# Bandit2

 Author Sean Submission date 2016-02-25 00:34:26.867859 Rating 7092 Matches played 412 Win rate 67.48

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
exp = math.exp
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
beat = (P, S, R)
name = ("R", "P", "S")
third = 1.0 / 3.0

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

def scores(self):
r = self.counts
p = self.counts
s = self.counts
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):
path = []
stop = False
path.append(self)
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()
stop = True
child = self.children[n]
self = child
path.append(self)
k = 3 * len(path)
nu = 0.95 / k
norm = 0
for n in path:
scores = n.scores()
norm += sum(exp(nu * s) for s in scores)
r = random.random()
x = 0
for n in path:
for i, s in enumerate(n.scores()):
x += (0.5 * exp(nu * s)) / norm + nu
if x >= r:
return i

tree = MarkovTree()
history = collections.deque([])

else:

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

tree.update(history, i)

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