Author | zdg |

Submission date | 2012-08-16 15:25:30.975406 |

Rating | 7283 |

Matches played | 742 |

Win rate | 76.01 |

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

```
# Name: zai_mix_markov0_bayes
# Author: zdg
# Email: rpscontest.b73@gishpuppy.com
# the email is disposable in case it gets spammed
#
# let's try out some bayes inference with 0-th order markov models as the hypotheses
# seems like some adhoc decay is needed for this to be really effective
# --------------------- initialization -----------------------------
if not input:
import random, collections, math
# micro-optimizations
rchoice = random.choice
# global constants and maps
# using lists and dictionaries because function call and arithmetic is slow
R, P, S = 0, 1, 2
RPS = [R, P, S]
T, W, L = R, P, S
PAYOFFS = RPS
tr = {'R':R, 'P':P, 'S':S, R:'R', P:'P', S:'S'}
sub = [[T, L, W], [W, T, L], [L, W, T]]
add = [[R, P, S], [P, S, R], [S, R, P]]
ties, beats, loses = add[T], add[W], add[L]
pts = [0, 1, -1]
near = [1, 0, 0]
enc1 = [1,2,3]
dec1 = [None, R, P, S]
enc2 = [[1,2,3], [4,5,6], [7,8,9]]
dec2 = [None,(R,R),(R,P),(R,S),(P,R),(P,P),(P,S),(S,R),(S,P),(S,S)]
seed = rchoice(RPS)
def pick_max(vec):
max_val = max(vec)
max_list = [i for i in xrange(len(vec)) if vec[i] == max_val]
return rchoice(max_list)
# calculate the hand with the best expected value against the given op hand
# random only in case of ties
def expected(vec):
expected_payoffs = [vec[S] - vec[P], vec[R] - vec[S], vec[P] - vec[R]]
max_expected = max(expected_payoffs)
max_list = [i for i in RPS if expected_payoffs[i] == max_expected]
return rchoice(max_list)
def normalize(vec):
factor = 1.0 / sum(vec)
for i in xrange(len(vec)):
vec[i] *= factor
# greedy history pattern matcher
# ORDER is the largest context size
# BASE is the base of the numerical encoding
# encodes sequences of numbers from 1...BASE as a BASE-adic number
# encodes the empty sequence as 0
# apparently this encoding is called a bijective base-BASE system on wikipedia
class GHPM:
def __init__(self, ORDER, BASE):
self.ORDER = ORDER
self.BASE = BASE
self.powers = [0] + [BASE ** i for i in xrange(ORDER)]
self.hist = []
self.contexts = collections.defaultdict(lambda: None)
self.pred = None
self.preds = [None] * (ORDER+1)
def update(self, next_val, up_fun):
self.hist.append(next_val)
# update the history, order 0 as a special case
up_ix = 0
self.contexts[0] = up_fun(self.contexts[0])
# start the prediction with the zeroth order
self.pred = self.contexts[0]
# update the higher orders and prediction
elems = len(self.hist)
for order in xrange(1, self.ORDER+1 if elems > self.ORDER else elems):
pred_ix = up_ix * self.BASE + next_val
up_ix += self.hist[-order-1] * self.powers[order]
self.contexts[up_ix] = up_fun(self.contexts[up_ix])
try_get = self.contexts[pred_ix]
self.preds[order] = try_get
if try_get is not None:
self.pred = try_get
NUM_BOTS = 9
BOTS = range(NUM_BOTS)
DECAY = 0.98
next_hands = [seed for _ in BOTS]
# contexts = [[0.0, 0.0, 0.0] for _ in BOTS]
contexts = [[1.0, 1.0, 1.0] for _ in BOTS]
# initialize history matching strategies
my_ghpm = GHPM(6, 3)
op_ghpm = GHPM(6, 3)
both_ghpm = GHPM(6, 9)
# first hand is completely random - no reason to do otherwise
next_hand = seed
output = tr[next_hand]
# bookkeeping
hands = 1
last_ix = 0
score = 0
# --------------------- turn -----------------------------
else:
last_my = tr[output]
last_op = tr[input]
last_payoff = sub[last_my][last_op]
# update the contexts for the markov models
for b in BOTS:
contexts[b][R] *= DECAY
contexts[b][P] *= DECAY
contexts[b][S] *= DECAY
contexts[b][sub[last_op][next_hands[b]]] += 1.0
# update the history matchers
my_ghpm.update(enc1[last_my], lambda _:last_ix)
op_ghpm.update(enc1[last_op], lambda _:last_ix)
both_ghpm.update(enc2[last_op][last_my], lambda _:last_ix)
# update predictions
next_hands[1] = last_op
next_hands[2] = last_my
# hpm
both_hist = both_ghpm.hist
# my hist
pred_op, pred_my = dec2[both_hist[my_ghpm.pred]]
next_hands[3] = pred_op
next_hands[4] = pred_my
# op hist
pred_op, pred_my = dec2[both_hist[op_ghpm.pred]]
next_hands[5] = pred_op
next_hands[6] = pred_my
# both hist
pred_op, pred_my = dec2[both_hist[both_ghpm.pred]]
next_hands[7] = pred_op
next_hands[8] = pred_my
# calculate likelihood of the bots
likelihoods = [None] * NUM_BOTS
for b in BOTS:
cur_context = contexts[b]
likelihoods[b] = (cur_context[R] ** cur_context[R]) * (cur_context[P] ** cur_context[P]) * (cur_context[S] ** cur_context[S])
normalize(likelihoods)
# mix together the bots weighted by likelihood
next_op_dist = [0.0, 0.0, 0.0]
for b in BOTS:
for h in RPS:
next_op_dist[add[h][next_hands[b]]] += likelihoods[b] * contexts[b][h]
normalize(next_op_dist)
# next_hand = beats[next_hands[pick_max(likelihoods)]]
next_hand = expected(next_op_dist)
output = tr[next_hand]
# bookkeeping
hands += 1
last_ix += 1
score += pts[last_payoff]
# if hands % 100 == 0:
# print contexts
# print likelihoods
# print next_op_dist, next_hand
# print
```