "$repo" worktree add -b "$branch" "$wt" "$base" 2>&1)" \
|| die "git worktree add -b $branch (base $base) failed:"$'\n'" $gerr"$'\n'" (a bare 'dev' branch blocks 'dev/*' names — try --prefix, e.g. nsbx dev $name --prefix wt/)"
fi
ok "worktree ready: $wt (branch $branch)"
# ---- 3) isolated engram ----
local eport="(none)"
if [ "$no_engram" -eq 1 ]; then
warn "--no-engram: skipped standing up an engram"
else
if [ "$build" -eq 1 ]; then
log "isolated engram: building the worktree's own runtime"
cmd_create "$sbx" ${port:+--port "$port"} --source "$wt" || die "engram create (--build) failed"
else
log "isolated engram: stock prod binary on a clone of the live store"
cmd_create "$sbx" ${port:+--port "$port"} || die "engram create failed"
fi
eport="$(mget "$sbx" "['port']")"
# record the dev linkage next to the sandbox so dev-down can clean up
python3 - "$(sdir "$sbx")/dev.json" "$name" "$branch" "$wt" "$repo" "$eport" <<'PY'
import json,sys
p,name,branch,wt,repo,port=sys.argv[1:7]
json.dump({"name":name,"branch":branch,"worktree":wt,"repo":repo,"port":int(port)},
open(p,'w'),indent=2)
PY
# ---- pin the WHOLE worktree to the CLONE ----
# Every var any El tooling in this worktree might read for an engram target now
# points at the isolated clone. Sourcing .nsbx-env makes hitting live :$LIVE_BIND_PORT
# or ~/.neuron/engram by accident structurally impossible from this shell.
local edata ekey eurl ebin
edata="$(sdir "$sbx")/data"; ekey="sbx-$sbx"; eurl="http://127.0.0.1:$eport"; ebin="$(sdir "$sbx")/bin/engram"
cat > "$wt/.nsbx-env" <<ENV
# nsbx dev env for '$name' — SOURCE this to pin THIS shell to the isolated clone.
# The live mind (:$LIVE_BIND_PORT engram / :$SOUL_PORT soul / $LIVE_DATA_DIR) is deliberately
# NOT referenced here. Regenerated by 'nsbx dev'. -> source .nsbx-env
export NSBX_NAME="$sbx"
export ENGRAM_URL="$eurl"
export ENGRAM_BIND=":$eport"
export ENGRAM_PORT="$eport"
export ENGRAM_HOST="127.0.0.1"
export ENGRAM_DATA_DIR="$edata"
export ENGRAM_API_KEY="$ekey"
export NEURON_ENGRAM_URL="$eurl"
export NEURON_ENGRAM_KEY="$ekey"
# nsbx run compatibility (same names 'nsbx run' exports)
export SBX_NAME="$sbx" SBX_PORT="$eport" SBX_URL="$eurl" SBX_KEY="$ekey" SBX_DATA="$edata" SBX_BIN="$ebin"
ENV
# direnv users get it automatically on cd; everyone else runs 'source .nsbx-env'
[ -e "$wt/.envrc" ] || printf 'source_env .nsbx-env 2>/dev/null || source .nsbx-env\n' > "$wt/.envrc"
ok "wrote $wt/.nsbx-env (pins this worktree to the clone)"
fi
# ---- summary ----
echo >&2
printf '%s DEV ENV READY — %s%s\n' "$C_BLD" "$name" "$C_0" >&2
printf ' %-10s %s\n' "worktree" "$wt" >&2
printf ' %-10s %s\n' "branch" "$branch" >&2
if [ "$no_engram" -ne 1 ]; then
printf ' %-10s %s\n' "engram" "http://127.0.0.1:$eport (isolated clone; prod :$LIVE_BIND_PORT untouchable)" >&2
printf ' %-10s %s\n' "sandbox" "$sbx" >&2
echo >&2
printf '%s env exported into %s/.nsbx-env (source it -> pinned to the clone):%s\n' "$C_DIM" "$wt" "$C_0" >&2
grep '^export' "$wt/.nsbx-env" | sed 's/^/ /' >&2
fi
echo >&2
info "code in: cd $wt && source .nsbx-env # now every engram var points at the clone"
[ "$no_engram" -ne 1 ] && info "poke it: nsbx run $sbx api /api/stats (or: make run NAME=$name)"
[ "$no_engram" -ne 1 ] && info "edit->test: make build NAME=$name && make run NAME=$name # El change -> clone, seconds"
info "tear down: nsbx dev-down $name (destroys engram + removes worktree; branch kept)"
