import graph as G import randomWalk import metric as m filename = "../data/10000x320000.data" nodes = 2000 graph = G.readGraph(filename) t = randomWalk.RandomWalk(graph,nodes=nodes) w=t.getGraph() print "degreeDist ",m.degreeDist(w) print "wcc ", m.wccDist(w)
import graph as G
import random
def randomNodeNeighbor(graph, nodes=1000):
nodes = min(nodes, len(G.getEdges(graph)))
#dataNodes = G.getNodes(getEdges)
sampleGraph = {}
sampleNodes = {}
while True:
if len(G.getNodes(sampleGraph)) >= nodes:
break
randomNode = random.choice(graph.keys())
sampleGraph[randomNode] = graph[randomNode]
return sampleGraph
if __name__ == "__main__":
print "fetching data"
data = G.readGraph("../data/5000x25000.data")
print "running randomNodeNeighbor"
sample = randomNodeNeighbor(data, nodes=2000)
print "vars: data, sample "
import json
path = "../data/smallGraph.data"
#path = "../data/smallGraph.data"
numNodes = 1000
fid = open("../out-final/inDegreeDist-original-graph.out", "w")
fod = open("../out-final/outDegreeDist-original-graph.out", "w")
fwcc = open("../out-final/wccDist-original-graph.out", "w")
fscc = open("../out-final/sccDist-original-graph.out", "w")
fhop = open("../out-final/hopDist-original-graph.out", "w")
fclust = open("../out-final/clustDist-original-graph.out", "w")
print "Reading graph..."
graph = G.readGraph(path)
# print "Generating samples..."
# print "\t* using random edge"
# sampleRE = RE.randomEdge(graph, nodes=numNodes)
# print "\t* using random node"
# sampleRN = RN.sampleRN(graph, nodes=numNodes)
# print "\t* using random node neighbour"
# sampleRNN = RNN.randomNodeNeighbor(graph, nodes=numNodes)
# print "\t* using random walk"
# rw = RW.RandomWalk(graph, nodes=numNodes)
# sampleRW = rw.getGraph()
def getAlgo(num):
if num == 1:
return "random edge"
elif num == 2:
from graph import Graph, readGraph
from console import Console
'''
Created on 18 Mar 2019
@author: Marius
'''
#g = readGraph("graph_file2.txt")
#g = readGraph("graph1k.txt")
g = readGraph("graph10k.txt")
c = Console(g)
c.run()
path.append(d)
return getOptimumPath(s, finalVD[d][1], path)
def dijkstrasShortestPathAlgo():
source, destination = raw_input(
"\nEnter source and destination (source-destination): ").strip().split(
'-')
verticesDistances = {}
for v in graph.graphAdjDict.keys():
verticesDistances[v] = 99999999
verticesDistances[source] = 0
markedVertices.append(source)
finalVD[source] = [0, None]
verticesDistances.pop(source)
findMinDistancesFromSourceToAllVertices(
source, verticesDistances
) #Calculating minimum distances for all vertices from
#Given source using dijkstras algorithm
path = getOptimumPath(source, destination, [])
path.reverse()
print "Optimum path for", source, "to", destination, "is:", path, "with distance:", finalVD[
destination][0]
graph.readGraph()
dijkstrasShortestPathAlgo()
from graph import MatrGraph, readGraph
from console import Console
'''
Created on 28 May 2019
@author: Marius
'''
g = readGraph("graphHam1.txt")
#g = readGraph("graph1k.txt")
c = Console(g)
c.run()