def create_graph_kronecker(objects,
properties): #uses alpha-beta method to build kron
initEdges = properties['init_edges']
alpha = properties['alpha']
beta = properties['beta']
k = properties['kron_interations']
tryAgain = True
init = InitMatrix(properties['kron_seed_nodes'])
init.make()
init.addSelfEdges()
for i in range(len(initEdges)):
pos = initEdges[i]
index1 = pos[0]
index2 = pos[1]
if (index1 > properties['kron_seed_nodes']
or index2 > properties['kron_seed_nodes']):
raise IOError("init_edges are out of range of seed matrix size")
else:
init.addEdge(index1, index2)
if (properties['kron_seed_self_loops_stochastic'] == False):
init.makeStochasticAB(alpha, beta, False)
else:
init.makeStochasticAB(alpha, beta)
while (
tryAgain == True
): #loop to make sure we are connected, should there be a limit to this?
final = KroneckerGenerator.generateStochasticKron(init, k, True)
is_conn = nx.is_connected(final)
if (is_conn == False):
print "Graph was not connected, trying again..."
tryAgain = True
else:
tryAgain = False
stats = create_graph_stats(final)
conn = nx.Graph()
for (i, j) in final.edges():
inode = objects[i]
jnode = objects[j]
conn.add_edge(inode, jnode)
return conn, stats
def create_graph_kronecker(objects, properties): # uses alpha-beta method to build kron
initEdges = properties["init_edges"]
alpha = properties["alpha"]
beta = properties["beta"]
k = properties["kron_interations"]
tryAgain = True
init = InitMatrix(properties["kron_seed_nodes"])
init.make()
init.addSelfEdges()
for i in range(len(initEdges)):
pos = initEdges[i]
index1 = pos[0]
index2 = pos[1]
if index1 > properties["kron_seed_nodes"] or index2 > properties["kron_seed_nodes"]:
raise IOError("init_edges are out of range of seed matrix size")
else:
init.addEdge(index1, index2)
if properties["kron_seed_self_loops_stochastic"] == False:
init.makeStochasticAB(alpha, beta, False)
else:
init.makeStochasticAB(alpha, beta)
while tryAgain == True: # loop to make sure we are connected, should there be a limit to this?
final = KroneckerGenerator.generateStochasticKron(init, k, True)
is_conn = nx.is_connected(final)
if is_conn == False:
print "Graph was not connected, trying again..."
tryAgain = True
else:
tryAgain = False
stats = create_graph_stats(final)
conn = nx.Graph()
for (i, j) in final.edges():
inode = objects[i]
jnode = objects[j]
conn.add_edge(inode, jnode)
return conn, stats
stats = {'cc':cc, 'bc':bc, 'deg':deg, \
'num_cc':num_cc, 'dens':dens}#, 'largest_cc':largest_cc}
return stats #conn,
#above are methods to make input for histogram
nodes = 2
init = InitMatrix(nodes)
init.make()
#Alpha Beta Method of Testing
init.addEdge(0, 1)
init.addSelfEdges()
init.makeStochasticAB(0.4, 0.2)
#Custom Method of Testing
#p = 15
#c = 6
#probArr = np.array([1, c*p, p/c, 0, 0, c*p, 1, p/c, 0, 0, p/c, p/c, 1, p/c, p/c, 0, 0, p/c, 1, c*p, 0, 0, p/c, c*p, 1])
#init.makeStochasticCustom(probArr)
#Networkx Graph Gen as Seed, Alpha Beta after Testing
#G = nx.watts_strogatz_graph(5, 2, 0.1)
#nx.draw(G)
#plt.show() # if you want to visualize your seed graph first
#init = InitMatrix(nodes)
#init = init.makeStochasticABFromNetworkxGraph(G, 0.75, 0.5)
dens = nx.density(x)
stats = {'cc':cc, 'bc':bc, 'deg':deg, \
'num_cc':num_cc, 'dens':dens}#, 'largest_cc':largest_cc}
return stats #conn,
#above are methods to make input for histogram
nodes = 2
init = InitMatrix(nodes)
init.make()
#Alpha Beta Method of Testing
init.addEdge(0, 1)
init.addSelfEdges()
init.makeStochasticAB(0.4, 0.2)
#Custom Method of Testing
#p = 15
#c = 6
#probArr = np.array([1, c*p, p/c, 0, 0, c*p, 1, p/c, 0, 0, p/c, p/c, 1, p/c, p/c, 0, 0, p/c, 1, c*p, 0, 0, p/c, c*p, 1])
#init.makeStochasticCustom(probArr)
#Networkx Graph Gen as Seed, Alpha Beta after Testing
#G = nx.watts_strogatz_graph(5, 2, 0.1)
#nx.draw(G)
#plt.show() # if you want to visualize your seed graph first
#init = InitMatrix(nodes)
#init = init.makeStochasticABFromNetworkxGraph(G, 0.75, 0.5)