# zai_counts4

 Author zdg Submission date 2011-09-07 22:28:53.012814 Rating 7342 Matches played 2687 Win rate 67.85

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

## Source code:

``````# weighted higher order frequency counting with decay
# this version uses exponential decay, and moves higher order matches up linearly

import random
from itertools import imap
from operator import mul

def to_num(h):
return 'RPS'.index(h)

def to_str(i):
return 'RPS'[i]

def play(h1, h2):
return (h1 - h2 + 4) % 3 - 1

def beats(h):
return (h + 1) % 3

def loses(h):
return (h + 2) % 3

def ties(h):
return h

def randix(pvec=None):
if pvec is None:
return random.randint(0,2)
r = random.uniform(0.0, sum(pvec))
acc = 0.0
for (i,p) in enumerate(pvec):
acc += p
if r <= acc:
return i
return random.randint(0,2)

def normalize(vec):
m = min(vec) / 2
# m = max(min(vec) - 1, 0)
for i, x in enumerate(vec):
vec[i] = x - m

# little endian ternary number
def ternary(vec):
return sum(imap(mul, vec, POWERS3))

def emptycounts(n):
return tuple(tuple([0.0] * 3 for x in RPS) for i in xrange(n))

# start
if input == '':
import random

ROUNDS = 1000
R = 0
P = 1
S = 2
RPS = [R,P,S]
WIN = 1
TIE = 0
LOSE = -1

ORDER_MAX = 7
POWERS3 = tuple(3 ** x for x in xrange(ORDER_MAX+1))

order = tuple(emptycounts(POWERS3[n]) for n in xrange(ORDER_MAX+1))

predictions = [0] * (ORDER_MAX + 1)

my_hands = []
op_hands = []

output = to_str(randix())
my_hands.append(to_num(output))
else:
op_hands.append(to_num(input))

hands = len(my_hands)

# update the counts

order_update_max = min(hands-1, ORDER_MAX)

for i in xrange(order_update_max + 1):
# don't include the last hand
index = ternary(op_hands[-i-1:-1])
order[i][index][play(op_hands[-1],my_hands[-1])][op_hands[-1]] += 2 ** (hands/14.0)

# use counts to make predictions

order_predict_max = min(hands, ORDER_MAX)

pvec = [0.0, 0.0, 0.0]
for i in xrange(order_predict_max + 1):
index = ternary(op_hands[-i:] if i != 0 else [])
curr_order = order[i][index]
curr_pvec = [curr_order[WIN][k] for k in RPS]
if sum(curr_pvec) > 2 ** ((hands-min(hands,100))/14.0):
pvec[0] += curr_pvec[0] * (2**(i*1.5))
pvec[1] += curr_pvec[1] * (2**(i*1.5))
pvec[2] += curr_pvec[2] * (2**(i*1.5))
# normalize(pvec)
prediction = randix(pvec)

pvec = [0.0, 0.0, 0.0]
pvec[beats(prediction)] = 0.7
pvec[ties(prediction)] = 0.3
output = to_str(randix(pvec))
# output = to_str(beats(prediction))
my_hands.append(to_num(output))``````