# UCB11

 Author Sean Submission date 2016-05-05 10:21:23.316674 Rating 7035 Matches played 410 Win rate 70.98

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")

def ucb(pos, neg, n, t):
a = gamma(pos + 1, 1)
b = gamma(neg + 1, 1)
s = (a - b) / (a + b)
n = n + 1
return s + sqrt(2 * log(t) / n)

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

def score(self, t):
return ucb(self.pos_total, self.neg_total, self.total_visits, t)

def select_move(self, t):
scores = [ucb(pos, neg, n, t) for pos, neg, n in zip(self.pos, self.neg, self.visits)]
best = max(scores)
return (best, scores.index(best))

def update(self, i):
for j in xrange(3):
if j == beat[i]:
self.pos[j] += 1
elif i == beat[j]:
self.neg[j] += 1

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)
t = sum(3 + sum(node.visits) for node in path)
best_score = float("-inf")
for n in path:
score, move = n.select_move(t)
if score >= best_score:
best_score = score
best_node = n
best_move = move
return (best_node, best_move)

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

node = tree

else:

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

node.visits[j] += 1
node.update(i)

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

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