Author | Sean |

Submission date | 2016-02-24 15:54:37.759064 |

Rating | 7503 |

Matches played | 406 |

Win rate | 72.91 |

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

```
if input == "":
import collections
import math
import random
gamma = random.gammavariate
sqrt = math.sqrt
log = math.log
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
beat = (P, S, R)
name = ("R", "P", "S")
def ucb(s, n, t):
return s + sqrt(2 * log(t) / n)
def belief(xs):
n = sum(xs)
m = sum(1 for x in xs if x)
if m == 0:
a = 1.0
else:
a = m / (6.0 * log((n + 1.0) / m))
return [gamma(x + a, 1) for x in xs]
class MarkovTree:
def __init__(self, parent = None):
self.counts = [0.0 for _ in xrange(3)]
self.visits = [0.0 for _ in xrange(3)]
self.children = None
self.parent = parent
def select_move(self):
r, p, s = belief(self.counts)
visits = belief(self.visits)
t = sum(visits) + 1.0
n = r + p + s
scores = [s - p, r - s, p - r]
scores = [ucb(s / n, k, t) for s, k in zip(scores, visits)]
best = max(scores)
i = scores.index(best)
self.visits[i] += 1
return i
def update(self, h, i):
for n in h:
self.counts[i] += 1
if self.children is None:
break
self = self.children[n]
if self is None:
break
def predict(self, h):
stop = False
d = 0
for d, n in enumerate(h):
if stop or d >= 16:
break
if self.children is None:
self.children = [None for _ in xrange(3)]
if self.children[n] is None:
self.children[n] = MarkovTree(self)
stop = True
child = self.children[n]
self = child
leaf = self
counts = [0, 0, 0]
while self is not None:
counts[self.select_move()] += 1
self = self.parent
r, p, s = counts
scores = [r - s, p - r, s - p]
s = max(scores)
m = random.choice([i for i, x in enumerate(scores) if x == s])
return (leaf, m)
tree = MarkovTree()
history = collections.deque([])
node = tree
else:
i = index[input]
j = index[output]
#while node is not None:
# node.visits[j] += 1
# node = node.parent
tree.update(history, i)
history.appendleft(i)
history.appendleft(j)
node, k = tree.predict(history)
output = name[k]
```