def NextNode(self, Target, deterministic=False): " determines the most attractive neighbouring node when heading to Target" if len(self.Table): if deterministic: return max([(self.Table[Target][n], n) for n in self.Table[Target]])[1] return self.Table[Target].keys()[ET.fortune_wheel(self.Table[Target].values())] return None
def NextNode(self, Target, deterministic=False):
" determines the most attractive neighbouring node when heading to Target"
if len(self.Table):
if deterministic:
return max([(self.Table[Target][n], n)
for n in self.Table[Target]])[1]
return list(self.Table[Target].keys())[ET.fortune_wheel(
self.Table[Target].values())]
return None
def Sniff(self): " Looks for the next place to go " # The ant makes a biased choice of its next location # More probable links leading from neighbouring nodes to the ant's origin are more likely to be chosen Neighbours = self.location.Neighbours[:] if self.Origin in Neighbours: Neighbours.remove(self.Origin) Probabilities = [n.Table[self.Origin][self.location] for n in Neighbours] if Probabilities: NodeRank = ET.fortune_wheel(Probabilities) # chooses a neighbour based on probabilities return Neighbours[NodeRank] return None
def Sniff(self):
" Looks for the next place to go "
# The ant makes a biased choice of its next location
# More probable links leading from neighbouring nodes to the ant's origin are more likely to be chosen
Neighbours = self.location.Neighbours[:]
if self.Origin in Neighbours: Neighbours.remove(self.Origin)
Probabilities = [
n.Table[self.Origin][self.location] for n in Neighbours
]
if Probabilities:
NodeRank = ET.fortune_wheel(
Probabilities) # chooses a neighbour based on probabilities
return Neighbours[NodeRank]
return None
