nodes = len(adj_mat)

dist = [INF for i in range(nodes)]

visit = [0 for x in xrange(nodes)]

dist[source] = 0

while True:

min_val = INF

current = -1

for i in range(nodes):

if visit[i] == 0:

if dist[i] < min_val:

min_val = dist[i]

current = i

if current == -1: #All nodes have been visited

return dist

visit[current] = 1

for n in range(nodes):

if visit[n] == 0 and adj_mat[current][n] == 1:

temp = dist[current] + 1

if temp < dist[n]:

n = 5 #Number of nodes

e = 7 #Number of edges

A = [[0 for x in xrange(n)] for x in xrange(n)] #Adjacency Matrix

i = 1 #iterator

while i <= e:

a = random.randint(0,n-1)

b = random.randint(0,n-1)

if a!=b and A[a][b]!=1:

A[a][b] = 1

A[b][a] = 1

else:

i=i-1

i+=1

print A, "\n"

total_cost = []

for i in xrange(len(A)):

start = i

costs = dijkstra(A, start)

print i, ":", costs,

total_cost.append(sum(costs))

print "\nTotal cost:", total_cost, "\n"

characterstic_pathlength = sum(total_cost)/float(n)

print "Characterstic pathlength:", characterstic_pathlength, "\n"

#Clustering Coefficient of the graph:

clustering_coefficient=clustering(A)

print "Clustering coefficients are:", clustering_coefficient, "\n"

#Average clustering coefficient of the graph

aver_clustering=sum(clustering_coefficient)/n

print "Average clustering coefficient of the network:", aver_clustering, "\n"