# CTS-Meta-2

 Author Sean Submission date 2016-08-26 10:55:55.223590 Rating 7033 Matches played 326 Win rate 73.62

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

## Source code:

``````if input == "":
import math
log = math.log
exp = math.exp
log_half = log(0.5)
third = 1.0 / 3.0
log_third = log(1.0/3.0)
log_two_thirds = log(2.0/3.0)

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

def log_sub(x, y):
d = y - x
return x + log(1.0 - exp(d))

def log_mean(x, y):

def log_mean_3(x, y, z):

def meta(k, c):
if k == c:
d = 0
elif k == beat[c]:
d = 1
else:
d = 2
return d

class ContextTree:
def __init__(self):
self.p = 0.0
self.p_children = 0.0
self.weights = [log_third for _ in xrange(3)]
self.counts = [0, 0, 0]
self.meta_counts = [0, 0, 0]
self.children = [None, None, None]
def update(self, alpha, beta, history, c, i=0):
counts = self.counts
meta_counts = self.meta_counts
scores = [0.0 for _ in xrange(3)]
for j in xrange(3):
scores[j] = counts[beaten[j]] - counts[beat[j]]
k = scores.index(max(scores))
d = meta(k, c)
rt = 1.0 / (sum(counts) + 1.0)
cond_p_self = log((counts[c] + third) * rt)
cond_p_meta = log((meta_counts[d] + third) * rt)
counts[c] += 1
meta_counts[d] += 1
if i >= min(len(history) - 1, 10):
self.p += cond_p_self
return
x = history[i]
if self.children[x] is None:
self.children[x] = ContextTree()
self.children[x].update(alpha, beta, history, c, i + 1)
p_children = 0.0
for child in self.children:
if child is not None:
p_children += child.p
w0, w1, w2 = self.weights
cond_p_children = p_children - self.p_children
self.p_children = p_children
probs = (cond_p_self, cond_p_meta, cond_p_children)
base = alpha + self.p
for i, (w, p) in enumerate(zip(self.weights, probs)):
self.weights[i] = log_add(base, beta + w + p)
def predict(self, history, ps, i=0):
counts = self.counts
meta_counts = self.meta_counts
scores = [0.0 for _ in xrange(3)]
for j in xrange(3):
scores[j] = counts[beaten[j]] - counts[beat[j]]
k = scores.index(max(scores))
rt = 1.0 / (sum(counts) + 1.0)
cond_p_self = (log((counts[c] + third) * rt) for c in xrange(3))
cond_p_meta = (log((meta_counts[meta(k, c)] + third) * rt) for c in xrange(3))
if i >= min(len(history) - 1, 10):
for i, p0 in enumerate(cond_p_self):
ps[i] += p0 + self.p
return
x = history[i]
p_children = [0.0 for _ in self.children]
factor = 0.0
for y, child in enumerate(self.children):
if child is not None:
if y == x:
child.predict(history, p_children, i + 1)
else:
factor += child.p
elif y == x:
factor += log_third
w0, w1, w2 = self.weights
w3 = w2 + factor - self.p_children
for i, (pse, pm, pc) in enumerate(zip(cond_p_self, cond_p_meta, p_children)):

import collections
import random

R, P, S = range(3)
index = {"R": R, "P": P, "S": S}
name = ("R", "P", "S")
beat   = (P, S, R)
beaten = (S, R, P)
model = ContextTree()
history = collections.deque([])
output = random.choice(name)
rnd = 0
else:
rnd += 1
i = index[input]
j = index[output]
alpha = 1.0 / (rnd + 2)
beta  = 1 - 2 * alpha
model.update(log(alpha), log(beta), history, i)
history.appendleft(i)
history.appendleft(j)
ps = [0.0, 0.0, 0.0]
model.predict(history, ps)
p0 = min(ps)
for i, p in enumerate(ps):
ps[i] = exp(p - p0)
scores = [0, 0, 0]
t = sum(ps)
for _ in xrange(3):
x = 0
r = random.uniform(0, t)
for k, p in enumerate(ps):
x += p
if x >= r:
break
scores[beat[k]]   += 1
scores[beaten[k]] -= 1
m = max(scores)
output = name[random.choice([k for k, x in enumerate(scores) if x == m])]``````