Files
SirRoboGarage/common_libs/tests/sweep_tm_pattern.nim
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SirStone 1ea72c7f14 TM gun round 2: base was never behind; RADIAL target beats Linear on bmPoint
=== TASK 1: MY PREMISE WAS REFUTED ===
I instructed the job to "fix the baseline" because an earlier measurement said the
TM gun's base did not iterate flight time like `LinearGun`. MEASURED: the new gun's
base is BYTE-FOR-BYTE `LinearGun` - 18/18 runs tie exactly, p=1.000, every per-run
row byte-identical. The "non-iterating baseline" belonged to the OLD `tsetlin.nim`,
not this gun. So no fix was needed, and the earlier inference should not have been
generalised to the new gun. (It did still align the zero-correction clamp to
LinearGun's exact [0, arena] range, and reports the old BotRadius-inset base was a
wash/marginally better at 34.2%/24.7%.)

=== TASK 2: THE RADIAL TARGET - A CONTROL-VALIDATED WIN, BUT ONLY ON bmPoint ===
Instead of the lateral (GF-bucket) component - which the linear lead already
captures - the TM now predicts the RADIAL component: will the enemy be nearer or
farther than the base prediction when our bullet arrives? A 5-class radial head
sharing the same 40-bit context and TM core; the readout advances/retards the aim
distance along the base bearing.

  under bmPath (the SHIPPED metric): STRUCTURAL NO-OP
    synthetic 8/8 exact ties, p=1.0; real 33.9%/24.1% vs Linear 34.0%/24.3%
  under bmPoint: A WIN, control-validated
    TMRadial 9.4% (6013/63785) / 5.8% (42079/726652)
    Linear   7.2% / 4.7%          overall 17/1, p=0.0001
    Tsetlin  7.0% / 4.8%          overall 15/3, p=0.0075
    shuffled 7.0% / 3.6%          early 17/1 p=0.0001; overall 18/0, p<0.0001
  radial head online accuracy 48.8% vs 19.9% shuffled chance and 36.7% majority
  -> it is CONDITIONAL learning, not a constant short-range bias.
Best config: TM_RADIAL_RANGE=60, TM_RAD_MARGIN=0.25, 5 classes.

CAVEAT THAT MATTERS: a win on `bmPoint` is NOT yet evidence of a real win. `bmPath`
is the shipped SELECTION metric precisely because it beat `bmPoint` on real hit
rate (7.43% vs 4.70%). But that A/B was about which gun to PICK, not about gun
QUALITY - a gun can be better in reality while scoring worse on the selection
metric. So this needs a LIVE test, and it is the decisive one.

=== TASK 3: REVERSAL TARGET - CLEAN NEGATIVE ===
The label positive rate is only 9.7% (rev=[24772,2673]) and the head's 86.8%
accuracy is BELOW the 90.3% majority baseline: it does not learn the positive
class at all. Hit-rate effect neutral (bmPath 19.5%/18.4% vs shuffled 19.1%/17.8%,
p=0.24/0.82). Dropped.

=== OVERALL ===
Not competitive on the shipped bmPath metric (gated GF 28.3%/22.2% vs Linear
34.0%/24.3%, p=0.0075). Better than Linear on bmPoint via TMRadial (+2.2pp early,
+1.1pp overall). Per-enemy reset exists; a fresh gun per round; NO cross-battle
persistence (the user's non-negotiable).

MEASURED LIMITATION: radial mode has a high labelMiss because aiming short
resolves BEFORE the base arrival tick, biasing training toward resolvable samples.
The metric win is label-independent. A deferred-label fix is the next refinement.
INFERRED: the mechanism is surfers being NEARER than the base prediction
(range-holding); a constant-short-offset ablation would separate a learned
short-range bias from genuine per-tick conditional prediction.
