TMComposites gate: per-gun confidence faithful for 3 guns; no pair composes
Adds a per-sample intrinsic-confidence field (GunPrediction.confidence, threaded through FeedbackEvent/VirtualBullet, populated by Pattern, DecayGF, KNN, GuessFactor, Tsetlin, TMHorizon) and an offline recorder + analyzer that reproduce the paper's Figure 2 per gun and its Eq-8 composite. Measured on 3 held-out tr-bridge DrussGT battles (33k ticks, ~133k samples/gun): - FAITHFUL: DecayGF (rho +0.133), KNN (+0.090), Pattern (+0.064, weak). - GuessFactor is ANTI-faithful (rho -0.067); Tsetlin c_max is useless (0.001). - No pair of guns specialises complementarily: the same gun dominates both high-confidence slices in every pair. - Eq-8 alpha-normalised confidence-weighted composite: 18.41% vs Pattern 20.45% (McNemar p=3.1e-126). Faithful-only variant 18.68%, still loses. Shuffle control passes weakly (composite > shuffle, p=4e-14) so ~0.7pp of competence is real but ~2pp short. Offline veto: design is dead. See docs/tmcomposites_gate.md.
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@@ -1091,13 +1091,16 @@ proc predict*(g: var TmHorizonGun, state: WorldState,
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let sgn = if side == 1: 1.0 else: -1.0
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shift = sgn * g.shiftDeg * magScale
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let pred =
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if shift == 0.0: base
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else: tmhApplyShift(state.selfX, state.selfY, base.x, base.y, shift)
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# TMComposites Eq 4 analogue: the side-machine's class-sum margin (normalised
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# by the clause half-count) is the per-sample confidence in WHICH WAY to shift.
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var predOut = base
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if shift != 0.0:
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predOut = tmhApplyShift(state.selfX, state.selfY, base.x, base.y, shift)
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if warm: predOut.confidence = ev.sideConf
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let aimDeg = radToDeg(arctan2(pred.y - state.selfY, pred.x - state.selfX))
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let aimDeg = radToDeg(arctan2(predOut.y - state.selfY, predOut.x - state.selfX))
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g.tmhLog(state, bulletSpeed, h, side, mag, ev.sideConf, shift, aimDeg)
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pred
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predOut
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proc onResult*(g: var TmHorizonGun, e: FeedbackEvent) =
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## The label comes from our own observation ring, not from virtual-bullet
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