Author | Sean |

Submission date | 2016-02-10 00:20:59.460212 |

Rating | 6676 |

Matches played | 372 |

Win rate | 65.86 |

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 = [0 for _ in xrange(3)]
else:
self.counts = counts
self.children = None
def split_edge(self, i):
old = self.children[i]
new = MarkovChain(old.counts)
self.children[i] = new
new.children = old.children
def transition(self, i, j):
self.visits += 1
self.counts[i] += 1
for i in xrange(3):
self.counts[i] *= 0.99
k = 3 * i + j
if self.children[k].visits >= 9:
self.split_edge(k)
return self.children[k]
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
name = ["R", "P", "S"]
l = 3
nodes = [[MarkovChain() for _ in xrange(9)] for _ in xrange(l)]
for i in xrange(l):
children = nodes[(i + 1) % l]
for j in xrange(9):
nodes[i][j].children = children
model = MarkovChain()
model.children = nodes[0]
else:
i = index[input]
j = index[output]
model = model.transition(i, j)
counts = model.counts
sample = [random.gammavariate(n + 1, 1) for n in counts]
expected_values = [sample[2] - sample[1],
sample[0] - sample[2],
sample[1] - sample[0]]
j = 0
e = 0
for i, x in enumerate(expected_values):
if x >= e:
j = i
e = x
output = name[j]
```