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BitBrain gun (id 16): Pattern-relative ADE+SBC aim corrector, default off

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Wire the verified common_libs/bitbrain ADE+SBC library into ModularBot as a
fine-grained angular corrector on top of Pattern's prediction, the shape the
offline gate test measured (argmax readout over N correction classes).

- common_libs/guns/bitbrain_gun.nim: new gun. Input = the existing TMHorizon
  53 bits (tmhBaseBits + tmhLits); output = argmax class centre over
  +-TR_BITBRAIN_RANGE, applied by rotating the Pattern point around the shooter
  exactly as tmhApplyShift does. Label = the +h-tick fact from TmHorizonGun's
  own observation ring (never across a round). Prequential (defer + resolve).
  AD layer synthesised online for our binary inputs (center=0): heuristic
  cold-start thresholds + running-histogram ~1% percentile init + the library's
  adaptThresholds. Memory modes perRound (default, measured best) / retained /
  decay (periodic partial SBC wipe). Lazy network build + local RNG, so the
  default path builds nothing and consumes no global randomness.
- tm_horizon.nim: export tmhUpdateHistory and add tmhObservedAt (label seam).
- selector.nim: register BITBRAIN at rack id 16, default rmOff, in the SAME
  commit as the id and the wiring (the aed579b admission bug is not repeated).
- ModularBot.nim: id 16 wired through predict/spawn/onResult/resets/colors,
  arrays grown 16->17, spawn gated on rack admission, per-round/per-battle/
  target reset hooks.
- env_report.nim: report every TR_BITBRAIN_* knob + add names to the known set.
- tests: update the rack length literals; new test_bitbrain_registration
  (default-parity: off, lazy, global-RNG clean).

Guard counts unchanged: rack 48, tm_pattern_registration 20, vbullet_admit 12,
env_report 25, and the rest of the suite green.
This commit is contained in:
Davide Cappellini
2026-09-24 22:25:04 +02:00
parent 1fec87537d
commit 795a0e59fe
8 changed files with 721 additions and 28 deletions
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common_libs/guns/bitbrain_gun.nim
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## bitbrain_gun.nim — BitBrain (ADE + SBC) FINE-GRAINED AIM CORRECTOR.
