def random_decision(self, fac, force_list=None):
"Creates a decision based on a random list in faction."
# decide which list to make decision from
decide_list = force_list
if force_list is None:
decide_list = fac.get_attr_by_type(list)
decide_list = self.rand_element(decide_list)
attr_list = decide_list[0].attr
attr = self.rand_element(attr_list.keys())
# value that we should search for the closest to
value_random = random.random()
if value_random < 0.333:
value = decision.minimum
elif value_random < 0.666:
value = decision.maximum
else:
value = self.rand_int(-100, 100)
weight = random.random() * 3.0
# possible add decisions on another factor from same list
dec = decision.decision(decide_list, attr, value, weight)
if random.random() < self.length_change:
dec = dec + random_decision(decide_list)
return dec
def on_data(self, data):
# print data
temp = json.loads(data)
if temp['coordinates'] != None:
coord = temp['coordinates']['coordinates']
t = langDetect.formBaseTweet(
temp['text'].encode('utf-8').replace('\n', ''))
if temp['user']['lang'].encode('utf-8') == None:
print ' user is None'
d, flag = decision.decision(
temp['user']['lang'].encode('utf-8'), t['text'])
if d:
t['langAlert'] = flag
t['author'] = temp['user']['name'].encode(
'utf-8'
) + ' aka ' + temp['user']['screen_name'].encode('utf-8')
t['created_at'] = temp['created_at']
t['userlang'] = temp['user']['lang'].encode('utf-8')
# print temp['coordinates']['coordinates']
t['lon'] = temp['coordinates']['coordinates'][0]
t['lat'] = temp['coordinates']['coordinates'][1]
print locationName, '|', t['userlang'].decode(
'utf-8', 'ignore'), '|', t['detectedLang'].decode(
'utf-8', 'ignore'
), '|', t['langAlert'], '|', t['text'].decode('utf-8')
# print '|'.join([str(x).decode('utf-8') for x in [locationName, t['userlang'], t['detectedLang'], t['langAlert'], t['text']]])
SaveToDB.SaveToDB(t, locationName)
return True
def run(self):
#extracting of command from xml file:
cmdCl = TransformXmlToCmd()
cmdCl.readXml('cmdCrTest.xml')
cmd = cmdCl.getcmd(self.cr.name)
#cmd is a list containing objects that contain the indicator and her command
# calling the obj_extract class to execute command and extract its result
ex=obj_extract()
result=ex.extract(self.host,cmd[0],self.cr.name)
# definition of fixed threshold of each indicator
fixedTh=fixed_thresholds(self.cr)
mxAdd=fixedTh.return_maxAdding()
mnRem=fixedTh.return_minRemoving()
# decision for each indicator
d=decision()
crDecision=d.optimize(result,mxAdd,mnRem)
# saving decision
queueLock.acquire()
f=open('content.log','a')
content=self.cr.name+' , '+crDecision
f.write(content)
f.close()
# saving crDecision
queueLock.release()
def __init__(self, fac):
self.build_city = lambda reward, resource: action.action(
{ "to_city" : decision.decision(
fac.potential_cities,
"area.potential." + resource,
decision.decision.maximum ),
"from_city" : decision.decision(
fac.cities,
"area.potential." + resource,
decision.decision.minimum )
},
[ action.evaluator(
"from_city",
city.change,
{ "population": action.evaluator(
"from_city", "population") - 10,
"military": action.evaluator(
"from_city", "military") - 5
}
),
action.evaluator(
"to_city",
city.change,
{ "faction_ident": fac.ident,
"population": 10,
"military": 5
}
)
],
{ "citizen" : lambda city: create_citizen(
fac,
city ),
"move" : lambda obj, city: action.movement(
obj,
city.pos,
reward=reward),
"action" : lambda obj, act: obj.give_action( act )
},
[ ["from_city"], ["citizen", "to_city"],
["citizen", "move"] ]
)
def accepted_handler(event: accepted):
global pid
global finished_paxos
acceptedBals[event.ballot] += 1
if acceptedBals[event.ballot] == 2:
