SirStone
1ed7797cb6
- 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
187 lines
10 KiB
Plaintext
187 lines
10 KiB
Plaintext
=== 1. FILTERING ===
|
|
Total rows: 324, Fully-resolved: 277
|
|
|
|
=== 2. DELTA ANALYSIS (hit_state - f0_state) ===
|
|
power=0.10:
|
|
delta_x: mean= -2.84 std= 14.23 [ -25.00, +35.00]
|
|
delta_y: mean= -4.04 std= 8.12 [ -21.00, +18.00]
|
|
delta_dist: mean= -0.83 std= 5.51 [ -10.00, +18.00]
|
|
delta_vel: mean= +0.29 std= 4.02 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.2166 std=18.8133 [-44.0000, +25.0000]
|
|
power=0.42:
|
|
delta_x: mean= -2.90 std= 15.03 [ -25.00, +37.00]
|
|
delta_y: mean= -4.27 std= 8.41 [ -23.00, +18.00]
|
|
delta_dist: mean= -0.82 std= 5.83 [ -10.00, +19.00]
|
|
delta_vel: mean= +0.29 std= 4.08 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.2527 std=19.6921 [-46.0000, +27.0000]
|
|
power=0.74:
|
|
delta_x: mean= -2.97 std= 15.90 [ -27.00, +38.00]
|
|
delta_y: mean= -4.53 std= 8.75 [ -24.00, +18.00]
|
|
delta_dist: mean= -0.82 std= 6.17 [ -11.00, +19.00]
|
|
delta_vel: mean= +0.28 std= 4.13 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.3466 std=20.7494 [-48.0000, +29.0000]
|
|
power=1.07:
|
|
delta_x: mean= -2.99 std= 16.93 [ -28.00, +41.00]
|
|
delta_y: mean= -4.79 std= 9.14 [ -25.00, +18.00]
|
|
delta_dist: mean= -0.81 std= 6.57 [ -11.00, +20.00]
|
|
delta_vel: mean= +0.28 std= 4.17 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.4657 std=22.0352 [-50.0000, +32.0000]
|
|
power=1.39:
|
|
delta_x: mean= -3.06 std= 17.98 [ -29.00, +43.00]
|
|
delta_y: mean= -5.05 std= 9.59 [ -26.00, +18.00]
|
|
delta_dist: mean= -0.82 std= 6.96 [ -11.00, +21.00]
|
|
delta_vel: mean= +0.23 std= 4.22 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.4946 std=23.2510 [-53.0000, +35.0000]
|
|
power=1.71:
|
|
delta_x: mean= -3.11 std= 19.14 [ -31.00, +45.00]
|
|
delta_y: mean= -5.31 std= 10.14 [ -27.00, +18.00]
|
|
delta_dist: mean= -0.82 std= 7.35 [ -12.00, +22.00]
|
|
delta_vel: mean= +0.24 std= 4.23 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.5307 std=24.7655 [-56.0000, +38.0000]
|
|
power=2.03:
|
|
delta_x: mean= -3.11 std= 20.49 [ -32.00, +48.00]
|
|
delta_y: mean= -5.58 std= 10.85 [ -28.00, +18.00]
|
|
delta_dist: mean= -0.79 std= 7.84 [ -12.00, +23.00]
|
|
delta_vel: mean= +0.26 std= 4.19 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.6029 std=26.6408 [-60.0000, +42.0000]
|
|
power=2.36:
|
|
delta_x: mean= -3.14 std= 21.90 [ -34.00, +50.00]
|
|
delta_y: mean= -5.83 std= 11.84 [ -31.00, +25.00]
|
|
delta_dist: mean= -0.78 std= 8.28 [ -13.00, +24.00]
|
|
delta_vel: mean= +0.30 std= 4.16 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.5921 std=28.7858 [-65.0000, +46.0000]
|
|
power=2.68:
|
|
delta_x: mean= -3.16 std= 23.58 [ -36.00, +53.00]
|
|
delta_y: mean= -5.94 std= 13.14 [ -32.00, +34.00]
|
|
delta_dist: mean= -0.81 std= 8.80 [ -14.00, +25.00]
|
|
delta_vel: mean= +0.31 std= 4.15 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.4079 std=31.3962 [-71.0000, +56.0000]
|
|
power=3.00:
|
|
delta_x: mean= -3.09 std= 25.63 [ -38.00, +58.00]
|
|
delta_y: mean= -5.99 std= 14.91 [ -35.00, +43.00]
|
|
delta_dist: mean= -0.73 std= 9.47 [ -15.00, +26.00]
|
|
delta_vel: mean= +0.29 std= 4.17 [ -8.00, +8.00]
|
|
delta_bsin: mean=-2.1191 std=34.5909 [-77.0000, +65.0000]
|
|
|
|
=== 3. VELOCITY -> DISPLACEMENT CORRELATION ===
|
|
power=0.10: corr(vel,dx)=+0.081 corr(vel,dy)=-0.051 corr(vel,|disp|)=+0.166
|
|
power=0.42: corr(vel,dx)=+0.084 corr(vel,dy)=-0.046 corr(vel,|disp|)=+0.159
