# CTF2

 Author Sean Submission date 2016-02-25 13:50:23.832804 Rating 5970 Matches played 393 Win rate 57.51

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

## Source code:

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

import array
import math
log = math.log
exp = math.exp
log_half = log(0.5)
log_third = log(1/3.0)
log_sixth = log(1/6.0)

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

def kt_smoothing(counts, i, x):
m = float(sum(1 for x in counts if x != 0))
n = sum(counts)
if i == 0:
used = 6
else:
used = 3
return log(counts[x] + 0.5) - log(n + 0.5 * used)

class ContextTree:
def __init__(self, n=6):
self.log_p_kt = 0.0
self.log_p = 0.0
self.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
self.log_p_kt += kt_smoothing(self.counts, i, x)
self.total += 1
self.counts[x] += 1
if self.total == 1 or i >= len(history) - 1 or i >= 16:
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
log_p_kt = self.log_p_kt + kt_smoothing(self.counts, i, x)
if self.total == 0 or i >= len(history) - 1 or i >= 16:
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 i == 0:
log_p_children += log_sixth
else:
log_p_children += log_third
return log_add(log_p_kt, log_p_children) + log_half

import collections
import random
index = {"R": 0, "P": 1, "S": 2}
name = ("R", "P", "S")
beat = (1, 2, 0)
model = ContextTree()
history = collections.deque([])
output = random.choice(name)
beta = -0.5
dq = None
sign = -1
learning_rate = 0.01
else:
i = index[input]
j = index[output]
if dq is not None:
dscorebeta = 0
for x in xrange(3):
if x == beat[i]:
s = 1
elif i == beat[x]:
s = -1
else:
s = 0
dscorebeta += dq[x] * s
if dscorebeta >= 0:
new_sign = 1
else:
new_sign = -1
if new_sign == sign:
learning_rate *= 1.05
else:
learning_rate *= 0.95
if learning_rate <= 0:
learning_rate = 0.0005
if learning_rate >= 0.5:
learning_rate = 0.5
sign = new_sign
beta += learning_rate * dscorebeta
if beta >= 10:
beta = 10
if beta <= -10:
beta = -10
dq = 1
history.appendleft(i)
model.update(history)
history.appendleft(j + 3)
model.update(history)
ps = [0.0, 0.0, 0.0]
for i1, _ in enumerate(ps):
history.appendleft(i1)
ps[i1] = model.predict(history)
history.popleft()
scores = [0, 0, 0]
t = ps[0]
t = log_add(t, ps[1])
t = log_add(t, ps[2])
ps = [exp(p - t) for p in ps]
qs = [0 for _ in xrange(3)]
dqplus = [0 for _ in xrange(3)]
dqminus = [0 for _ in xrange(3)]
scores = [0, 0, 0]
for y in xrange(3):
p = ps[y]
for x in xrange(3):
if x == beat[y]:
s = 1
elif y == beat[x]:
s = -1
else:
s = 0
qs[x] += p * s
r, p, s = ps
sr, sp, ss = scores
a = exp(beta * sr)
b = exp(beta * sp)
c = exp(beta * ss)
qs = [s * a, r * b, s * c]
dq = [qs[0] * sr, qs[1] * sp, qs[2] * ss]
t = sum(qs)
p = 0
r = random.uniform(0, t)
for x in xrange(3):
p += qs[x]
if r <= p:
break
output = name[x]``````