# zai_search_i2

 Author zdg Submission date 2011-09-06 03:53:30.624852 Rating 6813 Matches played 2714 Win rate 72.22

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

## Source code:

``````# searching for a past pattern and uses it to predict my next worst move
# some refactoring and different prediction calculation

import math
import random

# numerical representation
def to_num(h):
return 'RPS'.index(h)

# convert back to string
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

# returns a weighted random choice of R, P or S
# default with no arguments is uniformly random
def rand_hand(pvec=None):
if pvec is None:
pvec = [1.0/3.0] * 3
r = random.uniform(0.0, sum(pvec))
acc = 0.0
for (i,p) in enumerate(pvec):
acc += p
if r <= acc:
return i

# finds the max match from i1 and i2 leftward for lst
def search(lst, i1, i2, imin, smax):
s = 0
while i1 >= imin and i2 >= imin and s < smax and lst[i1] == lst[i2]:
i1 -= 1
i2 -= 1
s += 1
return s

# calculates the weight of a pattern match based on length and distance
# the distance is between the matches
def weight(s, d, dmax):
sw = s ** 2
# dw = 1
dw = ((dmax - d) / float(dmax))
return sw * dw

# calculates the weights of my next move
def weigh_matches(lst, i, dmax, smax):
imin = max(i - dmax, 0)
ws = [[0.0] * 3 for x in xrange(3)]
for j in xrange(i - 1, imin - 1, -1):
s = search(lst, j, i, imin, smax)
d = i - j
ws[play(myhands[j+1], ophands[j+1])][myhands[j+1]] += weight(s, d, dmax)
return ws

# uses the calculated weights to make a prediction about my worst next hand
def predict(lst, i, dmax, smax):
ws = weigh_matches(lst, i, dmax, smax)
return rand_hand([ws[LOSE][j] + ws[TIE][j] / 2 + ws[WIN][j] / 4 for j in xrange(3)])

# start of main code
if input == '':
ROUNDS = 1000
R = 0
P = 1
S = 2
WIN = 1
TIE = 0
LOSE = -1

myhands = []
ophands = []

output = to_str(rand_hand())
myhands.append(to_num(output))
else:
ophands.append(to_num(input))

dmax = 100
smax = 10

prediction = predict(myhands, len(myhands) - 1, dmax, smax)
pvec = [0.0] * 3
pvec[loses(prediction)] = 0.6
pvec[beats(prediction)] = 0.4
output = to_str(rand_hand(pvec))

myhands.append(to_num(output))``````