##
## THE BRIEF THIS IMPLEMENTS (from the offline gate test, docs/bitbrain_gate_test.md):
## * BASE — the shipped Pattern gun's prediction (`guns/pattern_matcher`).
## BitBrain supplies only a small ANGULAR CORRECTION on top of it,
## exactly the shape the gate test measured and the shape TMHorizon
## uses. That keeps the comparison against Pattern/TMHorizon
## apples-to-apples.
## * INPUT — the SAME 53 bits TMHorizon uses: the 49-bit draft spec
## (`tmhBaseBits`) PLUS a 4-bit horizon one-hot (`tmhLits`). These are
## reused from `guns/tm_horizon.nim`, not re-derived.
## * OUTPUT — a fine-grained angular-correction CLASS over ±`TR_BITBRAIN_RANGE`
## degrees (`TR_BITBRAIN_N` bins, default 32). The readout is the
## ARGMAX class centre (the gate test MEASURED that argmax is the
## winning readout; the count-weighted mean is a shrinkage predictor
## that lowers the hit rate). Zero correction when there is no
## evidence.
## * LABEL — the +h-tick FACT from our OWN observation ring (the ring the
## embedded TmHorizonGun maintains): `h = round(dist/speed)`,
## `speed = 20 - 3*power`, clamped to [10, 50]. Never crosses a round
## boundary (pending samples are dropped on a round reset).
## * TRAIN — ONLINE / PREQUENTIAL: predict, then learn the resolved fact when
## it becomes due `h` ticks later.
## * AD LAYER — synthesised for OUR data. The MNIST weights are useless.
## center = 0 (the inputs are BINARY; the reference 127 would collapse
## the code to a polarity count). Thresholds start from a small
## heuristic that fires ~1 % from the first ticks, are then calibrated
## from a running score histogram to the paper's ~1 % operating point
## (the gate test's percentile init, made online), and are nudged by
## the library's deterministic `adaptThresholds` homeostasis.
##
## MEMORY MODES (`TR_BITBRAIN_MEM`):
## perRound (DEFAULT) — wipe the SBCs every round. The gate test measured this
## as the WINNING regime.
## retained — accumulate across the whole battle/enemy and wipe only
## on a target change / new battle. This is what the user
## asked for, and the gate test measured it as the WEAKEST
## regime: the idempotent SBC only ADDS, so it saturates.
## decay — retained PLUS a periodic partial wipe of the SBC
## tensors (TR_BITBRAIN_DECAY every N samples, a fraction
## TR_BITBRAIN_DECAY_FRAC of words zeroed). This is the one
## mechanism with a measured diagnosis behind it: the SBC
## saturates and a bounded/decaying memory should help.
##
## DEFAULT OFF / PARITY: this gun is admitted ONLY when `TR_RACK_BITBRAIN` says so
## (default `off`) AND it never runs its network until `predict` is first called
## (`ensureInit`). With the shipped rack the live loop never calls `predict`, so
## no network is built, no RNG is touched and the shipped bot is unchanged.
import std/[math, os, strutils, strformat, random]
import gun_harness/gun_interface
import guns/tm_horizon
import guns/pattern_matcher
import bitbrain/bitbrain
const
## ── env knobs (all resolved once at gun construction) ─────────────────────
BB_MEM_ENV* = "TR_BITBRAIN_MEM" ## perRound|retained|decay
BB_N_ENV* = "TR_BITBRAIN_N" ## correction classes
BB_NADE_ENV* = "TR_BITBRAIN_NADE" ## ADEs per address decoder
BB_RANGE_ENV* = "TR_BITBRAIN_RANGE" ## class half-range, degrees
BB_LOG_ENV* = "TR_BITBRAIN_LOG" ## 1 = per-change [bb] log
BB_MIN_OBS_ENV* = "TR_BITBRAIN_MIN_OBS" ## resolved samples before correction
BB_WARMUP_ENV* = "TR_BITBRAIN_WARMUP" ## samples before percentile init
BB_ADAPT_ENV* = "TR_BITBRAIN_ADAPT" ## homeostasis interval (samples)
BB_CALIB_ENV* = "TR_BITBRAIN_CALIB" ## percentile recalibration interval
BB_DECAY_ENV* = "TR_BITBRAIN_DECAY" ## decay interval (samples)
BB_DECAY_FRAC_ENV* = "TR_BITBRAIN_DECAY_FRAC" ## fraction of words zeroed per decay
BB_SEED_ENV* = "TR_BITBRAIN_SEED" ## deterministic AD/decay seed
BB_RESET_ON_TARGET_ENV* = "TR_BITBRAIN_RESET_ON_TARGET"
## ── fixed geometry ────────────────────────────────────────────────────────
BB_WIDTHS* = [6, 8, 10, 12] ## the paper's multi-width ADs
BB_TARGET_RATE* = 0.01 ## the paper's ~1 % firing target
BB_PENDING_CAP* = 512 ## deferred-label queue (>= 4 buckets x 50 ticks)