2026-09-22 01:27:59 +02:00

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## Discrete-target Tsetlin gun sweep: tm_pattern vs its shuffled-feedback
## control, the current default Tsetlin gun, and Linear.
##
## Metric: EARLY virtual hit rate = resolutions in the first 100 ticks of each
## ROUND (the TM starts cold every round, so this is what "learns fast" means),
## plus first-300 and whole-round rates. Every fixture with a round sidecar is
## split into rounds and each round is replayed with a FRESH gun instance
## (cold every battle, overfit within the battle).
##
## Usage:
## nim c -r --path:common_libs -d:release common_libs/tests/sweep_tm_pattern.nim \
## --set=real --seeds=3
## Flags:
## --set=real|range|synthetic fixture set (default real)
## --seeds=N seeds for stochastic variants (default 3)
## --maxrounds=N cap rounds per fixture
## --variants=a,b,c subset of: linear,tsetlin,tmpat,tmpat_shuf
##
## Emits per-run CSV then a COMPARE section with per-run means, ranges and a
## paired sign test (exact binomial, two-sided) for every variant pair.
import std/[os, strformat, strutils, json, tables, math, random, algorithm, sequtils]
import gun_harness/offline_range
import range_guns
import guns/tm_pattern
import guns/linear
import guns/lead_forecast
type
LinearInsetGun = object ## pre-fix TMPattern base: same forecast, BotRadius clamp
proc predict*(g: var LinearInsetGun, state: WorldState, bulletSpeed: float): GunPrediction =
let f = forecastLinear(state, bulletSpeed)
GunPrediction(x: clamp(f.x, BotRadius, state.arenaWidth - BotRadius),
y: clamp(f.y, BotRadius, state.arenaHeight - BotRadius))
proc onResult*(g: var LinearInsetGun, e: FeedbackEvent) = discard
const repoRoot = currentSourcePath().parentDir.parentDir.parentDir
const fixturesDir = repoRoot / "tools" / "fixtures"
type
GunStats = object
obs, labelMiss, traceMiss: int
labHist, choHist: array[TM_CLASSES, int]
classCorrect, classTotal: int
radCorrect, radTotal, revCorrect, revTotal: int
Adapt = object
h100, n100, h300, n300, hall, nall, f100, m100: int
rounds: int
st: GunStats
RoundSpan = tuple[start, count: int]
Row = object
variant, fixture: string
seed: int
r: Adapt
VariantKind = enum
vLinear, vLinearOld, vTsetlin, vTmpat, vTmpatShuf, vTmpatBase,
vTmpatRad, vTmpatRadShuf, vTmpatRev, vTmpatRevShuf
proc variantName(v: VariantKind): string =
case v
of vLinear: "Linear"
of vLinearOld: "LinearOldClamp"
of vTsetlin: "Tsetlin"
of vTmpat: "TMPattern"
of vTmpatShuf: "TMPatternShuf"
of vTmpatBase: "TMPatternBase"
of vTmpatRad: "TMRadial"
of vTmpatRadShuf: "TMRadialShuf"
of vTmpatRev: "TMReversal"
of vTmpatRevShuf: "TMReversalShuf"
proc loadRounds(path: string): seq[RoundSpan] =
let dir = path.parentDir
let base = path.extractFilename
var side = dir / "drussgt_meta" / (base & ".rounds.json")
if not fileExists(side): side = dir / (base & ".rounds.json")
if not fileExists(side): return @[]
let node = parseJson(readFile(side))
if not node.hasKey("rounds"): return @[]
for r in node["rounds"]:
result.add (r["startTick"].getInt(), r["count"].getInt())
proc addAdapt(dst: var Adapt, src: Adapt) =
inc dst.rounds, src.rounds
dst.h100 += src.h100; dst.n100 += src.n100
dst.h300 += src.h300; dst.n300 += src.n300
dst.hall += src.hall; dst.nall += src.nall
dst.f100 += src.f100; dst.m100 += src.m100
