# Jepsen

 Author Sean Submission date 2016-05-04 10:31:31.892155 Rating 6492 Matches played 388 Win rate 63.92

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

## Source code:

``````if input == "":

import collections
import math
import random

gamma = random.gammavariate
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
beat = (P, S, R)
name = ("R", "P", "S")

def score(probs):
r, p, s = probs
scores = [s - p, r - s, p - r]
s = max(scores)
return (s, scores.index(s))

def score_belief(counts):
r, p, s = counts
r0 = s - p
p0 = r - s
s0 = p - r
r1 = gamma(s + 1, 1) - gamma(p + 1, 1)
p1 = gamma(r + 1, 1) - gamma(s + 1, 1)
s1 = gamma(p + 1, 1) - gamma(r + 1, 1)
scores = [r1, p1, s1]
s = max(scores)
return (s, scores.index(s))

class MarkovTree:

def __init__(self):
self.counts = [0.0 for _ in xrange(3)]
self.children = None

def select_move(self):
(s0, _) = score(self.counts)
while True:
(s1, _) = u = score_belief(self.counts)
if s1 >= s0:
return u

def update(self, h, i):
for d, n in enumerate(h):
if d >= 16:
return
self.counts[i] += 1
if self.children is None:
self.children = [MarkovTree() for _ in xrange(3)]
return
self = self.children[n]

def predict(self, h):
s0 = 0
for n in h:
s1, m1 = self.select_move()
if s1 >= s0:
m = m1
s0 = s1
if self.children is None:
return m
self = self.children[n]

tree = MarkovTree()
history = collections.deque([])
output = random.choice(name)

else:

i = index[input]
j = index[output]

tree.update(history, i)

history.appendleft(i)
history.appendleft(j)

output = name[tree.predict(history)]``````