for process in range(1, 6):
if process != pid:
sender = threading.Thread(
target=send_event,
args=[decision(ballotNum, current_transaction), process])
sender.daemon = True
sender.start()
append_block(current_transaction)
finished_paxos = True
def on_data(self, data):
# print data
temp = json.loads(data)
if temp['coordinates']!=None:
coord = temp['coordinates']['coordinates']
t = langDetect.formBaseTweet(temp['text'].encode('utf-8').replace('\n',''))
if temp['user']['lang'].encode('utf-8')==None: print ' user is None'
d, flag = decision.decision(temp['user']['lang'].encode('utf-8'), t['text'])
if d:
t['langAlert']=flag
t['author']=temp['user']['name'].encode('utf-8') + ' aka ' + temp['user']['screen_name'].encode('utf-8')
t['created_at']=temp['created_at']
t['userlang']= temp['user']['lang'].encode('utf-8')
# print temp['coordinates']['coordinates']
t['lon']= temp['coordinates']['coordinates'][0]
t['lat']= temp['coordinates']['coordinates'][1]
print locationName , '|', t['userlang'].decode('utf-8','ignore'), '|', t['detectedLang'].decode('utf-8','ignore'), '|', t['langAlert'] , '|', t['text'].decode('utf-8')
# print '|'.join([str(x).decode('utf-8') for x in [locationName, t['userlang'], t['detectedLang'], t['langAlert'], t['text']]])
SaveToDB.SaveToDB(t, locationName )
return True
def mk_decision():
a = decision(0, 0, 0, 0, 0, 0, "name")
a.name = input("what decision are you going to make ")
gh = (input("how happy can you tolerate on 0-100% "))
if (gh.isdigit()):
gh = int(gh)
if (gh > 0 and gh < 100):
a.h = gh
if (gh > 100):
a.h = 100
if (gh < 0):
a.h = 0
else:
print("enter a valid number from 1-100")
gh = input(" how much happy this decision makes you ")
if (gh.isdigit()):
gh = int(gh)
if (gh > 0 and gh < 100):
a.h = gh
if (gh > 100):
a.h = 100
if (gh < 0):
a.h = 0
else:
print("enter a valid number from 1-100")
gf = input(" how much fear this decision makes you ")
if (gf.isdigit()):
gf = int(gf)
if (gf > 0 and gf < 100):
a.f = gf
if (gf > 100):
a.f = 100
if (gf < 0):
a.f = 0
else:
print("enter a valid number from 1-100")
gs = input(" how much sadness this decision makes you ")
if (gs.isdigit()):
gs = int(gs)
if (gs > 0 and gs < 100):
a.s = gs
if (gs > 100):
a.s = 100
if (gs < 0):
a.s = 0
else:
print("enter a valid number from 1-100")
ga = input(" how much angry this decision makes you ")
if (ga.isdigit()):
ga = int(ga)
if (ga > 0 and ga < 100):
a.a = ga
if (ga > 100):
a.a = 100
if (ga < 0):
a.a = 0
else:
print("enter a valid number from 1-100")
gsu = input(" how much surprise this decision makes you ")
if (gsu.isdigit()):
gsu = int(gsu)
if (gsu > 0 and gsu < 100):
a.su = gsu
if (gsu > 100):
a.su = 100
if (gsu < 0):
a.su = 0
else:
print("enter a valid number from 1-100")
gd = input(" how much disgust this decision makes you ")
if (gd.isdigit()):
gd = int(gd)
if (gd > 0 and gd < 100):
a.d = gd
if (gd > 100):
a.d = 100
if (gd < 0):
a.d = 0
else:
print("enter a valid number from 1-100")
return a
nn_hdim = 3 # number of hidden variables
nn_output_dim = 2 # two classes
# Initial model
model = initialize(nn_input_dim, nn_hdim, nn_output_dim)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.ion()
fig.show()
fig.canvas.draw()
# Train a model
reg_lambda = 0.01
epsilon = 0.01
num_passes = 1000
for i in range(0, num_passes):
out = forward(model, X, y)
z1, a1, probs, yhat = out['z1'], out['a1'], out['probs'], out['yhat']
model = backward(model, X, y, probs, a1, reg_lambda, epsilon)
if i % 20 == 0:
# Plot the contour and training examples
out = decision(X, y, lambda x: predict(model, x))
xx, yy, Z = out["xx"], out["yy"], out["Z"]
ax.clear()
ax.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
plt.pause(0.1)
