SirStone
57b2ac3849
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.
38 lines
1.3 KiB
Nim
38 lines
1.3 KiB
Nim
## TM selector gun checks + interpretability dump.
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## Run: nim c -r common_libs/tests/test_tm_selector.nim
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import std/[strformat, math, random]
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import gun_harness/offline_range
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import range_guns
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import guns/tm_selector
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import guns/head_on
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import guns/linear
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import guns/circular
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import guns/wall_bounce
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import guns/accel_predictor
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proc main() =
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let names = ["decel-before-turn", "energy-threshold-turner", "oscillator", "random-walk"]
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for name in names:
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let fx = synthesizeByName(name)
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let (drv, gun) = makeTmSelectorDriver(seed = 1)
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let reps = replayFixture(fx, @[
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makeDriver("HeadOn", HeadOnGun()),
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makeDriver("Linear", LinearGun()),
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makeDriver("Circular", CircularGun()),
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makeDriver("WallBounce", initWallBounceGun()),
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makeDriver("Accel", initAccelGun()),
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drv], metric = bmPath)
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echo "=== ", name
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for r in reps: echo " ", formatReportRow(r)
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let st = gun[].selectorClauseStats()
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var wc = ""
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for c in 0..<N_EXPERTS: wc.add fmt" {ExpertNames[c]}={gun[].winCount[c]}"
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echo fmt" TMSelect trainCalls={gun[].trainCalls} traceMisses={gun[].traceMisses} " &
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fmt"clauses active={st.nActive}/{st.nClauses} meanIncl={st.meanIncluded:.1f}"
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echo " winner counts:", wc
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echo gun[].describeClauses(topN = 5)
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when isMainModule:
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main()
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