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greedy.py
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greedy.py
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import networkx as nx
import heapq
# assume cost function is decreasing convex
def normal_greedy(G, blackstart):
"""
conducts a greedy on the given graph G with blackstart and
size n using given heuristic f
installs nodes that have the lowest installation cost,
which is to say, nodes that have the most installed neighbors
"""
toInstall = [] # priority queue for things to be visited
order = [] # order of installation given by this algorithm
element_finder = {} # mapping of nodes to pairs (num_installed, node)
#put the blackstart in
elt = (0,blackstart)
element_finder[blackstart] = elt
heapq.heappush(toInstall, elt)
# while there are node to install
while toInstall:
# pop out the(a) node with lowest cost
cur = heapq.heappop(toInstall)[1]
order.append(cur)
# for each of cur's neighbors, if it's not in toInstall, add it, otherwise update
nb = G.neighbors(cur)
for n in nb:
if(n not in order):
if toInstall and n in zip(*toInstall)[1]:
# get the pair and the number of neighbors installed
pair = element_finder[n]
numN = pair[0]
# remove pair from heap and dict
toInstall.remove(pair)
element_finder.pop(n)
# push the relaxed value, minus one since we use -numNeighbor as priority
newPair = (numN-1,n)
else:
newPair = (-1,n)
element_finder[n] = newPair
heapq.heappush(toInstall, newPair)
return order
# assume cost function is decreasing convex
def percentage_greedy(G, blackstart):
"""
conducts a greedy on the given graph G with blackstart and
size n using given heuristic f
installs nodes that have the lowest installation percentage cost,
which is to say, nodes that have the most installed neighbor percentage
"""
toInstall = [] # priority queue for things to be visited
order = [] # order of installation given by this algorithm
element_finder = {} # mapping of nodes to (num_installed, node)
percent_finder = {} # mapping of nodes to (percentage_installed, node)
#put the blackstart in
elt = (0,blackstart)
element_finder[blackstart] = elt
percent_finder[blackstart] = elt
heapq.heappush(toInstall, elt)
# while there are node to install
while toInstall:
# pop out the(a) node with lowest cost
cur = heapq.heappop(toInstall)[1]
order.append(cur)
# for each of cur's neighbors, if it's not in toInstall, add it, otherwise update
nb = G.neighbors(cur)
for n in nb:
if(n not in order):
if toInstall and n in zip(*toInstall)[1]:
pair = percent_finder[n]
numN = element_finder[n][0]
# print (n,numN)
# remove pair from heap and dict
toInstall.remove(pair)
element_finder.pop(n)
percent_finder.pop(n)
numNeighbor = len(G.neighbors(n))
# print (n, numNeighbor)
newPerPair = ((numN-1.0)/numNeighbor,n)
newNumPair = (numN-1,n)
else:
numNeighbor = len(G.neighbors(n))
# print (n,numNeighbor)
newPerPair = (-1.0/numNeighbor,n)
newNumPair = (-1,n)
element_finder[n] = newNumPair
percent_finder[n] = newPerPair
heapq.heappush(toInstall, newPerPair)
return order