本文实例讲述了Python图算法。分享给大家供大家参考,具体如下:
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#encoding=utf-8
import networkx,heapq,sys
from matplotlib import pyplot
from collections import defaultdict,OrderedDict
from numpy import array
# Data in graphdata.txt:
# a b 4
# a h 8
# b c 8
# b h 11
# h i 7
# h g 1
# g i 6
# g f 2
# c f 4
# c i 2
# c d 7
# d f 14
# d e 9
# f e 10
def Edge(): return defaultdict(Edge)
class Graph:
def __init__( self ):
self .Link = Edge()
self .FileName = ''
self .Separator = ''
def MakeLink( self ,filename,separator):
self .FileName = filename
self .Separator = separator
graphfile = open (filename, 'r' )
for line in graphfile:
items = line.split(separator)
self .Link[items[ 0 ]][items[ 1 ]] = int (items[ 2 ])
self .Link[items[ 1 ]][items[ 0 ]] = int (items[ 2 ])
graphfile.close()
def LocalClusteringCoefficient( self ,node):
neighbors = self .Link[node]
if len (neighbors) < = 1 : return 0
links = 0
for j in neighbors:
for k in neighbors:
if j in self .Link[k]:
links + = 0.5
return 2.0 * links / ( len (neighbors) * ( len (neighbors) - 1 ))
def AverageClusteringCoefficient( self ):
total = 0.0
for node in self .Link.keys():
total + = self .LocalClusteringCoefficient(node)
return total / len ( self .Link.keys())
def DeepFirstSearch( self ,start):
visitedNodes = []
todoList = [start]
while todoList:
visit = todoList.pop( 0 )
if visit not in visitedNodes:
visitedNodes.append(visit)
todoList = self .Link[visit].keys() + todoList
return visitedNodes
def BreadthFirstSearch( self ,start):
visitedNodes = []
todoList = [start]
while todoList:
visit = todoList.pop( 0 )
if visit not in visitedNodes:
visitedNodes.append(visit)
todoList = todoList + self .Link[visit].keys()
return visitedNodes
def ListAllComponent( self ):
allComponent = []
visited = {}
for node in self .Link.iterkeys():
if node not in visited:
oneComponent = self .MakeComponent(node,visited)
allComponent.append(oneComponent)
return allComponent
def CheckConnection( self ,node1,node2):
return True if node2 in self .MakeComponent(node1,{}) else False
def MakeComponent( self ,node,visited):
visited[node] = True
component = [node]
for neighbor in self .Link[node]:
if neighbor not in visited:
component + = self .MakeComponent(neighbor,visited)
return component
def MinimumSpanningTree_Kruskal( self ,start):
graphEdges = [line.strip( '\n' ).split( self .Separator) for line in open ( self .FileName, 'r' )]
nodeSet = {}
for idx,node in enumerate ( self .MakeComponent(start,{})):
nodeSet[node] = idx
edgeNumber = 0 ; totalEdgeNumber = len (nodeSet) - 1
for oneEdge in sorted (graphEdges,key = lambda x: int (x[ 2 ]),reverse = False ):
if edgeNumber = = totalEdgeNumber: break
nodeA,nodeB,cost = oneEdge
if nodeA in nodeSet and nodeSet[nodeA] ! = nodeSet[nodeB]:
nodeBSet = nodeSet[nodeB]
for node in nodeSet.keys():
if nodeSet[node] = = nodeBSet:
nodeSet[node] = nodeSet[nodeA]
print nodeA,nodeB,cost
edgeNumber + = 1
def MinimumSpanningTree_Prim( self ,start):
expandNode = set ( self .MakeComponent(start,{}))
distFromTreeSoFar = {}.fromkeys(expandNode,sys.maxint); distFromTreeSoFar[start] = 0
linkToNode = {}.fromkeys(expandNode,'');linkToNode[start] = start
while expandNode:
# Find the closest dist node
closestNode = ''; shortestdistance = sys.maxint;
for node,dist in distFromTreeSoFar.iteritems():
if node in expandNode and dist < shortestdistance:
closestNode,shortestdistance = node,dist
expandNode.remove(closestNode)
print linkToNode[closestNode],closestNode,shortestdistance
for neighbor in self .Link[closestNode].iterkeys():
recomputedist = self .Link[closestNode][neighbor]
if recomputedist < distFromTreeSoFar[neighbor]:
distFromTreeSoFar[neighbor] = recomputedist
linkToNode[neighbor] = closestNode
def ShortestPathOne2One( self ,start,end):
pathFromStart = {}
pathFromStart[start] = [start]
todoList = [start]
while todoList:
current = todoList.pop( 0 )
for neighbor in self .Link[current]:
if neighbor not in pathFromStart:
pathFromStart[neighbor] = pathFromStart[current] + [neighbor]
if neighbor = = end:
return pathFromStart[end]
todoList.append(neighbor)
return []
def Centrality( self ,node):
path2All = self .ShortestPathOne2All(node)
