This program has been disqualified.
| Author | Kyle Miller |
| Submission date | 2011-08-01 22:51:21.832149 |
| Rating | 5706 |
| Matches played | 117 |
| Win rate | 57.26 |
# metameta.py by Kyle Miller, 2011
# I never meta strategy I didn't like
import random
if not input :
rps_to_num = {"R":0, "P":1, "S":2}
num_to_rps = {0:"R", 1:"P", 2:"S"}
def their_history_strategy(my_move, their_move, my_history, their_history) :
"""See if they've played these moves before, and mirror what
they're going to play."""
max_length = 0
best_move = random.randint(0, 2)
for i in xrange(max(0, len(their_history)-8), len(their_history)-1) :
if their_history[i] == their_move :
length = 1
for j in xrange(0, i) :
if their_history[i-j-1] == their_history[-j-1] :
length += 1
else :
break
if length >= max_length :
max_length = length
best_move = their_history[i+1]
return best_move
def our_history_strategy(my_move, their_move, my_history, their_history) :
"""See if they've played these moves before, and mirror what
they're going to play."""
max_length = 0
best_move = random.randint(0, 2)
for i in xrange(max(0, len(their_history)-8), len(their_history)-1) :
if their_history[i] == their_move and my_history[i] == my_move :
length = 1
for j in xrange(0, i) :
if their_history[i-j-1] == their_history[-j-1] and my_history[i-j-1] == my_history[-j-1]:
length += 1
else :
break
if length >= max_length :
max_length = length
best_move = their_history[i+1]
return best_move
last_guesses = None
metastrategies = []
def get_guesses(my_move, their_move, my_history, their_history) :
guesses = [0,
their_move, my_move,
their_history_strategy(my_move, their_move, my_history, their_history),
their_history_strategy(their_move, my_move, their_history, my_history),
our_history_strategy(my_move, their_move, my_history, their_history),
our_history_strategy(their_move, my_move, their_history, my_history)]
guesses2 = [(g+1)%3 for g in guesses]
guesses3 = [(g+2)%3 for g in guesses]
guesses.extend(guesses2)
guesses.extend(guesses3)
guesses.append(random.randint(0,2))
return guesses
metastrategy_scores = []
m_last_guesses = []
def add_metastrategy(m) :
metastrategies.append(m)
metastrategy_scores.append(0)
m_last_guesses.append(None)
def get_best(guesses, scores) :
best_score = -1000
best_guess = None
for g, s in zip(guesses, scores) :
if s > best_score :
best_score = s
best_guess = g
return g
class Metastrategy1 :
def __init__(self) :
self.scores = None
def get_best_move(self, their_move, last_guesses, guesses) :
if not self.scores :
self.scores = [0 for g in guesses]
scores = self.scores
for i in xrange(0, len(last_guesses)) :
if last_guesses[i] is not None :
if (last_guesses[i]-1)%3 == their_move : # got it!
scores[i] += 2
elif last_guesses[i] == their_move : # at least didn't lose...
scores[i] = max(scores[i]-1, 0)
else : # whoops...
scores[i] = scores[i]//2
return get_best(guesses, scores)
add_metastrategy(Metastrategy1())
class MetastrategyLinearFalloff :
def __init__(self) :
self.scores = None
def get_best_move(self, their_move, last_guesses, guesses) :
if not self.scores :
self.scores = [0 for g in guesses]
scores = self.scores
for i in xrange(0, len(guesses)) :
if last_guesses[i] is not None :
if (last_guesses[i]-1)%3 == their_move : # got it!
scores[i] += 1
elif last_guesses[i] == their_move : # at least didn't lose...
pass
else : # whoops...
scores[i] = max(0, scores[i]-1)
return get_best(guesses, scores)
add_metastrategy(MetastrategyLinearFalloff())
class MetastrategyTryThingsOnce :
def __init__(self) :
self.scores = None
self.j = -1
def get_best_move(self, their_move, last_guesses, guesses) :
if not self.scores :
self.scores = [[10,0] for g in guesses]
scores = self.scores
for i in xrange(0, len(guesses)) :
if last_guesses[i] is not None :
if (last_guesses[i]-1)%3 == their_move : # got it!
scores[i][0] += 1
if i == self.j :
scores[i][1] += 1
elif last_guesses[i] == their_move : # at least didn't lose...
pass
else : # whoops...
scores[i][0] = 0
scores[i][1] = 0
if i == self.j :
self.j = -1
return get_best(guesses, scores)
add_metastrategy(MetastrategyTryThingsOnce())
def get_best_move(my_move, their_move, my_history, their_history) :
global last_guesses, m_last_guesses
guesses = get_guesses(my_move, their_move, my_history, their_history)
if not last_guesses :
last_guesses = [None for g in guesses]
m_guesses = []
for i in xrange(0, len(metastrategies)) :
m_guess = metastrategies[i].get_best_move(their_move, last_guesses, guesses)
if m_last_guesses[i] is not None :
if (m_last_guesses[i]-1)%3 == their_move : # got it!
metastrategy_scores[i] += 2
elif m_last_guesses[i] == their_move : # tie
metastrategy_scores[i] = max(0, metastrategy_scores[i]-1)
else :
metastrategy_scores[i] = metastrategy_scores[i]//2
m_guesses.append(m_guess)
last_guesses = guesses
m_last_guesses = m_guesses
to_try = zip(m_guesses, metastrategy_scores, xrange(0, len(metastrategies)))
to_try.sort(key=lambda x : x[1])
return to_try[-1][0]
# at the beginning, we don't know what to do. so be random!
output = num_to_rps[random.randint(0, 2)]
my_history = []
their_history = []
else :
my_move = rps_to_num[output]
their_move = rps_to_num[input]
output = num_to_rps[get_best_move(my_move, their_move, my_history, their_history)]
my_history.append(my_move)
their_history.append(their_move)