import model
import networkx
if __name__ == '__main__':
n = 5000
D = 1
space = .1
n_data_vertices = 11
times = 1
data_vertices = []
data = [0.0]*n_data_vertices
for i in xrange(times):
G = model.make_opinion_graph(n - n_data_vertices, 0, D)
v = 0.0
for j in xrange(n_data_vertices):
G.add_node((v,))
data_vertices.append((v,))
v += space
model.construct(G, 6.5041*10**-8, 'c')
for j in xrange(n_data_vertices):
data[j] += G.degree(data_vertices[j]) / float(times)
outstring = ''
for i in xrange(n_data_vertices):
outstring += '{0}\t{1}\n'.format(space * i, data[i])
k = 4
D = 1
times = 5*10**4
stats_size = 1
alpha = 2
outstring = 'N\tMean Pair-Wise Distance\n'
print 'Working on ',
print 'opPairwise_{0}k_{1}D_{2}alpha_{3}min_{4}max_{5}times_{6}size.dat'.format(k, D, alpha, n_min, n_max, times, stats_size)
print 'N\tMean Pair-Wise Distance'
for n in xrange(n_min, n_max + n_step, n_step):
print "Starting n = %d" % n
pairwise_total = 0
for j in xrange(stats_size):
G = model.make_opinion_graph(n, .5 * n * k, D, alpha)
for i in xrange(n*times):
model.iterate(G)
giant_component = nx.connected_component_subgraphs(G)[0]
iter_pw_distance = 0 #diameter may not be diameter
for vertex_one in giant_component.nodes():
for vertex_two in giant_component.nodes():
if vertex_one == vertex_two:
break
iter_pw_distance += nx.shortest_path_length(
giant_component, vertex_one, vertex_two)
buf = giant_component.number_of_nodes()
import model
import networkx
if __name__ == '__main__':
n = 5000
D = 1
space = .1
n_data_vertices = 11
times = 1
data_vertices = []
data = [0.0] * n_data_vertices
for i in xrange(times):
G = model.make_opinion_graph(n - n_data_vertices, 0, D)
v = 0.0
for j in xrange(n_data_vertices):
G.add_node((v, ))
data_vertices.append((v, ))
v += space
model.construct(G, 6.5041 * 10**-8, 'c')
for j in xrange(n_data_vertices):
data[j] += G.degree(data_vertices[j]) / float(times)
outstring = ''
for i in xrange(n_data_vertices):
outstring += '{0}\t{1}\n'.format(space * i, data[i])
print "storing degree informations"
obs_degrees = {}
for x in G.nodes_iter():
if G.degree(x) in obs_degrees:
obs_degrees[G.degree(x)] += 1
else:
obs_degrees[G.degree(x)] = 1
outstring = "degree\tfrequency\n"
for key in obs_degrees.iterkeys():
outstring += "%s\t%d\n" % (key, obs_degrees[key])
# save data
print "saving data"
outfile = open("observed_degree_{0}n_{1}k_{2}D.dat".format(n, d_mean, D), "w")
outfile.write(outstring)
outfile.close()
if __name__ == "__main__":
n = 2000
d_mean = 4.0
D = 1
times = 10 ** 6
alpha = 2
G = model.make_opinion_graph(n, 0.5 * d_mean * n, D, alpha)
write_expected_deg(G, n, d_mean, D, times)
n_step = 100
n_max = 1200
k = 4
D = 1
times = 1*10**3
stats_size = 50
alpha = 2
outstring = 'N\tDiameter\n'
print 'N\tDiameter'
for n in xrange(n_min, n_max + n_step, n_step):
print "Starting n = %d" % n
diam_total = 0
for j in xrange(stats_size):
G = model.make_opinion_graph(n, .5 * n * k, D, alpha)
for i in xrange(n*times):
model.iterate(G)
subgraphs = nx.connected_component_subgraphs(G)
max_diameter = 0 #diameter may not be diameter
