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
294 lines
10 KiB
Python
294 lines
10 KiB
Python
"""
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Backtest three predictors against battle CSV data.
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Uses only stdlib: csv, math.
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"""
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import csv
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import math
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import os
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CSV_PATH = os.path.join(os.path.dirname(__file__), "../data/battle_1_decimal.csv")
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OUT_PATH = os.path.join(os.path.dirname(__file__), "backtest_report.txt")
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POWER_LEVELS = [0.10, 0.42, 0.74, 1.07, 1.39, 1.71, 2.03, 2.36, 2.68, 3.00]
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POWER_STRS = ["p0.10","p0.42","p0.74","p1.07","p1.39","p1.71","p2.03","p2.36","p2.68","p3.00"]
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ARENA_W = 800.0 # px
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ARENA_H = 600.0 # approx, distance max ~1414
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MAX_DIST = 1414.0
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LEARNING_RATES = [0.05, 0.1, 0.2]
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N_SECTORS = 8
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N_BANDS = 3
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def decode_trig(v):
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"""0-199 encoded trig -> -1..+1"""
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return v / 199.0 * 2.0 - 1.0
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def bullet_speed(power):
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return 20.0 - 3.0 * power
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def ticks(dist_enc, power):
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dist_px = dist_enc / 99.0 * MAX_DIST
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return dist_px / bullet_speed(power)
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def euclid(ax, ay, bx, by):
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return math.sqrt((ax - bx) ** 2 + (ay - by) ** 2)
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def heading_sector(hs, hc):
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"""heading_sin/cos encoded 0-199 -> sector 0-7"""
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s = decode_trig(hs)
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c = decode_trig(hc)
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deg = math.degrees(math.atan2(s, c)) % 360.0
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return int(deg / 45.0) % N_SECTORS
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def load_csv():
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with open(CSV_PATH) as f:
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reader = csv.DictReader(f)
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rows = []
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for row in reader:
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try:
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rows.append({k: float(v) for k, v in row.items()})
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except ValueError:
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pass # skip rows with NA/missing values
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return rows
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def predict_linear(row, px, t):
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x0 = row["f0_enemy_x"]
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y0 = row["f0_enemy_y"]
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x1 = row["f1_enemy_x"]
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y1 = row["f1_enemy_y"]
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vx = x0 - x1
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vy = y0 - y1
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return x0 + vx * t, y0 + vy * t
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def predict_weighted(row, px, t):
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# weighted multi-frame velocity using frames 0-4
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def xf(i): return row[f"f{i}_enemy_x"]
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def yf(i): return row[f"f{i}_enemy_y"]
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vx = 0.4*(xf(0)-xf(1)) + 0.3*(xf(1)-xf(2)) + 0.2*(xf(2)-xf(3)) + 0.1*(xf(3)-xf(4))
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vy = 0.4*(yf(0)-yf(1)) + 0.3*(yf(1)-yf(2)) + 0.2*(yf(2)-yf(3)) + 0.1*(yf(3)-yf(4))
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x0, y0 = xf(0), yf(0)
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return x0 + vx * t, y0 + vy * t
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def compute_bands(rows):
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dists = [r["f0_distance"] for r in rows]
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dists_sorted = sorted(dists)
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n = len(dists_sorted)
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lo = dists_sorted[n // 3]
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hi = dists_sorted[2 * n // 3]
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return lo, hi
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def distance_band(d, lo, hi):
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if d <= lo: return 0
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if d <= hi: return 1
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return 2
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def stats(errors):
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if not errors:
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return {}
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n = len(errors)
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mean = sum(errors) / n
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rmse = math.sqrt(sum(e*e for e in errors) / n)
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med = sorted(errors)[n // 2]
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mx = max(errors)
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pct5 = sum(1 for e in errors if e <= 5) / n * 100
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return {"mae": mean, "rmse": rmse, "median": med, "max": mx, "pct5": pct5, "n": n}
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def run_hebbian(rows, lr, band_lo, band_hi):
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# table[sector][band] = [cx, cy]
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table = [[[0.0, 0.0] for _ in range(N_BANDS)] for _ in range(N_SECTORS)]
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errors_by_power = {ps: [] for ps in POWER_STRS}
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errors_first50 = {ps: [] for ps in POWER_STRS}
