| Author | rspk |
| Submission date | 2013-04-29 07:17:05.772838 |
| Rating | 7584 |
| Matches played | 684 |
| Win rate | 77.78 |
Use rpsrunner.py to play unranked matches on your computer.
#uses stuff from http://www.ofb.net/~egnor/iocaine.html , 2 history matchers and 2 frequency predictors
#hist() matcher code, final metastrat. selector, quintuple henny influenced by momo's 'galton' bot
#improvement over scattershot:
#- better panic mode
#licatj @ no-spam-pls rpi.edu
import random
from collections import defaultdict
wins = ['RS','SP','PR']
encode = {'RR':'a', 'RP':'b', 'RS':'c', 'PR':'d', 'PP':'e', 'PS':'f', 'SR':'g', 'SP':'h', 'SS':'i'}
decode = {'a':'RR', 'b':'RP', 'c':'RS', 'd':'PR', 'e':'PP', 'f':'PS', 'g':'SR', 'h':'SP', 'i':'SS', 'x':'XX'}
numMeta = 3 #num of meta strategies for each predictor
if input=='': #first round; initialize everything
output = input = random.choice('RPS')
currSet = 0
score = 0
consecLosses = 0
lastStratChosen = -2
allMoves = '' #history of all moves made
myMoves = ''
hisMoves = ''
#initialize strategies
def alwaysRock():
return 'R'
def alwaysPaper():
return 'P'
def alwaysScissors():
return 'S'
def rand():
return random.choice('RPS')
#old version of hist, this one does reverse search for a sequence of max length
def hist_fwd_rvr(allMoves):
numToCheck = 2 #numer of matches at each end to look for
toReturn = []#random.choice('abcdefghi')
lam = len(allMoves)
maxDepth = 100
maxLength = 6
lastStartF = 0
lastStartR = lam-1
l = min(maxLength,lam-1)
#search forwards strings of decreasing length
for i in range(numToCheck):
foundAt = -1
while l>0 and foundAt==-1:
foundAt = allMoves.find(allMoves[lam-l:lam],lastStartF,lam-1)
if foundAt == -1:
l -= 1
#Post condition: l is length of best match found, foundAt is its start point (if not -1)
#also, l is the length of the longest substring terminating at current iter that is repeated
#elsewhere in string. l will not change for the rest of this function's execution.
if foundAt != -1:
lastStartF = foundAt+l
toReturn += [allMoves[lastStartF]]
else:
lastStartF = lam
toReturn += ['x']
#search backwards strings of decreasing length
foundAt = allMoves.rfind(allMoves[lam-l:lam],0,lastStartR)
if foundAt != -1:
lastStartR = foundAt+l-1
toReturn += allMoves[foundAt+l]
else:
lastStartF = 0
toReturn += ['x']
#print toReturn
return toReturn
stored = ''
def histFAll1():
global allMoves,stored
stored = hist_fwd_rvr(allMoves)
return decode[stored[0]][0]
def histFAll2():
global stored
return decode[stored[0]][1]
def histRAll1():
global stored
return decode[stored[1]][0]
def histRAll2():
global stored
return decode[stored[1]][1]
def histFAll3():
global stored
return decode[stored[2]][0]
def histFAll4():
global stored
return decode[stored[2]][1]
def histRAll3():
global stored
return decode[stored[3]][1]
def histRAll4():
global stored
return decode[stored[3]][1]
histAnalysis = defaultdict(list)
considerLengths = [3,4]
histResults = ['']*len(considerLengths)
def hist(allMoves,considerLengths):
global histAnalysis,histResults
lam = len(allMoves)-1
#learn from previous example
keys = []
for l in considerLengths:
if lam < l: #can't learn this history
keys.append('')
continue
key = allMoves[-l-1:-1]
histAnalysis[key] += [allMoves[-1]]
keys.append(key)
#make predictions
for i in xrange(len(considerLengths)):
if lam < considerLengths[i]: #key will not be valid
histResults[i] = random.choice('abcdefghi')
continue
cand = histAnalysis[keys[i]]
k = xrange(len(cand))
#skew toward more recent ones
histResults[i] = cand[max(random.choice(k),random.choice(k))]
