| Author | sampa |
| Submission date | 2011-06-12 14:15:42.853096 |
| Rating | 2721 |
| Matches played | 5420 |
| Win rate | 24.13 |
Use rpsrunner.py to play unranked matches on your computer.
import math
import random
# a max of the best on the leaderboard...
# credit in full to the original authors
input = ''
class RPSPlayer:
# 123456789.123456789.123456789.123456789.123456789.123456789.123456789.12
DEFAULT_PLAY = "RSPRSPSSSSSSPSPRPPRPPSSPSSPPSSSPPRPPRPSPRSSSPSRRPRPSRPPRRSSSRRRPRRRRRSSP"
SCORE = {"PR": 1, "RS": 1, "SP": 1, "RP": -1, "SR": -1, "PS": -1, "RR": 0, "SS": 0, "PP" : 0}
KEYS = list(enumerate([[1,3,5,7], [2,4,6,8], [3,5,7], [4,6,8], [1,2,3,4], [2,3,4], [3,4], [1,3,5,7,9], [2,4,6,8,10]]))
ADJ = {1: [0, 1, 0], -1: [0, 0, 1], 0: [1, 0, 0]}
def __init__(self):
self.round = 0
self.hist_us = ""
self.hist_them = ""
self.hist_pred = "SSSSSS"
self.weighting = [9, 1 , 0] # win, block, reverse
self.memory = {}
self.score = 0
self.memory_length = 5
def default_for_round(self):
return self.DEFAULT_PLAY[self.round % 72]
def tally_score(self):
last_round = self.hist_us[-1:] + self.hist_them[-1:]
#print last_round
self.score += self.SCORE[last_round]
#
last_result = self.SCORE[self.hist_pred[-1:] + self.hist_them[-1:]]
adjustment = self.ADJ[last_result]
for i in range(3):
self.weighting[i] += adjustment[i]
def get_keys(self):
def get_segment(s):
# !!TODO suggests that it is about time to change this structure...
if s == 1: return self.hist_them[len(self.hist_them) - 1]
if s == 2: return self.hist_us [len(self.hist_us ) - 1]
if s == 3: return self.hist_them[len(self.hist_them) - 2]
if s == 4: return self.hist_us [len(self.hist_us ) - 2]
if s == 5: return self.hist_them[len(self.hist_them) - 3]
if s == 6: return self.hist_us [len(self.hist_us ) - 3]
if s == 7: return self.hist_them[len(self.hist_them) - 4]
if s == 8: return self.hist_us [len(self.hist_us ) - 4]
if s == 9: return self.hist_them[len(self.hist_them) - 5]
if s ==10: return self.hist_us [len(self.hist_us ) - 5]
for key_no, segment in self.KEYS:
key = str(key_no) + ':'
for s in segment:
key += get_segment(s)
yield key
def remember_opponent(self, move):
def add_key(key, move):
# !!TODO forgetfullness so that
# we do not get trained into a strategy...
if key not in self.memory:
self.memory[key] = {"R": 0, "P": 0, "S": 0}
self.memory[key][move] += 1
#print self.hist_us
#print self.hist_them
#print key, move, self.memory[key]
if len(self.hist_them) > self.memory_length:
for key in self.get_keys():
add_key(key, move)
self.hist_them += move
if self.round % 500 == 0:
for key in self.memory:
self.memory[key]["R"] /= 2
self.memory[key]["P"] /= 2
self.memory[key]["S"] /= 2
def remember_me(self, move):
self.hist_us += move
def dump(self):
print self.memory
print 'weighting: ', self.weighting
print self.hist_us
print self.hist_them
print self.hist_pred
print "score", self.score
def predict(self):
def find(key):
if key not in self.memory:
return {"R": 0, "P": 0, "S": 0}
return self.memory[key]
pr = pp = ps = 0
for key in self.get_keys():
value = find(key)
pr += value["R"]
pp += value["P"]
ps += value["S"]
if pr == pp == ps == 0:
return self.default_for_round()
prediction = random.choice("R" * pr + "P" * pp + "S" * ps)
self.hist_pred += prediction
if random.random() < .95:
return {"R" : "P", "P" : "S", "S" : "R"}[prediction]