## ── shipped defaults ──────────────────────────────────────────────────────
BB_N_DEF = 32
BB_NADE_DEF = 256
BB_RANGE_DEF = 40.0
BB_MIN_OBS_DEF = 8
BB_WARMUP_DEF = 400
BB_ADAPT_DEF = 32
BB_CALIB_DEF = 512
BB_DECAY_DEF = 250
BB_DECAY_FRAC_DEF = 0.02
BB_SEED_DEF = 20240921
BB_RESET_ON_TARGET_DEF = true
type
BitMemMode* = enum
bmPerRound, bmRetained, bmDecay
BbPending = object
## One deferred training sample. `lits` is the exact literal vector the ADs
## saw at fire time; the label is resolved `horizon` ticks later.
fireTick: int
horizon: int
selfX*, selfY: float
baseBearing: float
lits: array[TMH_NLITS, uint8]
BitBrainGun* = object
tmh: TmHorizonGun
bb: BitBrain
initialized: bool
# ── resolved config ──────────────────────────────────────────────────────
nClasses*: int
maxDeg*: float
nAde*: int
memMode*: BitMemMode
logEnabled*: bool
minObs*: int
warmupN*: int
adaptEvery*: int
calibEvery*: int
decayEvery*: int
decayFrac*: float
seed*: int64
resetOnTarget*: bool
# ── AD calibration state ─────────────────────────────────────────────────
rng: Rand
hist: seq[seq[int32]] ## per-AD raw-score histogram (bins 2w+1)
histTotal: int
sampleCount*: int
sinceAdapt: int
sinceCalib: int
sinceDecay: int
decays*: int
# ── scratch (avoid per-sample allocation) ────────────────────────────────
scratch: seq[seq[int32]]
counts: seq[int]
# ── deferred labels ──────────────────────────────────────────────────────
pending: array[BB_PENDING_CAP, BbPending]
pendingCount*: int
pendingDropped*: int
# ── per-tick caches ──────────────────────────────────────────────────────
lastTick: int
lastEnqTick: int
lastEnqBucket: int
cachedBits: array[TMH_N_BASE, uint8]
cachedBitsTick: int
bitsValid: bool
# ── accounting / logging ─────────────────────────────────────────────────
trained*: int
lastBest: int
lastShift*: float
corrections*: int
lastLogKey: string
lastLogTick: int
observedTargetId*: int
# ── small pure helpers ───────────────────────────────────────────────────────
proc wrapRadBB(r: float): float {.inline.} =
result = r
while result > PI: result -= 2.0 * PI
while result < -PI: result += 2.0 * PI
proc memModeName*(m: BitMemMode): string =
case m
of bmPerRound: "perRound"
of bmRetained: "retained"
of bmDecay: "decay"
proc parseMemMode*(value: string): BitMemMode =
## Empty / unknown values fall back to the shipped `perRound` (the measured
## winning regime), so a typo cannot silently select another regime.
case value.strip().toLowerAscii()
of "retained", "retain", "accum", "accumulate": bmRetained
of "decay", "forget", "age": bmDecay
else: bmPerRound
proc envFloatBB(name: string, default: float): float =
let v = getEnv(name, "")
if v.len == 0: return default
try: parseFloat(v.strip()) except ValueError: default
proc envIntBB(name: string, default: int): int =
let v = getEnv(name, "")
if v.len == 0: return default
try: parseInt(v.strip()) except ValueError: default
proc envBoolBB(name: string, default: bool): bool =
case getEnv(name, "").strip().toLowerAscii()
of "1", "true", "yes", "on": true
of "0", "false", "no", "off": false
else: default
proc bbCenterDeg*(k, nClasses: int, maxDeg: float): float =
## Centre (degrees) of correction class `k` over ±maxDeg.
let w = 2.0 * maxDeg / float(nClasses)
-maxDeg + (float(k) + 0.5) * w
proc bbClassOf*(errRad: float, nClasses: int, maxDeg: float): int =
## Bin a signed angular error (radians) into one of `nClasses` bins over
## [−maxDeg, +maxDeg] (the gate test's `binOf`).
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
# ── construction / lazy network build ────────────────────────────────────────
proc initBitBrainGun*(): BitBrainGun =
result.nClasses = clamp(envIntBB(BB_N_ENV, BB_N_DEF), 2, 512)
result.nAde = clamp(envIntBB(BB_NADE_ENV, BB_NADE_DEF), 8, 4096)
result.maxDeg = clamp(envFloatBB(BB_RANGE_ENV, BB_RANGE_DEF), 1.0, 180.0)
result.memMode = parseMemMode(getEnv(BB_MEM_ENV, ""))
result.logEnabled = envBoolBB(BB_LOG_ENV, false)
result.minObs = max(1, envIntBB(BB_MIN_OBS_ENV, BB_MIN_OBS_DEF))
result.warmupN = max(0, envIntBB(BB_WARMUP_ENV, BB_WARMUP_DEF))
result.adaptEvery = max(1, envIntBB(BB_ADAPT_ENV, BB_ADAPT_DEF))
result.calibEvery = max(1, envIntBB(BB_CALIB_ENV, BB_CALIB_DEF))
result.decayEvery = max(1, envIntBB(BB_DECAY_ENV, BB_DECAY_DEF))
result.decayFrac = clamp(envFloatBB(BB_DECAY_FRAC_ENV, BB_DECAY_FRAC_DEF), 0.0, 1.0)
result.seed = int64(envIntBB(BB_SEED_ENV, BB_SEED_DEF))
result.resetOnTarget = envBoolBB(BB_RESET_ON_TARGET_ENV, BB_RESET_ON_TARGET_DEF)
result.lastTick = -1
result.lastEnqTick = -1
result.lastEnqBucket = -1
result.observedTargetId = -1
result.rng = initRand(result.seed + 991)