#does history matching of length 3 (or whatever considerLengths[0] is)
def hist1_1():
global allMoves,considerLengths,histResults,decode
hist(allMoves,considerLengths)
return decode[histResults[0]][0]
#history matching of length 3 (or whatever considerLenghts[0] is), but assumes they're using predictor against you
def hist1_2():
global histResults,decode
return decode[histResults[0]][1]
def hist2_1():
global histResults,decode
return decode[histResults[1]][0]
def hist2_2():
global histResults,decode
return decode[histResults[1]][1]
frq = [0,0,0]
def freq1(): #predicts they will make the move they make most often
global allMoves,frq,decode
#learn from last move made
if len(allMoves)==0:
return 'X'
frq[ 'RPS'.index(decode[allMoves[-1]][1]) ] += 1
util = [frq[2]-frq[1],frq[0]-frq[2],frq[1]-frq[0]]
return 'RPS'[util.index(max(util))]
frq2 = [0,0,0]
def freq2(): #like freq, but assumes they think we're playing with freq and they want to beat it
global allMoves,frq2,decode
#learn from last move made
if len(allMoves)==0:
return 'X'
frq2[ 'RPS'.index(decode[allMoves[-1]][0]) ] += 1
util = [frq2[2]-frq2[1],frq2[0]-frq2[2],frq2[1]-frq2[0]]
return 'PSR'[util.index(max(util))]
allStrats = [freq1,freq2,hist1_1,hist1_2,hist2_1,hist2_2,histFAll1,histRAll1]
allStrats += [histFAll2,histRAll2,histFAll3,histRAll3,histFAll4,histRAll4,rand]
lasn = len(allStrats)*numMeta
allScores = [0]*lasn
allPredictions = ['X']*lasn
#for metameta level:
mmScor = 0
mmPred = 'X'
else:
allMoves = allMoves + encode[output + input]
myMoves = myMoves + output
hisMoves = hisMoves + input
scoreChange = 0
if (output+input) in wins:
score += 1
scoreChange = 1
consecLosses = 0
elif (input+output) in wins:
score -= 1
scoreChange = -1
consecLosses += 1
if score < -50:
output = 'X'#don't-know choice, taken care of later
else: #actually try something
beat = {'R':'P', 'P':'S', 'S':'R', 'X':'X'}#random.choice('RPS')}
for i in xrange(len(allStrats)):
nmi = numMeta*i
for j in xrange(nmi, nmi+3):
allScores[j] *= 0.85#max(0,allScores[j]-1) #decay
if allPredictions[j] == input:
allScores[j] += 5
elif allPredictions[j] == beat[input]: #this prediction led to a loss, punish
allScores[j] = max(0,allScores[j]-5)
predBase = allStrats[i]() #update predictions:
jumpUp = {'R':'S', 'P':'R', 'S':'P', 'X':'X'}#random.choice('RPS')}
allPredictions[nmi] = predBase #P.0 : assume they'll follow the move exactly predicted
allPredictions[nmi+1] = jumpUp[predBase] #P.1 - assume they predict we'll try to beat predBase
allPredictions[nmi+2] = jumpUp[jumpUp[predBase]] #P.2 - assume they predict we'll try to beat jumpUp[predBase]
#for meta meta
mmScor *= 0.85 #decay
if mmPred == input:
mmScor += 3
elif mmPred == beat[input]:
mmScor -= 3
#give a bonus to the last strategy chosen if it was successful
if lastStratChosen != -2:
if lastStratChosen == -1:
mmScor += scoreChange
else:
allScores[lastStratChosen] += scoreChange
#select the best one deterministically method 1: all who have max score, choose one randomly
mx = max(allScores)
i = random.choice([j for j in xrange(len(allScores)) if allScores[j]==mx])
output = beat[allPredictions[i]]
lastStratChosen = i
mmPred = beat[output]
if mmScor > allScores[i]:
output = mmPred
lastStratChosen = -1
if consecLosses > 3: #panic!
output = 'X'
lastStratChosen = -2
if output=='X':
if len(allMoves) > 2:
#use triple henny, from momo's 'galton' bot and http://webdocs.cs.ualberta.ca/~darse/rsbpc.html
choices = []
for h in range(5):
choices.append(decode[allMoves[random.randint(0,len(allMoves))-1]][1])
output = beat[random.choice(choices)]
else:
output = random.choice('RPS')
currSet += 1