# The average of the distances of all the reachable nodes
return float ( sum ([ len (path) - 1 for path in path2All.itervalues()])) / len (path2All)
def SingleSourceShortestPath_Dijkstra( self ,start):
expandNode = set ( self .MakeComponent(start,{}))
distFromSourceSoFar = {}.fromkeys(expandNode,sys.maxint); distFromSourceSoFar[start] = 0
while expandNode:
# Find the closest dist node
closestNode = ''; shortestdistance = sys.maxint;
for node,dist in distFromSourceSoFar.iteritems():
if node in expandNode and dist < shortestdistance:
closestNode,shortestdistance = node,dist
expandNode.remove(closestNode)
for neighbor in self .Link[closestNode].iterkeys():
recomputedist = distFromSourceSoFar[closestNode] + self .Link[closestNode][neighbor]
if recomputedist < distFromSourceSoFar[neighbor]:
distFromSourceSoFar[neighbor] = recomputedist
for node in distFromSourceSoFar:
print start,node,distFromSourceSoFar[node]
def AllpairsShortestPaths_MatrixMultiplication( self ,start):
nodeIdx = {}; idxNode = {};
for idx,node in enumerate ( self .MakeComponent(start,{})):
nodeIdx[node] = idx; idxNode[idx] = node
matrixSize = len (nodeIdx)
MaxInt = 1000
nodeMatrix = array([[MaxInt] * matrixSize] * matrixSize)
for node in nodeIdx.iterkeys():
nodeMatrix[nodeIdx[node]][nodeIdx[node]] = 0
for line in open ( self .FileName, 'r' ):
nodeA,nodeB,cost = line.strip( '\n' ).split( self .Separator)
if nodeA in nodeIdx:
nodeMatrix[nodeIdx[nodeA]][nodeIdx[nodeB]] = int (cost)
nodeMatrix[nodeIdx[nodeB]][nodeIdx[nodeA]] = int (cost)
result = array([[ 0 ] * matrixSize] * matrixSize)
for i in xrange (matrixSize):
for j in xrange (matrixSize):
result[i][j] = nodeMatrix[i][j]
for itertime in xrange ( 2 ,matrixSize):
for i in xrange (matrixSize):
for j in xrange (matrixSize):
if i = = j:
result[i][j] = 0
continue
result[i][j] = MaxInt
for k in xrange (matrixSize):
result[i][j] = min (result[i][j],result[i][k] + nodeMatrix[k][j])
for i in xrange (matrixSize):
for j in xrange (matrixSize):
if result[i][j] ! = MaxInt:
print idxNode[i],idxNode[j],result[i][j]
def ShortestPathOne2All( self ,start):
pathFromStart = {}
pathFromStart[start] = [start]
todoList = [start]
while todoList:
current = todoList.pop( 0 )
for neighbor in self .Link[current]:
if neighbor not in pathFromStart:
pathFromStart[neighbor] = pathFromStart[current] + [neighbor]
todoList.append(neighbor)
return pathFromStart
def NDegreeNode( self ,start,n):
pathFromStart = {}
pathFromStart[start] = [start]
pathLenFromStart = {}
pathLenFromStart[start] = 0
todoList = [start]
while todoList:
current = todoList.pop( 0 )
for neighbor in self .Link[current]:
if neighbor not in pathFromStart:
pathFromStart[neighbor] = pathFromStart[current] + [neighbor]
pathLenFromStart[neighbor] = pathLenFromStart[current] + 1
if pathLenFromStart[neighbor] < = n + 1 :
todoList.append(neighbor)
for node in pathFromStart.keys():
if len (pathFromStart[node]) ! = n + 1 :
del pathFromStart[node]
return pathFromStart
def Draw( self ):
G = networkx.Graph()
nodes = self .Link.keys()
edges = [(node,neighbor) for node in nodes for neighbor in self .Link[node]]
G.add_edges_from(edges)
networkx.draw(G)
pyplot.show()
if __name__ = = '__main__' :
separator = '\t'
filename = 'C:\\Users\\Administrator\\Desktop\\graphdata.txt'
resultfilename = 'C:\\Users\\Administrator\\Desktop\\result.txt'
myGraph = Graph()
myGraph.MakeLink(filename,separator)
print 'LocalClusteringCoefficient' ,myGraph.LocalClusteringCoefficient( 'a' )
print 'AverageClusteringCoefficient' ,myGraph.AverageClusteringCoefficient()
print 'DeepFirstSearch' ,myGraph.DeepFirstSearch( 'a' )
print 'BreadthFirstSearch' ,myGraph.BreadthFirstSearch( 'a' )
print 'ShortestPathOne2One' ,myGraph.ShortestPathOne2One( 'a' , 'd' )
print 'ShortestPathOne2All' ,myGraph.ShortestPathOne2All( 'a' )
print 'NDegreeNode' ,myGraph.NDegreeNode( 'a' , 3 ).keys()
print 'ListAllComponent' ,myGraph.ListAllComponent()
print 'CheckConnection' ,myGraph.CheckConnection( 'a' , 'f' )
print 'Centrality' ,myGraph.Centrality( 'c' )
myGraph.MinimumSpanningTree_Kruskal( 'a' )
myGraph.AllpairsShortestPaths_MatrixMultiplication( 'a' )
myGraph.MinimumSpanningTree_Prim( 'a' )
myGraph.SingleSourceShortestPath_Dijkstra( 'a' )
# myGraph.Draw()
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希望本文所述对大家Python程序设计有所帮助。