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.

common_libs/guns/pattern_matcher.nim

@@ -50,6 +50,10 @@ type
     cacheValid:     bool
     cacheTick:      int
     bestMatch:      int          ## -1 = no usable match (linear fallback)
+    lastMatchScore*: float       ## best pattern-match cost (lower = better);
+                                 ## set by findBestMatch, exposed as the gun's
+                                 ## intrinsic per-sample confidence
+                                 ## (TMComposites gate, docs/tmcomposites_gate.md)
     playStart:      int
     playAvail:      int
     pathX:          array[HistorySize + 1, float]
@@ -87,9 +91,11 @@ proc linearPredict(state: WorldState, bulletSpeed: float): (float, float) =
 
 # --- pattern search + play-forward ---
 
-proc findBestMatch(g: PatternMatcherGun): int =
+proc findBestMatch(g: var PatternMatcherGun): int =
   ## Speed-independent history search. Returns the start index of the best
-  ## matching pattern, or -1 when there is not enough history.
+  ## matching pattern, or -1 when there is not enough history. Stores the best
+  ## match cost in `g.lastMatchScore` for the confidence readout.
+  g.lastMatchScore = Inf
   if g.count < PatternLen * 2:
     return -1
 
@@ -111,6 +117,7 @@ proc findBestMatch(g: PatternMatcherGun): int =
     if score < bestScore:
       bestScore = score
       bestMatch = i
+  g.lastMatchScore = bestScore
   bestMatch
 
 proc buildPath(g: var PatternMatcherGun, state: WorldState, bestMatch: int) =
@@ -222,7 +229,10 @@ proc predict*(g: var PatternMatcherGun, state: WorldState,
     return g.applyRadial(state, px, py)
 
   let (px, py) = g.projectFromPath(state, bulletSpeed)
-  g.applyRadial(state, px, py)
+  result = g.applyRadial(state, px, py)
+  # Match quality as a confidence: a perfect historical match (cost 0) gives 1.0,
+  # a worse match decays toward 0. Deterministic and per-sample.
+  result.confidence = 1.0 / (1.0 + max(0.0, g.lastMatchScore))
 
 proc onResult*(g: var PatternMatcherGun, e: FeedbackEvent) =
   discard  # pattern matcher learns from movement observation, not feedback