proc resetThresholdsHeuristic(g: var BitBrainGun) =
## Cold-start thresholds: a small multiple of the raw-score standard deviation
## puts every ADE near the paper's ~1 % firing rate from the FIRST ticks, so
## the SBCs see a useful (sparse) coincidence set immediately and inference
## never degenerates into an O(nAde^2) dense scan. The running-histogram
## percentile calibration replaces these once warmup has passed.
for a in 0..<g.bb.ades.len:
let w = float(g.bb.ades[a].width)
let thr = int32(round(2.33 * sqrt(w * 0.28)))
let scaled = int32(g.bb.ades[a].scale) * thr
for e in 0..<g.bb.ades[a].nAde:
g.bb.ades[a].thresholds[e] = scaled
proc ensureInit*(g: var BitBrainGun) =
## Build the AD/SBC network on first use. Never runs on the shipped default
## path (the rack does not admit BitBrain), so the default bot is untouched.
if g.initialized: return
g.initialized = true
g.tmh = initTmHorizonGun()
var rng = initRand(g.seed)
var ades: seq[AddressDecoder]
for w in BB_WIDTHS:
ades.add initRandomAddressDecoder(g.nAde, w, TMH_N_BITS, rng,
scale = DefaultScale, center = 0,
threshold = 0'i32)
g.bb = initBitBrain(ades, crossPairs(ades.len), g.nClasses)
g.hist = newSeq[seq[int32]](ades.len)
g.scratch = newSeq[seq[int32]](ades.len)
for a in 0..<ades.len:
g.hist[a] = newSeq[int32](2 * ades[a].width + 1)
g.counts = newSeq[int](g.nClasses)
g.resetThresholdsHeuristic()
# ── AD calibration (online percentile init + library homeostasis) ────────────
proc calibrate(g: var BitBrainGun) =
## Set every ADE's threshold to the raw score whose `count >= t` is CLOSEST to
## `BB_TARGET_RATE * total` — the gate test's percentile init, run online over
## the running histogram. This is what pins the realised firing rate near 1 %.
if g.histTotal <= 0: return
let target = BB_TARGET_RATE * float(g.histTotal)
for a in 0..<g.bb.ades.len:
let w = g.bb.ades[a].width
let sc = g.bb.ades[a].scale
var cum = 0
var bestRaw = w
var bestDiff = Inf
for raw in countdown(w, -w):
cum += int(g.hist[a][raw + w])
let d = abs(float(cum) - target)
if d < bestDiff:
bestDiff = d
bestRaw = raw
let t = int32(bestRaw) * int32(sc)
for e in 0..<g.bb.ades[a].nAde:
g.bb.ades[a].thresholds[e] = t
proc afterSample(g: var BitBrainGun) =
## Post-sample calibration/homeostasis schedule.
inc g.sampleCount
if g.sampleCount == g.warmupN:
g.calibrate()
g.sinceAdapt = 0
g.sinceCalib = 0
elif g.sampleCount > g.warmupN:
inc g.sinceAdapt
inc g.sinceCalib
if g.sinceAdapt >= g.adaptEvery:
for a in 0..<g.bb.ades.len:
g.bb.ades[a].adaptThresholds(g.adaptEvery, BB_TARGET_RATE, 1)
g.sinceAdapt = 0
if g.sinceCalib >= g.calibEvery:
g.calibrate()
g.sinceCalib = 0
# ── one AD pass: firing counts + histogram + inference ───────────────────────
proc bbObserve(g: var BitBrainGun, lits: array[TMH_NLITS, uint8]) =
## Drive every ADE: update its firing accumulator and the score histogram,
## collect the active list, then infer the class counts into `g.counts`.
for a in 0..<g.bb.ades.len:
let w = g.bb.ades[a].width
let sc = g.bb.ades[a].scale
g.scratch[a].setLen(0)
for e in 0..<g.bb.ades[a].nAde:
var raw = 0
let off = e * w
for j in 0..<w:
let c = g.bb.ades[a].codes[off + j]
let idx = if c > 0'i32: int(c) - 1 else: int(-c) - 1
let pol = if c > 0'i32: 1 else: -1
raw += pol * int(lits[idx])
inc g.hist[a][raw + w]
if raw * sc >= int(g.bb.ades[a].thresholds[e]):
g.scratch[a].add int32(e)
inc g.bb.ades[a].fireCounts[e]
inc g.histTotal
for k in 0..<g.counts.len: g.counts[k] = 0
for sl in 0..<g.bb.sbcs.len:
let spec = g.bb.specs[sl]
g.bb.sbcs[sl].infer(g.scratch[spec.row], g.scratch[spec.col], g.counts)