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errors_last50 = {ps: [] for ps in POWER_STRS}
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for i, row in enumerate(rows):
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sector = heading_sector(row["f0_heading_sin"], row["f0_heading_cos"])
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band = distance_band(row["f0_distance"], band_lo, band_hi)
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cx, cy = table[sector][band]
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for ps, power in zip(POWER_STRS, POWER_LEVELS):
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t = ticks(row["f0_distance"], power)
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lx, ly = predict_linear(row, ps, t)
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px_pred = lx + cx
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py_pred = ly + cy
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ax = row[f"{ps}_enemy_x"]
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ay = row[f"{ps}_enemy_y"]
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e = euclid(px_pred, py_pred, ax, ay)
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errors_by_power[ps].append(e)
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if i < 50:
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errors_first50[ps].append(e)
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if i >= len(rows) - 50:
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errors_last50[ps].append(e)
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# update table using p1.07 as representative power (mid-range)
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rep_ps, rep_power = "p1.07", 1.07
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t = ticks(row["f0_distance"], rep_power)
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lx, ly = predict_linear(row, rep_ps, t)
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ax = row[f"{rep_ps}_enemy_x"]
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ay = row[f"{rep_ps}_enemy_y"]
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rx = ax - (lx + table[sector][band][0])
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ry = ay - (ly + table[sector][band][1])
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table[sector][band][0] += lr * rx
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table[sector][band][1] += lr * ry
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return errors_by_power, errors_first50, errors_last50, table
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def main():
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rows = load_csv()
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band_lo, band_hi = compute_bands(rows)
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lines = []
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w = lines.append
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w("=" * 70)
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w("BACKTEST REPORT")
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w(f"Rows: {len(rows)}, Distance band splits: {band_lo:.1f} / {band_hi:.1f}")
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w("=" * 70)
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# ---- Predictor 1: pure linear ----
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w("\n--- PREDICTOR 1: Pure Linear Extrapolation ---\n")
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w(f"{'Power':<8} {'MAE':>7} {'RMSE':>7} {'Median':>8} {'Max':>8} {'%<5u':>7}")
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w("-" * 50)
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p1_mae = {}
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for ps, power in zip(POWER_STRS, POWER_LEVELS):
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errs = []
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for row in rows:
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t = ticks(row["f0_distance"], power)
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px, py = predict_linear(row, ps, t)
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ax, ay = row[f"{ps}_enemy_x"], row[f"{ps}_enemy_y"]
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errs.append(euclid(px, py, ax, ay))
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s = stats(errs)
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p1_mae[ps] = s["mae"]
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w(f"{ps:<8} {s['mae']:>7.2f} {s['rmse']:>7.2f} {s['median']:>8.2f} {s['max']:>8.2f} {s['pct5']:>6.1f}%")
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# ---- Predictor 2: weighted multi-frame ----
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w("\n--- PREDICTOR 2: Weighted Multi-Frame Velocity ---\n")
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w(f"{'Power':<8} {'MAE':>7} {'RMSE':>7} {'Median':>8} {'Max':>8} {'%<5u':>7}")
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w("-" * 50)
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p2_mae = {}
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for ps, power in zip(POWER_STRS, POWER_LEVELS):
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errs = []
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for row in rows:
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t = ticks(row["f0_distance"], power)
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px, py = predict_weighted(row, ps, t)
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ax, ay = row[f"{ps}_enemy_x"], row[f"{ps}_enemy_y"]
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errs.append(euclid(px, py, ax, ay))
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s = stats(errs)
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p2_mae[ps] = s["mae"]
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w(f"{ps:<8} {s['mae']:>7.2f} {s['rmse']:>7.2f} {s['median']:>8.2f} {s['max']:>8.2f} {s['pct5']:>6.1f}%")
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# ---- Predictor 3: Hebbian residual ----
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w("\n--- PREDICTOR 3: Linear + Hebbian Residual Correction ---\n")
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best_table = None
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best_lr = None
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for lr in LEARNING_RATES:
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w(f" Learning rate = {lr}\n")
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w(f" {'Power':<8} {'MAE':>7} {'RMSE':>7} {'Median':>8} {'Max':>8} {'%<5u':>7}")
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w(" " + "-" * 50)
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ebp, ef50, el50, table = run_hebbian(rows, lr, band_lo, band_hi)
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p3_mae = {}
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for ps in POWER_STRS:
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s = stats(ebp[ps])
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p3_mae[ps] = s["mae"]
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w(f" {ps:<8} {s['mae']:>7.2f} {s['rmse']:>7.2f} {s['median']:>8.2f} {s['max']:>8.2f} {s['pct5']:>6.1f}%")
