Are you a lucker?

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

Source code:

#This one is just for the proof that luck plays an important role in the leaderboard
#only 200 matches but there are more than 650 ai score > 5000 
import random

num_predictors =27
num_meta= 18

if input =="":
    len_rfind = [20]
    limit = [10,20,60]
    beat = { "P":"S" , "R":"P" , "S":"R" }
    not_lose = { "R":"PR", "P":"SP", "S":"RS" } 
    your_his =""
    my_his = ""
    both_his=""
    both_his2=""
    length =0
    score1=[3]*num_predictors
    score2=[3]*num_predictors
    score3=[3]*num_predictors
    score4=[3]*num_predictors
    score5=[3]*num_predictors
    score6=[3]*num_predictors
    metascore=[3]*num_meta
    temp1 = { "PP":"1","PR":"2","PS":"3",
              "RP":"4","RR":"5","RS":"6",
              "SP":"7","SR":"8","SS":"9"}
    temp2 = { "1":"PP","2":"PR","3":"PS",
                "4":"RP","5":"RR","6":"RS",
                "7":"SP","8":"SR","9":"SS"} 
    who_win = { "PP": 0, "PR":1 , "PS":-1,
                "RP": -1,"RR":0, "RS":1,
                "SP": 1, "SR":-1, "SS":0}
    index = { "P":0, "R":1, "S":2 }
    chance =[0]*num_predictors
    chance2 =[0]*num_predictors
    output = random.choice("RPS")
    predictors = [output]*num_predictors
    metapredictors = [output]*num_meta
else:
    #calculate score
    for i in range(num_predictors):
        #meta 1
        score1[i]*=0.8
        if input==predictors[i]:
            score1[i]+=3
        else:
            score1[i]-=3
        #meta 2
        if input==predictors[i]:
            score2[i]+=3
        else:
            score2[i]=0
        #meta 3
        score3[i]*=0.8
        if output==predictors[i]:
            score3[i]+=3
        else:
           score3[i]-=3
        #meta 4
        if output==predictors[i]:
            score4[i]+=3
        else:
            score4[i]=0
        #meta 5
        score5[i]*=0.8
        if input==predictors[i]:
            score5[i]+=3
        else:
            if chance[i]==1:
                chance[i]=0
                score5[i]-=3
            else:
                chance[i]=1
                score5[i]=0
        #meta 6
        score6[i]*=0.8
        if output==predictors[i]:
            score6[i]+=3
        else:
            if chance2[i]==1:
                chance2[i]=0
                score6[i]-=3
            else:
                chance2[i]=1
                score6[i]=0
    #calculate metascore
    for i in range(num_meta):
        metascore[i]*=0.9
        if input==metapredictors[i]:
            metascore[i]+=3
        else:
            metascore[i]=0
    #Predictors
    #if length>1:
    #    output=beat[predict]
    your_his+=input
    my_his+=output
    both_his+=temp1[(input+output)]
    both_his2+=temp1[(output+input)]
    length+=1

    #history matching
    for i in range(1):
        len_size = min(length,len_rfind[i])
        j=len_size
        #both_his
        while j>=1 and not both_his[length-j:length] in both_his[0:length-1]:
            j-=1
        if j>=1:
            k = both_his.rfind(both_his[length-j:length],0,length-1)
            predictors[0+6*i] = your_his[j+k]
            predictors[1+6*i] = beat[my_his[j+k]]
        else:
            predictors[0+6*i] = random.choice("RPS")
            predictors[1+6*i] = random.choice("RPS")
        j=len_size
        #your_his
        while j>=1 and not your_his[length-j:length] in your_his[0:length-1]:
            j-=1
        if j>=1:
            k = your_his.rfind(your_his[length-j:length],0,length-1)
            predictors[2+6*i] = your_his[j+k]
            predictors[3+6*i] = beat[my_his[j+k]]
        else:
            predictors[2+6*i] = random.choice("RPS")
            predictors[3+6*i] = random.choice("RPS")
        j=len_size
        #my_his
        while j>=1 and not my_his[length-j:length] in my_his[0:length-1]:
            j-=1
        if j>=1:
            k = my_his.rfind(my_his[length-j:length],0,length-1)
            predictors[4+6*i] = your_his[j+k]
            predictors[5+6*i] = beat[my_his[j+k]]
        else:
            predictors[4+6*i] = random.choice("RPS")
            predictors[5+6*i] = random.choice("RPS")
    
    #Reverse
    for i in range(3):
        temp =""
        search = temp1[(output+input)] #last round
        for start in range(2, min(limit[i],length) ):
            if search == both_his2[length-start]:
                temp+=both_his2[length-start+1]
        if(temp==""):
            predictors[6+i] = random.choice("RPS")
        else:
            collectR = {"P":0,"R":0,"S":0} #take win/lose from opponent into account
            for sdf in temp:
                next_move = temp2[sdf]
                if(who_win[next_move]==-1):
                    collectR[temp2[sdf][1]]+=3
                elif(who_win[next_move]==0):
                    collectR[temp2[sdf][1]]+=1
                elif(who_win[next_move]==1):
                    collectR[beat[temp2[sdf][0]]]+=1
            max1 = -1
            p1 =""
            for key in collectR:
                if(collectR[key]>max1):
                    max1 = collectR[key]
                    p1 += key
            predictors[6+i] = random.choice(p1)
    
    for i in range(9,27):
        predictors[i]=beat[beat[predictors[i-9]]]
    #find prediction for each meta
    metapredictors[0]=predictors[score1.index(max(score1))]
    metapredictors[1]=predictors[score2.index(max(score2))]
    metapredictors[2]=beat[predictors[score3.index(max(score3))]]
    metapredictors[3]=beat[predictors[score4.index(max(score4))]]
    metapredictors[4]=predictors[score5.index(max(score5))]
    metapredictors[5]=beat[predictors[score6.index(max(score6))]]
    for i in range(6,18):
        metapredictors[i] = beat[metapredictors[i-6]]
    
    predict = metapredictors[metascore.index(max(metascore))]
    output = beat[predict]
    #output = random.choice(not_lose[predict])