def __init__(self, name, preconditions=None, effects=None):
# excellent resource on weighted selection, i.e. loaded dice (i.e. PDF):
# http://www.keithschwarz.com/darts-dice-coins/
self.name = name
self.preconditions = [
(set(), set())
] if not preconditions else preconditions # multiple conditions may apply
self.effects = [] if effects is None else effects
total_probability = sum([effect.probability for effect in effects])
if total_probability > 1: # test whether the total probability does not exceed 1
raise AttributeError(
"The probability of the effects of an action must sum up to 1 or less than 1."
)
elif total_probability < 1: # if the probability is less than 1, add the default effect where nothing changes
self.effects.append(Effect(set(), set(), 1 - total_probability))
# sort the effects so that the most likely effect is on top; use for efficient rollouts along most probable path
self.effects = sorted(self.effects,
key=lambda effect: effect.probability,
reverse=True)
self._vose = Vose([(effect.probability, effect)
for effect in self.effects])
}
def check(prompt, ans):
k = ''.join(ans.lower().split())
if k not in smappings:
return False
return smappings[k] == prompt
prompts = tuple(mappings.keys())
accuracy = [CMA() for i in range(len(mappings))]
total_accuracy = CMA()
last_i = None
while True:
dice = Vose(*(1 - cma.value() for cma in accuracy))
i = next(dice)
if last_i is not None and i == last_i:
continue
else:
last_i = i
prompt = prompts[i]
ans = input(f'{prompt} = ')
if check(prompt, ans):
completion = int(100 * (1 - (len(prompts) / len(mappings))))
accuracy[i].update(1)
total_accuracy.update(1)
prn_accuracy = int(total_accuracy.value() * 100)
print(f'correct ({prn_accuracy}% accurate)')
else:
accuracy[i].update(0)
