feat(guns): scale-aware power selection (+52% damage); TM classifier gun built, measured, DISABLED

TASK 2 - power selection, a clear win. bestPower used an ABSOLUTE
MinHitRate = 0.40 bar. Measured per-bin virtual rates (rolling-100 fraction)
show no bin ever clears 40%, so 11 of 14 guns were stuck at bin 0 (power 1.0)
even where higher bins were comparable:
  Linear  p1.0 44% p1.5 39% p2.0 30% p3.0 29%   old bin 0 -> new bin 3
  Accel   p1.0 44% p1.5 40% p2.0 26% p3.0 29%   old bin 1 -> new bin 3
  Pattern p1.0 50% p1.5 40% p2.0 27% p3.0 12%   old bin 1 -> new bin 2
Replaced with a scale-aware PowerBarFrac = 0.50 (a dimensionless FRACTION of
the gun's own best bin rate). 13 of 14 selections now pick heavier bullets.
Real effect vs DrussGT (8 rounds x 3 runs): hit rate unchanged (7.56% ->
7.47%) but damage dealt +52% (157 -> 239 per run) and rounds end faster.
Same accuracy, half the shots, half again more damage.

TASK 1 - the TM pattern-classifier gun does NOT earn its slot. It was built as
a mixture of experts with a corrected-Granmo TM as a multi-class gate over
HeadOn/Linear/Circular/WallBounce/Accel, labelled by which expert's prediction
was closest to the actual enemy position (an exact, supervised, per-shot
label - no delayed credit). Offline it loses to the best of its OWN experts on
essentially every fixture, and against DrussGT it cost real performance:
  baseline (path+relative)  7.56% real hit rate, damage 157
  + power fix               7.47%,                 damage 239
  + power fix + TM gun      5.59%,                 damage 133
The gun was selected on 806 ticks and fired 24 real shots at 4.2%.
So the tree ships with EnableTmSelector = false: code and wiring kept intact
for re-enabling, but it is not in the active rack.

Worth recording from the clause dump: the gate DOES latch onto meaningful
structure. On energy-threshold-turner, HeadOn's clauses key on the energy bits
(the rule's own driving variable) while Circular keys on distance/velocity. So
the TM is learning something real and interpretable - it simply cannot beat
'always pick the best expert'. Root cause (INFERRED): the closest-expert label
is noisy because several experts are near-tied, and under the path metric the
winner varies by power bin while the gate sees one shared per-tick input, so a
one-vs-rest gate over a saturated 870-bit clause space has no margin to exploit.
(Zero-padding the 2-frame window was tried first and saturated every clause at
256-755 included literals; alternating the two real frames fixed that.)

Also factors the corrected feedback into an exported tmLearnDir and exports the
encoding/TM primitives; the Tsetlin tests still reproduce the documented
mean=13.8 included literals, so the refactor is behaviour-preserving.

Verified: 33/33 guard checks, tsetlin tests green, metric checks green, new
power-selection guard green (13/14 selections change; relative bar still picks
bin 1 and not bin 3 for a [30,25,12,5]% profile), 12/12 offline==online
acceptance under the shipped default.

common_libs/tests/acceptance_offline_vs_online.nim

@@ -34,6 +34,9 @@ const
   runnerJar     = "/home/davide/Projects/tank-royale/runner/examples/lib/robocode-tankroyale-runner.jar"
 
 const TsetlinId = 2
+const TmSelectorId = 13  ## also stochastic (rand() in Gate choose + TM feedback)
+
+proc isStochastic(id: int): bool = id == TsetlinId or id == TmSelectorId
 
 proc lastOnlineRound(path: string): JsonNode =
   result = nil
@@ -78,12 +81,12 @@ proc main() =
   let reports = replayFixture(fx, buildAllGunDrivers(), liveActual = true)
 
   # Map online stats by gun id.
-  var onShots: array[13, int]
-  var onHits: array[13, int]
-  var onNames: array[13, string]
+  var onShots: array[14, int]
+  var onHits: array[14, int]
+  var onNames: array[14, string]
   for g in online["guns"]:
     let id = g["id"].getInt()
-    if id >= 0 and id < 13:
+    if id >= 0 and id < 14:
       onShots[id] = g["vShots"].getInt()
       onHits[id]  = g["vHits"].getInt()
       onNames[id] = g["name"].getStr()
@@ -96,11 +99,11 @@ proc main() =
   echo "-----------------------------------------------------------------------"
   var matches = 0
   var deterministic = 0
-  for id in 0..<13:
+  for id in 0..<14:
     let r = reports[id]
     let match = r.hits == onHits[id] and r.shots == onShots[id]
     var verdict: string
-    if id == TsetlinId:
+    if isStochastic(id):
       verdict = if match: "MATCH (stochastic)" else: "differs (stochastic, expected)"
     else:
       inc deterministic
@@ -117,7 +120,7 @@ proc main() =
     echo "VERDICT: FAIL — offline range does NOT reproduce the live metric."
     quit(1)
   echo "VERDICT: PASS — offline == online for all 12 deterministic guns."
-  echo "(Tsetlin is stochastic and is allowed to differ.)"
+  echo "(Tsetlin and TMSelect are stochastic and are allowed to differ.)"
 
 when isMainModule:
   main()