# Ent13

This program has been disqualified.

 Author Sean Submission date 2016-03-02 10:39:25.270585 Rating 5486 Matches played 159 Win rate 55.97

## 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
R, P, S = 0, 1, 2
beat = (P, S, R)
beat2 = (S, R, P)

def match_entropy(v, h0):
h0 = -h0
h1 = h0
p = [third, third, third]
k = -0.5
error = 1
n = 0
while exp(abs(error)) - 1 >= 0.00000000001:
p = [exp(k * vi) for vi in v]
t = sum(p)
f = 1.0 / t
p = [pi * f for pi in p]
n += 1
h = [log(pi) * pi for pi in p]
h1 = sum(h)
dp = [0 for _ in xrange(3)]
dt = sum(pi * vi for pi, vi in zip(p, v))
dh = sum((log(pi) + 1) * pi * (vi - dt) 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.us = [0 for _ in xrange(3)]
self.them = [0 for _ in xrange(3)]
self.meta = [0 for _ in xrange(3)]
self.children = None

def update(self, h, i, j):
stop = False
for d, k in enumerate(h):
r, p, s = self.us
scores = [s - p, r - s, p - r]
m = scores.index(max(scores))
if m == i:
self.meta += 1
elif m == beat[i]:
self.meta += 1
else:
self.meta += 1
self.us[j] += 1
self.them[i] += 1
if d >= 16 or stop:
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
self = self.children[k]

def predict(self, h):
best_score = float("-inf")
best_counts = [0, 0, 0]
best_scores = [0, 0, 0]
i = 0
for i, k in enumerate(h):
counts = [gamma(n + 1, 1) for n in self.them]
f = 1.0
r, p, s = counts
scores = [f * (s - p), f * (r - s), f * (p - r)]
score = max(scores)
if score >= best_score:
best_score = score
best_counts = self.them
best_scores = scores
r, p, s = self.us
scores = [s - p, r - s, p - r]
m = scores.index(max(scores))
counts = [gamma(n + 1, 1) for n in self.meta]
f = 1.0 / sum(counts)
a, b, c = counts
scores = [0, 0, 0]
scores[m] = c - b
scores[beat[m]] = a - c
scores[beat2[m]] = b - a
score = max(scores)
if score >= best_score:
best_score = score
best_scores = scores
best_counts = self.meta
if self.children is None:
break
child = self.children[k]
if child is None:
break
self = child
return best_counts, best_scores

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)

counts, scores = tree.predict(history)
u = 1.0 / (abs(max(scores)) + 1)
scores = [x * u for x in scores]
counts = [max(1, gamma(n + 1, 1)) for n in counts]
u = 1.0 / sum(counts)
p = [x * u for x in counts]
h = -sum(pi * log(pi) for pi in p)
ps = match_entropy([-x for x in scores], h)
h1 = -sum(pi * log(pi) for pi in ps)
r = random.uniform(0, sum(ps))
x = 0
for i in xrange(3):
x += ps[i]
if r <= x:
break
output = name[i]``````