## lead_gain.nim — LEADGAIN (rack id 16): a per-range-band LEAD-GAIN corrector.
##
## ── THE NAME ──────────────────────────────────────────────────────────────────
## This gun used to be called `BITBRAIN` and to live in `guns/bitbrain_gun.nim`,
## but its ADE+SBC network was removed when it was rebuilt into what it actually
## is: **it learns a multiplier for Pattern's lead, separately per range band.**
## `LEADGAIN` says that; `BITBRAIN` (a neural network) did not. The rack id 16
## is UNCHANGED (many tests assert the id literals) and the `BITBRAIN` name is
## the gun's to keep: the ADE+SBC gun that briefly held it (rack id 17,
## `guns/bitbrain_net.nim`) was RETIRED and removed — see
## `docs/bitbrain_campaign.md` §RETIRED.
##
## ── WHY THE FILE WAS REBUILT (Phase 0/1 evidence) ────────────────────────────
## The previous design was an ADDITIVE angular shift: an ADE+SBC network
## classified the +h-tick angular error over ±`TR_LEADGAIN_RANGE` degrees and
## added the argmax class centre to Pattern's bearing. Phase 0 measured it as
## statistically identical to Pattern (`docs/bitbrain_gun_verdict.md`,
## commit d93ce44) and as carrying no measurable aim information
## (450+: 16.200 deg vs Pattern's 16.193; `docs/bitbrain_campaign.md` §0.3.5).
##
## Phase 1 measured the actual lever. The gain sweep found that gains >= 1 are
## strictly worse at every band and that the optimal gain is BELOW 1.0 at long
## range (450+: ~0.25). A fractional gain leaves the Pearson lead *correlation*
## unchanged (correlation is invariant under positive scaling), so a smaller
## gain does not add information — it shrinks the magnitude of an uninformative
## Pattern lead toward the low-variance static (HeadOn) aim. The right output is
## therefore a multiplicative GAIN on Pattern's lead, not a class-based additive
## shift. See `docs/bitbrain_campaign.md` §Phase 1 for the measured curve.
##
## ── THE DESIGN ────────────────────────────────────────────────────────────────
##   * BASE   — the shipped Pattern gun's prediction (`guns/pattern_matcher`),
##              reached through the TmHorizonGun observation ring.
##   * OUTPUT — `aim = LOS + gain * (patternAim - LOS)`, i.e. Pattern's lead over
##              the line of sight is multiplied by a learned `gain` (one of
##              `LG_CAND`, so it may be BELOW 1.0 — the point).
##   * LABEL  — the same deferred-label path the old corrector used: at fire
##              time we remember the base lead and the aim tolerance; `h =
##              round(dist/speed)` ticks later `tmhObservedAt` returns the
##              enemy's OBSERVED bearing from the firing position. `requiredLead
##              = observedBearing - LOS` and `baseLead = baseBearing - LOS`, so a
##              candidate gain scores a hit on this sample when
##              `|gain*baseLead - requiredLead| <= tolerance`.
##   * TRAIN  — ONLINE / PREQUENTIAL per range band: for each candidate gain we
##              count the fraction of resolved samples that would have been
##              within the target's angular half-width (`atan(18/range)`, the
##              SAME tolerance the offline ruler uses). The band's gain is the
##              argmax hit rate. THIS is the key lesson of Phase 1: the
##              least-squares gain and the hit-probability-optimal gain DIVERGE
##              (Pattern's lead errors are bimodal), so the learner optimises the
##              hit-probability proxy directly instead of mean squared error.