else:
return prediction
def next(self, last_move):
if last_move != "":
self.remember_opponent(last_move)
self.tally_score()
if len(self.hist_us) < self.memory_length:
result = self.default_for_round()
else:
result = self.predict()
self.round += 1
self.remember_me(result)
return result
####################################################################################################
# Similar to switching3, but a different strategy selection:
# always use best performed strategy
####################################################################################################
if input == "":
dna = ""
opp = []
anti_expected = {'P': 'S', 'S': 'R', 'R': 'P'}
dna_encode = {'PP': '1', 'PR': '2', 'PS': '3', 'RP': '4', 'RS': '5','RR': '6','SS': '7','SP': '8', 'SR': '9',}
dna_decode = {'1':'PP', '2':'PR', '3':'PS','4':'RP','5':'RS','6':'RR','7':'SS','8':'SP','9':'SR',}
score = {'RR': 0, 'PP': 0, 'SS': 0, 'PR': 1, 'RS': 1, 'SP': 1,'RP': -1, 'SR': -1, 'PS': -1,}
output1 = random.choice(["R", "P", "S"])
index = random.randint(0,2)
candidates = [output1] * 3
performance = [0, 0, 0]
total = 0
else:
dna += dna_encode[input + output]
opp.append(input)
sc = score[output+input]
total += sc
for i in range(3):
s = score[candidates[i]+input]
if s == -1:
performance[i] = 0
elif s == 1:
performance[i] += 1
elif total < 0 and s == 0:
performance[i] = 0
m = max(performance)
bestlist = [i for i, c in enumerate(performance) if c == m]
index = random.choice(bestlist)
candidates[0] = anti_expected[random.choice(opp)]
candidates[1] = anti_expected[random.choice(opp)]
candidates[2] = anti_expected[random.choice(opp)]
for length in range(min(4, len(dna)-1), 0, -1):
search = dna[-length:]
idx = dna.rfind(search, 0, -1)
if idx != -1:
answered = dna_decode[dna[idx + length]]
candidates[1] = anti_expected[answered[0]]
candidates[2] = anti_expected[anti_expected[answered[1]]]
break
output1 = candidates[index]
##################################################################################################
##################################################################################################
#DNA Polymerase
#This enzyme helps with DNA replication.
##################################################################################################
if not input:
limit = 11
telomere = 30
DNA_strand0=""
DNA_strand1=""
DNA_strand2=""
DNA_strand3="" #Unlike most other DNA, this one has four strands
base_pairs = range(telomere)
for i in range(telomere):
base_pairs[i] = random.choice(['R','P','S'])
nucleotide_frequency = range(telomere,0,-1)
helicase=[0,0,0,0,0,0]
deoxyribonuclease = {'RP':'a','PS':'a','SR':'a','PR':'b','SP':'b','RS':'b','RR':'c','PP':'c','SS':'c'}
ribonuclease = {'RP':'I','PS':'A','SR':'M','PR':'S','SP':'U','RS':'P','RR':'E','PP':'R','SS':'B'}
phosphodiester = dict()
historase = ""
endonuclease = 8
exoplastase = 0.825
output3 = random.choice(['R','P','S'])
else:
for i in range(telomere):
nucleotide_frequency[i]*=0.87
if input==mRNA[i]:
nucleotide_frequency[i]+=telomere*0.5
elif input=={'R':'S', 'P':'R', 'S':'P'}[mRNA[i]]:
nucleotide_frequency[i]-=telomere*0.48
else:
nucleotide_frequency[i]-=telomere*0.02
j=limit
DNA_strand1+=input
DNA_strand2+=output
historase+=input
historase+=output
DNA_strand3+=deoxyribonuclease[input+output]
DNA_strand0+=ribonuclease[input+output]
