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
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"""
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WiSARD hyperparameter sweep.
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Stdlib only: csv, math, random.
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"""
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import csv
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import math
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import random
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import os
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DATA_DIR = os.path.join(os.path.dirname(__file__), "../out/data")
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OUT_PATH = os.path.join(os.path.dirname(__file__), "sweep_wisard_results.txt")
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# Use all target battle decimal CSVs for more data
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CSV_FILES = [
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os.path.join(DATA_DIR, f"target_battle_{i}_decimal.csv") for i in range(1, 6)
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]
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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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MAX_DIST = 1414.0
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HIT_RADIUS = 18.0 # px, bot half-width for hit-rate calc
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REP_POWER = 1.07
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REP_POWER_STR = "p1.07"
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K_VALUES = [4, 6, 8, 10, 12, 14, 16, 20]
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XOR_OPTIONS = [False, True]
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BLEACH_VALS = [0, 1, 2, 3]
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SEEDS = [42, 137, 2718]
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# ---------------------------------------------------------------------------
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# Bit encoding (mirrors backtest_wisard.py)
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# ---------------------------------------------------------------------------
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def _to_gray(v):
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return v ^ (v >> 1)
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def int_to_bits(value, nbits):
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gray = _to_gray(int(value))
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return [(gray >> (nbits - 1 - i)) & 1 for i in range(nbits)]
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def encode_frame(row, prefix):
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bits = []
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bits += int_to_bits(row[prefix + "bearing_sin"], 8)
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bits += int_to_bits(row[prefix + "bearing_cos"], 8)
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bits += int_to_bits(row[prefix + "distance"], 7)
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bits += int_to_bits(row[prefix + "velocity"], 5)
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bits += int_to_bits(row[prefix + "heading_sin"], 8)
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bits += int_to_bits(row[prefix + "heading_cos"], 8)
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bits += int_to_bits(row[prefix + "enemy_x"], 7)
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bits += int_to_bits(row[prefix + "enemy_y"], 7)
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bits += int_to_bits(row[prefix + "enemy_energy"],11)
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return bits # 69 bits
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def build_input(row, use_xor=False):
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frames = [encode_frame(row, f"f{i}_") for i in range(4)]
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bits = []
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for f in frames:
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bits += f
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if use_xor:
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bits += [a ^ b for a, b in zip(frames[0], frames[1])]
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return bits # 276 or 345 bits
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# ---------------------------------------------------------------------------
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# Physics helpers
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# ---------------------------------------------------------------------------
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def bullet_speed(power):
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return 20.0 - 3.0 * power
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def flight_ticks(dist_enc, power):
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return (dist_enc / 99.0 * MAX_DIST) / 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 predict_linear(row, t):
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x0, y0 = row["f0_enemy_x"], row["f0_enemy_y"]
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x1, y1 = row["f1_enemy_x"], row["f1_enemy_y"]
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return x0 + (x0 - x1) * t, y0 + (y0 - y1) * t
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# ---------------------------------------------------------------------------
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# WiSARD with bleaching
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# ---------------------------------------------------------------------------
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class WiSARD:
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def __init__(self, n_bits, k, seed=42):
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self.k = k
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n_bits_pad = math.ceil(n_bits / k) * k
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self.n_nodes = n_bits_pad // k
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rng = random.Random(seed)
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indices = list(range(n_bits)) + [0] * (n_bits_pad - n_bits)
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self.perm = indices[:]
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rng.shuffle(self.perm)
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self.tables = [{} for _ in range(self.n_nodes)]
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def _addresses(self, bits):
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addrs = []
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for node in range(self.n_nodes):
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addr = 0
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for bi in range(self.k):
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pi = node * self.k + bi
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b = bits[self.perm[pi]] if pi < len(bits) else 0
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addr = (addr << 1) | b
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addrs.append(addr)
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return addrs
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def predict(self, bits, bleach=0):
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addrs = self._addresses(bits)
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cx_sum = cy_sum = 0.0
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count = 0
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for node, addr in enumerate(addrs):
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entry = self.tables[node].get(addr)
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if entry and entry[2] > bleach:
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cx_sum += entry[0] / entry[2]
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cy_sum += entry[1] / entry[2]
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count += 1
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if count == 0:
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return 0.0, 0.0
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return cx_sum / count, cy_sum / count
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def learn(self, bits, rx, ry):
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for node, addr in enumerate(self._addresses(bits)):
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entry = self.tables[node].get(addr)
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if entry is None:
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self.tables[node][addr] = [rx, ry, 1]
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else:
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entry[0] += rx
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entry[1] += ry
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entry[2] += 1
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# ---------------------------------------------------------------------------
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# Data loading
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# ---------------------------------------------------------------------------
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def load_rows():
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rows = []
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for path in CSV_FILES:
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if not os.path.exists(path):
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continue
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with open(path) as f:
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for row in csv.DictReader(f):
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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
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return rows
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# ---------------------------------------------------------------------------
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# Run one config
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# ---------------------------------------------------------------------------
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def run_config(rows, k, use_xor, bleach, seed):
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n_bits = 276 + (69 if use_xor else 0)
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net = WiSARD(n_bits, k, seed)
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n = len(rows)
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all_errors = []
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errors_first50 = []
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errors_last50 = []
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hits = 0
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for i, row in enumerate(rows):
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bits = build_input(row, use_xor)
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cx, cy = net.predict(bits, bleach)
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t = flight_ticks(row["f0_distance"], REP_POWER)
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lx, ly = predict_linear(row, t)
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px = lx + cx
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py = ly + cy
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ax, ay = row[f"{REP_POWER_STR}_enemy_x"], row[f"{REP_POWER_STR}_enemy_y"]
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e = euclid(px, py, ax, ay)
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all_errors.append(e)
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if e <= HIT_RADIUS:
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hits += 1
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if i < 50:
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errors_first50.append(e)
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if i >= n - 50:
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errors_last50.append(e)
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# Online learn: residual on top of current prediction
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rx = ax - px
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ry = ay - py
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net.learn(bits, rx, ry)
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mae = sum(all_errors) / n
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hit_rate = hits / n * 100
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mae_f50 = sum(errors_first50) / len(errors_first50) if errors_first50 else float("nan")
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mae_l50 = sum(errors_last50) / len(errors_last50) if errors_last50 else float("nan")
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return mae, hit_rate, mae_f50, mae_l50
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main():
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rows = load_rows()
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print(f"Loaded {len(rows)} rows from {sum(os.path.exists(p) for p in CSV_FILES)} files")
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# Baseline: pure linear extrapolation (no correction)
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baseline_errors = []
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baseline_hits = 0
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for row in rows:
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t = flight_ticks(row["f0_distance"], REP_POWER)
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px, py = predict_linear(row, t)
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ax, ay = row[f"{REP_POWER_STR}_enemy_x"], row[f"{REP_POWER_STR}_enemy_y"]
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e = euclid(px, py, ax, ay)
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baseline_errors.append(e)
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if e <= HIT_RADIUS:
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baseline_hits += 1
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baseline_mae = sum(baseline_errors) / len(baseline_errors)
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baseline_hitrate = baseline_hits / len(rows) * 100
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print(f"Baseline linear extrapolation: MAE={baseline_mae:.2f}px hit%={baseline_hitrate:.1f}%")
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print(f"\nRunning sweep: {len(K_VALUES)} K × {len(XOR_OPTIONS)} XOR × {len(BLEACH_VALS)} bleach × {len(SEEDS)} seeds = "
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f"{len(K_VALUES)*len(XOR_OPTIONS)*len(BLEACH_VALS)*len(SEEDS)} configs...")
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results = [] # (avg_mae, hit_rate, mae_f50, mae_l50, std_mae, k, use_xor, bleach)
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total = len(K_VALUES) * len(XOR_OPTIONS) * len(BLEACH_VALS)
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done = 0
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for k in K_VALUES:
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for use_xor in XOR_OPTIONS:
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for bleach in BLEACH_VALS:
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done += 1
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seed_maes = []
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seed_hits = []
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seed_f50 = []
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seed_l50 = []
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for seed in SEEDS:
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mae, hit_rate, mae_f50, mae_l50 = run_config(rows, k, use_xor, bleach, seed)
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seed_maes.append(mae)
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seed_hits.append(hit_rate)
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seed_f50.append(mae_f50)
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seed_l50.append(mae_l50)
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avg_mae = sum(seed_maes) / len(seed_maes)
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avg_hit = sum(seed_hits) / len(seed_hits)
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avg_f50 = sum(seed_f50) / len(seed_f50)
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avg_l50 = sum(seed_l50) / len(seed_l50)
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mean_m = avg_mae
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std_mae = math.sqrt(sum((x - mean_m) ** 2 for x in seed_maes) / len(seed_maes))
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results.append((avg_mae, avg_hit, avg_f50, avg_l50, std_mae, k, use_xor, bleach))
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print(f" [{done:3d}/{total}] K={k:2d} xor={int(use_xor)} bleach={bleach} MAE={avg_mae:.2f}±{std_mae:.2f} hit%={avg_hit:.1f}%")
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results.sort(key=lambda r: r[0]) # sort by MAE ascending
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# ---- Format output ----
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lines = []
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w = lines.append
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w("=" * 90)
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w("WiSARD HYPERPARAMETER SWEEP — sorted by MAE (rep power=1.07, hit_radius=18px)")
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w(f"Dataset: {len(rows)} rows (target battles 1-5), 3 seeds per config")
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w("=" * 90)
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w("")
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w(f"{'Config':<30} {'MAE':>8} {'±std':>6} {'hit%':>7} {'MAE f50':>8} {'MAE l50':>8} {'vs base':>8}")
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w("-" * 90)
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w(f"{'P1 linear baseline':<30} {baseline_mae:>8.2f} {'':>6} {baseline_hitrate:>7.1f}% {'':>8} {'':>8} {'':>8}")
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w("-" * 90)
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for avg_mae, avg_hit, avg_f50, avg_l50, std_mae, k, use_xor, bleach in results:
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n_bits = 276 + (69 if use_xor else 0)
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xor_tag = "+XOR" if use_xor else " "
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delta = avg_mae - baseline_mae
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label = f"K={k:2d} {xor_tag} bleach={bleach} ({n_bits}b)"
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w(f"{label:<30} {avg_mae:>8.2f} {std_mae:>6.2f} {avg_hit:>7.1f}% {avg_f50:>8.2f} {avg_l50:>8.2f} {delta:>+8.2f}")
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w("")
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w("Columns: MAE=mean abs error(px) ±std=across 3 seeds hit%=shots within 18px")
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w(" MAE f50/l50=first/last 50 samples (learning curve) vs base=delta to P1")
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report = "\n".join(lines)
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print("\n" + 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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