dst.st.obs += src.st.obs; dst.st.labelMiss += src.st.labelMiss
dst.st.traceMiss += src.st.traceMiss
dst.st.classCorrect += src.st.classCorrect
dst.st.classTotal += src.st.classTotal
dst.st.radCorrect += src.st.radCorrect
dst.st.radTotal += src.st.radTotal
dst.st.revCorrect += src.st.revCorrect
dst.st.revTotal += src.st.revTotal
for c in 0..<TM_CLASSES:
dst.st.labHist[c] += src.st.labHist[c]
dst.st.choHist[c] += src.st.choHist[c]
proc replayRound(states: seq[WorldState], lastSeen: seq[int], enemyId, baseTick: int,
driver: GunDriver, metric: BulletMetric,
obsBefore: GunStats, obsCount: proc(): GunStats): Adapt =
var tracker = initTracker(1, metric)
var res: Adapt
inc res.rounds
for si in 0..<states.len:
let state = states[si]
var preds: array[len(PowerBins), GunPrediction]
for i in 0..<len(PowerBins):
preds[i] = driver.predictCb(state, bulletSpeed(PowerBins[i]))
let ready = if driver.readyCb == nil: true else: driver.readyCb()
if ready:
tracker.spawnBullets(0, preds, state, enemyId)
var enemyPositions: Table[int, tuple[x, y: float, lastSeenTick: int, alive: bool]]
var lst = state.tick
if si < lastSeen.len and lastSeen[si] >= 0: lst = lastSeen[si]
if state.enemies.len > 0:
for e in state.enemies:
enemyPositions[e.id] = (x: e.x, y: e.y, lastSeenTick: lst, alive: true)
else:
enemyPositions[enemyId] = (x: state.enemyX, y: state.enemyY,
lastSeenTick: lst, alive: true)
let localTick = state.tick - baseTick
tracker.tickBullets(state, enemyPositions,
proc(gunId: GunId, binIdx: int, e: FeedbackEvent) =
inc res.nall
if e.hit: inc res.hall
if localTick < 100:
inc res.n100
if e.hit: inc res.h100
if localTick < 300:
inc res.n300
if e.hit: inc res.h300
let fireTick = e.fireTick - baseTick
if fireTick < 100:
inc res.m100
if e.hit: inc res.f100
driver.resultCb(e))
let after = obsCount()
res.st.obs = after.obs - obsBefore.obs
res.st.labelMiss = after.labelMiss - obsBefore.labelMiss
res.st.traceMiss = after.traceMiss - obsBefore.traceMiss
res.st.classCorrect = after.classCorrect - obsBefore.classCorrect
res.st.classTotal = after.classTotal - obsBefore.classTotal
res.st.radCorrect = after.radCorrect - obsBefore.radCorrect
res.st.radTotal = after.radTotal - obsBefore.radTotal
res.st.revCorrect = after.revCorrect - obsBefore.revCorrect
res.st.revTotal = after.revTotal - obsBefore.revTotal
for c in 0..<TM_CLASSES:
res.st.labHist[c] = after.labHist[c] - obsBefore.labHist[c]
res.st.choHist[c] = after.choHist[c] - obsBefore.choHist[c]
result = res
proc replayFixture(fx: Fixture, path: string, driver: GunDriver, metric: BulletMetric,
obsBefore: GunStats, obsCount: proc(): GunStats,
maxRounds = 0): Adapt =
var spans =
if fx.meta.source == "synthetic": @[(start: 0, count: fx.states.len)]
else: loadRounds(path)
if maxRounds > 0 and spans.len > maxRounds: spans.setLen(maxRounds)
if spans.len == 0:
return replayRound(fx.states, fx.lastSeen, fx.enemyId, 0, driver, metric,
obsBefore, obsCount)
for sp in spans:
var st: seq[WorldState]
var ls: seq[int]
for i in 0..<fx.states.len:
let t = fx.states[i].tick
if t >= sp.start and t < sp.start + sp.count:
st.add fx.states[i]
ls.add(if i < fx.lastSeen.len: fx.lastSeen[i] else: -1)
if st.len == 0: continue
addAdapt(result, replayRound(st, ls, fx.enemyId, sp.start, driver, metric,
obsBefore, obsCount))