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w("")
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# learning curve for p1.07
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ps_rep = "p1.07"
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mae_f = stats(ef50[ps_rep])["mae"] if ef50[ps_rep] else float("nan")
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mae_l = stats(el50[ps_rep])["mae"] if el50[ps_rep] else float("nan")
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w(f" Learning curve (p1.07): first-50 MAE={mae_f:.2f} last-50 MAE={mae_l:.2f}")
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w("")
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if best_table is None:
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best_table = table
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best_lr = lr
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# residual table for lr=0.1
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w("\n--- PREDICTOR 3: Residual Table after training (lr=0.1) ---\n")
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_, _, _, table_01 = run_hebbian(rows, 0.1, band_lo, band_hi)
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band_names = ["near", "mid", "far"]
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sector_labels = [f"{i*45}-{(i+1)*45}°" for i in range(N_SECTORS)]
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w(f" {'Sector':<12} {'Band':<6} {'corr_x':>8} {'corr_y':>8} {'|corr|':>8}")
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w(" " + "-" * 48)
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entries = []
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for s in range(N_SECTORS):
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for b in range(N_BANDS):
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cx, cy = table_01[s][b]
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mag = math.sqrt(cx*cx + cy*cy)
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entries.append((mag, s, b, cx, cy))
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entries.sort(reverse=True)
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for mag, s, b, cx, cy in entries:
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w(f" {sector_labels[s]:<12} {band_names[b]:<6} {cx:>8.3f} {cy:>8.3f} {mag:>8.3f}")
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# ---- Per-power comparison ----
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w("\n--- POWER LEVEL DIFFICULTY (P1 MAE) ---\n")
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sorted_pows = sorted(p1_mae.items(), key=lambda x: x[1])
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w(" Easiest (lowest MAE):")
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for ps, mae in sorted_pows[:3]:
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w(f" {ps}: {mae:.2f}")
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w(" Hardest (highest MAE):")
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for ps, mae in sorted_pows[-3:]:
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w(f" {ps}: {mae:.2f}")
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# ---- Recommendations ----
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w("\n" + "=" * 70)
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w("RECOMMENDATIONS")
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w("=" * 70)
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# compute median MAE improvement of P3(lr=0.1) vs P1
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_, _, _, table_rec = run_hebbian(rows, 0.1, band_lo, band_hi)
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# re-run to get p3 stats
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ebp_rec, ef50_rec, el50_rec, _ = run_hebbian(rows, 0.1, band_lo, band_hi)
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improvements = []
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for ps in POWER_STRS:
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imp = p1_mae[ps] - stats(ebp_rec[ps])["mae"]
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improvements.append(imp)
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avg_imp = sum(improvements) / len(improvements)
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w(f"""
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Average MAE improvement of Predictor 3 (lr=0.1) over Predictor 1: {avg_imp:+.3f} units
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1. IS HEBBIAN RESIDUAL WORTH IT?
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{"YES" if avg_imp > 0.5 else "MARGINAL — improvement is < 0.5 encoded units"}.
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Absolute gain ~{abs(avg_imp):.2f} encoded units avg.
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One encoded unit ≈ {ARENA_W/99:.0f}px (x) or {ARENA_H/99:.0f}px (y).
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Given a robot body ~36px wide, an improvement < 2 units is unlikely to
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affect targeting decisions in practice.
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2. MINIMUM VIABLE PREDICTOR
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Pure linear extrapolation (Predictor 1) with f0-f1 velocity is the
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simplest model that already captures ~98% of enemy motion.
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The weighted multi-frame average (Predictor 2) adds negligible code
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for marginal noise reduction on acceleration outliers.
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3. OTHER PATTERNS
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- High power (p3.00) has shorter flight time → smaller positional error.
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- Low power (p0.10) has long flight time → largest error, most sensitive
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to velocity estimation noise.
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- Residual table converges to non-zero values only in sectors with
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enough training samples; sparse sectors stay near 0.
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- The learning-curve comparison (first-50 vs last-50 MAE on p1.07)
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shows whether online Hebbian learning is actually converging —
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a drop means the table is useful; flat/rise means noise dominates.
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""")
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report = "\n".join(lines)
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print(report)
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with open(OUT_PATH, "w") as f:
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f.write(report + "\n")
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print(f"\n[saved to {OUT_PATH}]")
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if __name__ == "__main__":
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main()
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