# ga2

This program has been disqualified.

 Author momo Submission date 2012-08-31 13:02:15.065627 Rating 6529 Matches played 35 Win rate 68.57

## 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])
def cumsum(iterable):
iterable= iter(iterable)
s= iterable.next()
yield s
for c in iterable:
s= s+ c
yield s
def weightedchoice(v, w, no):
ww = sum(w)
if (ww == 0): return random.choice(v)

w = [(ws/ww)**no for ws in w]
ww = sum(w)

ra = ww*random.random()
j = 0
for i in cumsum(w):
# print(r,i)
if ra <= i: break
j+= 1
return v[j]

if(1):
if (input == ""):
N = 1
AR1 = 0.95
states = ["R","S","P"]
st = [0,1,2]
sdic = {"R":0, "S":1, "P":2}
table = {}
cutoff = 400
#     hennies = 5
res = [[0, 1, -1], [-1, 0, 1], [1, -1, 0]]
r=0
MEM = [(0,.9),(1,0.9),(2,0.9),(3,0.90),(4,0.90), (5,0.90)]

MEM2 = [3,4,5]
M = len(MEM)*2 + len(MEM2)*2

models = [.6,1,1,1,1,0.6]*(len(MEM2))+ [1,0.6,0.6]
state =  * (M*3)
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 = []
pyo = [[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],[0,0,0],[0,0,0],[0,0,0]]
ptu = [[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],[0,0,0],[0,0,0],[0,0,0]]

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)]
if (res[yo][tu] == 1):
state = [state[i] + 1.5*(yo==prognosis[i]) for i in range(M*3)]
else:
state = [state[i] + (1+res[prognosis[i]][tu]) for i in range(M*3)]

r = res[yo][tu]

i = 0

prop =  [random.choice(st) for j in range(len(MEM2)*2)]
for m in MEM2:
if(N + 1> m):
key = tuple(hi[-m-1:-1])
if (key in table):
table[key] += [pa]
else:
table[key] = [pa]
if(N > m):
key = tuple(hi[-m:])

if (key in table):
ch = table[key]
k = len(ch)
k = max(random.choice(range(0,k)),random.choice(range(0,k)))

prop[i] = ch[k]
prop[i+1] = ch[k]

i += 2

for m in range(len(MEM)):

h = MEM[m]
r = MEM[m]
if h == 0:
pyo[yo] = pyo[yo]*r + 1
ptu[tu] = ptu[tu]*r + 1
elif h < N:
z = (hi[N-h-1]-yo)%3
pyo[m][z] = pyo[m][z]*r + 1

z = (hi[N-h-1]-tu )%3
ptu[m][z] = ptu[m][z]* r + 1

i = -3
prognosis = [random.choice(st) for l in range(M*3)]

for m in range(len(MEM)):

h = MEM[m]
if h == 0:
i += 3; prognosis[i] = best(ptu[m])
i += 3; prognosis[i] = highest(pyo[m])
else:
i += 3; prognosis[i] = (tu + best(ptu[m])) % 3
i += 3; prognosis[i] = (yo + highest(pyo[m])) % 3

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]

ms = min(state)
state2 = [s - ms for s in state]

b = weightedchoice(range(M*3), state2, 2)
yo = prognosis[b]

output = states[yo]

N = N + 1``````