}
# nsbx dev-down <name> [--delete-branch] [--repo R]
# Teardown counterpart: destroy the isolated engram, remove the git worktree,
# and (optionally) delete the branch. Live prod is never touched.
cmd_dev_down(){
local name="" repo="$EL_REPO" del_branch=0
[ $# -gt 0 ] && [ "${1#-}" = "$1" ] && { name="$1"; shift; } || die "usage: nsbx dev-down <name> [--delete-branch]"
while [ $# -gt 0 ]; do case "$1" in
--repo) repo="$2"; shift 2;;
--delete-branch) del_branch=1; shift;;
*) die "unknown flag: $1";;
esac; done
local sbx="dev-${name}" wt="" branch="dev/${name}"
# recover worktree/branch/repo from the dev linkage if present
if mexists "$sbx" && [ -f "$(sdir "$sbx")/dev.json" ]; then
local dj; dj="$(sdir "$sbx")/dev.json"
wt="$(python3 -c "import json;print(json.load(open('$dj'))['worktree'])" 2>/dev/null)"
branch="$(python3 -c "import json;print(json.load(open('$dj'))['branch'])" 2>/dev/null)"
repo="$(python3 -c "import json;print(json.load(open('$dj'))['repo'])" 2>/dev/null)"
fi
# 1) engram
if mexists "$sbx"; then cmd_destroy "$sbx"; else info "no engram sandbox '$sbx'"; fi
# 2) worktree
if [ -n "$wt" ] && [ -d "$wt" ]; then
log "removing git worktree $wt"
git -C "$repo" worktree remove --force "$wt" 2>/dev/null || rm -rf "$wt"
git -C "$repo" worktree prune 2>/dev/null || true
ok "worktree removed"
else info "no worktree to remove"; fi
# 3) branch (opt-in)
if [ "$del_branch" -eq 1 ]; then
git -C "$repo" branch -D "$branch" 2>/dev/null && ok "deleted branch $branch" || warn "could not delete branch $branch"
else info "branch $branch kept (use --delete-branch to remove)"; fi
ok "dev-down '$name' complete (live untouched)"
}
usage(){ cat >&2 <<EOF
${C_BLD}nsbx${C_0} — Neuron Sandbox: experiments + code changes against the REAL engram
runtime on an isolated snapshot of the live mind, with a gated promote-to-prod path.
nsbx dev <name> [--base REF] [--worktree DIR] ONE command onboarding: new branch (dev/<name>) + git
[--port N] [--prefix P] [--build] worktree (persistent, never /tmp) + isolated engram on a
[--no-engram] [--repo R] non-default port. clone -> coding on the mind in one shot.
nsbx dev-down <name> [--delete-branch] [--repo R] teardown: destroy the engram + remove the worktree
(branch kept unless --delete-branch). live untouched.
nsbx up [name] [flags…] one command: your private, isolated copy of the mind
(creates on first run, starts thereafter; prod untouchable)
nsbx create [name] [--port N] [--source DIR | --branch REF [--repo R] | --binary PATH]
clone live store+WAL+config, place/build the runtime, boot on an
isolated port (never :$LIVE_BIND_PORT/:$SOUL_PORT). Default runtime = stock prod binary.
nsbx build <name> --source DIR | --branch REF rebuild the runtime from a code change + hot-restart
nsbx run <name> <cmd...> | api <path> [json] run an experiment; capture output + metrics
nsbx validate <name> rails as checks: zero-loss(load+reboot), reboot-prove,
RSS bound, retrieval parity, keystone integrity
nsbx promote <name> [--data] [--i-approve-prod-cutover] GATED rails cutover to prod (DRY-RUN without approval)
nsbx destroy <name> stop daemon, free port, remove clone (live untouched)
nsbx list | nsbx status <name>
Env in 'run' cmds: \$SBX_URL \$SBX_PORT \$SBX_KEY \$SBX_DATA \$SBX_BIN \$SBX_NAME
EOF
}
main(){
local cmd="${1:-}"; shift || true
case "$cmd" in
dev) cmd_dev "$@";;
dev-down) cmd_dev_down "$@";;
up) cmd_up "$@";;
create) cmd_create "$@";;
build) cmd_build "$@";;
run) cmd_run "$@";;
validate) cmd_validate "$@";;
promote) cmd_promote "$@";;
destroy) cmd_destroy "$@";;
list|ls) cmd_list "$@";;
status) cmd_status "$@";;
""|-h|--help|help) usage;;
*) die "unknown command: $cmd (try: nsbx help)";;
esac
}
main "$@"