This program has been disqualified.
| Author | muddyshoes |
| Submission date | 2011-06-12 07:36:48.120892 |
| Rating | 8001 |
| Matches played | 172 |
| Win rate | 79.65 |
import random
import math
class TurnHistory:
outcomes = { "RR":0,"RP":-1, "RS":1, "PR":1, "PP":0, "PS":-1, "SR":-1,"SP":1,"SS":0}
def __init__(self):
self.turns = []
def win_lose_or_draw(self, p1, p2 ):
return TurnHistory.outcomes[p1+p2]
def append_turn(self,me,opp):
turn_data = (me, opp, self.win_lose_or_draw(me, opp))
self.turns.append(turn_data)
class Predictor(object):
scoring_ratios = [0.1,0.15,0.23,0.33,0.45,0.59,0.72,0.85,1.0]
winning_moves = {'R':'P','P':'S','S':'R'}
def __init__(self, turn_history):
self.prediction_history = []
self.prediction_score_history = []
self.turn_history = turn_history
def get_total_score(self):
return sum(self.prediction_score_history)
def get_prediction_score(self):
hist_length = min(len(Predictor.scoring_ratios),len(self.prediction_score_history))
if hist_length > 0:
ratios = Predictor.scoring_ratios[-hist_length:]
scores = map(lambda x,i: x*i,self.prediction_score_history[-hist_length:],ratios)
return sum(scores)
return 0
def turn_added(self):
if len(self.prediction_history) > 0:
predicted_move = Predictor.winning_moves[self.prediction_history[-1]]
whatif_result = self.turn_history.win_lose_or_draw(predicted_move,self.turn_history.turns[-1][1])
self.prediction_score_history.append(whatif_result)
self.inner_turn_added()
self.prediction_history.append(self.predict_next_opp_move())
def inner_turn_added(self):
pass
def predict_next_opp_move(self):
pass
def choose_best(self,scores):
counts = [x[0] for x in scores]
m = max(counts)
return scores[counts.index(m)][1]
def choose_dist(self,scores):
counts = [x[1] for x in scores]
total = sum(counts)
if total == 0:
return random.choice(['R','P','S'])
pick = (random.randint(1,1000)/1000.0)*total
if( scores[0][1] > pick):
return scores[0][0]
elif( scores[0][1] + scores[1][1] > pick):
return scores[1][0]
else:
return scores[2][0]
class IOPairsPredictor(Predictor):
def __init__(self, turn_history):
Predictor.__init__(self,turn_history)
self.history = {}
def inner_turn_added(self):
for item in self.history.values():
for key in item.keys():
item[key] *= 0.95
if len(self.turn_history.turns)>1:
pairkey = self.turn_history.turns[-2][0]+self.turn_history.turns[-2][1]
if pairkey not in self.history:
self.history[pairkey] = {'R':0,'P':0,'S':0}
self.history[pairkey][self.turn_history.turns[-1][1]] += 1
def predict_next_opp_move(self):
pairkey = self.turn_history.turns[-1][0]+self.turn_history.turns[-1][1]
if pairkey in self.history:
return self.choose_dist(self.history[pairkey].items())
else:
return random.choice(['R','P','S'])
class AntiIOPairsPredictor(IOPairsPredictor):
def predict_next_opp_move(self):
return Predictor.winning_moves[IOPairsPredictor.predict_next_opp_move(self)]
class LastInputPredictor(Predictor):
def __init__(self, turn_history):
Predictor.__init__(self,turn_history)
self.history = {}
def inner_turn_added(self):
for item in self.history.values():
item['R'] *= 0.95
item['P'] *= 0.95
item['S'] *= 0.95
if len(self.turn_history.turns)>1:
if self.turn_history.turns[-2][1] not in self.history:
self.history[self.turn_history.turns[-2][1]] = {'R':0,'P':0,'S':0}
self.history[self.turn_history.turns[-2][1]][self.turn_history.turns[-1][1]] += 1
def predict_next_opp_move(self):
if self.turn_history.turns[-1][1] in self.history:
return self.choose_dist(self.history[self.turn_history.turns[-1][1]].items())
else:
return random.choice(['R','P','S'])
class AntiLastInputPredictor(LastInputPredictor):
def predict_next_opp_move(self):
move = super(AntiLastInputPredictor,self).predict_next_opp_move()
return Predictor.winning_moves[move]
class LastOutputPredictor(Predictor):
def __init__(self, turn_history):
Predictor.__init__(self,turn_history)
self.history = {}
def inner_turn_added(self):
for item in self.history.values():
item['R'] *= 0.95
item['P'] *= 0.95
item['S'] *= 0.95