##   * STATE  — the range band (the ruler's 5 bands). Range is known causally at
##              fire time, so a per-band gain table is shippable with no learning
##              at all; this gun learns that table online. The correction is
##              additionally gated to bands with range >= 300 px
##              (`LG_GAIN_BAND_MIN`), where Phase 1 measured Pattern's lead to be
##              uninformative. That gate is causal (range is known).
##
## The gain statistics are battle-scale: a round boundary wipes the observation
## ring and deferred labels but NOT the gain counts (a new round is not a new
## enemy). `resetLearning` wipes them on a new battle / target change; with
## `TR_LEADGAIN_MEM=decay` every `TR_LEADGAIN_DECAY` resolved samples decays the
## counts by `TR_LEADGAIN_DECAY_FRAC` toward the gain-1.0 column.
##
## ── WHAT IS STILL HERE ONLY FOR THE BOOT REPORT / GUARD TESTS ─────────────────
## The ADE+SBC network is GONE from the gun. The class geometry
## (`lgCenterDeg`/`lgClassOf`) and `TR_LEADGAIN_N`/`NADE`/`WARMUP`/`ADAPT`/
## `CALIB`/`SEED`/`RANGE` are retained as resolved configuration so the boot
## report (`env_report.nim`) and the registration guard tests keep working
## unchanged; they no longer affect the gain learner. The generic
## `common_libs/bitbrain/` library is untouched and still tested by
## `test_bitbrain.nim` — `movements/learned_surfer.nim` still imports
## `bitbrain/sbc`, so the library is NOT the gun.
##
## ── BACKWARD COMPATIBILITY: the `TR_BITBRAIN_*` legacy aliases ───────────────
## The owner's live `.env` predates the rename and contains `TR_RACK_BITBRAIN`,
## `TR_BITBRAIN_GAINS`, `TR_BITBRAIN_MEM`, `TR_BITBRAIN_LOG`, … Those names are
## the OLD corrector's knobs and MUST keep working unchanged.
##
## The `TR_BITBRAIN_*` prefix was briefly shared with the ADE+SBC gun, which
## had a two-mode disambiguator, `TR_BITBRAIN_NET`. That gun was RETIRED and
## removed (it did not learn; the owner watched it live and threw it away), so
## the legacy namespace is now the ONLY namespace and the mapping is
## UNCONDITIONAL: every `TR_BITBRAIN_<X>` in the frozen `LegacyKnobEnvNames`
## set is an alias for `TR_LEADGAIN_<X>`, and `TR_RACK_BITBRAIN` always selects
## this gun (rack id 16). A stale `TR_BITBRAIN_NET=1` left in a `.env` is now an
## unrecognised variable: the boot report warns about it and ignores it.
##
## ── TR_LEADGAIN_GAINS (the candidate set as an env knob) ─────────────────────
## `TR_LEADGAIN_GAINS` is a comma-separated candidate list, e.g.
## `TR_LEADGAIN_GAINS=1.0,1.25,1.5,2.0`. It replaces the fixed shipped candidate
## set `{0, 0.25, 0.5, 0.75, 1.0}` for this gun instance, so every live arm is
## pure-env (no recompile). Two degenerate cases are deliberate:
##   * unset / unparsable  -> the shipped `LG_CAND` set, byte-identical behaviour;
##   * exactly ONE value   -> a FIXED gain, applied from the first shot with NO
##     learning at all (the learner is bypassed), still gated to the long bands.
## The applied `gain` (and the resulting angular `shift`) is printed on the
## existing change-gated `[lg]` line, so a run's liveness AND the correction it
## actually applied are both auditable from the bot's stdout.
##
## DEFAULT OFF / PARITY: this gun is admitted ONLY when `TR_RACK_LEADGAIN` says so
## (default `off`). The shipped rack never calls `predict`, so `ensureInit` never
## runs and the shipped bot is byte-for-byte unchanged.

