# SuperMix

 Author Sean Submission date 2016-02-22 15:49:00.930269 Rating 5377 Matches played 422 Win rate 57.11

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

## Source code:

``````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")

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

def scores(self):
r = gamma(self.counts[0] + 0.5, 1)
p = gamma(self.counts[1] + 0.5, 1)
s = gamma(self.counts[2] + 0.5, 1)
return [s - p, r - s, p - r]

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):
scores = [0, 0, 0]
for n in h:
for i, s in enumerate(self.scores()):
scores[i] += s
if self.children is None:
break
self = self.children[n]
if self is None:
break
return scores.index(max(scores))
tree = MarkovTree()
history = collections.deque([])

node = tree

else:

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

tree.update(history, i)

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

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