if len(self.turn_history.turns)>1 and self.turn_history.turns[-2][0] not in self.history:
self.history[self.turn_history.turns[-2][0]] = {'R':0,'P':0,'S':0}
if len(self.turn_history.turns)>1:
self.history[self.turn_history.turns[-2][0]][self.turn_history.turns[-1][1]] += 1
def predict_next_opp_move(self):
if self.turn_history.turns[-1][0] in self.history:
return self.choose_dist(self.history[self.turn_history.turns[-1][0]].items())
else:
return random.choice(['R','P','S'])
class RecentInputsPredictor(Predictor):
recent_length = 5
def __init__(self, turn_history):
Predictor.__init__(self,turn_history)
self.history = []
self.classes = []
self.nb= None
def inner_turn_added(self):
mapping = {'R':0,'P':1,'S':2}
if len(self.turn_history.turns)>RecentInputDistributionPredictor.recent_length:
recent = [ mapping[x[1]] for x in self.turn_history.turns[-(RecentInputDistributionPredictor.recent_length+1):-1] ]
self.history.append(recent)
self.classes.append(self.turn_history.turns[-1][1])
self.nb = NaiveBayes.train(self.history,self.classes,typecode = 'i')
def predict_next_opp_move(self):
if self.nb:
mapping = {'R':0,'P':1,'S':2}
recent = [ mapping[x[1]] for x in self.turn_history.turns[-RecentInputDistributionPredictor.recent_length:] ]
return self.nb.classify(recent)
else:
return random.choice(['R','P','S'])
class RecentInputDistributionPredictor(Predictor):
recent_length = 5
def __init__(self, turn_history):
Predictor.__init__(self,turn_history)
self.history = []
self.classes = []
self.nb= None
def inner_turn_added(self):
if len(self.turn_history.turns)>RecentInputDistributionPredictor.recent_length:
recent = [ x[1] for x in self.turn_history.turns[-(RecentInputDistributionPredictor.recent_length+1):-1] ]
self.history.append([recent.count('R'),recent.count('P'),recent.count('S')])
self.classes.append(self.turn_history.turns[-1][1])
self.nb = NaiveBayes.train(self.history,self.classes,typecode = 'i')
def predict_next_opp_move(self):
if self.nb:
recent = [ x[1] for x in self.turn_history.turns[-RecentInputDistributionPredictor.recent_length:] ]
return self.nb.classify([recent.count('R'),recent.count('P'),recent.count('S')])
else:
return random.choice(['R','P','S'])
class SimplePatternPredictor(Predictor):
def __init__(self, turn_history):
Predictor.__init__(self,turn_history)
self.singles = {}
self.doubles = {}
self.triples = {}
self.nb= None
def inner_turn_added(self):
turn = len(self.turn_history.turns)
if turn < 2:
return
mapping = {'R':0,'P':1,'S':2}
singledecay = 0.7
doubledecay = 0.95
tripledecay = 0.99
turn = len(self.turn_history.turns)
lastpair = (self.turn_history.turns[-2][0],self.turn_history.turns[-2][1])
if lastpair not in self.singles:
self.singles[lastpair] = [0.0,0.0,0.0,turn-1]
else:
decay_factor = pow(singledecay,turn - self.singles[lastpair][3])
self.singles[lastpair][3] = turn
self.singles[lastpair][0] *= decay_factor
self.singles[lastpair][1] *= decay_factor
self.singles[lastpair][2] *= decay_factor
self.singles[lastpair][mapping[self.turn_history.turns[-1][1]]] += 1.0
if turn > 2:
prevdouble = tuple([(x[0],x[1]) for x in self.turn_history.turns[-3:-1]])
if prevdouble not in self.doubles:
self.doubles[prevdouble] = [0.0,0.0,0.0,turn-1]
else:
decay_factor = pow(doubledecay,turn - self.doubles[prevdouble][3])
self.doubles[prevdouble][3] = turn
self.doubles[prevdouble][0] *= decay_factor
self.doubles[prevdouble][1] *= decay_factor
self.doubles[prevdouble][2] *= decay_factor
self.doubles[prevdouble][mapping[self.turn_history.turns[-1][1]]] += 1.0
if turn > 3:
prevtriple = tuple([(x[0],x[1]) for x in self.turn_history.turns[-4:-1]])
if prevtriple not in self.triples:
self.triples[prevtriple] = [0.0,0.0,0.0,turn-1]
else:
decay_factor = pow(tripledecay,turn - self.triples[prevtriple][3])
self.triples[prevtriple][3] = turn
self.triples[prevtriple][0] *= decay_factor
self.triples[prevtriple][1] *= decay_factor
self.triples[prevtriple][2] *= decay_factor
self.triples[prevtriple][mapping[self.turn_history.turns[-1][1]]] += 1.0