proc emptyStats(): GunStats = GunStats()
proc makeTmpatDriver(seed: int, shuffle: bool, forceBase = false,
mode = tmGF):
tuple[driver: GunDriver, gun: ref TmPatternGun] =
let g = new(TmPatternGun)
g[] = initTmPatternGun()
g[].shuffleLabels = shuffle
g[].forceBase = forceBase
g[].targetMode = mode
if seed >= 0: randomize(seed)
result.gun = g
result.driver = GunDriver(
name: (if forceBase: "TMPatternBase" elif shuffle: "TMPatternShuf" else: "TMPattern"),
predictCb: proc(state: WorldState, bulletSpeed: float): GunPrediction =
g[].predict(state, bulletSpeed),
resultCb: proc(e: FeedbackEvent) = g[].onResult(e),
readyCb: proc(): bool = g[].isWarmedUp())
proc tmpatStats(g: ref TmPatternGun): GunStats =
result.obs = g[].totalObs
result.labelMiss = g[].labelMisses
result.traceMiss = g[].traceMisses
result.labHist = g[].labelHist
result.choHist = g[].chosenHist
result.classCorrect = g[].classCorrect
result.classTotal = g[].classTotal
result.radCorrect = g[].radCorrect
result.radTotal = g[].radTotal
result.revCorrect = g[].revCorrect
result.revTotal = g[].revTotal
proc fixtureSet(name: string): seq[string] =
case name
of "synthetic", "":
for n in SyntheticFixtureNames: result.add n
of "real":
for n in ["drussgt_vs_crazy", "drussgt_vs_spinbot", "drussgt_vs_drussgt",
"tr_drussgt_vs_crazy", "tr_drussgt_vs_spinbot",
"tr_drussgt_vs_modularbot"]:
result.add(fixturesDir / (n & ".jsonl"))
of "range":
for n in SyntheticFixtureNames: result.add n
for n in ["drussgt_vs_crazy", "drussgt_vs_spinbot", "tr_drussgt_vs_crazy"]:
result.add(fixturesDir / (n & ".jsonl"))
else: discard
proc resolve(name: string): tuple[fx: Fixture, path: string] =
let p = if fileExists(name): name else: fixturesDir / (name & ".jsonl")
(loadFixture(p), p)
# ── stats ────────────────────────────────────────────────────────────────────
proc rateStr(h, n: int): string =
if n == 0: " n/a " else: &"{h.float / n.float * 100.0:5.1f}%"
proc binomPmf(k, n: int): float =
if k < 0 or k > n: return 0.0
var lg = 0.0
for i in 1..k: lg += ln(float(n - k + i)) - ln(float(i))
exp(lg - float(n) * ln(2.0))
proc signTestP(wins, n: int): float =
## exact two-sided binomial p under p=0.5
if n == 0: return 1.0
let lo = min(wins, n - wins)
var s = 0.0
for k in 0..lo: s += binomPmf(k, n)
min(1.0, 2.0 * s)
proc main() =
var nSeeds = 3
var set = "real"
var maxRounds = 0
var metricName = "path"
var variants: seq[VariantKind] = @[vLinear, vTsetlin, vTmpat, vTmpatShuf]
for i in 1..paramCount():
let a = paramStr(i)
if a.startsWith("--seeds="): nSeeds = parseInt(a[8..^1])
elif a.startsWith("--set="): set = a[6..^1]
elif a.startsWith("--metric="): metricName = a[9..^1]
elif a.startsWith("--maxrounds="): maxRounds = parseInt(a[12..^1])
elif a.startsWith("--variants="):
variants = @[]
for tok in a[11..^1].split(','):
case tok.strip()
of "linear": variants.add vLinear
of "linear_old": variants.add vLinearOld
of "tsetlin": variants.add vTsetlin
of "tmpat": variants.add vTmpat
of "tmpat_shuf": variants.add vTmpatShuf
of "tmbase": variants.add vTmpatBase
of "tmrad": variants.add vTmpatRad
of "tmrad_shuf": variants.add vTmpatRadShuf
of "tmrev": variants.add vTmpatRev
of "tmrev_shuf": variants.add vTmpatRevShuf
else: discard
let metric = if metricName == "point": bmPoint else: bmPath
let names = fixtureSet(set)
