clean up code
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42d51c896a
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@ -1,4 +1,4 @@
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from typing import Dict, Optional, Tuple
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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# import numpy.typing as npt
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@ -9,15 +9,17 @@ from scipy.stats.stats import F_onewayResult
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class ClocktowerManager:
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__slots__ = ['bounds', 'data', 'rng', 'significance', 'best_guess', 'min_correct_tries',
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'anova', 'use_smart_strategy']
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'anova', 'use_adaptive', 'min_required_count', 'favor_candidate_chance']
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bounds: Tuple[int, int]
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data: Dict[int, np.ndarray]
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rng: npr.Generator
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significance: float
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min_correct_tries: int
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best_guess: Optional[int]
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anova: Optional[F_onewayResult]
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use_smart_strategy: bool
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use_adaptive: bool
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min_correct_tries: int
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min_required_count: int
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favor_candidate_chance: float
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def __init__(self, bounds: Tuple[int, int] = (0, 256),
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significance: float = 0.01,
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@ -32,25 +34,41 @@ class ClocktowerManager:
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self.min_correct_tries = min_correct_tries
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self.best_guess = None
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self.anova = None
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self.use_smart_strategy = True
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self.use_adaptive = True
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self.min_required_count = 3
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self.favor_candidate_chance = 0.5
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def next_guess(self) -> Optional[int]:
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min_count = min([len(x) for x in self.data.values()])
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if self.use_adaptive:
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return self._next_guess_adaptive()
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else:
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return self._next_guess_naive()
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def _next_guess_adaptive(self) -> Optional[int]:
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counts = [len(x) for x in self.data.values()]
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min_count = min(counts)
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low_count = np.quantile(counts, 0.5)
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low_keys = [k for k in self.data.keys() if len(self.data[k]) <= low_count]
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min_keys = [k for k in self.data.keys() if len(self.data[k]) == min_count]
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if self.use_smart_strategy:
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if (self.best_guess is not None and
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len(self.data[self.best_guess]) >= self.min_correct_tries):
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return None
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if min_count < 3 or self.rng.uniform(0, 1) > 0.8:
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if min_count < self.min_required_count:
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return self.rng.choice(min_keys)
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elif self.rng.uniform(0, 1) > self.favor_candidate_chance:
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return self.rng.choice(low_keys)
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elif self.best_guess is not None:
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return self.best_guess
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else:
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means = {k: v.mean() for k, v in self.data.items()}
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return max(means.items(), key=lambda x: x[1])[0]
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else:
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def _next_guess_naive(self) -> Optional[int]:
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min_count = min([len(x) for x in self.data.values()])
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min_keys = [k for k in self.data.keys() if len(self.data[k]) == min_count]
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if self.best_guess is not None:
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return None
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else:
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@ -68,7 +86,8 @@ class ClocktowerManager:
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return
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self.anova = f_oneway(*inputs)
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if self.anova.pvalue <= self.significance:
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self.best_guess = max(self.data.items(), key=lambda v: v[1].mean())[0]
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self.best_guess = max(self.data.items(),
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key=lambda v: v[1].mean() if len(v[1]) > 0 else 0)[0]
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else:
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self.best_guess = None
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@ -83,12 +102,14 @@ def main() -> None:
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# stdev = 7059
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# u0 = 500000
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# u1 = 506046
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# stdev = 1000
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# u0 = 500000
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# u1 = 506046
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stdev = 7000 * 2
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stdev = 1000
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u0 = 500000
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u1 = 506046
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# stdev = 7000 * 2
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# u0 = 500000
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# u1 = 506046
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correct_guess = 0x42
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def sample(guess):
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@ -111,3 +132,4 @@ def main() -> None:
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print("state", mgr.anova)
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print(f"answer 0x{mgr.get_best_guess():02x}")
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print("took", num_guesses, "guesses")
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print("stats:", ", ".join([f"{i:02x} {len(mgr.data[i]):04d}" for i in range(256)]))
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