# Ent8

 Author Sean Submission date 2016-03-01 16:39:56.451653 Rating 6905 Matches played 413 Win rate 70.94

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

## Source code:

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

import collections
import random
import math

exp = math.exp
log = math.log
third = 1.0 / 3
expected_entropy = -log(third)
gamma = random.gammavariate

def match_entropy(v, h0):
h0 = -h0
p = [third, third, third]
k0 = -0.05
k = k0
error = 1
n = 0
while abs(error) >= 0.0001 * -h0:
if n >= 9:
break
n += 1
p = [exp(k * vi) for vi in v]
t = sum(p)
f = 1.0 / t
p = [pi * f for pi in p]
if any(x == 0 for x in p):
return p
h = [log(pi) * pi for pi in p]
h1 = sum(h)
dh = sum((log(pi) + 1) * vi * pi for vi, pi in zip(v, p))
if dh == 0:
return p
error = h1 - h0
k = k - error / dh
return p

class MarkovTree:
def __init__(self):
self.them = [0 for _ in xrange(3)]
self.children = None

def update(self, h, i, j):
stop = False
for d, k in enumerate(h):
self.them[i] += 1
if stop or d >= 16:
return
if self.children is None:
self.children = [None for _ in xrange(3)]
if self.children[k] is None:
self.children[k] = MarkovTree()
stop = True
return
self = self.children[k]

def predict(self, h):
best_score = float("-inf")
counts = [0, 0, 0]
probs = [third, third, third]
for i, k in enumerate(h):
ps = [gamma(n + 1.0, 1) for n in self.them]
f = 1.0 / sum(ps)
r, p, s = ps
score = max([s - p, r - s, p - r])
if score >= best_score:
best_score = score
counts = self.them
if self.children is None:
break
child = self.children[k]
if child is None:
break
self = child
return counts
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
name = ("R", "P", "S")
tree = MarkovTree()
history = collections.deque([])
epoch = 1.0

else:

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

tree.update(history, i, j)
history.appendleft(i)
history.appendleft(j)
them = tree.predict(history)
r, p, s = [n + 1.0 for n in them]
u = 1.0 / (r + p + s)
scores = [-(s - p) * u, -(r - s) * u, -(p - r) * u]
them = [n + 7.0 for n in them]
u = 1.0 / sum(them)
p = [x * u for x in them]
h_them = -sum(pi * log(pi) for pi in p)
ps = match_entropy(scores, h_them)
r = random.random()
x = 0
for i in xrange(3):
x += ps[i]
if r <= x:
break
output = name[i]``````