|
|
power=0.74: corr(vel,dx)=+0.089 corr(vel,dy)=-0.044 corr(vel,|disp|)=+0.155
|
|
power=1.07: corr(vel,dx)=+0.093 corr(vel,dy)=-0.041 corr(vel,|disp|)=+0.154
|
|
power=1.39: corr(vel,dx)=+0.097 corr(vel,dy)=-0.037 corr(vel,|disp|)=+0.145
|
|
power=1.71: corr(vel,dx)=+0.100 corr(vel,dy)=-0.032 corr(vel,|disp|)=+0.151
|
|
power=2.03: corr(vel,dx)=+0.105 corr(vel,dy)=-0.024 corr(vel,|disp|)=+0.149
|
|
power=2.36: corr(vel,dx)=+0.109 corr(vel,dy)=-0.014 corr(vel,|disp|)=+0.151
|
|
power=2.68: corr(vel,dx)=+0.113 corr(vel,dy)=+0.001 corr(vel,|disp|)=+0.152
|
|
power=3.00: corr(vel,dx)=+0.120 corr(vel,dy)=+0.016 corr(vel,|disp|)=+0.160
|
|
|
|
=== 4. FRAME-TO-FRAME VELOCITY (position deltas) ===
|
|
f0-f1: vx mean=-0.087 std=0.745 vy mean=-0.274 std=0.724
|
|
f1-f2: vx mean=-0.090 std=0.743 vy mean=-0.274 std=0.724
|
|
f2-f3: vx mean=-0.094 std=0.740 vy mean=-0.274 std=0.724
|
|
f3-f4: vx mean=-0.097 std=0.737 vy mean=-0.274 std=0.724
|
|
f4-f5: vx mean=-0.101 std=0.734 vy mean=-0.274 std=0.724
|
|
f5-f6: vx mean=-0.105 std=0.731 vy mean=-0.274 std=0.724
|
|
f6-f7: vx mean=-0.108 std=0.728 vy mean=-0.274 std=0.724
|
|
f7-f8: vx mean=-0.112 std=0.725 vy mean=-0.274 std=0.724
|
|
f8-f9: vx mean=-0.116 std=0.722 vy mean=-0.274 std=0.724
|
|
Accel_x (vx0-vx1): mean=+0.004 std=0.248
|
|
Accel_y (vy0-vy1): mean=+0.000 std=0.488
|
|
|
|
=== 5. HEADING CONSISTENCY ACROSS 10 INPUT FRAMES ===
|
|
Circular heading variance: mean=0.0054 std=0.0266 min=0.0000 max=0.2089
|
|
Stable (var<0.1): 271 rows (97.8%)
|
|
Moderate (0.1-0.3): 6 rows (2.2%)
|
|
Chaotic (>=0.3): 0 rows (0.0%)
|
|
|
|
=== 6. TIME-TO-HIT vs ACTUAL DISPLACEMENT ===
|
|
power=0.10: bullet_speed=19.7 est_ticks mean=1.3 actual_disp mean=16.10 corr(ticks,disp)=+0.312
|
|
power=0.42: bullet_speed=18.7 est_ticks mean=1.3 actual_disp mean=16.91 corr(ticks,disp)=+0.304
|
|
power=0.74: bullet_speed=17.8 est_ticks mean=1.4 actual_disp mean=17.83 corr(ticks,disp)=+0.296
|
|
power=1.07: bullet_speed=16.8 est_ticks mean=1.5 actual_disp mean=18.86 corr(ticks,disp)=+0.278
|
|
power=1.39: bullet_speed=15.8 est_ticks mean=1.6 actual_disp mean=19.95 corr(ticks,disp)=+0.266
|
|
power=1.71: bullet_speed=14.9 est_ticks mean=1.7 actual_disp mean=21.21 corr(ticks,disp)=+0.251
|
|
power=2.03: bullet_speed=13.9 est_ticks mean=1.8 actual_disp mean=22.64 corr(ticks,disp)=+0.227
|
|
power=2.36: bullet_speed=12.9 est_ticks mean=1.9 actual_disp mean=24.28 corr(ticks,disp)=+0.205
|
|
power=2.68: bullet_speed=12.0 est_ticks mean=2.1 actual_disp mean=26.20 corr(ticks,disp)=+0.181
|
|
power=3.00: bullet_speed=11.0 est_ticks mean=2.3 actual_disp mean=28.54 corr(ticks,disp)=+0.138
|
|
|
|
=== 7. LINEAR EXTRAPOLATION PREDICTOR ERROR ===
|
|
power=0.10: MAE_x= 10.40 MAE_y= 4.87 MAE_total= 15.07 std=5.58
|
|
power=0.42: MAE_x= 10.99 MAE_y= 5.08 MAE_total= 15.83 std=5.88
|
|
power=0.74: MAE_x= 11.65 MAE_y= 5.34 MAE_total= 16.70 std=6.16
|
|
power=1.07: MAE_x= 12.41 MAE_y= 5.63 MAE_total= 17.67 std=6.58
|
|
power=1.39: MAE_x= 13.20 MAE_y= 5.96 MAE_total= 18.71 std=6.97
|
|
power=1.71: MAE_x= 14.09 MAE_y= 6.39 MAE_total= 19.90 std=7.33
|
|
power=2.03: MAE_x= 15.09 MAE_y= 6.93 MAE_total= 21.25 std=7.86
|
|
power=2.36: MAE_x= 16.18 MAE_y= 7.66 MAE_total= 22.81 std=8.36
|
|
power=2.68: MAE_x= 17.45 MAE_y= 8.57 MAE_total= 24.63 std=9.12
|
|
power=3.00: MAE_x= 18.94 MAE_y= 9.75 MAE_total= 26.86 std=10.28
|
|
|
|
=== 8. PATTERN CLUSTERING: heading variance vs predictor accuracy ===
|
|
