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tune testing

This commit is contained in:
xenia 2021-06-05 03:04:14 -04:00
parent 021d8724cd
commit 42d51c896a
1 changed files with 39 additions and 24 deletions
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63
clocktower/__init__.py
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@ -4,16 +4,20 @@ import numpy as np
# import numpy.typing as npt
import numpy.random as npr
from scipy.stats import f_oneway
from scipy.stats.stats import F_onewayResult
class ClocktowerManager:
__slots__ = ['bounds', 'data', 'rng', 'significance', 'best_guess', 'min_correct_tries']
__slots__ = ['bounds', 'data', 'rng', 'significance', 'best_guess', 'min_correct_tries',
'anova', 'use_smart_strategy']
bounds: Tuple[int, int]
data: Dict[int, np.ndarray]
rng: npr.Generator
significance: float
min_correct_tries: int
best_guess: Optional[int]
anova: Optional[F_onewayResult]
use_smart_strategy: bool
def __init__(self, bounds: Tuple[int, int] = (0, 256),
significance: float = 0.01,
@ -27,21 +31,30 @@ class ClocktowerManager:
self.rng = rng
self.min_correct_tries = min_correct_tries
self.best_guess = None
self.anova = None
self.use_smart_strategy = True
def next_guess(self) -> Optional[int]:
if (self.best_guess is not None and
len(self.data[self.best_guess]) >= self.min_correct_tries):
return None
min_count = min([len(x) for x in self.data.values()])
if min_count < 3 or self.rng.uniform(0, 1) > 0.5:
min_keys = [k for k in self.data.keys() if len(self.data[k]) == min_count]
return self.rng.choice(min_keys)
elif self.best_guess is not None:
return self.best_guess
min_keys = [k for k in self.data.keys() if len(self.data[k]) == min_count]
if self.use_smart_strategy:
if (self.best_guess is not None and
len(self.data[self.best_guess]) >= self.min_correct_tries):
return None
if min_count < 3 or self.rng.uniform(0, 1) > 0.8:
return self.rng.choice(min_keys)
elif self.best_guess is not None:
return self.best_guess
else:
means = {k: v.mean() for k, v in self.data.items()}
return max(means.items(), key=lambda x: x[1])[0]
else:
means = {k: v.mean() for k, v in self.data.items()}
return max(means.items(), key=lambda x: x[1])[0]
if self.best_guess is not None:
return None
else:
return self.rng.choice(min_keys)
def update(self, guess: int, value: float) -> None:
if guess not in self.data.keys():
@ -53,10 +66,8 @@ class ClocktowerManager:
return
if max([len(v) for v in inputs]) < 2:
return
res = f_oneway(*inputs)
print("results", res)
if res.pvalue <= self.significance:
print("significant!")
self.anova = f_oneway(*inputs)
if self.anova.pvalue <= self.significance:
self.best_guess = max(self.data.items(), key=lambda v: v[1].mean())[0]
else:
self.best_guess = None
@ -69,12 +80,15 @@ class ClocktowerManager:
def main() -> None:
gen = npr.default_rng()
stdev = 7059
u0 = 500000
u1 = 506046
# stdev = 7059
# u0 = 500000
# u1 = 506046
# stdev = 1000
# u0 = 500000
# u1 = 506046
stdev = 7000 * 2
u0 = 500000
u1 = 506046
correct_guess = 0x42
def sample(guess):
@ -86,13 +100,14 @@ def main() -> None:
num_guesses += 1
guess = mgr.next_guess()
if guess is None:
print("answer", hex(mgr.get_best_guess()))
break
print("guessing", hex(guess), "(guess", num_guesses, ")")
print("guessing", f"0x{guess:02x}", "(guess", num_guesses, ")", "(state", mgr.anova, ")")
value = sample(guess)
mgr.update(guess, value)
if len(mgr.data[correct_guess]) > 0:
print("means", mgr.data[correct_guess].mean(),
np.hstack([v for k, v in mgr.data.items() if k != correct_guess]).mean())
# if len(mgr.data[correct_guess]) > 0:
# print("means", mgr.data[correct_guess].mean(),
# np.hstack([v for k, v in mgr.data.items() if k != correct_guess]).mean())
print("state", mgr.anova)
print(f"answer 0x{mgr.get_best_guess():02x}")
print("took", num_guesses, "guesses")