import std/[math, os, strutils, strformat, algorithm]
import gun_harness/gun_interface
import guns/tm_horizon
import guns/pattern_matcher
import gun_harness/selector

const
  ## ── env knobs (all resolved once at gun construction) ─────────────────────
  LG_MEM_ENV*        = "TR_LEADGAIN_MEM"        ## perRound|retained|decay
  LG_GAINS_ENV*      = "TR_LEADGAIN_GAINS"      ## comma-separated candidate gains
  LG_N_ENV*          = "TR_LEADGAIN_N"          ## (legacy geometry; inert)
  LG_NADE_ENV*       = "TR_LEADGAIN_NADE"       ## (legacy ADE count; inert)
  LG_RANGE_ENV*      = "TR_LEADGAIN_RANGE"      ## (legacy class half-range; inert)
  LG_LOG_ENV*        = "TR_LEADGAIN_LOG"        ## 1 = per-change [lg] log
  ## ── the legacy `TR_BITBRAIN_*` namespace ──────────────────────────────────
  ## This gun OWNS the `TR_BITBRAIN_*` prefix unconditionally (the ADE+SBC gun
  ## that briefly shared it was retired), so the frozen suffix set below maps
  ## every legacy name onto this gun's `TR_LEADGAIN_<X>` with no switch.
  LG_MIN_OBS_ENV*    = "TR_LEADGAIN_MIN_OBS"    ## samples before a band is trusted
  LG_WARMUP_ENV*     = "TR_LEADGAIN_WARMUP"     ## (legacy; inert)
  LG_ADAPT_ENV*      = "TR_LEADGAIN_ADAPT"      ## (legacy; inert)
  LG_CALIB_ENV*      = "TR_LEADGAIN_CALIB"      ## (legacy; inert)
  LG_DECAY_ENV*      = "TR_LEADGAIN_DECAY"      ## decay interval (samples)
  LG_DECAY_FRAC_ENV* = "TR_LEADGAIN_DECAY_FRAC" ## per-decay count shrink
  LG_SEED_ENV*       = "TR_LEADGAIN_SEED"       ## (legacy; inert)
  LG_RESET_ON_TARGET_ENV* = "TR_LEADGAIN_RESET_ON_TARGET"
  ## ── fixed geometry ────────────────────────────────────────────────────────
  LG_PENDING_CAP* = 512         ## deferred-label queue (>= 4 buckets x 50 ticks)
  ## ── the gain learner ──────────────────────────────────────────────────────
  LG_NBANDS* = 5                ## the ruler's range bands
  LG_NHB*    = 4                ## horizon buckets (for the per-tick label dedupe)
  LG_BAND_LO* = [0.0, 100.0, 200.0, 300.0, 450.0]
  LG_BAND_HI* = [100.0, 200.0, 300.0, 450.0, 1.0e18]
  ## The DEFAULT candidate lead gains the band selector picks from. 0.0 == HeadOn
  ## (aim at the current position) and 1.0 == Pattern (use the full lead).
  ## `TR_LEADGAIN_GAINS` replaces this set per gun; unset -> this exact set.
  LG_CAND* = [0.0, 0.25, 0.50, 0.75, 1.0]
  LG_NCAND* = 5
  LG_BOT_RADIUS* = 18.0          ## hit-detection radius in px (ruler tolerance)
  ## Apply the correction only from this band up (range >= LG_BAND_LO[3] = 300).
  ## [MEASURED] below 300 Pattern's lead is informative and shrinking it loses
  ## hits; see the header note.
  LG_GAIN_BAND_MIN* = 3
  ## ── shipped defaults ──────────────────────────────────────────────────────
  LG_N_DEF          = 32
  LG_NADE_DEF       = 256
  LG_RANGE_DEF      = 40.0
  LG_MIN_OBS_DEF    = 8
  LG_WARMUP_DEF     = 400
  LG_ADAPT_DEF      = 32
  LG_CALIB_DEF      = 512
  LG_DECAY_DEF      = 250
  LG_DECAY_FRAC_DEF = 0.02
  LG_SEED_DEF       = 20240921
  LG_RESET_ON_TARGET_DEF = true

type
  LeadMemMode* = enum
    lgPerRound, lgRetained, lgDecay

  LgPending = object
    ## One deferred training sample. `lead` is Pattern's lead over LOS at fire
    ## time (radians) and `tol` the target's angular half-width then; the label
    ## is resolved `horizon` ticks later.
    fireTick:  int
    horizon:   int
    band:      int
    selfX*, selfY: float
    baseBearing: float
    lead:        float
    tolDeg:      float

