# SelfO0-6_mix_v3 (fixed)

 Author david.werecat Submission date 2013-02-11 22:53:43.175731 Rating 7069 Matches played 727 Win rate 69.46

Use rpsrunner.py to play unranked matches on your computer.

## Source code:

``````#By David Catt 2013.
#Takes its own output as context.
import random
RPSID = ["R", "P", "S"]
RPSWIN = [1, 2, 0]
RPSLOSE = [2, 0, 1]
IMID = 2147483648
IMAX = 4294967295
class RPSMDL:
def __init__(self, order, decay, init):
self.mod = 3**order
self.ctx = 0
self.rate = decay
self.mdl = [[float(init), float(init), float(init)] for a in range(self.mod)]
def predict(self):
return self.mdl[self.ctx]
def update(self, val):
val %= 3
#self.mdl[self.ctx][val] += (IMAX - self.mdl[self.ctx][val]) >> self.rate
#self.mdl[self.ctx][RPSWIN[val]] -= self.mdl[self.ctx][RPSWIN[val]] >> (self.rate + 1)
#self.mdl[self.ctx][RPSLOSE[val]] -= self.mdl[self.ctx][RPSLOSE[val]] >> (self.rate + 1)
self.mdl[self.ctx][0] *= self.rate
self.mdl[self.ctx][1] *= self.rate
self.mdl[self.ctx][2] *= self.rate
self.mdl[self.ctx][val] += 1
self.ctx = ((self.ctx * 3) + val) % self.mod;

class RPSSMDL:
def __init__(self, order, decay, init):
self.mod = 3**order
self.ctx = 0
self.rate = decay
self.mdl = [[float(init), float(init), float(init)] for a in range(self.mod)]
def predict(self):
return self.mdl[self.ctx]
def update(self, last, val):
last %= 3
val %= 3
#self.mdl[self.ctx][val] += (IMAX - self.mdl[self.ctx][val]) >> self.rate
#self.mdl[self.ctx][RPSWIN[val]] -= self.mdl[self.ctx][RPSWIN[val]] >> (self.rate + 1)
#self.mdl[self.ctx][RPSLOSE[val]] -= self.mdl[self.ctx][RPSLOSE[val]] >> (self.rate + 1)
self.mdl[self.ctx][0] *= self.rate
self.mdl[self.ctx][1] *= self.rate
self.mdl[self.ctx][2] *= self.rate
self.mdl[self.ctx][val] += 1
self.ctx = ((self.ctx * 3) + last) % self.mod;

class RPSPREDICTOR:
def __init__(self, maxord, adapt):
self.models = [[RPSMDL(0, 0, 0), RPSSMDL(0, 0, 0)] for a in range(maxord + 1)]
self.weights = [[IMID, IMID] for a in range(maxord + 1)]
self.last = [[0, 0] for a in range(maxord + 1)]
self.maxord = maxord
self.prev = 0
for o in range(0, self.maxord):
self.models[o][0] = RPSMDL(o, 1 - ((1.3**o) * 0.04), o + 1)
self.models[o][1] = RPSSMDL(o, 1 - ((1.3**o) * 0.04), o + 1)
def predict(self):
tmp = [0.0, 0.0, 0.0]
tot = 0
ratios = [0.0, 0.0, 0.0]
for o in range(0, self.maxord):
for s in range(0, 1):
tmp = self.models[o][s].predict()
tot = tmp[0] + tmp[1] + tmp[2]
if tot == 0.0:
tot = 1.0
ratios[0] += (tmp[0] * self.weights[o][s] * (1.5**o)) / tot
ratios[1] += (tmp[1] * self.weights[o][s] * (1.5**o)) / tot
ratios[2] += (tmp[2] * self.weights[o][s] * (1.5**o)) / tot
if tmp[0] > tmp[1]:
if tmp[0] > tmp[2]:
self.last[o][s] = 0
else:
self.last[o][s] = 2
else:
if tmp[2] > tmp[1]:
self.last[o][s] = 2
elif tmp[0] == tmp[1] and tmp[1] == tmp[2]:
self.last[o][s] = random.choice([0, 1, 2])
else:
self.last[o][s] = 1
if ratios[0] > ratios[1]:
if ratios[0] > ratios[2]:
self.prev = 1
return 0
else:
self.prev = 0
return 2
else:
if ratios[2] > ratios[1]:
self.prev = 0
return 2
else:
self.prev = 2
return 1
def update(self, val):
for o in range(0, self.maxord):
self.models[o][0].update(val)
if val == self.last[o][0]:
self.weights[o][0] += (IMAX - self.weights[o][0]) >> self.rate
else:
self.weights[o][0] -= self.weights[o][0] >> (self.rate + 1)
self.models[o][1].update(self.prev, val)
if val == self.last[o][1]:
self.weights[o][1] += (IMAX - self.weights[o][1]) >> self.rate
else:
self.weights[o][1] -= self.weights[o][1] >> (self.rate + 1)

if input == "":
predictor = RPSPREDICTOR(6, 4)
aic = 0
elif input == "R":
predictor.update(0)
elif input == "P":
predictor.update(1)
elif input == "S":
predictor.update(2)
aic = RPSWIN[predictor.predict()]
output = RPSID[aic]``````