def updateNewMonkeyToChosenSetDistanceVector(self, graph=None, oldShortestDistanceVectorData=None, \
newlyChosenMonkeyID=None, minShortestDistance=0.4):
"""
2012.11.26
supplement to constructNewMonkeyToChosenSetDistanceVector()
"""
oldShortestDistanceToChosenSet_monkeyID_ls = oldShortestDistanceVectorData.shortestDistanceToChosenSet_monkeyID_ls
sys.stderr.write("Updating the shortest-distance to chosen set vector (%s elements) because monkeys %s has been added into the chosen set ..."%\
(len(oldShortestDistanceToChosenSet_monkeyID_ls), newlyChosenMonkeyID))
returnData = PassingData(shortestDistanceToChosenSet_monkeyID_ls = [])
counter = 0
real_counter = 0
spanStartPos = 0
monkey2ID = newlyChosenMonkeyID
for element in oldShortestDistanceToChosenSet_monkeyID_ls:
shortestDistance, monkey1ID, shortestDistanceToThisChosenMonkey = element[:3]
if monkey1ID!=monkey2ID: #skip the chosen monkey
counter += 1
#get the shortest path
#short path in graph, sum all the edge weights, A* star algorithm seems to be much faster (>100%) than default shortest_path.
#distance = nx.shortest_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
distance = nx.astar_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
if distance<shortestDistance:
shortestDistance = distance
shortestDistanceToThisChosenMonkey = monkey2ID
element = (shortestDistance, monkey1ID, shortestDistanceToThisChosenMonkey)
real_counter += 1
if shortestDistance>=minShortestDistance:
returnData.shortestDistanceToChosenSet_monkeyID_ls.append(element)
spanStartPos += shortestDistance #increase the distance.
returnData.totalDistance = spanStartPos
sys.stderr.write("%s (out of %s) monkeys changed their shortest distance to the chosen set (%s elements). \n\
total distance to the chosen set is %s.\n"%\
(real_counter, counter, len(returnData.shortestDistanceToChosenSet_monkeyID_ls), spanStartPos))
return returnData
def constructNewMonkeyToChosenSetDistanceVector(self, graph=None, preChosenMonkeyIDSet=None, minShortestDistance=0.4):
"""
2012.11.26
each monkey is assigned a probability mass based on its geographic distance to the closest monkey
in the preChosenMonkeyIDSet.
"""
sys.stderr.write("Constructing distance vector from new monkey to %s chosen monkeys (%s total monkeys) ..."%\
(len(preChosenMonkeyIDSet), len(graph)))
counter = 0
real_counter = 0
spanStartPos = 0
unChosenMonkeyIDSet = set(graph.nodes()) - preChosenMonkeyIDSet
preChosenAndInGraphMonkeyIDSet = set(graph.nodes()) - unChosenMonkeyIDSet
returnData = PassingData(shortestDistanceToChosenSet_monkeyID_ls = [])
for monkey1ID in unChosenMonkeyIDSet:
counter += 1
shortestDistance = None
shortestDistanceToThisChosenMonkey = None
for monkey2ID in preChosenAndInGraphMonkeyIDSet:
#get the shortest path
#distance = nx.shortest_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
distance = nx.astar_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
#short path in graph, sum all the edge weights
if shortestDistance is None or distance<shortestDistance:
shortestDistance = distance
shortestDistanceToThisChosenMonkey = monkey2ID
if shortestDistance is not None and shortestDistance>=minShortestDistance: #ignore monkeys from same sites
returnData.shortestDistanceToChosenSet_monkeyID_ls.append((shortestDistance, monkey1ID, shortestDistanceToThisChosenMonkey))
real_counter += 1
spanStartPos += shortestDistance #increase the distance.
returnData.totalDistance = spanStartPos
sys.stderr.write("%s (out of %s candidate) monkeys added into candidate vector, total distance to the chosen set is %s.\n"%\
(real_counter, counter, spanStartPos))
return returnData