length = len(DNA_strand2)
i = DNA_strand2.rfind(DNA_strand2[length-j:length-1],0,length-2)
while i==-1:
j-=1
i = DNA_strand2.rfind(DNA_strand2[length-j:length-1],0,length-2)
if j<2:
break
if i==-1 or j+i>=length:
base_pairs[0] = base_pairs[2] = random.choice(['R','P','S'])
else:
base_pairs[0] = DNA_strand1[j+i]
base_pairs[1] = {'R':'P','P':'S','S':'R'}[DNA_strand2[j+i]]
j=limit
i = DNA_strand1.rfind(DNA_strand1[length-j:length-1],0,length-2)
while i==-1:
j-=1
i = DNA_strand1.rfind(DNA_strand1[length-j:length-1],0,length-2)
if j<2:
break
if i==-1 or j+i>=length:
base_pairs[2] = base_pairs[3] = random.choice(['R','P','S'])
else:
base_pairs[2] = DNA_strand1[j+i]
base_pairs[3] = {'R':'P','P':'S','S':'R'}[DNA_strand2[j+i]]
j=limit
i = DNA_strand0.rfind(DNA_strand0[length-j:length-1],0,length-2)
while i==-1:
j-=1
i = DNA_strand0.rfind(DNA_strand0[length-j:length-1],0,length-2)
if j<2:
break
if i==-1 or j+i>=length:
base_pairs[4] = base_pairs[5] = random.choice(['R','P','S'])
else:
base_pairs[4] = DNA_strand1[j+i]
base_pairs[5] = {'R':'P','P':'S','S':'R'}[DNA_strand2[j+i]]
j=limit
i = DNA_strand3.rfind(DNA_strand3[length-j:length-1],0,length-2)
while i==-1:
j-=1
i = DNA_strand3.rfind(DNA_strand3[length-j:length-1],0,length-2)
if j<2:
break
if i==-1 or j+i>=length:
base_pairs[6] = base_pairs[7] = random.choice(['R','P','S'])
else:
base_pairs[6] = DNA_strand1[j+i]
base_pairs[7] = {'R':'P','P':'S','S':'R'}[DNA_strand2[j+i]]
for i in range(8,24):
base_pairs[i] = {'R':'S','P':'R','S':'P'}[base_pairs[i-8]]
base_pairs[26] = random.choice(['R','P','S'])
#Helicase
helicase[0] = helicase[0]*0.95+{'R':0,'P':-0.1,'S':0.1}[DNA_strand2[length-1]]
helicase[1] = helicase[1]*0.95+{'R':0.1,'P':0,'S':-0.1}[DNA_strand2[length-1]]
helicase[2] = helicase[2]*0.95+{'R':-0.1,'P':0.1,'S':0}[DNA_strand2[length-1]]
base_pairs[24] = {0:'R',1:'P',2:'S',3:'R',4:'P',5:'S'}[helicase.index(max(helicase[0:3]))]
helicase[3] = helicase[3]*0.95+{'R':0.1,'P':0,'S':-0.1}[input]
helicase[4] = helicase[4]*0.95+{'R':-0.1,'P':0.1,'S':0}[input]
helicase[5] = helicase[5]*0.95+{'R':0,'P':-0.1,'S':0.1}[input]
base_pairs[25] = {0:'R',1:'P',2:'S',3:'R',4:'P',5:'S'}[helicase.index(max(helicase[3:6]))]
#Adenosine triphosphate
l=len(historase)
if l >= endonuclease+2:
codon = str(historase[l-endonuclease-2])
for i in range(-endonuclease-1,-2):
codon = codon+str(historase[l+i])
if not codon in phosphodiester:
phosphodiester[codon] = dict()
if historase[l-1] in phosphodiester[codon]:
phosphodiester[codon][historase[l-1]]+=1
else:
phosphodiester[codon][historase[l-1]]=1
if length>50:
guess=str(historase[l-endonuclease])
for i in range(-endonuclease+1,0):
guess = guess+str(historase[l+i])
if guess in phosphodiester:
base_pairs[27] = max(phosphodiester[guess], key = lambda x: phosphodiester[guess].get(x) )
else:
base_pairs[27] = random.choice(['R','P','S'])
for i in range(28,30):
base_pairs[i] = {'R':'S','P':'R','S':'P'}[base_pairs[i-1]]
else:
for i in range(27,30):
nucleotide_frequency[i] = 0
output3 = {'R':'P', 'P':'S', 'S':'R'}[base_pairs[nucleotide_frequency.index(max(nucleotide_frequency))]]
output3 = {0:output3,1:random.choice(['R','P','S'])}[ random.random() < 0.2 or max(nucleotide_frequency)<0 ]
mRNA = base_pairs
if input == "":
player = RPSPlayer()
output2 = player.next(input)
output = random.choice([output1, output2, output3])