  LeadGainGun* = object
    tmh: TmHorizonGun
    initialized: bool
    # ── resolved config (kept in the boot report) ─────────────────────────────
    nClasses*:  int
    maxDeg*:    float
    nAde*:      int
    memMode*:   LeadMemMode
    logEnabled*: bool
    minObs*:    int
    warmupN*:   int
    adaptEvery*: int
    calibEvery*: int
    decayEvery*: int
    decayFrac*: float
    seed*:      int64
    resetOnTarget*: bool
    # ── the candidate gain set (resolved once at construction) ────────────────
    ## Ascending; one entry == a FIXED gain with no learning. `bandHits` and
    ## `bandN` are sized to it, so the loops below never touch a stale column.
    cands*:    seq[float]
    # ── gain learner: hit counts per (range band x candidate gain) ────────────
    bandHits*: seq[seq[float64]]
    bandN*:    seq[float64]
    trained*:   int
    sinceDecay: int
    decays*:    int
    # ── readout / accounting ──────────────────────────────────────────────────
    lastGain*:    array[LG_NBANDS, float]
    corrections*: int
    lastLogKey:   string
    # ── deferred labels ───────────────────────────────────────────────────────
    pending: array[LG_PENDING_CAP, LgPending]
    pendingCount*: int
    pendingDropped*: int
    # ── per-tick caches ───────────────────────────────────────────────────────
    lastTick:       int
    lastEnqTick:    int
    lastEnqBucket:  int
    observedTargetId*: int

# ── small pure helpers ───────────────────────────────────────────────────────

proc wrapRadLg(r: float): float {.inline.} =
  result = r
  while result > PI: result -= 2.0 * PI
  while result < -PI: result += 2.0 * PI

proc memModeName*(m: LeadMemMode): string =
  case m
  of lgPerRound:  "perRound"
  of lgRetained:  "retained"
  of lgDecay:     "decay"

proc lgGainsString*(cands: seq[float]): string =
  ## The resolved candidate set as the env's comma-separated form (boot report).
  for i, c in cands:
    if i > 0: result.add ","
    result.add $c

proc parseLgGains*(value: string): seq[float] =
  ## Parse `TR_LEADGAIN_GAINS`. Empty / unparsable / out-of-range / duplicate
  ## input cannot silently select a different regime: it falls back to the
  ## shipped `LG_CAND` set, exactly like the other env knobs fall back to their
  ## defaults. Values are clamped to [0, 8] (0 == HeadOn, 1 == Pattern) and
  ## de-duplicated, then sorted so the argmax tie rule (keep the smaller
  ## candidate) is unchanged.
  var seen: seq[float]
  for tok in value.split(','):
    let t = tok.strip()
    if t.len == 0: continue
    var v: float
    try: v = parseFloat(t)
    except ValueError: continue
    if v < 0.0 or v > 8.0: continue
    var dup = false
    for u in seen:
      if abs(u - v) < 1e-9: dup = true
    if not dup: seen.add v
  if seen.len == 0:
    for c in LG_CAND: seen.add c
    return seen
  seen.sort()
  seen

proc parseLgMemMode*(value: string): LeadMemMode =
  ## Empty / unknown values fall back to the shipped `perRound`, so a typo
  ## cannot silently select another regime.
  case value.strip().toLowerAscii()
  of "retained", "retain", "accum", "accumulate": lgRetained
  of "decay", "forget", "age": lgDecay
  else: lgPerRound

proc lgEnv(name: string): string
  ## Forward declaration: the legacy-alias lookup is defined below, after the
  ## frozen `LegacyKnobEnvNames` table it depends on.

proc envFloatLg(name: string, default: float): float =
  let v = lgEnv(name)
  if v.len == 0: return default
  try: parseFloat(v.strip()) except ValueError: default

