SirStone
e53690036b
Four guns cached a whole prediction per tick while predict() is called once per power bin, so every bin after the first (and the real fired shot, which shares lastState) reused the power-1.0 lead. Fixed by caching only the speed-INDEPENDENT derived state and recomputing the lead per requested speed: - stop_shot: also fixes prevSpeed being written before it was read, which made abs(speed) < abs(prev) permanently false and the entire stop-prediction branch unreachable (it was just Linear). - displacement: the cache key included bulletSpeed, so the guard missed on all four bins and the 15-tick window advanced ~4x/tick, making the inferred velocity ~4x too small. - averaged_lead: tick cache removed outright. pattern_matcher: split into speed-independent match+path and per-call lead. FeedbackEvent gains fireTick/powerBin (additive; only virtual_bullets constructs one) so guns can pair feedback to the exact shot instead of guessing by coordinates. tsetlin uses it: traces are now keyed exactly by (fireTick, powerBin) with a 1024-slot ring, and the 10-frame window shifts at most once per tick (it was shifting ~4-5x/tick, so isWarmedUp tripped after ~2 ticks). KNOWN INCOMPLETE: tsetlin still does not diverge from Linear in battle. The two named bugs are fixed (a 600-tick sim shows trainedShots=2141, traceMisses=0, and a fixed-input probe converges to a 9.6px correction), but the TM's clause feedback itself is broken: ~131 of 1740 literals end up included per clause, so its conjunction never fires. Sweeping TM_S, TM_N_CLAUSES and a two-branch Type-I update did not change the correction from 0. Needs a real TM fix or removal, not another bug fix. First-ever guard tests for the gun selector: common_libs/tests/ test_gun_harness.nim (14 checks, headless, no Java). There were none before, which is how six broken guns survived a full analysis cycle. Against the previous HEAD, 5 of these checks FAIL - that is the regression guard.
36 lines
1.4 KiB
Nim
36 lines
1.4 KiB
Nim
## Averaged-lead gun: mean prediction from linear, circular, and wall-bounce guns.
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## Smooths noise when enemy movement is between analytical models.
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import gun_harness/gun_interface
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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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type AveragedLeadGun* = object
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linear: LinearGun
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circular: CircularGun
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wallBounce: WallBounceGun
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debugGraphics*: bool
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proc initAveragedLeadGun*(): AveragedLeadGun =
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AveragedLeadGun(wallBounce: initWallBounceGun(), debugGraphics: false)
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proc predict*(g: var AveragedLeadGun, state: WorldState, bulletSpeed: float): GunPrediction =
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# No tick cache: every sub-gun's lead depends on bulletSpeed (dist/bulletSpeed),
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# so caching one result per tick and reusing it for all four power bins would
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# silently collapse every bin onto the first. linear/wallBounce are stateless
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# and cheap; circular only caches its speed-independent omega internally.
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let lp = g.linear.predict(state, bulletSpeed)
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let cp = g.circular.predict(state, bulletSpeed)
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let wp = g.wallBounce.predict(state, bulletSpeed)
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var px = (lp.x + cp.x + wp.x) / 3.0
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var py = (lp.y + cp.y + wp.y) / 3.0
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px = clamp(px, BotRadius, state.arenaWidth - BotRadius)
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py = clamp(py, BotRadius, state.arenaHeight - BotRadius)
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GunPrediction(x: px, y: py)
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proc onResult*(g: var AveragedLeadGun, e: FeedbackEvent) =
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discard # analytical average — no learning
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