print features.prep_features(sys.argv[1:])
f = sys.stdin
while True:
line = f.readline()
if line == '':
break
if line == '\n':
continue
try:
k, tag = line.strip().split("\t")
except:
break ## can finish earlier
print k, tag
k = int(k)
best_w = None
for j in xrange(k):
logprob = float(f.readline().strip())
parse = f.readline().strip()
tree = Tree.parse(parse)
##print tree
if j < maxk:
## print tree
features.evaluate(tree, tree.get_sent(), j)
##print features.pp_fv(fvector, j)
##fvector = features.extract(tree, tree.get_sent())
import copy, time
class Fortify_Evaluator():
def __init__(self):
pass
# action = (source_territory, destination_territory, num_armies)
def evaluate_action(self, action, map_layout, player_status, enemy_status):
(sim_player_status, sim_enemy_status) = self._simulate(action, player_status, enemy_status)
return features.evaluate_fortify(map_layout, sim_player_status, sim_enemy_status)
def _simulate(self, action, player_status, enemy_status):
''' Takes away num_armies from source, and add num_armies to the destination '''
source, destination, num_armies = action
sim_player_status = copy.deepcopy(player_status)
sim_source = get_territory_by_id(source['territory'], sim_player_status['territories'])
sim_source['num_armies'] -= num_armies
sim_dest = get_territory_by_id(destination['territory'], sim_player_status['territories'])
sim_dest['num_armies'] += num_armies
return (sim_player_status, enemy_status)
if __name__ == "__main__":
import EvaluatorAI
b = EvaluatorAI.EvaluatorAI()
time.sleep(2)
r = Fortify_Evaluator()
b._refresh_state()
source, dest = b._create_set_of_legal_fortifications()[0]
possible_action = (source, dest, source['num_armies'] - 1)
print features.evaluate(b.map_layout, b.player_status, b.get_enemy_status())
print r.evaluate_action(possible_action, b.map_layout, b.player_status, b.get_enemy_status())
(sim_player_status,
sim_enemy_status) = self._simulate(action, player_status,
enemy_status)
return features.evaluate_fortify(map_layout, sim_player_status,
sim_enemy_status)
def _simulate(self, action, player_status, enemy_status):
''' Takes away num_armies from source, and add num_armies to the destination '''
source, destination, num_armies = action
sim_player_status = copy.deepcopy(player_status)
sim_source = get_territory_by_id(source['territory'],
sim_player_status['territories'])
sim_source['num_armies'] -= num_armies
sim_dest = get_territory_by_id(destination['territory'],
sim_player_status['territories'])
sim_dest['num_armies'] += num_armies
return (sim_player_status, enemy_status)
if __name__ == "__main__":
import EvaluatorAI
b = EvaluatorAI.EvaluatorAI()
time.sleep(2)
r = Fortify_Evaluator()
b._refresh_state()
source, dest = b._create_set_of_legal_fortifications()[0]
possible_action = (source, dest, source['num_armies'] - 1)
print features.evaluate(b.map_layout, b.player_status,
b.get_enemy_status())
print r.evaluate_action(possible_action, b.map_layout, b.player_status,
b.get_enemy_status())
[1,2,3,4], #text + tweet + user + time
[1,2,3,5], #text + tweet + user + time2
[1,3], #text + user + time
[1,3,4], #text + user + time
[1,3,5], #text + user + time
]
threshold = int(len(tweets)*0.8)
print(len(tweets))
# Training
for i in range(len(result)):
featuresets = map_async(get_features,tweets,[screen_names,hashtags,words,links,users,result[i]])
classifier = nltk.NaiveBayesClassifier.train(featuresets[:threshold])
x = evaluate(classifier, featuresets[threshold:])
classifier.show_most_informative_features(n=10)
result[i] = x
for i in result:
print(i)
# mostrare features più interessanti
#==============================================================================
# Prova su 6000 sconosciuti
#==============================================================================