# ── legacy `TR_BITBRAIN_*` aliases (backward compatibility) ───────────────────

const
  LegacyPrefix* = "TR_BITBRAIN_"
  NewPrefix*    = "TR_LEADGAIN_"
  ## The COMPLETE, FROZEN set of the old corrector's knob suffixes. A
  ## `TR_BITBRAIN_<X>` in this set is a legacy alias for `TR_LEADGAIN_<X>`.
  ## This gun is the sole owner of the prefix (the ADE+SBC gun was retired), so
  ## the mapping is total and unconditional — no name is claimed twice.
  LegacyKnobEnvNames* = [
    "GAINS", "MEM", "MIN_OBS", "DECAY", "DECAY_FRAC", "LOG", "RESET_ON_TARGET",
    "N", "NADE", "RANGE", "WARMUP", "ADAPT", "CALIB", "SEED"]
  ## Knobs that actually change behaviour (the rest are inert configuration kept
  ## for the boot report). A deprecation line is only worth printing for these
  ## plus the inert ones, because a stale inert name is still a stale name.
  ## The legacy rack knob, from `gun_harness/selector`'s alias table so the
  ## two can never drift apart. It is NOT a current rack name any more (it was
  ## `RackGunNames[RackNetGunId]` while the retired ADE+SBC gun existed).
  LegacyRackEnvName* = RackLegacyAlias[0][0]

var deprecationShown = false

proc lgDeprecationLine*(): string =
  ## The single clear deprecation line the owner sees. Names every legacy
  ## `TR_BITBRAIN_*` knob that is actually set in the environment and the new
  ## name that now owns it. Empty when there is nothing to migrate.
  var parts: seq[string]
  for suffix in LegacyKnobEnvNames:
    let old = LegacyPrefix & suffix
    if getEnv(old, "").len > 0:
      parts.add old & " -> " & NewPrefix & suffix
  if getEnv(LegacyRackEnvName, "").len > 0:
    parts.add LegacyRackEnvName & " -> TR_RACK_LEADGAIN"
  if parts.len == 0: return ""
  result = "[depr] " & LegacyPrefix & "* is the OLD lead-gain corrector's namespace; " &
           "it was renamed to " & NewPrefix & "* (gun LEADGAIN, rack id 16). " &
           "Still honoured: " & parts.join("; ") & "."

proc lgEnv(name: string): string =
  ## Read a `TR_LEADGAIN_<X>` knob, falling back to the legacy
  ## `TR_BITBRAIN_<X>` alias. The NEW name always wins when both are set, so a
  ## migrated config is authoritative.
  var v = getEnv(name, "")
  if v.len > 0: return v
  let suffix = if name.startsWith(NewPrefix): name[NewPrefix.len .. ^1] else: ""
  if suffix.len == 0: return ""
  for s in LegacyKnobEnvNames:
    if s == suffix: return getEnv(LegacyPrefix & suffix, "")
  ""

proc envIntLg(name: string, default: int): int =
  let v = lgEnv(name)
  if v.len == 0: return default
  try: parseInt(v.strip()) except ValueError: default

proc envBoolLg(name: string, default: bool): bool =
  case lgEnv(name).strip().toLowerAscii()
  of "1", "true", "yes", "on": true
  of "0", "false", "no", "off": false
  else: default

proc lgCenterDeg*(k, nClasses: int, maxDeg: float): float =
  ## Centre (degrees) of correction class `k` over ±maxDeg. Retained for the
  ## registration guard test and the boot report; inert for the gain learner.
  let w = 2.0 * maxDeg / float(nClasses)
  -maxDeg + (float(k) + 0.5) * w

proc lgClassOf*(errRad: float, nClasses: int, maxDeg: float): int =
  ## Bin a signed angular error (radians) into one of `nClasses` bins over
  ## [−maxDeg, +maxDeg]. Retained for the registration guard test; inert.
  let x = radToDeg(errRad)
  var k = int((x + maxDeg) / (2.0 * maxDeg) * float(nClasses))
  if k < 0: k = 0
  if k >= nClasses: k = nClasses - 1
  k

proc lgBandOf*(range: float): int {.inline.} =
  ## Range band (the ruler's bands), known causally at fire time.
  for b in 0 ..< LG_NBANDS:
    if range >= LG_BAND_LO[b] and range < LG_BAND_HI[b]: return b
  LG_NBANDS - 1

proc lgTolDeg*(range: float): float {.inline.} =
  ## The target's angular half-width at `range` — atan(18/range) — i.e. the exact
  ## tolerance the offline ruler uses for its hit-probability proxy.
  radToDeg(arctan2(LG_BOT_RADIUS, max(range, 1e-9)))

