# Bog

This program has been disqualified.

 Author Sean Submission date 2016-02-25 10:05:50.714374 Rating 6468 Matches played 66 Win rate 65.15

## Source code:

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

import collections
import math
import random

gamma = random.gammavariate
sqrt = math.sqrt
log = math.log
R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
beat = (P, S, R)
name = ("R", "P", "S")
third = 1.0 / 3.0
plus1 = [1, 2, 0]
plus2 = [2, 0, 1]

class MarkovTree:
def __init__(self, parent = None):
self.counts = [0.0 for _ in xrange(3)]
self.meta = [0.0 for _ in xrange(3)]
self.visits = [1.0 for _ in xrange(3)]
self.children = None
self.prev = None
self.parent = parent

def select_move(self, k):
r = gamma(self.counts[0] + 1, 1)
p = gamma(self.counts[1] + 1, 1)
s = gamma(self.counts[2] + 1, 1)
u = 1.0 / (r + p + s)
scores = [(s - p) * u, (r - s) * u, (p - r) * u]
m = scores.index(max(scores))
self.prev = m
meta = [gamma(n + 1.0, 1) for n in self.meta]
v = 1.0 / sum(meta)
meta = [n * v for n in meta]
meta_scores = [0, 0, 0]
meta_scores[m] = meta[0] - meta[2]
meta_scores[plus2[m]] = meta[1] - meta[0]
meta_scores[plus1[m]] = meta[2] - meta[1]
bounds = [sqrt(max(log(1000 / (3 * v)), 0) / v) for v in self.visits]
for i, (s, bound) in enumerate(zip(scores, bounds)):
scores[i] = s + bound
for i, (s, v) in enumerate(zip(meta_scores, bounds)):
meta_scores[i] = s + bound
best = max(scores)
meta_best = max(meta_scores)
if best >= 0.7 * meta_best:
return (best, scores.index(best))
return (meta_best, meta_scores.index(meta_best))

def update(self, h, i):
for n in h:
self.counts[i] += 1
if self.children is None:
break
self = self.children[n]
if self is None:
break

def predict(self, h):
path = []
stop = False
path.append(self)
for d, n in enumerate(h):
if stop or d >= 16:
break
if self.children is None:
self.children = [None for _ in xrange(3)]
if self.children[n] is None:
self.children[n] = MarkovTree(self)
stop = True
child = self.children[n]
self = child
path.append(self)
best_score = float("-inf")
best_node = None
k = 3 * len(path) + 1
leaf = self
for n in path:
score, move = n.select_move(k)
if score >= best_score:
best_score = score
best_node = n
best_move = move
return (leaf, best_move, best_score)

both = MarkovTree()
us = MarkovTree()
them = MarkovTree()
history = collections.deque([])
our_history = collections.deque([])
their_history = collections.deque([])
a, b, c = None, None, None

else:

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

bt = beat[i]
for node in (a, b, c):
while node is not None:
node.visits[j] += 1
if node.prev == bt:
node.meta[0] += 1
elif plus2[node.prev] == bt:
node.meta[1] += 1
else:
node.meta[2] += 1
node = node.parent
both.update(history, i)
us.update(our_history, i)
them.update(their_history, i)

history.appendleft(i)
history.appendleft(j)
our_history.appendleft(j)
their_history.appendleft(i)

a, m0, s0 = both.predict(history)
b, m1, s1 = them.predict(their_history)
c, m2, s2 = us.predict(our_history)
m = m0
s = s0
if s1 > s:
m = m1
s = s1
if s2 > s:
m = m2
s = s2
output = name[m]``````