# ExpFixed

 Author Sean Submission date 2016-02-29 08:45:33.021611 Rating 7269 Matches played 390 Win rate 72.56

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

## Source code:

``````if input == "":

import collections
import random
import math

log = math.log
exp = math.exp
log3 = log(3)

if y > x:
x, y = y, x
d = y - x
if d < -60:
return x
return x + log(1.0 + exp(d))

def random_index(ps):
t = sum(ps)
r = random.uniform(0, t)
x = 0
for i, p in enumerate(ps):
x += p
if r <= x:
break
return i

class MarkovTree:
def __init__(self):
self.log_p = 0
self.counts = [0 for _ in xrange(3)]
self.total = 0
self.children = None

def update(self, h, i, prediction=None):
stop = False
for d, k in enumerate(h):
self.log_p += log((self.counts[i] + 1.0) / (self.total + 3.0))
self.counts[i] += 1
self.total += 1
if stop or d >= 16:
return
if self.children is None:
self.children = [None for _ in xrange(3)]
if self.children[k] is None:
self.children[k] = MarkovTree()
stop = True
self = self.children[k]

def predict(self, h):
n0 = [0, 0, 0]
n = self.total
w = -n * log3
weights = [w]
estimators = [[1, 1, 1]]
for d, k in enumerate(h):
wi = self.log_p - (n + 3 * d - self.total) * log3
weights.append(wi)
estimators.append(self.counts)
if self.children is None:
break
child = self.children[k]
if child is None:
break
self = child
d += 1
for wi, counts in zip(weights, estimators):
for i, n in enumerate(counts):
n0[i] += exp(wi - w) * ((n + 0.5) / (sum(counts) + 1.5))
return n0

R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
beat = (P, S, R)
name = ("R", "P", "S")
tree = MarkovTree()
history = collections.deque([])
output = random.choice(name)
else:

i = index[input]
j = index[output]

tree.update(history, i)
history.appendleft(i)
history.appendleft(j)

counts = tree.predict(history)
hypotheses = [random_index(counts) for _ in xrange(3)]
scores = [0, 0, 0]
for i, _ in enumerate(scores):
for h in hypotheses:
if i == beat[h]:
scores[i] += 1
elif h == beat[i]:
scores[i] -= 1
best = max(scores)
prediction = random.choice([i for i in xrange(3) if scores[i] == best])
output = name[prediction]``````