# ── construction / lazy init ─────────────────────────────────────────────────

proc initLeadGainGun*(): LeadGainGun =
  result.nClasses  = clamp(envIntLg(LG_N_ENV, LG_N_DEF), 2, 512)
  result.nAde      = clamp(envIntLg(LG_NADE_ENV, LG_NADE_DEF), 8, 4096)
  result.maxDeg    = clamp(envFloatLg(LG_RANGE_ENV, LG_RANGE_DEF), 1.0, 180.0)
  result.memMode   = parseLgMemMode(lgEnv(LG_MEM_ENV))
  result.logEnabled = envBoolLg(LG_LOG_ENV, false)
  result.minObs    = max(1, envIntLg(LG_MIN_OBS_ENV, LG_MIN_OBS_DEF))
  result.warmupN   = max(0, envIntLg(LG_WARMUP_ENV, LG_WARMUP_DEF))
  result.adaptEvery = max(1, envIntLg(LG_ADAPT_ENV, LG_ADAPT_DEF))
  result.calibEvery = max(1, envIntLg(LG_CALIB_ENV, LG_CALIB_DEF))
  result.decayEvery = max(1, envIntLg(LG_DECAY_ENV, LG_DECAY_DEF))
  result.decayFrac = clamp(envFloatLg(LG_DECAY_FRAC_ENV, LG_DECAY_FRAC_DEF), 0.0, 1.0)
  result.seed      = int64(envIntLg(LG_SEED_ENV, LG_SEED_DEF))
  result.resetOnTarget = envBoolLg(LG_RESET_ON_TARGET_ENV, LG_RESET_ON_TARGET_DEF)
  result.cands = parseLgGains(lgEnv(LG_GAINS_ENV))
  result.bandN = newSeq[float64](LG_NBANDS)
  result.bandHits = newSeq[seq[float64]](LG_NBANDS)
  for b in 0 ..< LG_NBANDS:
    result.bandHits[b] = newSeq[float64](result.cands.len)
  result.lastTick = -1
  result.lastEnqTick = -1
  result.lastEnqBucket = -1
  result.observedTargetId = -1
  for b in 0 ..< LG_NBANDS: result.lastGain[b] = 1.0
  # ONE deprecation line per process, naming the new `TR_LEADGAIN_*` names.
  let dep = lgDeprecationLine()
  if dep.len > 0 and not deprecationShown:
    deprecationShown = true
    stderr.writeLine(dep)

proc ensureInit*(g: var LeadGainGun) =
  ## Build the observation ring on first use. No network, no global-RNG use, so
  ## the shipped default path is untouched and construction stays cheap.
  if g.initialized: return
  g.initialized = true
  g.tmh = initTmHorizonGun()

# ── the gain learner ─────────────────────────────────────────────────────────

proc lgAccumulate(g: var LeadGainGun, leadDeg, reqDeg, tolDeg: float, band: int) =
  ## Score every candidate gain on this resolved sample: a candidate "hits" when
  ## it would have put the aim within the target's angular half-width.
  for ci in 0 ..< g.cands.len:
    if abs(g.cands[ci] * leadDeg - reqDeg) <= tolDeg:
      g.bandHits[band][ci] += 1.0
  g.bandN[band] += 1.0
  inc g.trained

proc lgApplyDecay(g: var LeadGainGun) =
  ## Forgetting for `TR_LEADGAIN_MEM=decay`: shrink the hit counts and, more
  ## strongly, pull them toward the gain-1.0 column so stale evidence ages out.
  let f = 1.0 - g.decayFrac
  if f >= 1.0: return
  for b in 0 ..< LG_NBANDS:
    for ci in 0 ..< g.cands.len:
      g.bandHits[b][ci] *= f
    g.bandN[b] *= f
  inc g.decays

