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TestGraph.py
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TestGraph.py
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import misc
from Graph import Vertex
from Graph import Edge
from Graph import Graph
from Downey.GraphWorld import *
from RandomGraph import RandomGraph
from SmallWorldGraph import SmallWorldGraph
from matplotlib import pyplot as plt
import numpy as np
import string
def testrandomcomplete(rn = 15):
for n in range(2, rn):
for p in range(10):
prob = p / 10.0
labels = string.ascii_lowercase + string.ascii_uppercase
vs = [Vertex(c) for c in labels[:n]]
g = RandomGraph(vs)
g.add_random_edges(prob)
print "%s, %s, %s" % (n, prob, g.is_connected())
### chap 4, ex 04.3 BEGIN ###
def test_clustercoefficient():
ps = np.arange(0, 1, 0.01)
n = 1000
labels = string.ascii_lowercase + string.ascii_uppercase
vs = []
iter = misc.gen_identifier()
for i in range(n):
vs.append(Vertex(iter.next()))
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(10)
c0 = g.get_clustering_coefficient()
xs = []
ys = []
for p in ps:
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(10)
g.rewire(p)
ys.append(g.get_clustering_coefficient() / c0)
xs.append(p)
fig = plt.figure(dpi = 100)
plt.subplot(1,1,1)
plt.plot(xs, ys)
plt.xscale('log')
plt.show()
### chap 4, ex 04.3 END ###
### chap 4, ex 02.2 BEGIN ###
def test_clustercoefficient():
ps = np.arange(0, 1, 0.01)
n = 1000
e = 10
labels = string.ascii_lowercase + string.ascii_uppercase
vs = []
iter = misc.gen_identifier()
for i in range(n):
vs.append(Vertex(iter.next()))
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
c0 = g.get_clustering_coefficient()
xs = []
ys = []
for p in ps:
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
g.rewire(p)
ys.append(g.get_clustering_coefficient() / c0)
xs.append(p)
fig = plt.figure(dpi = 100)
plt.subplot(1,1,1)
plt.plot(xs, ys)
plt.xscale('log')
plt.show()
### chap 4, ex 04.3 END ###
### chap 4, ex 05.2 BEGIN ###
def test_average_path_len():
ps = np.arange(0, 1, 0.05)
print ps
n = 1000
e = 10
labels = string.ascii_lowercase + string.ascii_uppercase
vs = []
iter = misc.gen_identifier()
for i in range(n):
vs.append(Vertex(iter.next()))
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
l0 = g.get_averaged_shortest_path()
layout = CircleLayout(g)
xs = []
ys = []
for p in ps:
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
g.rewire(p)
l1 = g.get_averaged_shortest_path() / l0
ys.append(l1)
xs.append(p)
print l1, p
fig = plt.figure(dpi = 100)
plt.subplot(1,1,1)
plt.plot(xs, ys)
plt.xscale('log')
plt.show()
### chap 4, ex 05.2 END ###
def main(script, n='10', *args):
# create n Vertices
n = int(n)
labels = string.ascii_lowercase + string.ascii_uppercase
vs = []
iter = misc.gen_identifier()
for i in range(n):
vs.append(Vertex(iter.next()))
vs = [Vertex(c) for c in labels[:n]]
#test_average_path_len()
# create a graph and a layout
#g = Graph(vs)
#g = RandomGraph(vs)
#g.is_connected
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(4)
print g.all_pairs_shortest_path()
#v = vs[0]
#print v
#print g.shortest_path(v)
#w = vs[3]
#print w
#print g.shortest_path(v, w)
#print g.get_max_neighbors()
#print g.get_clustering_coefficient()
#p = 0.8
#g.rewire(p)
#print g.get_clustering_coefficient()
#test_clustercoefficient()
#g.add_random_edges(1.0)
#print g.is_connected()
# draw the graph
layout = CircleLayout(g)
gw = GraphWorld()
gw.show_graph(g, layout)
gw.mainloop()
if __name__ == '__main__':
import sys
main(*sys.argv)
#testrandomcomplete()