def predict_next_opp_move(self):
scores = [0.0,0.0,0.0]
lastpair = (self.turn_history.turns[-1][0],self.turn_history.turns[-1][1])
if lastpair in self.singles:
scores[0] += self.singles[lastpair][0]
scores[1] += self.singles[lastpair][1]
scores[2] += self.singles[lastpair][2]
prevdouble = tuple([(x[0],x[1]) for x in self.turn_history.turns[-2:]])
if prevdouble in self.doubles:
scores[0] += self.doubles[prevdouble][0]
scores[1] += self.doubles[prevdouble][1]
scores[2] += self.doubles[prevdouble][2]
prevtriple = tuple([(x[0],x[1]) for x in self.turn_history.turns[-3:]])
if prevtriple in self.triples:
scores[0] += self.triples[prevtriple][0]
scores[1] += self.triples[prevtriple][1]
scores[2] += self.triples[prevtriple][2]
return self.choose_dist([('R',scores[0]),('P',scores[1]),('S',scores[2])])
class AntiSimplePatternPredictor(SimplePatternPredictor):
def predict_next_opp_move(self):
return Predictor.winning_moves[SimplePatternPredictor.predict_next_opp_move(self)]
#The code below is a modified version of part of biopython
# Biopython License Agreement
#
#Permission to use, copy, modify, and distribute this software and its
#documentation with or without modifications and for any purpose and
#without fee is hereby granted, provided that any copyright notices
#appear in all copies and that both those copyright notices and this
#permission notice appear in supporting documentation, and that the
#names of the contributors or copyright holders not be used in
#advertising or publicity pertaining to distribution of the software
#without specific prior permission.
#
#THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DISCLAIM ALL
#WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED
#WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL THE
#CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY SPECIAL, INDIRECT
#OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
#OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
#OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
#OR PERFORMANCE OF THIS SOFTWARE.
class NaiveBayes:
"""Holds information for a NaiveBayes classifier.
Members:
classes List of the possible classes of data.
p_conditional CLASS x DIM array of dicts of value -> P(value|class,dim)
p_prior List of the prior probabilities for every class.
dimensionality Dimensionality of the data.
"""
def __init__(self):
self.classes = []
self.p_conditional = None
self.p_prior = []
self.dimensionality = None
def calculate(self, observation, scale=0):
"""calculate(nb, observation[, scale]) -> probability dict
Calculate log P(class|observation) for each class. nb is a NaiveBayes
classifier that has been trained. observation is a list representing
the observed data. scale is whether the probability should be
scaled by P(observation). By default, no scaling is done. The return
value is a dictionary where the keys is the class and the value is the
log probability of the class.
"""
# P(class|observation) = P(observation|class)*P(class)/P(observation)
# Taking the log:
# lP(class|observation) = lP(observation|class)+lP(class)-lP(observation)
# Make sure the observation has the right dimensionality.
if len(observation) != self.dimensionality:
raise ValueError, "observation in %d dimension, but classifier in %d" \
% (len(observation), self.dimensionality)
# Calculate log P(observation|class) for every class.
lp_observation_class = [] # list of log P(observation|class)
for i in range(len(self.classes)):
# log P(observation|class) = SUM_i log P(observation_i|class)
probs = [None] * len(observation)
for j in range(len(observation)):
probs[j] = self.p_conditional[i][j].get(observation[j], 0)
lprobs = [math.log(x) if x > 0 else -10000 for x in probs]
lprob = sum(lprobs)
lp_observation_class.append(lprob)
# Calculate log P(class).
lp_prior = map(math.log, self.p_prior)
# Calculate log P(observation).
lp_observation = 0.0 # P(observation)
if scale: # Only calculate this if requested.