echo "# set=", set, " seeds=", nSeeds, " metric=", metricName, " variants=", variants.mapIt(variantName(it)).join(",")
echo "variant,fixture,seed,h100,n100,h300,n300,hall,nall,f100,m100,rounds,obs,labelMiss,traceMiss"
var rows: seq[Row]
var revLabTab = initTable[string, array[2, int]]()
var radLabTab = initTable[string, array[TM_CLASSES, int]]()
for name in names:
let (fx, path) = resolve(name)
let fxName = path.extractFilename.replace(".jsonl", "")
for v in variants:
let nIter = if v in [vLinear, vLinearOld, vTmpatBase]: 1 else: nSeeds
for seed in 1..nIter:
var drv: GunDriver
var gun: ref TmPatternGun
var obsCount: proc(): GunStats = emptyStats
case v
of vLinear:
drv = makeDriver("Linear", LinearGun())
of vLinearOld:
drv = makeDriver("LinearOldClamp", LinearInsetGun())
of vTsetlin:
let pair = makeTsetlinDriver(seed = seed)
drv = pair.driver
of vTmpat, vTmpatShuf, vTmpatBase, vTmpatRad, vTmpatRadShuf,
vTmpatRev, vTmpatRevShuf:
let mode =
case v
of vTmpatRad, vTmpatRadShuf: tmRadial
of vTmpatRev, vTmpatRevShuf: tmReversal
else: tmGF
let shuf = v in [vTmpatShuf, vTmpatRadShuf, vTmpatRevShuf]
let pair = makeTmpatDriver(seed = seed, shuffle = shuf,
forceBase = (v == vTmpatBase), mode = mode)
drv = pair.driver
gun = pair.gun
obsCount = proc(): GunStats = tmpatStats(gun)
let r = replayFixture(fx, path, drv, metric, emptyStats(), obsCount, maxRounds)
rows.add Row(variant: variantName(v), fixture: fxName, seed: seed, r: r)
if gun != nil:
if variantName(v) notin revLabTab: revLabTab[variantName(v)] = [0, 0]
if variantName(v) notin radLabTab:
radLabTab[variantName(v)] = default(array[TM_CLASSES, int])
for c in 0..<2: revLabTab[variantName(v)][c] += gun[].revLabelHist[c]
for c in 0..<TM_CLASSES: radLabTab[variantName(v)][c] += gun[].radLabelHist[c]
echo &"{variantName(v)},{fxName},{seed},{r.h100},{r.n100},{r.h300},{r.n300}," &
&"{r.hall},{r.nall},{r.f100},{r.m100},{r.rounds},{r.st.obs},{r.st.labelMiss},{r.st.traceMiss}"
# ── per-variant pooled summary ──
echo "\n# ── pooled summary ──"
echo "variant,runs,h100,n100,early%,h300,n300,early300%,hall,nall,overall%,obs,labelMiss,traceMiss"
var pooled = initTable[string, Adapt]()
for v in variants: pooled[variantName(v)] = Adapt()
for row in rows: addAdapt(pooled[row.variant], row.r)
for v in variants:
let a = pooled[variantName(v)]
echo &"{variantName(v)},{a.rounds},{a.h100},{a.n100},{rateStr(a.h100, a.n100)}," &
&"{a.h300},{a.n300},{rateStr(a.h300, a.n300)},{a.hall},{a.nall}," &
&"{rateStr(a.hall, a.nall)},{a.st.obs},{a.st.labelMiss},{a.st.traceMiss}"
# ── label vs chosen class histogram (all TM variants) ──
for v in [vTmpat, vTmpatShuf, vTmpatRad, vTmpatRadShuf, vTmpatRev, vTmpatRevShuf]:
if v in variants:
let a = pooled[variantName(v)]
var ls, cs: string
for c in 0..<TM_CLASSES:
ls.add &"{a.st.labHist[c]},"
cs.add &"{a.st.choHist[c]},"
echo &"\n# class histogram {variantName(v)}: labels=[{ls}] chosen=[{cs}] " &
&"onlineAcc={a.st.classCorrect}/{a.st.classTotal} " &
&"radAcc={a.st.radCorrect}/{a.st.radTotal} " &
&"revAcc={a.st.revCorrect}/{a.st.revTotal}"
if variantName(v) in revLabTab:
var rl, rdl: string
for c in 0..<2: rl.add &"{revLabTab[variantName(v)][c]},"
for c in 0..<TM_CLASSES: rdl.add &"{radLabTab[variantName(v)][c]},"
echo &"# label hist {variantName(v)}: radial=[{rdl}] rev=[{rl}]"