proc bbLearn(g: var BitBrainGun, lits: array[TMH_NLITS, uint8], cls: int) =
## Recompute the active lists for a resolved sample and set its class bits in
## every SBC (idempotent, so a repeat is a no-op).
for a in 0..<g.bb.ades.len:
let w = g.bb.ades[a].width
let sc = g.bb.ades[a].scale
g.scratch[a].setLen(0)
for e in 0..<g.bb.ades[a].nAde:
var raw = 0
let off = e * w
for j in 0..<w:
let c = g.bb.ades[a].codes[off + j]
let idx = if c > 0'i32: int(c) - 1 else: int(-c) - 1
let pol = if c > 0'i32: 1 else: -1
raw += pol * int(lits[idx])
if raw * sc >= int(g.bb.ades[a].thresholds[e]):
g.scratch[a].add int32(e)
for sl in 0..<g.bb.sbcs.len:
let spec = g.bb.specs[sl]
discard g.bb.sbcs[sl].learn(g.scratch[spec.row], g.scratch[spec.col], cls)
proc applyDecay(g: var BitBrainGun) =
## Age the SBC tensors: zero a fraction of their 32-bit words. This is the
## bounded-memory mechanism the gate test's diagnosis called for (the
## idempotent SBC otherwise only ADDS and saturates with stale class bits).
let cut = int(g.decayFrac * 1000.0)
if cut <= 0: return
for sl in 0..<g.bb.sbcs.len:
for wi in 0..<g.bb.sbcs[sl].bits.len:
if g.rng.rand(999) < cut:
g.bb.sbcs[sl].bits[wi] = 0'u32
inc g.decays
# ── deferred-label resolution (prequential learning) ─────────────────────────
proc resolvePending(g: var BitBrainGun, 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 = wrapRadBB(obs.bearing - p.baseBearing)
let cls = bbClassOf(err, g.nClasses, g.maxDeg)
g.bbLearn(p.lits, cls)
inc g.trained
inc g.sinceDecay
if g.memMode == bmDecay and g.sinceDecay >= g.decayEvery:
g.applyDecay()
g.sinceDecay = 0
else:
inc g.pendingDropped
else:
inc g.pendingDropped
g.pendingCount = w
# ── logging ──────────────────────────────────────────────────────────────────
proc bbLog(g: var BitBrainGun, state: WorldState, h, bucket, total, best: int) =
## ONE change-gated `[bb]` line (behind TR_BITBRAIN_LOG=1) so the user tailing
## the GUI log sees what the corrector is thinking, not one line per tick.
if not g.logEnabled: return
let shift = bbCenterDeg(best, g.nClasses, g.maxDeg)
let key = fmt"{best}|{shift:.1f}"
if key == g.lastLogKey: return
if state.tick == g.lastLogTick: return
g.lastLogKey = key
g.lastLogTick = state.tick
var nz = 0
for k in 0..<g.counts.len:
if g.counts[k] > 0: inc nz
echo fmt"[bb] t={state.tick} h={h} bucket={bucket} cls={best}/{g.nClasses} " &
fmt"shift={shift:+.1f}deg cnt={g.counts[best]}/{total} nz={nz} " &
fmt"trained={g.trained} samples={g.sampleCount} pend={g.pendingCount} " &
fmt"mode={memModeName(g.memMode)} warm={(if g.trained >= g.minObs: 1 else: 0)}"
# ── reset hooks (mirroring TmHorizonGun) ─────────────────────────────────────
proc resetRound(g: var BitBrainGun) =
## PER-ROUND wipe. Always clear the observation ring, deferred labels and
## per-tick caches (the bots teleport between rounds). In `perRound` mode the
## SBCs are wiped too; `retained`/`decay` keep them across the round.
g.tmh.resetRoundState()
g.pendingCount = 0
g.lastTick = -1
g.lastEnqTick = -1
g.lastEnqBucket = -1
g.bitsValid = false
g.lastLogKey = ""
g.lastLogTick = -1
if g.memMode == bmPerRound:
g.bb.resetLearning()
g.trained = 0
proc resetRoundState*(g: var BitBrainGun) =
if not g.initialized: return
g.resetRound()
