# Farn M30

 Author momo Submission date 2012-04-27 11:37:03.386867 Rating 6576 Matches played 800 Win rate 67.75

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 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
states = ["R","S","P"]
st = [0,1,2]
sdic = {"R":0, "S":1, "P":2}

res = [[0, 1, -1], [-1, 0, 1], [1, -1, 0]]

MEM = [(0,.9),(1,0.9),(2,0.9),(3,0.90),(4,0.90), (5,0.90), (12,0.90), (30,0.90)]
M = len(MEM)*2

state =  * (M*3)

yo = random.choice(st)
tu = random.choice(st)

pa = (yo, tu)
hi = [pa]
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]

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)]

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

i += 3;

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

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``````