# meta-henny-pmarkov-full-ew

 Author locutus Submission date 2018-09-20 10:44:20.082068 Rating 7080 Matches played 272 Win rate 71.69

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

## Source code:

``````import random
import math

def weighted_choice(weights):
total = sum(weights.values())
treshold = random.uniform(0, total)
for k in weights.keys():
total -= weights[k]
if total < treshold:
return k
return random.choice(weights.keys())

def selectBest(s):
return random.choice([i for i in range(len(s)) if max(s) == s[i]])

def selectBestDict(s):
total = sum(s.values())
if total > 0:
B = A / total
else:
B = 0.0
ew = {i:math.exp(B * (s[beatedBy[beatedBy[i]]] - s[beatedBy[i]])) for i in s.keys()}
return weighted_choice(ew)
#return random.choice([i for i in ew.keys() if max(ew.values()) == ew[i]])

if not input:
debug = 0

USE_RANDOM = 1
USE_HENNY = 1
USE_MARKOW = 1

LAST_ROUND = 1000
ROUND = 1

history = []
moves = ["R","P","S"]
beatedBy = {"R":"P", "P":"S", "S":"R"}
result = {"R":{"R":0, "P":-1, "S":1}, "P":{"R":1, "P":0, "S":-1}, "S":{"R":-1, "P":1, "S":0}}

A = 1.0
M = 0

if USE_RANDOM == 1:
M += 1

if USE_HENNY == 1:
M += 6

if USE_MARKOW == 1:
markov_orders = [0,1,2,3,4,5,6]
historyCount = {}
M += 6 * len(markov_orders)

weight = [1] * M
decay = [0.85] * M

score = [0] * M
selected = [0] * M
move = [random.choice(moves) for i in range(M)]
else:
ROUND += 1
history += [(last,input)]
score = [ decay[i] * score[i] + weight[i] * result[move[i]][input] for i in range(M)]

index = 0
# random optimal
if USE_RANDOM == 1:
move[index] = random.choice(moves)
# adjust random optimal score to zero
score[index] = 0
index += 1

first_meta_index = index

if USE_HENNY == 1:
# henny with meta strategies
k = random.choice(range(len(history)))
move[index]   = history[k][0]
move[index+3] = history[k][1]
index += 6

if USE_MARKOW == 1:
# markow with meta strategies
for m in markov_orders:
if len(history) > m:
key = tuple(history[-m-1:-1])
if not (key in historyCount):
historyCount[key] = [{"R":0,"P":0,"S":0},{"R":0,"P":0,"S":0}]
historyCount[key][0][history[-1][0]] += 1
historyCount[key][1][history[-1][1]] += 1

for m in markov_orders:
if len(history) >= m:
key = tuple(history[-m:])
if key in historyCount:
move[index]   = selectBestDict(historyCount[key][0])
move[index+3] = selectBestDict(historyCount[key][1])
else:
move[index]   = random.choice(moves)
move[index+3] = random.choice(moves)
else:
move[index]   = random.choice(moves)
move[index+3] = random.choice(moves)
index += 6

# set other meta strategies
for i in range(first_meta_index, M, 3):
move[i+1] = beatedBy[move[i]]
move[i+2] = beatedBy[move[i+1]]

best = selectBest(score)
selected[best] += 1
output = move[best]
last = output

if debug == 1:
if ROUND == LAST_ROUND:
print "score =", score
print "selected =", selected``````