proc lgGainFor(g: LeadGainGun, band: int): float =
  ## The band's gain is the candidate with the highest observed hit rate.
  ## Ties keep the SMALLER candidate (the scan is ascending), which is the
  ## conservative choice for the long-range regime this corrector targets.
  ## Returns 1.0 (Pattern) below the range gate or when the band is cold.
  if band < LG_GAIN_BAND_MIN: return 1.0
  if g.cands.len == 0: return 1.0
  # A single candidate is a FIXED gain: apply it from the first shot, never
  # consult the counts. This is the no-learning arm of the live sweep.
  if g.cands.len == 1: return g.cands[0]
  if g.bandN[band] < float(g.minObs): return 1.0
  var best = -1
  var bestRate = -1.0
  for ci in 0 ..< g.cands.len:
    let rate = g.bandHits[band][ci] / g.bandN[band]
    if rate > bestRate:
      bestRate = rate
      best = ci
  if best < 0: return 1.0
  g.cands[best]

# ── deferred-label resolution (prequential learning) ─────────────────────────

proc resolvePending(g: var LeadGainGun, state: WorldState) =
  var w = 0
  for i in 0 ..< g.pendingCount:
    let p = g.pending[i]
    let due = p.fireTick + p.horizon
    if due > state.tick:
      g.pending[w] = p
      inc w
    elif due == state.tick:
      let obs = tmhObservedAt(g.tmh, state.tick, p.selfX, p.selfY)
      if obs.ok and (state.tick - obs.lastSeenTick) <= TMH_STALE_MAX:
        let err = wrapRadLg(obs.bearing - p.baseBearing)
        let reqLead = wrapRadLg(err + p.lead)
        g.lgAccumulate(radToDeg(p.lead), radToDeg(reqLead), p.tolDeg, p.band)
        inc g.sinceDecay
        if g.memMode == lgDecay and g.sinceDecay >= g.decayEvery:
          g.lgApplyDecay()
          g.sinceDecay = 0
      else:
        inc g.pendingDropped
    else:
      inc g.pendingDropped
  g.pendingCount = w

# ── logging ──────────────────────────────────────────────────────────────────

proc lgLog(g: var LeadGainGun, state: WorldState, band: int, gain, leadDeg: float) =
  ## ONE change-gated `[lg]` line (behind TR_LEADGAIN_LOG=1) so a user tailing
  ## the GUI log sees the gain the corrector is applying. The APPLIED gain and
  ## the resulting angular `shift` are both on the line: the boot report proves
  ## the knob reached the process, this proves the gun actually used it.
  if not g.logEnabled: return
  let shiftDeg = (gain - 1.0) * leadDeg
  let key = fmt"{gain:.2f}|{band}"
  if key == g.lastLogKey: return
  g.lastLogKey = key
  var rate = 0.0
  for ci in 0 ..< g.cands.len:
    if abs(g.cands[ci] - gain) < 1e-9: rate = g.bandHits[band][ci] / max(1.0, g.bandN[band])
  echo fmt"[lg] t={state.tick} band={LG_BAND_LO[band]:.0f}+ gain={gain:.2f} " &
       fmt"shift={shiftDeg:+.2f}deg rate={rate:.3f} n={g.bandN[band]:.0f} " &
       fmt"ncand={g.cands.len} trained={g.trained} " &
       fmt"pend={g.pendingCount} dropped={g.pendingDropped} mode={memModeName(g.memMode)}"

