4657fe715e
The audit inferred (from code) that GF/DecayGF/KNN pop the OLDEST wave on resolution, while under bmPath bullets leave the arena in NON-FIFO order - so an outcome could be attached to the wrong wave. It also noted that `starved=0` does NOT rule this out. Both halves are now MEASURED. MISPAIRING RATE (10 DrussGT fixtures, real VirtualTracker, 344k resolutions/gun): gun bmPath mispair label err bmPoint mispair label err GuessFactor 36.48% 19.39% 18.24% 7.62% DecayGF 36.85% 19.52% 20.57% 8.64% KNN 57.91% 27.63% 29.75% 11.58% (starved = 0 everywhere, exactly as the audit predicted) So ~1 in 5 GF/DecayGF learning samples and ~1 in 4 KNN samples carried a WRONG guess-factor bin. This is a material corruption of the learning signal. FIX: the same fireTick-keyed ring scheme `tsetlin.nim`/`tm_selector.nim` already use - `slot = (fireTick*4 + bin) mod 1024` (period 256 ticks, longer than the ~91-tick max flight), looked up by exact key. Public interfaces unchanged; added `waveResolved`/`waveMispaired` integrity counters. AFTER: mispaired = 0 and starved = 0, both metrics, all three guns. EFFECT ON HIT RATE: SMALL AND NOT SIGNIFICANT. bmPath 4000 samples/gun: GuessFactor 23.20% -> 23.02% (-0.18pp, per-run sign-flip p=0.750) DecayGF 23.80% -> 24.25% (+0.45pp, p=0.625) KNN 18.27% -> 18.80% (+0.53pp, p=0.547) bmPoint: +0.05 / +0.33 / -0.15pp, p = 1.00 / 0.50 / 0.50. Per-run ranges overlap almost completely. A bullet-level z-test is anti-conservative (bullets within a fixture share a trajectory) and its KNN p=1.9e-16 cannot be trusted given ~10 effective independent runs. PLAIN READING: this is a CORRECTNESS fix, not a measurable hit-rate win. It removes a 36-58% mislabelling of the learning signal; the point estimates move by at most ~0.5pp, within run-to-run noise. Stated plainly rather than oversold. A REGRESSION IT CAUGHT IN ITSELF (and this explains the SIGSEGV another job saw and correctly attributed to a concurrent knn_gun.nim rewrite): the first implementation put an inline `array[1024, KNNWave]` (~100KB) inside each gun, which overflowed the default 8MB stack and made `test_power_selection` SIGSEGV. Causation was proven by stashing only the three gun files (test passed), then fixed by making the rings heap-backed `seq`. Verified: `test_power_selection` 3 PASS on the default stack, and zero inline `array[1024]` remain. Guards: test_wave_pairing 17 (new, pure), test_gun_harness 39, test_vbullet_metric 11, test_power_selection 3, test_adaptive_radar 41, test_tfil_ring_weights 24, test_power_policy 26, test_ram_decision 28. ModularBot compiles. Adds audit_wave_pairing.nim and compare_pairing.nim.
152 lines
5.0 KiB
Nim
152 lines
5.0 KiB
Nim
## Recency-weighted GF gun: exponential decay on histogram bins.
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## decay=0.998/tick gives ~350-tick half-life — adapts to mid-battle strategy shifts.
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## Everything else identical to guess_factor.nim, including exact (fireTick,
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## powerBin)-keyed wave pairing (see guess_factor.nim for the measured FIFO defect).
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import std/math
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import gun_harness/gun_interface
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import gun_harness/virtual_bullets as vb # PowerBins
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import guns/lead_forecast
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const
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GFBins = 31
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GFPrior = 0.1
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DecayRate = 0.998 # ponytail: single global decay, tune if adaptation too slow/fast
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DecayWaveRingSlots = 1024 # (fireTick, powerBin) ring; see guess_factor.nim
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type
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DWave = object
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fireX, fireY: float
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fireBearing: float
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fireTick: int
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bin: int
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alive: bool
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DecayGFGun* = object
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bins: array[GFBins, float]
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# Exact (fireTick, powerBin)-keyed ring; a resolved bullet is matched to the
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# wave it actually fired, no matter how many other shots resolved first.
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# Heap-backed (seq): see guess_factor.nim.
