# Skip

 Author Sean Submission date 2018-07-10 11:34:13.544840 Rating 7364 Matches played 269 Win rate 69.14

Use rpsrunner.py to play unranked matches on your computer.

## Source code:

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

import collections
import random

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):
for j in xrange(p, len(h)):
if j or not skips:
k = h[j]
self.counts[i] += 1
self.total += 1
if (d >= 5 or skips >= 4) and j:
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()
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, 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.predict_helper(h, j + 1, n0)
child = self.children[k]
if child is None:
return
self = child

def predict(self, h):
n0 = [1, 1, 1]
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)
beaten = (S, R, P)

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)
hypotheses = [random_index(counts) for _ in xrange(3)]
scores = [0, 0, 0]
for i, _ in enumerate(scores):
for h in hypotheses:
if i == beat[h]:
scores[i] += 1
elif h == beat[i]:
scores[i] -= 1
best = max(scores)
m = random.choice([i for i in xrange(3) if scores[i] == best])
output = name[m]``````