detailedbalanceT7

This program has been disqualified.

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 seqfreq(hi, l):
    N = len(hi)
    count =  [[0,0,0],[0,0,0]]
    a = 0
    b = 0
    for pos in range(max(l, N-cutoff), N):
              j = 0
              inc = 1 + (pos * decay)
              while (hi[pos-j] == hi[N-1 - j]) and j < l:
                  j += 1
              if (j == l):
                  count[0][hi[pos-j][0]] += inc
                  count[1][(hi[pos-j][1]+a)%3] += inc
              
              j0 = j
              while (hi[pos-j][0] == hi[N-1 - j][0]) and j < l:
                  j += 1
              if (j == l):
                  count[0][hi[pos-j][0]] += inc
                  count[1][(hi[pos-j][1]+a)%3] += inc
              j = j0
              while (hi[pos-j][1] == hi[N-1 - j][1]) and j < l:
                  j += 1
              if (j == l):
                  count[0][hi[pos-j][0]] += inc
                  count[1][(hi[pos-j][1]+a)%3] += inc
    return count
 

if (1):
    if (input == ""):
        N = 1
        L = 4
        cutoff = 320
        AR1 = 0.88 #0.85
        states = ["R","S","P"]
        st = [0,1,2]
        sdic = {"R":0, "S":1, "P":2}
        decay = 0.001
        decay2 = 0.5
        res = [[0, 1, -1], [-1, 0, 1], [1, -1, 0]]
        total=0
        r=0
        M = 3
        models = [1]*(M*3+1)
        
        state = [1]*(M*3+1)
        yo = random.choice(st)
        tu = random.choice(st)
        
        pa = (yo, tu)
        hi = [pa]
        prognosis = [random.choice(st) for i in range(M*3+1)]
        choices = []


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

          hi += [pa]

          state = [ AR1 * state[i] + res[prognosis[i]][tu] * models[i] for i in range(M*3+1)]

          r = res[yo][tu]
          total = total + r
          
    count0 = seqfreq(hi, L)
    count = [[count0[0][i] + count0[1][(i+0)% 3] for i in st]]
    count += [[count0[0][i] + count0[1][(i+1)% 3] for i in st]]
    count += [[count0[0][i] + count0[1][(i+2)% 3] for i in st]]
    

    i = 0;  prognosis[i] = best(count[0])
    i += 3; prognosis[i] = best(count[1])
    i += 3; prognosis[i] = best(count[2])
    assert(i+3==3*M)

    

    # modelrandom
    prognosis[3*M] = random.choice(st)
    
 
    for i in range(M):
      prognosis[i*3 + 1] = (prognosis[i*3] + 1) % 3
      prognosis[i*3 + 2] = (prognosis[i*3+1] + 1) % 3

    if(random.choice([0,1])): thebest = highest(state[0:-1])
    else:
        thebest = highest(state)
    choices += [thebest]
    
    yo = prognosis[thebest]
    
    output = states[yo]  
        
    N = N + 1