# stolen_from_zdg

 Author Sean Submission date 2016-02-12 10:38:43.598409 Rating 7864 Matches played 396 Win rate 75.25

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

## Source code:

``````# Name: zai_switch_markov1_ml
# Author: zdg
# Email: rpscontest.b73@gishpuppy.com
# the email is disposable in case it gets spammed
#
# let's try out some bayes inference with 1-st order markov model wrappers on
#    various strategies as the hypotheses
# uses last diff as well as last payoff to transition for the 1-st order
# uses ML i.e. maximum likelihood learning by minimizing the entropy
# also have some ad-hoc decay which helps a lot

# --------------------- initialization -----------------------------
if not input:
import random, collections, math

# micro-optimizations
rchoice = random.choice
log = math.log

# 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]]

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)

# entropy of a 3 vector - doesn't need to be normalized
def entropy(vec):
v1, v2, v3 = vec
# force coerce to float
total = v1 + v2 + v3 + 0.0
# normalize
p1, p2, p3 = v1 / total, v2 / total, v3 / total
if total > 0.0:
return (-((p1 * log(p1)) if v1 > 0.0 else 0.0)
- ((p2 * log(p2)) if v2 > 0.0 else 0.0)
- ((p3 * log(p3)) if v3 > 0.0 else 0.0))
else:
# entropy of nothing is undefined, but here it's useful to define it
#    as the same as completely random
return 1.0

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 =  + [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 = up_fun(self.contexts)

# start the prediction with the zeroth order
self.pred = self.contexts

# 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]

# 1st order transition matrix
# use both last diff and last payoff for each bot
markov = [[[1.0 for _ in RPS] for _ in xrange(9)] 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]

bot_diffs = [[] for _ in BOTS]
bot_payoffs = [[] for _ in BOTS]

# 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]

# keep track of how bots have been playing
for b in BOTS:
bot_diffs[b].append(sub[last_op][next_hands[b]])
bot_payoffs[b].append(sub[last_my][next_hands[b]])

# update the zeroth order markov models
# for b in BOTS:
# contexts[b][T] *= DECAY
# contexts[b][W] *= DECAY
# contexts[b][L] *= DECAY
# contexts[b][bot_diffs[b][-1]] += 1.0

# update the first order markov models
if hands >= 2:
for b in BOTS:
last_last_diff, last_diff = bot_diffs[b][-2], bot_diffs[b][-1]
last_last_payoff = bot_payoffs[b][-2]
last_last_state = enc2[last_last_diff][last_last_payoff]-1

markov[b][last_last_state][T] *= DECAY
markov[b][last_last_state][W] *= DECAY
markov[b][last_last_state][L] *= DECAY
markov[b][last_last_state][last_diff] += 1.0

# use first order markov to calculate each bot's next state distribution
next_dist = [None] * NUM_BOTS
for b in BOTS:
last_state = enc2[bot_diffs[b][-1]][bot_payoffs[b][-1]]-1
next_dist[b] = markov[b][last_state]

# 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 = last_op
next_hands = last_my

# hpm
both_hist = both_ghpm.hist
# my hist
pred_op, pred_my = dec2[both_hist[my_ghpm.pred]]
next_hands = pred_op
next_hands = pred_my
# op hist
pred_op, pred_my = dec2[both_hist[op_ghpm.pred]]
next_hands = pred_op
next_hands = pred_my
# both hist
pred_op, pred_my = dec2[both_hist[both_ghpm.pred]]
next_hands = pred_op
next_hands = pred_my

# find the most likely bot i.e. one with least entropy
entropies = [-entropy(next_dist[b]) for b in BOTS]
most_likely = pick_max(entropies)

pred_next = [None] * 3
for h in RPS:

t = sum(pred_next)
r = random.uniform(0, t)
x = 0
for i, p in enumerate(pred_next):
x += p
if r <= x:
break
output = tr[beats[i]]

# bookkeeping
hands += 1
last_ix += 1
score += pts[last_payoff]

# if hands % 100 == 0:
# print markov
# print next_dist
# print``````