# Ent2

This program has been disqualified.

 Author Sean Submission date 2016-02-29 18:19:15.819010 Rating 5617 Matches played 151 Win rate 55.63

## 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]
k = -0.05
error = 1
while abs(error) >= 0.00000001 * -h0:
if k < -20 or k >= 0:
return p
p = [exp(k * vi) for vi in v]
t = sum(p)
f = 1.0 / t
p = [pi * f for pi in 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

def random_index(ps):
t = sum(ps)
r = random.uniform(0, t)
x = 0
for i, p in enumerate(ps):
x += p
if r <= x:
break
return i

class MarkovTree:
def __init__(self, us=None, them=None):
self.entropy = 0
self.total = 0
if us is None:
self.us = [0 for _ in xrange(3)]
else:
self.us = list(us)
if them is None:
self.them = [0 for _ in xrange(3)]
else:
self.them = list(them)
self.children = None

def update(self, h, i, j):
stop = False
for k in h:
p_them = (self.them[i] + 0.5) / (self.total + 1.5)
p_us = (self.us[i] + 0.5) / (self.total + 1.5)
self.entropy -= log(p_them)
self.them[i] += 1
self.us[j] += 1
self.total += 1
if self.children is None:
self.children = [None for _ in xrange(3)]
if self.children[k] is None:
self.children[k] = MarkovTree(self.us, self.them)
return
self = self.children[k]

def predict(self, h):
us = [1, 1, 1]
them = [1, 1, 1]
entropy = float("inf")
t = 0.0
for i, k in enumerate(h):
entropy += self.entropy
t += self.total
for i in xrange(3):
us[i] += self.us[i]
them[i] += self.them[i]
if self.children is None:
break
child = self.children[k]
if child is None:
break
self = child
return (us, them)

R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
name = ("R", "P", "S")
tree = MarkovTree()
history = collections.deque([])

else:

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

tree.update(history, i, j)
history.appendleft(i)
history.appendleft(j)

us, them = tree.predict(history)
us = [gamma(n + 1, 1) for n in us]
them = [gamma(n + 1, 1) for n in them]
t_us = sum(us)
t_them = sum(them)
r, p, s = them
scores = [-(s - p), -(r - s), -(p - r)]
p_us = [n / t_us for n in us]
p_them = [n / t_them for n in them]
h_us = -sum(pi * log(pi) for pi in p_us)
h_them = -sum(pi * log(pi) for pi in p_them)
delta = max(h_them - h_us, 0)
h = expected_entropy + 0.5 * delta
ps = match_entropy(scores, h)
output = name[random_index(ps)]``````