# SimpleThompson

 Author Sean Submission date 2016-02-25 02:26:48.656368 Rating 5683 Matches played 332 Win rate 55.12

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")
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 select_move(self):
dr = gamma(self.counts[0] + 1, 1)
dp = gamma(self.counts[1] + 1, 1)
ds = gamma(self.counts[2] + 1, 1)
l = 1.0 / (dr + dp + ds)
r, p, s = self.counts
r += dr * l
p += dp * l
s += ds * l
scores = [s - p, r - s, p - r]
best = max(scores)
return (best, scores.index(best))

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)
best_score = float("-inf")
for n in path:
score, move = n.select_move()
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
tree.update(history, i)

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

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