Author | Sean |

Submission date | 2016-02-15 16:21:19.804908 |

Rating | 7020 |

Matches played | 408 |

Win rate | 68.38 |

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

```
if input == "":
import collections
import random
class MarkovTree:
def __init__(self, counts = None):
self.counts = [0 for _ in xrange(3)]
self.children = None
self.visits = 0
def update(self, h, i):
stop = False
for d, k in enumerate(h):
self.counts[i] += 2
self.visits += 1
if stop:
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
if self.visits < 9:
stop = True
self = self.children[k]
def predict(self, h, n0 = None):
if n0 is None:
n0 = [0, 0, 0]
for d, k in enumerate(h):
for i, x in enumerate(self.counts):
n0[i] += x
if self.children is None:
break
child = self.children[k]
if child is None:
break
self = child
return n0
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
beat = ("P", "S", "R")
us = MarkovTree()
them = MarkovTree()
both = MarkovTree()
us_history = collections.deque([])
them_history = collections.deque([])
both_history = collections.deque([])
else:
i = index[input]
j = index[output]
us.update(us_history, i)
them.update(them_history, i)
both.update(both_history, i)
us_history.appendleft(j)
them_history.appendleft(i)
both_history.appendleft(i)
both_history.appendleft(j)
counts = us.predict(us_history)
them.predict(them_history, counts)
both.predict(both_history, counts)
ps = [random.gammavariate(n + 1, 1) for n in counts]
t = sum(ps)
r = random.uniform(0, t)
x = 0
for i, p in enumerate(counts):
x += p
if r <= x:
break
output = beat[i]
```