Author | Sean |

Submission date | 2016-02-11 15:29:40.486051 |

Rating | 4841 |

Matches played | 432 |

Win rate | 44.21 |

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
def update(self, h, i):
stop = False
for d, k in enumerate(h):
self.counts[i] += 1
if stop or d >= 128:
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]
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")
tree = MarkovTree()
history = collections.deque([])
else:
i = index[input]
j = index[output]
tree.update(history, i)
history.appendleft(i)
history.appendleft(j)
counts = tree.predict(history)
observations = sum(tree.counts)
t = sum(counts)
if t != 0:
u = observations / float(t)
normalized_counts = [x * u + 0.5 for x in counts]
r = random.uniform(0, sum(normalized_counts))
x = 0
for i, p in enumerate(counts):
x += p
if r <= x:
break
else:
i = random.randrange(0, 3)
output = beat[i]
```