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waves: seq[DWave]
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waveStoredTick: array[len(vb.PowerBins), int] # last tick a wave was queued for this bin
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vt: VelocityTracker # enemy velocity history (base selection)
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cachedTick: int # last tick bins were decayed
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wavePushes*: int
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waveStarved*: int
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waveResolved*: int
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waveMispaired*: int # ring-slot collision (impossible by design)
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debugGraphics*: bool
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proc initDecayGFGun*(): DecayGFGun =
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result.cachedTick = -1
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result.debugGraphics = false
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result.waves = newSeq[DWave](DecayWaveRingSlots)
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for b in 0..<len(vb.PowerBins):
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result.waveStoredTick[b] = -1
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let center = (GFBins - 1) div 2
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for i in 0..<GFBins:
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let d = abs(i - center)
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result.bins[i] = GFPrior + 0.5 / float(1 + d)
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proc gfToIndex(gf: float): int {.inline.} =
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clamp(int(round((gf + 1.0) * 0.5 * float(GFBins - 1))), 0, GFBins - 1)
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proc indexToGF(idx: int): float {.inline.} =
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float(idx) / float(GFBins - 1) * 2.0 - 1.0
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proc peakBin*(g: DecayGFGun): int =
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var best = 0
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for i in 1..<GFBins:
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if g.bins[i] > g.bins[best]:
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best = i
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best
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proc binForSpeed(spd: float): int {.inline.} =
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for i in 0..<len(vb.PowerBins):
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if abs(spd - bulletSpeed(vb.PowerBins[i])) < 1e-6:
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return i
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-1
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proc binForPower(power: float): int {.inline.} =
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for i in 0..<len(vb.PowerBins):
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if abs(power - vb.PowerBins[i]) < 1e-6:
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return i
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-1
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proc waveSlot(fireTick, binIdx: int): int {.inline.} =
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((fireTick * len(vb.PowerBins)) + binIdx) mod DecayWaveRingSlots
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proc predict*(g: var DecayGFGun, state: WorldState, bulletSpeed: float): GunPrediction =
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if bulletSpeed <= 0.0:
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return GunPrediction(x: state.enemyX, y: state.enemyY)
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let mea = arcsin(clamp(8.0 / bulletSpeed, -1.0, 1.0))
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if state.tick != g.cachedTick:
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g.cachedTick = state.tick
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g.vt.observe(state)
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# Decay all bins once per tick
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for i in 0..<GFBins:
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g.bins[i] *= DecayRate
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# Base forecast: the GF learns the residual against a self-consistent base
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# prediction (see lead_forecast.nim).
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let f = forecastRadialBlend(state, bulletSpeed, g.vt)
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# Queue at most one wave per (tick, power bin); the fire site's extra predict()
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# call for the selected bin lands on the same tick and reuses the queued wave.
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let binIdx = binForSpeed(bulletSpeed)
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if binIdx >= 0 and g.waveStoredTick[binIdx] != state.tick:
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let slot = waveSlot(state.tick, binIdx)
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g.waves[slot] = DWave(fireX: state.selfX, fireY: state.selfY,
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fireBearing: f.bearing, fireTick: state.tick,
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bin: binIdx, alive: true)
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g.waveStoredTick[binIdx] = state.tick
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inc g.wavePushes
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let peak = g.peakBin()
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let peakGF = indexToGF(peak)
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let gfAngle = f.bearing + peakGF * mea
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let px = state.selfX + cos(gfAngle) * f.dist
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let py = state.selfY + sin(gfAngle) * f.dist
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GunPrediction(
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x: clamp(px, BotRadius, state.arenaWidth - BotRadius),
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y: clamp(py, BotRadius, state.arenaHeight - BotRadius),
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)
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proc onResult*(g: var DecayGFGun, e: FeedbackEvent) =
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let binIdx =
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if e.powerBin >= 0 and e.powerBin < len(vb.PowerBins): e.powerBin
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else: binForPower(e.bulletPower)
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if binIdx < 0: return
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let slot = waveSlot(e.fireTick, binIdx)
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var w = addr g.waves[slot]
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if not w.alive:
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inc g.waveStarved
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return
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if w.fireTick != e.fireTick:
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inc g.waveMispaired
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inc g.waveStarved
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return
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inc g.waveResolved
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let speed = bulletSpeed(e.bulletPower)
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let mea = arcsin(clamp(8.0 / speed, -1.0, 1.0))
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let actualDx = e.actualX - w.fireX
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let actualDy = e.actualY - w.fireY
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let actualBearing = arctan2(actualDy, actualDx)
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var bearingDelta = actualBearing - w.fireBearing
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while bearingDelta > PI: bearingDelta -= 2.0*PI
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while bearingDelta < -PI: bearingDelta += 2.0*PI
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let gf = if mea > 1e-10: clamp(bearingDelta / mea, -1.0, 1.0) else: 0.0
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let centerIdx = gfToIndex(gf)
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for i in 0..<GFBins:
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let d = abs(i - centerIdx)
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g.bins[i] += 1.0 / float(1 + d)
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w.alive = false
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