for SG in subgraphs: #of largest connected subgraph
if SG.number_of_nodes() < 2:
break
diameter = nx.diameter(SG)
if diameter > max_diameter:
max_diameter = diameter
diam_total += max_diameter
import model
if __name__ == '__main__':
n = 200
k_mean = 4
D = 1
times = 10**7
period = 10**5
G = model.make_opinion_graph(n, .5 * n * k_mean, D)
outstring = ''
for i in xrange(times):
model.iterate(G)
if i % period == 0:
s = '{0}\t{1}\n'.format(i, model.mean_distance(G))
outstring += s
if i % (10*period) == 0:
print s
outfile = open('mixing.dat', 'w')
outfile.write(outstring)
import model
if __name__ == '__main__':
n = 200
k_mean = 4
D = 1
times = 10**7
period = 10**5
G = model.make_opinion_graph(n, .5 * n * k_mean, D)
outstring = ''
for i in xrange(times):
model.iterate(G)
if i % period == 0:
s = '{0}\t{1}\n'.format(i, model.mean_distance(G))
outstring += s
if i % (10 * period) == 0:
print s
outfile = open('mixing.dat', 'w')
outfile.write(outstring)
else:
G.remove_edges_from(G.edges())
model.add_random_edges(G, G.m)
#There are two Methods to add edges:
# 1. By using nx.gnm_random (Must iterate for graph to reach steady state)
# 2. By using model.construct (Theorem 2 - construct a graph at the steady state)
#This is a test of Theorem 2, so Method 1 is used
if __name__ == '__main__':
n = 50
d_mean = 2.0
D = 3
iterations = 10**4
times = 100
sample_rate = 100
throw_away = 10**3
iterations_for_getting_C = 100
#Method 1
G = model.make_opinion_graph(n, (d_mean * n) / 2, D)
#Method 2
#G = model.make_graph(n, 0, D)
#model.construct(G, d_mean)
#required for expected calculation
G.c = model.getC(G, iterations_for_getting_C)
#G.c = 0.0001778
write_edge_existence(G, iterations, times, n, d_mean, D, sample_rate, throw_away)
model.add_random_edges(G, G.m)
#There are two Methods to add edges:
# 1. By using nx.gnm_random (Must iterate for graph to reach steady state)
# 2. By using model.construct (Theorem 2 - construct a graph at the steady state)
#This is a test of Theorem 2, so Method 1 is used
if __name__ == '__main__':
n = 50
d_mean = 2.0
D = 3
iterations = 10**4
times = 100
sample_rate = 100
throw_away = 10**3
iterations_for_getting_C = 100
#Method 1
G = model.make_opinion_graph(n, (d_mean * n) / 2, D)
#Method 2
#G = model.make_graph(n, 0, D)
#model.construct(G, d_mean)
#required for expected calculation
G.c = model.getC(G, iterations_for_getting_C)
#G.c = 0.0001778
write_edge_existence(G, iterations, times, n, d_mean, D, sample_rate,
throw_away)
print "storing degree informations"
obs_degrees = {}
for x in G.nodes_iter():
if G.degree(x) in obs_degrees:
obs_degrees[G.degree(x)] += 1
else:
obs_degrees[G.degree(x)] = 1
outstring = "degree\tfrequency\n"
for key in obs_degrees.iterkeys():
outstring += "%s\t%d\n" % (key, obs_degrees[key])
#save data
print "saving data"
outfile = open('observed_degree_{0}n_{1}k_{2}D.dat'.format(n, d_mean, D),
'w')
outfile.write(outstring)
outfile.close()
if __name__ == '__main__':
n = 2000
d_mean = 4.0
D = 1
times = 10**6
alpha = 2
G = model.make_opinion_graph(n, 0.5 * d_mean * n, D, alpha)
write_expected_deg(G, n, d_mean, D, times)