# ── per-run distributions (a "run" = one fixture × one seed) ──
# Linear is deterministic: replicate its one row per fixture across seeds so a
# paired comparison against a stochastic variant has a partner per run.
type Key = tuple[fixture: string, seed: int]
var byVariant = initTable[string, Table[Key, float]]() # early rate
var byVariantAll = initTable[string, Table[Key, float]]() # overall rate
for v in variants:
byVariant[variantName(v)] = initTable[Key, float]()
byVariantAll[variantName(v)] = initTable[Key, float]()
for row in rows:
let early = if row.r.n100 > 0: row.r.h100.float / row.r.n100.float else: 0.0
let overall = if row.r.nall > 0: row.r.hall.float / row.r.nall.float else: 0.0
byVariant[row.variant][(row.fixture, row.seed)] = early
byVariantAll[row.variant][(row.fixture, row.seed)] = overall
if vLinear in variants:
for row in rows:
if row.variant == "Linear":
for s in 2..nSeeds:
byVariant["Linear"][(row.fixture, s)] = byVariant["Linear"][(row.fixture, 1)]
byVariantAll["Linear"][(row.fixture, s)] = byVariantAll["Linear"][(row.fixture, 1)]
if vLinearOld in variants:
for row in rows:
if row.variant == "LinearOldClamp":
for s in 2..nSeeds:
byVariant["LinearOldClamp"][(row.fixture, s)] = byVariant["LinearOldClamp"][(row.fixture, 1)]
byVariantAll["LinearOldClamp"][(row.fixture, s)] = byVariantAll["LinearOldClamp"][(row.fixture, 1)]
if vTmpatBase in variants:
for row in rows:
if row.variant == "TMPatternBase":
for s in 2..nSeeds:
byVariant["TMPatternBase"][(row.fixture, s)] = byVariant["TMPatternBase"][(row.fixture, 1)]
byVariantAll["TMPatternBase"][(row.fixture, s)] = byVariantAll["TMPatternBase"][(row.fixture, 1)]
echo "\n# ── per-run early-rate distribution (mean / min / max, n runs) ──"
echo "variant,earlyMean%,earlyMin%,earlyMax%,overallMean%,overallMin%,overallMax%,n"
for v in variants:
var es: seq[float]
var os: seq[float]
for r in byVariant[variantName(v)].values: es.add r
for r in byVariantAll[variantName(v)].values: os.add r
if es.len == 0: continue
es.sort(); os.sort()
echo &"{variantName(v)},{es.sum/float(es.len)*100:.2f},{es[0]*100:.2f},{es[^1]*100:.2f}," &
&"{os.sum/float(os.len)*100:.2f},{os[0]*100:.2f},{os[^1]*100:.2f},{es.len}"
echo "\n# ── pairwise paired sign tests (rows = fixture×seed) ──"
echo "A,B,metric,nA>B,nB>A,ties,p"
for i in 0..<variants.len:
for j in 0..<variants.len:
if i == j: continue
let aName = variantName(variants[i])
let bName = variantName(variants[j])
for (label, tab) in [("early", byVariant), ("overall", byVariantAll)]:
var winsA, winsB, ties, n = 0
for k, va in tab[aName].pairs:
if k notin tab[bName]: continue
let vb = tab[bName][k]
inc n
if va > vb: inc winsA
elif vb > va: inc winsB
else: inc ties
if n == 0: continue
echo &"{aName},{bName},{label},{n},{winsA},{winsB},{ties},{signTestP(winsA, n - ties):.4f}"
when isMainModule:
main()