Squad IV

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

Source code:

import random

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

def lowest(v):
    return random.choice([i for i in range(len(v)) if min(v) == v[i]])

def best(c):
    return highest([c[1]-c[2], c[2]-c[0], c[0]-c[1]])

def mean(c):
    return sum(c)/length(c)

# alpha in [0,1]: greediness 
def attack(yo, tu, alpha):
    r = res[yo][tu]
    p1 = yo
    if r == -1:
        p1 = (yo + 1) % 3
    elif r == 0 and random.random() < alpha:
        p1 = (yo + 2) % 3
    return p1
                   
if(1):
    if (input == ""):
        N = 1
        AR1 = .95#0.85
        states = ["R","S","P"]
        st = [0,1,2]
        sdic = {"R":0, "S":1, "P":2}
        forwardbias = 2
        res = [[0, 1, -1], [-1, 0, 1], [1, -1, 0]]
        MEM1 = MEM2 = MEM3 = []
        
        MEM4 = [6]
    
        M1 = len(MEM1)*3
        M2 = len(MEM2)*2
        M3 = len(MEM3)
        M4 = len(MEM4)*3
        M = M1 + M2 + M3 + M4
        models = ([1,.7,.7]*M4)
      
        
        state = [0] * (M*3)
      
        yo = random.choice(st)
        tu = random.choice(st)

        pa = (yo, tu)
        hi = [pa]
        hiyt = states[yo]+states[tu]
        hit = states[yo]+" "
        hiy = " " + states[tu]
        prognosis = [random.choice(st) for i in range(M*3)]
        choices = []


    else:
          tu = sdic[input]
          pa = (yo,tu)
          hi += [pa]

          hiyt += states[yo]+states[tu]
          hit += states[yo]+" "
          hiy += " " + states[tu]
          state = [ AR1 * state[i] + res[prognosis[i]][tu] * models[i] for i in range(M*3)]
        
 

    i = -3
    # Squad
    for h in MEM4:
        proby = [0.0,0.0,0.0]
        probt = [0.0,0.0,0.0]
        
        for j in range(h):
            k = max([random.choice(range(N)) for l in range(forwardbias)])
            if hi[k][1]!= tu:
                k = max([random.choice(range(N)) for l in range(forwardbias)])
            if k<N-1:
                k += 1
            proby[(hi[k][0])]+= 1
            probt[(hi[k][1])]+= 1
        i += 3; prognosis[i] = best([probt[l] +proby[(l+0)% 3] for l in st])
        i += 3; prognosis[i] = best([probt[l] +proby[(l+2)% 3] for l in st])
        i += 3; prognosis[i] = best([probt[l] +proby[(l+1)% 3] for l in st])
    
        #i += 3; prognosis[i] = (best(proby))
        #i += 3; prognosis[i] = (best(probt))

   


    i += 3; assert(i==3*M)


   
  
    for j in range(M1 + M2+M4):
        prognosis[j*3 + 1] = (prognosis[j*3] + 1) % 3
        prognosis[j*3 + 2] = (prognosis[j*3+1] + 1) % 3

    best = highest(state)   
    yo = prognosis[best]

    
    output = states[yo]  
        
    N = N + 1