feat(ModularBot): 6 guns, pattern matcher, melee modules, adversarial bots

- New guns: guess-factor (GF histogram), pattern-matcher (movement tape replay)
- New modules: minimum-risk melee movement, spinning melee radar
- New test bots: PatternMover, RandomMover, WaveSurfer
- Fixed: FeedbackEvent now carries actualX/actualY for proper GF learning
- Fixed: TM gun warmup gating + directional residuals
- Fixed: circular gun integrated formula + multi-bin omega cache
- Fixed: oscillator wall-bounce lockout
- Fixed: phantom meteor perpendicular body orientation
- 6/6 battle wins across all enemy types

BNNBot_garage/analysis/alternatives_results.txt

@@ -0,0 +1,40 @@
+================================================================================
+ALTERNATIVE LEARNING METHODS BACKTEST
+Dataset: 277 rows | Rep power: p1.07 | Hit threshold: 18.0px
+Baselines: linear MAE≈12.24 | WiSARD K=12 MAE≈9.93
+================================================================================
+
+Method                                 MAE     Hit%  F50 MAE  L50 MAE   vs Lin
+--------------------------------------------------------------------------------
+Linear (baseline)                    10.18    80.1%    20.74     8.19      ---
+WiSARD K=12 (reference)               9.93     ~55%      ---      ---    -2.31
+1. Echo State Net (res=512)           8.29    85.2%    15.76     2.11    -1.89
+2. Kanerva SDM (addr=2000)           10.16    80.1%    20.74     8.19    -0.02
+3. N-gram Markov (4×69 chunks)       10.11    80.5%    20.36     8.19    -0.07
+4. Bloom Filter (8192 slots)         17.08    59.2%    23.67    13.15    +6.90
+5. HDC (n_hd=2000, 32 cls)           65.33     0.0%    56.11    65.33   +55.15
+6. RandSubspace (30×50bits)           8.31    90.6%    15.45     6.00    -1.88
+7. WiSARD+Elig (k=12,tr=5)            8.24    90.3%    15.21     5.74    -1.94
+
+================================================================================
+RANKING by MAE (lower is better, rep power p1.07)
+================================================================================
+
+Rank  Method                                 MAE     Hit%      F50      L50
+----------------------------------------------------------------------
+1     7. WiSARD+Elig (k=12,tr=5)            8.24    90.3%    15.21     5.74
+2     1. Echo State Net (res=512)           8.29    85.2%    15.76     2.11
+3     6. RandSubspace (30×50bits)           8.31    90.6%    15.45     6.00
+4     3. N-gram Markov (4×69 chunks)       10.11    80.5%    20.36     8.19
+5     2. Kanerva SDM (addr=2000)           10.16    80.1%    20.74     8.19
+6     4. Bloom Filter (8192 slots)         17.08    59.2%    23.67    13.15
+7     5. HDC (n_hd=2000, 32 cls)           65.33     0.0%    56.11    65.33
+
+  Linear baseline:   MAE=10.18  Hit=80.1%
+  WiSARD K=12 ref:   MAE=9.93   Hit=~55%
+
+Notes:
+  F50/L50 = MAE on first/last 50 samples (learning speed proxy).
+  Hit% = fraction within 18px (Robocode bullet half-width).
+  All methods: online, binary input, no gradients, no supervised labels.
+  Learning signal: residual correction after linear extrapolation at p1.07.