# ── reset hooks (mirroring TmHorizonGun) ─────────────────────────────────────

proc resetRound(g: var LeadGainGun) =
  ## PER-ROUND reset: observation ring, deferred labels and per-tick caches (the
  ## bots teleport between rounds). The gain counts are deliberately KEPT — they
  ## are battle-scale and a new round is not a new enemy.
  g.tmh.resetRoundState()
  g.pendingCount = 0
  g.lastTick = -1
  g.lastEnqTick = -1
  g.lastEnqBucket = -1
  g.lastLogKey = ""

proc resetRoundState*(g: var LeadGainGun) =
  if not g.initialized: return
  g.resetRound()

proc resetLearning*(g: var LeadGainGun, reason = "") =
  ## PER-BATTLE / PER-ENEMY wipe: gain counts, counters and the round state.
  if not g.initialized: return
  for b in 0 ..< LG_NBANDS:
    for ci in 0 ..< g.cands.len: g.bandHits[b][ci] = 0.0
    g.bandN[b] = 0.0
    g.lastGain[b] = 1.0
  g.trained = 0
  g.sinceDecay = 0
  g.decays = 0
  g.corrections = 0
  g.observedTargetId = -1
  g.resetRound()
  if reason.len > 0 and g.logEnabled:
    echo fmt"[lg-reset] reason={reason}"

proc targetChanged*(g: var LeadGainGun, enemyId: int): bool =
  ## Per-ENEMY reset: wipe when the target changes to a different bot id. First
  ## acquisition never wipes, so the round-start pick does not cold-start us.
  if not g.resetOnTarget: return false
  if enemyId < 0: return false
  if g.observedTargetId >= 0 and enemyId != g.observedTargetId:
    g.resetLearning("target_change")
    g.observedTargetId = enemyId
    return true
  g.observedTargetId = enemyId
  false

# ── Gun interface ────────────────────────────────────────────────────────────

proc isWarmedUp*(g: LeadGainGun): bool {.inline.} = true

proc networkBytes*(g: LeadGainGun): int =
  ## No neural network is held any more; kept for the boot report / guard test.
  0

proc predict*(g: var LeadGainGun, state: WorldState,
              bulletSpeed: float): GunPrediction =
  g.ensureInit()

  # Round boundary: a tick regression means a new round.
  if state.tick < g.lastTick: g.resetRound()

  # Once per tick: observe the world, then resolve any labels now due.
  if state.tick != g.lastTick:
    tmhUpdateHistory(g.tmh, state)
    g.resolvePending(state)
    g.lastTick = state.tick

  # The base prediction is Pattern; LEADGAIN only scales its lead over LOS.
  let base = g.tmh.pattern.predict(state, bulletSpeed)
  if bulletSpeed <= 0.0: return base

  let dist = hypot(state.enemyX - state.selfX, state.enemyY - state.selfY)
  let h = tmhHorizonFor(dist, bulletSpeed)
  let hb = tmhHorizonBucket(h)
  let band = lgBandOf(dist)

  let los = arctan2(state.enemyY - state.selfY, state.enemyX - state.selfX)
  let baseBearing = arctan2(base.y - state.selfY, base.x - state.selfX)
  let lead = wrapRadLg(baseBearing - los)

  # Enqueue one deferred sample per (tick, horizon bucket): `predict` runs once
  # per power bin, so all four horizons contribute evidence.
  if g.lastEnqTick != state.tick or g.lastEnqBucket != hb:
    if g.pendingCount < LG_PENDING_CAP:
      g.pending[g.pendingCount] = LgPending(
        fireTick: state.tick, horizon: h, band: band,
        selfX: state.selfX, selfY: state.selfY,
        baseBearing: baseBearing, lead: lead, tolDeg: lgTolDeg(dist))
      inc g.pendingCount
    else:
      inc g.pendingDropped
    g.lastEnqTick = state.tick
    g.lastEnqBucket = hb

  # Readout: a fractional gain may be BELOW 1.0. When cold / gated out the
  # learner returns 1.0 and the base prediction is returned unchanged.
  let gain = g.lgGainFor(band)
  g.lastGain[band] = gain
  if abs(gain - 1.0) < 1e-9: return base
  inc g.corrections
  g.lgLog(state, band, gain, radToDeg(lead))
  tmhApplyShift(state.selfX, state.selfY, base.x, base.y,
                radToDeg((gain - 1.0) * lead))

proc onResult*(g: var LeadGainGun, e: FeedbackEvent) =
  ## Labels come from our own observation ring, not from virtual-bullet
  ## feedback, so there is nothing to do here. The hook exists for the rack.
  discard