# Skint

This program has been disqualified.

 Author Sean Submission date 2016-03-02 18:21:02.235215 Rating 6839 Matches played 66 Win rate 71.21

## Source code:

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

import collections
import random
import math

log = math.log
exp = math.exp
third = 1.0 / 3

def match_entropy(v, h0):
h0 = -h0
p = [third, third, third]
k = -0.5
error = 1
n = 0
while exp(abs(error)) - 1 >= 0.00000000001:
try:
p = [exp(k * vi) for vi in v]
except OverflowError:
return p
t = sum(p)
f = 1.0 / t
p = [pi * f for pi in p]
if any(pi == 0 for pi in p):
return 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

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, counts = None):
self.counts = [0 for _ in xrange(3)]
self.children = None
self.total = 0

def update_helper(self, h, i, p, d, skips):
stop = False
for j in xrange(p, len(h)):
k = h[j]
self.counts[i] += 2
self.total += 2
if stop or d >= 9 or skips >= 3:
return
d += 1
if self.children is None:
self.children = [None for _ in xrange(4)]
self.children[3] = MarkovTree()
if self.children[k] is None:
self.children[k] = MarkovTree()
stop = True
self.children[3].update_helper(h, i, j + 1, d, skips + 1)
self = self.children[k]

def update(self, h, i):
self.update_helper(h, i, 0, 0, 0)

def predict_helper(self, h, p, n0):
for j in xrange(p, len(h)):
k = h[j]
for i, x in enumerate(self.counts):
n0[i] += x
if self.children is None:
return
self.children[3].predict_helper(h, j + 1, n0)
child = self.children[k]
if child is None:
return
self = child

def predict(self, h):
n0 = [0, 0, 0]
self.predict_helper(h, 0, n0)
return n0

R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
name = ("R", "P", "S")
beat = (P, S, R)

tree = MarkovTree()

history = collections.deque([])

else:

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

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

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