Author | Sean |

Submission date | 2016-02-09 16:29:25.807676 |

Rating | 6500 |

Matches played | 349 |

Win rate | 63.32 |

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

```
import random
class MarkovChain:
def __init__(self, counts = None):
self.visits = 0
if counts is None:
self.counts = [1, 1, 1]
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 + 1 for n in self.counts]
def transition(self, i):
self.visits += 1
self.counts[i] += 1
for i in xrange(3):
self.counts[i] *= 0.98
if self.children[i].visits >= 2:
self.split_edge(i)
return self.children[i]
index = {"R": 0,
"P": 1,
"S": 2}
beat = {0: "P",
1: "S",
2: "R"}
if input == "":
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
else:
i = index[input]
j = index[output]
model = model.transition(i)
model = model.transition(j)
probs = model.unnormalized_probabilities()
t = sum(probs)
r = random.uniform(0, t)
x = 0
for i, p in enumerate(probs):
x += p
if r <= x:
output = beat[i]
break
```