def finalAnswers(self):
# in accordance to the user reports we conclude if the node is "good" or "bad" during the round
lista = []
scores = self.scoring()
for i in range(len(self.nodes)):
sumYes, sumNo, noNo, noYes, yesScore, noScore = 0, 0, 0, 0, 0, 0
usersOnNode = compFunctions.indices(self.assignement, i)
for user in usersOnNode:
if self.informationReports[user] == 0:
sumNo += scores[user]
noNo += 1
elif self.informationReports[user] == 1:
sumYes += scores[user]
noYes += 1
if noNo == 0:
noScore = 0
else:
noScore = sumNo / noNo
if noYes == 0:
yesScore = 0
else:
yesScore = sumYes / noYes
if yesScore > noScore:
lista.append(1)
else:
lista.append(0)
return lista, scores
def computeCongs(self, assignment):
# computing the Conegstion (on each node) values, according to the formulas
lista = []
totalItus = compFunctions.addIUS(self.users)
for i in range(self.k):
nodeISum = 0
usersOnNode = compFunctions.indices(assignment, i)
for userIndex in usersOnNode:
nodeISum += self.users[userIndex].ongoingTask[0]
lista.append(nodeISum / totalItus)
return np.array(lista)
def getPeersRefs(self):
# calculating the peers and reference nodes for each node
references = []
peers = []
for i in range(len(self.users)):
nodeIndex = self.assignement[i]
usersOnNode = compFunctions.indices(self.assignement, nodeIndex)
onNode = len(usersOnNode)
userIndex = usersOnNode.index(i)
references.append(usersOnNode[(userIndex + 1) % onNode])
peers.append(usersOnNode[(userIndex + 2) % onNode])
return references, peers
def computeUGGP(self, assignment, tOver, eOver):
# computing the UGGP values, according to the formulas
lista = []
for i in range(self.k):
nodeSum = 0
usersOnNode = compFunctions.indices(assignment, i)
onNode = len(usersOnNode)
for userIndex in usersOnNode:
nodeSum += tOver[userIndex] / constants.TIMESLOTDURATION
nodeSum += eOver[userIndex] / self.users[userIndex].eu
if onNode == 0:
lista.append(0)
else:
lista.append(nodeSum / onNode)
return np.array(lista)
def computeRFKs(self, assignment, fuks):
# computing the Reputation Factor (used in the reward calculation below)
lista = []
for i in range(self.k):
perNode = 0
usersOnNode = compFunctions.indices(assignment, i)
onNode = len(usersOnNode)
for userIndex in usersOnNode:
perNode += fuks[userIndex]
if onNode == 0:
lista.append(0)
else:
lista.append(perNode / onNode)
divisor = sum(lista)
array = np.array(lista)
array /= divisor
return array