# log P(observation) = log SUM_i P(observation|class_i)P(class_i)
obs = [ 0.0 for i in range(len(self.classes))]
for i in range(len(self.classes)):
obs[i] = math.exp(lp_prior[i]+lp_observation_class[i])
lp_observation = math.log(sum(obs))
# Calculate log P(class|observation).
lp_class_observation = {} # Dict of class : log P(class|observation)
for i in range(len(self.classes)):
lp_class_observation[self.classes[i]] = \
lp_observation_class[i] + lp_prior[i] - lp_observation
return lp_class_observation
def classify(self, observation):
"""classify(nb, observation) -> class
Classify an observation into a class.
"""
# The class is the one with the highest probability.
probs = self.calculate(observation, scale=0)
max_prob = max_class = None
for klass in self.classes:
if max_prob is None or probs[klass] > max_prob:
max_prob, max_class = probs[klass], klass
return max_class
@classmethod
def train(cls,training_set, results, priors=None, typecode=None):
"""train(training_set, results[, priors]) -> NaiveBayes
Train a naive bayes classifier on a training set. training_set is a
list of observations. results is a list of the class assignments
for each observation. Thus, training_set and results must be the same
length. priors is an optional dictionary specifying the prior
probabilities for each type of result. If not specified, the priors
will be estimated from the training results.
"""
if not len(training_set):
raise ValueError, "No data in the training set."
if len(training_set) != len(results):
raise ValueError, "training_set and results should be parallel lists."
# If no typecode is specified, try to pick a reasonable one. If
# training_set is a Numeric array, then use that typecode.
# Otherwise, choose a reasonable default.
# XXX NOT IMPLEMENTED
# Check to make sure each vector in the training set has the same
# dimensionality.
dimensions = [len(x) for x in training_set]
if min(dimensions) != max(dimensions):
raise ValueError, "observations have different dimensionality"
nb = NaiveBayes()
nb.dimensionality = dimensions[0]
# Get a list of all the classes.
nb.classes = list(set(results))
nb.classes.sort() # keep it tidy
# Estimate the prior probabilities for the classes.
if priors is not None:
percs = priors
else:
percs = {}
numclasses = len(nb.classes)
for c in nb.classes:
percs[c] = float(results.count(c))/numclasses
#percs = listfns.contents(results)
nb.p_prior = [ percs[nb.classes[i]] for i in range(len(nb.classes)) ]
# Collect all the observations in class. For each class, make a
# matrix of training instances versus dimensions. I might be able
# to optimize this with Numeric, if the training_set parameter
# were guaranteed to be a matrix. However, this may not be the
# case, because the client may be hacking up a sparse matrix or
# something.
c2i = dict([(c,nb.classes.index(c)) for c in nb.classes]) # class to index of class
observations = [[] for c in nb.classes] # separate observations by class
for i in range(len(results)):
klass, obs = results[i], training_set[i]
observations[c2i[klass]].append(obs)
# Now make the observations Numeric matrics.
#for i in range(len(observations)):
# XXX typecode must be specified!
# observations[i] = array.array(typecode,observations[i])
# Calculate P(value|class,dim) for every class.
# This is a good loop to optimize.
nb.p_conditional = []
for i in range(len(nb.classes)):
class_observations = observations[i] # observations for this class
nb.p_conditional.append([None] * nb.dimensionality)
for j in range(nb.dimensionality):
# Collect all the values in this dimension.
values = [x[j] for x in class_observations]
# Add pseudocounts here. This needs to be parameterized.
#values = list(values) + range(len(nb.classes)) # XXX add 1
# Estimate P(value|class,dim)
d = {}
unique_values = list(set(values))
numvalues = float(len(unique_values))
for v in unique_values:
d[v] = float(values.count(v))/numvalues
nb.p_conditional[i][j] = d
return nb
#End of Biopython-derived code
winning_moves = {'R':'P','P':'S','S':'R'}
if input == "":
turn_history = TurnHistory()
predictors = [ RecentInputDistributionPredictor(turn_history),
#RecentInputsPredictor(turn_history),
LastInputPredictor(turn_history), LastOutputPredictor(turn_history),
IOPairsPredictor(turn_history),
SimplePatternPredictor(turn_history)]
#Someething is broken with inheritance here
#,AntiSimplePatternPredictor(turn_history),
# AntiLastInputPredictor(turn_history),AntiIOPairsPredictor(turn_history)]
output = random.choice(["R","P","S"])
else:
turn_history.append_turn(output,input)
for predictor in predictors:
predictor.turn_added()
scores = []
for predictor in predictors:
scores.append(predictor.get_total_score() + predictor.get_prediction_score()*1000)
m = max(scores)
move = predictors[scores.index(m)].predict_next_opp_move()
output = winning_moves[move]