proc resetLearning*(g: var BitBrainGun, reason = "") =
## PER-BATTLE / PER-ENEMY wipe: SBCs, AD thresholds, histograms and counters.
if not g.initialized: return
g.bb.resetLearning()
g.resetThresholdsHeuristic()
for a in 0..<g.hist.len:
for i in 0..<g.hist[a].len: g.hist[a][i] = 0
g.histTotal = 0
g.sampleCount = 0
g.sinceAdapt = 0
g.sinceCalib = 0
g.sinceDecay = 0
g.decays = 0
g.trained = 0
g.observedTargetId = -1
g.rng = initRand(g.seed + 991)
g.resetRound()
if reason.len > 0 and g.logEnabled:
echo fmt"[bb-reset] reason={reason}"
proc targetChanged*(g: var BitBrainGun, 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: BitBrainGun): bool {.inline.} = true
proc networkBytes*(g: BitBrainGun): int =
## Bytes held by the AD/SBC network (0 until the network is built).
if g.initialized: g.bb.memoryBytes else: 0
proc predict*(g: var BitBrainGun, 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
g.bitsValid = false
# The base prediction is Pattern; BitBrain only corrects its bearing.
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 bucket = tmhHorizonBucket(h)
if not g.bitsValid or g.cachedBitsTick != state.tick:
g.cachedBits = tmhBaseBits(g.tmh, state)
g.cachedBitsTick = state.tick
g.bitsValid = true
let lits = tmhLits(g.cachedBits, bucket)
# Observe this input (AD pass + inference) and advance the calibration clock.
g.bbObserve(lits)
g.afterSample()
# Enqueue one deferred sample per (tick, bucket): predict runs once per power
# bin, so all four horizons contribute evidence.
if g.lastEnqTick != state.tick or g.lastEnqBucket != bucket:
if g.pendingCount < BB_PENDING_CAP:
g.pending[g.pendingCount] = BbPending(
fireTick: state.tick, horizon: h,
selfX: state.selfX, selfY: state.selfY,
baseBearing: arctan2(base.y - state.selfY, base.x - state.selfX),
lits: lits)
inc g.pendingCount
else:
inc g.pendingDropped
g.lastEnqTick = state.tick
g.lastEnqBucket = bucket
# Readout: argmax class centre, zero correction with no evidence / cold.
var shiftDeg = 0.0
if g.trained >= g.minObs:
var total = 0
for k in 0..<g.counts.len: total += g.counts[k]
if total > 0:
var best = 0
for k in 1..<g.counts.len:
if g.counts[k] > g.counts[best]: best = k
shiftDeg = bbCenterDeg(best, g.nClasses, g.maxDeg)
g.lastBest = best
g.lastShift = shiftDeg
inc g.corrections
g.bbLog(state, h, bucket, total, best)
if shiftDeg == 0.0: return base
tmhApplyShift(state.selfX, state.selfY, base.x, base.y, shiftDeg)
proc onResult*(g: var BitBrainGun, 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
common_libs/guns/tm_horizon.nim
+14 -1
View File
@@ -546,6 +546,19 @@ proc ringValidCount*(g: TmHorizonGun): int =
for i in 0..<TMH_POS_RING:
if g.ringValid[i]: inc result
proc tmhObservedAt*(g: TmHorizonGun, tick: int, selfX, selfY: float
): tuple[ok: bool, bearing, dist: float, lastSeenTick: int] =
## Exported seam for the BitBrain corrector: the enemy's OBSERVED bearing and
## distance (relative to `(selfX, selfY)`) at `tick`, if the observation ring
## holds that exact tick. `lastSeenTick` lets the caller apply the same
## staleness rule TMHorizon uses before trusting the label. `ok = false` when
## the tick is missing (e.g. across a round boundary or before warm-up).
let r = g.ringAt(tick)
if not r.ok:
return (false, 0.0, 0.0, 0)
(true, arctan2(r.t.ey - selfY, r.t.ex - selfX),
hypot(r.t.ex - selfX, r.t.ey - selfY), r.t.lastSeenTick)
proc ringHas*(g: TmHorizonGun, tick: int): bool =
## Observability / test seam: is there a valid observation exactly at `tick`?
## Used to prove a round boundary drops the old positions (so a label can never
@@ -598,7 +611,7 @@ proc bulletFeatures(g: var TmHorizonGun, state: WorldState): tuple[tta: int, lat
result.lat = b.ux * vy - b.uy * vx
g.bulletCount = w
proc tmhUpdateHistory(g: var TmHorizonGun, state: WorldState) =
proc tmhUpdateHistory*(g: var TmHorizonGun, state: WorldState) =
## Once per tick: write the observation ring, advance the reversal counter and
## the self-energy-drop bullet proxy.
let slot = ((state.tick mod TMH_POS_RING) + TMH_POS_RING) mod TMH_POS_RING