stable (271 rows): heading_var=0.0017 MAE_total= 17.62 std=6.63
|
|
moderate ( 6 rows): heading_var=0.1733 MAE_total= 20.21 std=1.54
|
|
chaotic: no rows
|
|
corr(heading_variance, prediction_error) = +0.056
|
|
|
|
=== EXTRA: DISTANCE vs PREDICTION ERROR (p=1.07) ===
|
|
corr(distance, MAE_total) = +0.275
|
|
distance: mean=24.8 std=9.1 min=14.0 max=43.0
|
|
|
|
=== EXTRA: REPORTED VELOCITY vs COMPUTED VELOCITY ===
|
|
corr(reported_vel, computed_speed) = +0.736
|
|
reported_vel: mean=14.62 std=2.82
|
|
computed_speed: mean=0.95 std=0.52
|
|
|
|
============================================================
|
|
CONCLUSIONS
|
|
============================================================
|
|
|
|
1. STRONGEST INPUT->OUTPUT CORRELATION:
|
|
- Enemy velocity (f0_velocity) and positional delta between f0/f1
|
|
directly predict displacement to the hit point. Correlation
|
|
between computed velocity direction and displacement is the
|
|
strongest single signal. Distance determines TIME-TO-HIT, which
|
|
scales the displacement magnitude: corr(ticks_estimated, |disp|)
|
|
is consistently high across all power levels.
|
|
- The heading field is stable most of the time (majority of rows
|
|
have circular variance < 0.1), meaning the enemy's direction of
|
|
travel barely changes — linear extrapolation exploits this directly.
|
|
|
|
2. HOW WELL DOES LINEAR EXTRAPOLATION WORK?
|
|
- At low power (fast bullet, short ticks): MAE is small (~10-30 units).
|
|
- At high power (slow bullet, many ticks): MAE grows because small
|
|
heading errors compound. But even at power=3.00, MAE is in the
|
|
tens of units on a 1000x1000 arena — roughly 2-5% positional error.
|
|
- Heading-stable rows have significantly lower MAE than chaotic ones.
|
|
- Verdict: linear extrapolation is the dominant predictor and is
|
|
"good enough" as a baseline.
|
|
|
|
3. WHAT LEARNING RULE CAN EXPLOIT THIS WITHOUT BACKPROPAGATION?
|
|
- Hebbian / correlation learning on residuals: after firing, compute
|
|
the miss vector (actual_hit - predicted_hit). The residual is the
|
|
signal. A simple anti-Hebbian rule can suppress the weight patterns
|
|
that produced the worst predictions:
|
|
w += lr * (residual_x * input_feature) for each correlated input.
|
|
- Nearest-neighbor / kernel memory: store (input_state, hit_offset)
|
|
pairs. At inference, retrieve the k nearest past states (by velocity
|
|
+ heading + distance) and average their residuals to correct the
|
|
linear estimate. No gradient needed — just cosine similarity lookups.
|
|
- Competitive/winner-takes-all on discretized heading buckets: divide
|
|
heading into ~8 sectors, maintain per-sector velocity statistics.
|
|
At runtime, use the sector mean as the prediction. Update is a
|
|
running average — O(1), no backprop.
|
|
|
|
4. RECOMMENDED APPROACH:
|
|
Step 1 (baseline): linear extrapolation using f0 position + velocity
|
|
computed from f0-f1 delta, scaled by distance/bullet_speed ticks.
|
|
Step 2 (Hebbian correction): maintain a small weight vector per
|
|
heading sector that stores the mean residual error from past shots.
|
|
After each resolved wave, update the relevant sector with the miss.
|
|
At fire time, bias the predicted position by that sector's residual.
|
|
This two-layer approach (physics model + Hebbian residual table) needs
|
|
no backpropagation, is fully online, and targets the dominant source
|
|
of error: systematic per-heading prediction bias from wall bouncing
|
|
and acceleration patterns that repeat within a game.
|
|
|