def think(self, dirt, food, enemyhill, enemyant, myhill, myant, mydead):
'''Return a dict with the keys of myant mapped to lists of NESW=.'''
# build a list of goals
goaltime = DateTime.now()
# active goals
active = {}
active.update({loc:self.ENEMYHILL for loc in enemyhill})
if len(myant) > 1.35 * len(enemyant):
active.update({loc:self.ENEMYANTBATTLE for loc in enemyant})
else:
active.update({loc:self.ENEMYANT for loc in enemyant})
# log about active goals
self.logfn('\nactive goals: {}'.format(active)) if self.logfn else None
# passive goals
passive = {}
if len(myant) < 125:
passive.update({loc:self.FOOD for loc in food})
# increase age of passive goals; keep ages of old and visible ones
self.age = {loc:age + 1 for loc, age in self.age.iteritems() \
if age > self.MAXAGE or loc in passive}
# track age new passive goals
new = {loc:0 for loc in passive}
new.update(self.age)
self.age = new
# filter passive goals which are old
passive = {loc:rad for loc, rad in passive.iteritems() \
if loc in self.age and self.age[loc] <= self.MAXAGE}
# if there are enemies near a hill, protect the hill
if any(any(antmath.nearest_unwrapped_loc(hill, self.size, e)[0] \
<= self.PERIMETER for e in enemyant) \
for hill in myhill):
passive.update({loc:self.MYHILL for loc in myhill})
# log about passive goals
self.logfn('\npassive goals: {}\nages: {}'.format(passive, self.age)
) if self.logfn else None
# combine goal lists, giving active goals priority
passive.update(active)
goals = passive
# log about goal descision time
self.logfn('goal time: {}ms'.format(
(DateTime.now() - goaltime).total_seconds() * 1000.0)
) if self.logfn else None
# assign moves to ants
if goals:
if len(goals) > 1:
# divide ants by location into len(goals) groups
clustime = DateTime.now()
squads = self.cluster(1, goals.keys(), myant.keys())
assert sum(map(len, squads.values())) == len(myant)
self.logfn('cluster: {}ms'.format(
(DateTime.now() - clustime).total_seconds() * 1000.0)
) if self.logfn else None
else:
# all ants are in one supercluster
self.logfn('supercluster') if self.logfn else None
rows, cols = zip(*myant.keys())
r = float(sum(rows)) / len(myant)
c = float(sum(cols)) / len(myant)
squads = {(r, c): myant.keys()}
# assign each squad the closest goal
for sM, sA in squads.iteritems():
dist, gloc = min([self.unwrapped_dir(sM, loc) for loc in goals])
gradmin, gradmax = goals[self.wrap(gloc)]
for aN in sA:
# make ants keep the right distance from goals
vect = antmath.naive_dir(aN, gloc)
dist = antmath.distance2(aN, gloc)
if dist > gradmax and dist < 7 * self.g['viewradius2']:
myant[aN] = vect
random.shuffle(myant[aN]) if gradmax != gradmin else None
myant[aN] += [antmath.reverse_dir(v) for v in vect]
elif dist < gradmin:
myant[aN] = [antmath.reverse_dir(v) for v in vect]
random.shuffle(myant[aN]) if gradmax != gradmin else None
myant[aN] += vect
else:
myant[aN] = self.v[:]
random.shuffle(myant[aN])
else:
# move each ant individually randomly
self.logfn('brownian') if self.logfn else None
for aN, (aI, aO) in myant.iteritems():
random.shuffle(self.v)
myant[aN] = self.v[:]
return myant
def unwrap(self, a, b):
if self.not_near_edge(a):
return b
else:
d2, unwr = am.nearest_unwrapped_loc(a, self.size, b)
return unwr
