This program has been disqualified.

 Author Sean Submission date 2016-02-20 21:08:12.264119 Rating 5166 Matches played 34 Win rate 52.94

## Source code:

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

import array
import math
import collections
import random

log = math.log
exp = math.exp
third = 1/3.0
log_half = log(0.5)
log_third = log(third)
log_sixth = log(1/6.0)
log2 = log(2.0)
log3 = log(3.0)
log5 = log(5.0)
log7 = log(7.0)
R, P, S = 0, 1, 2
strategies = ((R, P, S), (P, S, R), (S, R, P))
index = {"R": 0, "P": 1, "S": 2}
name = ("R", "P", "S")
beat = (1, 2, 0)

if y > x:
x, y = y, x
d = y - x
if d < -60:
return x
return x + log(1.0 + exp(d))

def laplace_smoothing(counts, n, x):
return (counts[x] + 1.0) / (n + len(counts))

def update_meta_counts(counts, meta_counts, x):
if x >= 3:
o = 3
x -= 3
else:
o = 0
y = 0
z = -1
for y1 in xrange(3):
z1 = counts[o + y1]
if z1 > z:
z = z1
y = y1
for j, s in enumerate(strategies):
if s[y] == x:
meta_counts[o + j] += 1

def generate_probs(counts, meta_counts, x):
if x >= 3:
x -= 3
p = 0
m0 = sum(1 for y in counts if y)
n0 = sum(counts)
m1 = sum(1 for y in meta_counts if y)
n1 = sum(meta_counts)
for q, s in enumerate(strategies):
for k, y in enumerate(s):
if y == x:
p += laplace_smoothing(meta_counts, n1, q) * \
laplace_smoothing(counts, n0, k)
return p

class ContextTree:
def __init__(self, n):
self.log_p_kt = 0.0
self.log_p = 0.0
self.counts = array.array('i',(0 for _ in xrange(n)))
self.meta_counts = array.array('i',(0 for _ in xrange(n)))
self.total = 0
self.children = None
def update(self, history, i=0):
x = history[i]
if i and (x >= 3):
x -= 3
p = generate_probs(self.counts, self.meta_counts, x)
self.log_p_kt += log(p)
update_meta_counts(self.counts, self.meta_counts, x)
self.total = (self.total + 1) * 0.995
self.counts[x] += 1
for y, _ in enumerate(self.counts):
self.counts[y] *= 0.995
if self.total == 1 or i >= len(history) - 1 or i >= 8:
self.log_p = self.log_p_kt
return
if self.children is None:
self.children = [None for _ in xrange(len(self.counts))]
if self.children[x] is None:
self.children[x] = ContextTree(3)
self.children[x].update(history, i + 1)
log_p_children = 0
for child in self.children:
if child is not None:
log_p_children += child.log_p
self.log_p = log_add(self.log_p_kt, log_p_children) + log_half
def predict(self, history, i=0):
x = history[i]
if i and x >= 3:
x -= 3
p = generate_probs(self.counts, self.meta_counts, x)
log_p_kt = self.log_p_kt + log(p)
if self.total == 0 or i >= len(history) - 1 or i >= 8:
return log_p_kt
log_p_children = 0
if self.children is not None:
for y, child in enumerate(self.children):
if child is not None:
if y == x:
log_p_children += child.predict(history, i + 1)
else:
log_p_children += child.log_p
elif y == x:
if len(self.counts) == 3:
log_p_children += log_sixth
else:
log_p_children += log_third
both = ContextTree(6)
history = collections.deque([])
output = random.choice(name)
pred = None
else:
i = index[input]
j = index[output]
history.appendleft(i)
both.update(history)
history.appendleft(3 + j)
both.update(history)
ps = [0.0, 0.0, 0.0]
uniform_log_p = -(len(history) +  1) * log3
for i1, _ in enumerate(ps):
history.appendleft(i1)
a = both.predict(history)
ps[i1] = a - log2
history.popleft()
scores = [0, 0, 0]
for _ in xrange(3):
r = t + log(random.random())
for k, log_p in enumerate(ps):
if k == 0:
x = log_p
else: