This program has been disqualified.
| Author | chriiscardozo |
| Submission date | 2016-12-15 22:28:34.268291 |
| Rating | 3661 |
| Matches played | 4 |
| Win rate | 0.0 |
import random
class Addressing:
def __call__(self, binCode): # binCode is a list of values of selected points of entry
index = 0
for i,e in enumerate(binCode):
if e > 0:
index += e * pow(3,i)
return index
class RAM:
def __init__(self, addressSize, entrySize, addressing):
self.addressing = addressing
self.ram = {}
self.address = [ random.randint(0, entrySize-1) for x in range(addressSize) ]
def _addressToIndex(self, entry):
binCode = []
for i in self.address:
binCode.append(entry[i])
return self.addressing(binCode)
def _acumulateRam(self, index):
if index not in self.ram:
self.ram[index] = 0
self.ram[index] += 1
def _getValue(self, index):
if index not in self.ram:
return 0
else:
return self.ram[index]
def train(self, entry):
index = self._addressToIndex(entry)
self._acumulateRam(index)
def classify(self, entry):
index = self._addressToIndex(entry)
return self._getValue(index)
class Discriminator:
def __init__(self, name, entrySize, addressSize, addressing, numberOfRAMS=None):
if numberOfRAMS is None:
numberOfRAMS = int(entrySize/addressSize)
self.rams = [ RAM(addressSize, entrySize, addressing) for x in range(numberOfRAMS) ]
def train(self, entry):
for ram in self.rams:
ram.train(entry)
def classify(self, entry):
return [ ram.classify(entry) for ram in self.rams ]
class WiSARD:
def __init__(self,
addressSize = 3,
numberOfRAMS = None,
bleachingActivated = True,
seed = random.randint(0, 1000000),
verbose = True,
addressing=Addressing()):
self.seed = seed
self.verbose = verbose
random.seed(seed)
if addressSize < 3:
self.addressSize = 3
else:
self.addressSize = addressSize
self.numberOfRAMS = numberOfRAMS
self.discriminators = {}
self.bleachingActivated = bleachingActivated
self.addressing = addressing
def _makeBleaching(self, discriminatorsoutput):
bleaching = 0
ambiguity = True
biggestVote = 2
while ambiguity and biggestVote > 1:
bleaching += 1
biggestVote = None
ambiguity = False
for key in discriminatorsoutput:
discriminator = discriminatorsoutput[key]
limit = lambda x: 1 if x >= bleaching else 0
discriminator[1] = sum(map(limit, discriminator[0]))
if biggestVote is None or discriminator[1] > biggestVote:
biggestVote = discriminator[1]
ambiguity = False
elif discriminator[1] == biggestVote:
ambiguity = True
if self.bleachingActivated:
break
return discriminatorsoutput
def train(self, entries, classes):
sizeOfEntry = len(entries[0])
for i,entry in enumerate(entries):
if self.verbose:
print("\rtraining "+str(i+1)+" of "+str(len(entries)))
aclass = str(classes[i])
if aclass not in self.discriminators:
self.discriminators[aclass] = Discriminator(aclass, sizeOfEntry, self.addressSize, self.addressing, self.numberOfRAMS)
self.discriminators[aclass].train(entry)
if self.verbose:
print("\r")
def classifyEntry(self, entry):
discriminatorsoutput = {}
for keyClass in self.discriminators:
discriminatorsoutput[keyClass] = [self.discriminators[keyClass].classify(entry),0]
discriminatorsoutput = self._makeBleaching(discriminatorsoutput)
calc = lambda key: (key, float(discriminatorsoutput[key][1])/len(discriminatorsoutput[key][0]))
classes = list(map(calc,discriminatorsoutput))
classes.sort(key=lambda x: x[1], reverse=True)
return classes
def classify(self, entries):
output=[]
for i,entry in enumerate(entries):
if self.verbose:
print("\rclassifying "+str(i+1)+" of "+str(len(entries)))
aclass = self.classifyEntry(entry)[0][0]
output.append((entry, aclass))
if self.verbose:
print("\r")
return output
if input == "":
count = -1
history = []
correctly_predictions = {}
last_prediction = {}
prediction = {}
memorizes = [5, 10, 20, 50, 100]
max_win_memorize=50
win = []
losses = []
draw = []
init_random_choices = []
for m in range(memorizes[0]):
init_random_choices.append("R")
init_random_choices.append("S")
init_random_choices.append("P")
net_wisard = {}
for m in memorizes:
net_wisard[m] = WiSARD(addressSize=5,verbose=False)
correctly_predictions[m] = []
char2num = {"R":0, "P":1, "S":2}
num2char = {0:"R", 1:"P", 2:"S"}
defeated_by = {"R":"P", "P":"S", "S":"R"}
output = random.choice(["R","P","S"])
prediction[memorizes[0]] = output
elif len(history) < memorizes[0] or count < 100:
output = random.choice(["R","P","S"])
prediction[memorizes[0]] = output
history.append(char2num[last])
history.append(char2num[input])
correctly_predictions[memorizes[0]].append(1 if last_prediction == input else 0)
else:
# training
for m in memorizes:
if(len(history) < m):
continue
if(m in last_prediction):
correctly_predictions[m].append(1 if last_prediction[m] == input else 0)
if len(correctly_predictions[m]) > max_win_memorize: correctly_predictions[m].pop(0)
net_wisard[m].train([history[len(history)-m:len(history)]], [char2num[input]])
# updating history
history.append(char2num[last])
history.append(char2num[input])
prediction = {}
# classifying
for m in memorizes:
if(len(history) < m):
continue
result = net_wisard[m].classifyEntry(history[len(history)-m:len(history)])
max_value = 0.0
for r in result:
if(r[1] >= max_value):
prediction[m] = int(r[0])
max_value = r[1]
# updating history
if(len(history) > memorizes[len(memorizes)-1]):
history.pop(0)
history.pop(0)
# voting
vote = [0,0,0]
for p in prediction:
vote[prediction[p]] += 1
max_vote = 0
for i,v in enumerate(vote):
if(v > max_vote):
max_vote = v
output = num2char[i]
if(count%100 == 0):
output = random.choice(["R","S","P"])
output = defeated_by[output]
if(last == input):
win.append(1)
elif(last == defeated_by[input]):
losses.append(1)
else:
draw.append(1)
if(count%200 == 0):
print(str(sum(win)) + "/" + str(sum(losses)) + " de " + str(count))
last = output
last_prediction = prediction
count += 1