# schwarz und rot

 Author momo Submission date 2011-09-03 11:02:01.402354 Rating 7371 Matches played 2861 Win rate 76.9

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

## Source code:

``````import random, math

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-c, c-c, c-c])

if(1):
if (input == ""):
N = 1
AR1 = 0.85
states = ["R","S","P"]
st = [0,1,2]
sdic = {"R":0, "S":1, "P":2}
table = {}
cutoff = 400
decay1 = 0.98
decay2 = 0.5
hennies = 5
res = [[0, 1, -1], [-1, 0, 1], [1, -1, 0]]
total=0
r=0
MEM = [] # 3, 5
MEM2 = [3,4,5]
M = len(MEM)*3 + len(MEM2)*2 + 1

models = [1,1,1,1,1,1,1,1,1,1,1,1,.2,.2,.2,.2,.2,.2]*  len(MEM) + [.3,.3,1]*(len(MEM2)*2)+ [1,0.6,0.6]#[1,0.5,0.5] #*(M*3+1)
state =  * (M*3)
yo = random.choice(st)
tu = random.choice(st)

pa = (yo, tu)
hi = [pa]
hit = states[yo]+states[tu]
prognosis = [random.choice(st) for i in range(M*3+1)]
choices = []

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

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

r = res[yo][tu]
total = total + r

count = [[[0,0,0],[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0],[0,0,0]]]
for m in range(len(MEM)):
mem = MEM[m]
if (N > mem + 1):

p = hi[N-mem-1:N-1]

s = hi[N-mem-2]

key0 = p
for key in [key0, [(i,-1) for i in key0], [ (-1,i) for i in key0]]:
k = tuple([s] + key)
if (k in table): table[k] = weight
else: table[k]= weight

for y in st:
for t in st:
key0 = p
for key in [key0, [(i,-1) for i in key0], [(-1,i) for i in key0]]:
k = tuple([(y,t)] + key)
if (k in table):
z = table[k]
count[m][y] += z
count[m][t] += z

countagg = [[],[],[],[],[],[],[],[],[]]
for  m in range(len(MEM)):
countagg[m] = [[count[m][i] + count[m][(i+0)% 3] for i in st]]
countagg[m] += [[count[m][i] + count[m][(i+1)% 3] for i in st]]
countagg[m] += [[count[m][i] + count[m][(i+2)% 3] for i in st]]
i = 0

prop =  [random.choice(st) for j in range(len(MEM2)*2)]
for m in MEM2:
if(N > m):
key = hit[-2*m:]
pos = N*2 - m*2
#prop[i] = sdic[hit[pos-1]]
#prop[i+1] = sdic[hit[pos-2]]

if (random.random() < decay1):
#prop[i] = (best(prob) + 1) % 3
#prop[i+1] = (best(prob)+2) % 3

while 1:
pos = hit.rfind(key,max(0, N-cutoff),pos)
if pos > 1:
# prop[i] = sdic[hit[pos-1]]
# prop[i+1] = sdic[hit[pos-2]]
prop[i] = sdic[hit[pos + 2*m]]
prop[i+1] = sdic[hit[pos + 2*m+1]]

else:
break
if (random.random() < decay2): break
i += 2

i = -3;
for m in range(len(MEM)):
i += 3; prognosis[i] = best(countagg[m])
i += 3; prognosis[i] = best(countagg[m])
i += 3; prognosis[i] = best(countagg[m])

for m in range(len(MEM2)):
i += 3; prognosis[i] = (prop[m])
i += 3; prognosis[i] = (prop[m+1])

prob = [0,0,0]
# triplehenny
for j in range(hennies):
prob[(hi[random.choice(range(max(0,N-cutoff),N))])]+=1
i += 3; prognosis[i] = (best(prob))
i += 3

assert(i==3*M)

for j in range(M):
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``````