Author | Sean |

Submission date | 2016-02-09 21:04:56.702687 |

Rating | 5218 |

Matches played | 366 |

Win rate | 48.91 |

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

```
if input == "":
import random
class MarkovChain:
def __init__(self, counts = None):
self.visits = 0
if counts is None:
self.counts = [3, 3, 3]
else:
self.counts = counts
self.children = [self, self, self]
def split_edge(self, i):
old = self.children[i]
new = MarkovChain(old.counts)
self.children[i] = new
new.children = old.children
def unnormalized_probabilities(self):
return [n + 0 for n in self.counts]
def transition(self, i):
self.visits += 1
self.counts[i] += 1
for i in xrange(3):
self.counts[i] *= 0.99
if self.children[i].visits >= 8:
self.split_edge(i)
return self.children[i]
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
names = ("R", "P", "S")
beat = (P, S, R)
namebeat = ("P", "S", "R")
rel = {}
for i in xrange(3):
for j in xrange(3):
a = names[i]
b = names[j]
if beat[i] == j:
rel[(a, b)] = 0
elif beat[j] == i:
rel[(a, b)] = 2
else:
rel[(a, b)] = 1
unrel = {}
for i in xrange(3):
for j in xrange(3):
a = names[i]
if j == 1:
unrel[(a, j)] = i
elif j == 0:
unrel[(a, j)] = beat[i]
else:
unrel[(a, j)] = beat[beat[i]]
r0 = MarkovChain()
p0 = MarkovChain()
s0 = MarkovChain()
r1 = MarkovChain()
p1 = MarkovChain()
s1 = MarkovChain()
r2 = MarkovChain()
p2 = MarkovChain()
s2 = MarkovChain()
children0 = [r0, p0, s0]
children1 = [r1, p1, s1]
children2 = [r2, p2, s2]
for c in children0:
c.children = children1
for c in children1:
c.children = children2
for c in children2:
c.children = children0
model = MarkovChain()
model.children = children0
output = random.choice(names)
else:
i = rel[(prev_output, input)]
j = rel[(input, output)]
model = model.transition(i)
model = model.transition(j)
counts = model.unnormalized_probabilities()
t = sum(counts)
r = random.uniform(0, t)
x = 0
for k, p in enumerate(counts):
x += p
if r <= x:
output = namebeat[unrel[(output, k)]]
break
prev_output = output
```