# SkipSwitch9/3

This program has been disqualified.

 Author Sean Submission date 2016-02-22 14:54:08.129743 Rating 6250 Matches played 31 Win rate 67.74

## Source code:

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

import collections
import random
gamma = random.gammavariate
third = 1.0 / 3

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

def select_move(self):
r = self.counts + third
p = self.counts + third
s = self.counts + third
scores = [s - p, r - s, p - r]
s = max(scores)
return (s, scores.index(s))

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 = MarkovTree()
if self.children[k] is None:
self.children[k] = MarkovTree()
stop = True
self.children.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, s, m):
for j in xrange(p, len(h)):
k = h[j]
s1, m1 = self.select_move()
if s1 >= s:
s = s1
m = m1
if self.children is None:
return (s, m)
s, m = self.children.predict_helper(h, j + 1, s, m)
child = self.children[k]
if child is None:
return (s, m)
self = child
return (s, m)

def predict(self, h):
s = 0
m = random.randrange(0, 3)
s, m = self.predict_helper(h, 0, s, m)
return m

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)
history.appendleft(i)
history.appendleft(j)

output = name[tree.predict(history)]``````