def test1():
original = nx.uniform_random_intersection_graph(n=1000, m=10, p=0.1)
ws = watts_strogatz_replicate(original, params={'k': 4})
liu_chung = expected_degree_replicate(original)
er = er_replicate(original)
rand_noise1 = random_noise_replicate(original,
params={
'epsilon': .24,
'preserve_degree': False
})
rand_noise2 = random_noise_replicate(original,
params={
'epsilon': .24,
'preserve_degree': True
})
kron = kronecker_replicate(original=nx.path_graph(10),
params={'num_iterations': 4})
import simpletesters
assert graphutils.graph_santity_test(ws)
assert graphutils.graph_santity_test(liu_chung)
assert graphutils.graph_santity_test(er)
assert graphutils.graph_santity_test(rand_noise1)
assert graphutils.graph_santity_test(rand_noise2)
assert graphutils.graph_santity_test(kron)
print('Test 1 passed!')
def intersection_graphs():
print("Intersection graphs")
print("Uniform random intersection graph")
G = nx.uniform_random_intersection_graph(n=14, m=5, p=0.07)
draw_graph(G)
print("K-random intersection graphs")
G = nx.k_random_intersection_graph(n=12, m=6, k=5)
draw_graph(G)
print("General random intersection graphs")
G = nx.general_random_intersection_graph(n=6,
m=6,
p=(0.25, 0.23, 0.8, 0.1, 0.8,
0.06))
draw_graph(G)
def test1():
original = nx.uniform_random_intersection_graph(n=1000, m=10, p=0.1)
ws = watts_strogatz_replicate(original, params={'k':4})
liu_chung = expected_degree_replicate(original)
er = er_replicate(original)
rand_noise1 = random_noise_replicate(original, params={'epsilon':.24, 'preserve_degree':False})
rand_noise2 = random_noise_replicate(original, params={'epsilon':.24, 'preserve_degree':True})
kron = kronecker_replicate(original=nx.path_graph(10), params={'num_iterations':4})
import simpletesters
assert graphutils.graph_santity_test(ws)
assert graphutils.graph_santity_test(liu_chung)
assert graphutils.graph_santity_test(er)
assert graphutils.graph_santity_test(rand_noise1)
assert graphutils.graph_santity_test(rand_noise2)
assert graphutils.graph_santity_test(kron)
print 'Test 1 passed!'
def test_random_intersection_graph(self):
G = nx.uniform_random_intersection_graph(10, 5, 0.5)
assert_equal(len(G), 10)
def test_random_intersection_graph(self):
G = nx.uniform_random_intersection_graph(10, 5, 0.5)
assert len(G) == 10
def gen_laplacian(data_num=DATA_NUM, opt=27, cache=False):
label = None
if cache:
print('Loading cached graph')
graph = pk.load(open('tmp/g.pk', 'rb'))
else:
print('Generating graph opt {}'.format(opt))
if 1 == opt:
graph = gen_rand_graph(data_num=data_num)
if 2 == opt:
top_num = random.randint(1, data_num)
bottom_num = data_num - top_num
graph = nx.bipartite.random_graph(top_num, bottom_num, 0.9)
label = [d['bipartite'] for n, d in graph.nodes(data=True)]
elif 3 == opt:
graph = nx.balanced_tree(4, 5)
elif 4 == opt:
graph = nx.complete_graph(data_num)
elif 5 == opt:
no1 = random.randint(1, data_num)
no2 = random.randint(1, int(data_num / no1))
no3 = data_num / no1 / no2
graph = nx.complete_multipartite_graph(no1, no2, no3)
elif 6 == opt:
graph = nx.circular_ladder_graph(data_num)
elif 7 == opt:
graph = nx.cycle_graph(data_num)
elif 8 == opt:
graph = nx.dorogovtsev_goltsev_mendes_graph(5)
elif 9 == opt:
top_num = int(random.random() * data_num)
bottom_num = data_num / top_num
graph = nx.grid_2d_graph(top_num, bottom_num)
elif 10 == opt:
no1 = random.randint(1, data_num)
no2 = random.randint(1, int(data_num / no1))
no3 = data_num / no1 / no2
graph = nx.grid_graph([no1, no2, no3])
elif 11 == opt:
graph = nx.hypercube_graph(10)
elif 12 == opt:
graph = nx.ladder_graph(data_num)
elif 13 == opt:
top_num = int(random.random() * data_num)
bottom_num = data_num - top_num
graph = nx.lollipop_graph(top_num, bottom_num)
elif 14 == opt:
graph = nx.path_graph(data_num)
elif 15 == opt:
graph = nx.star_graph(data_num)
elif 16 == opt:
graph = nx.wheel_graph(data_num)
elif 17 == opt:
graph = nx.margulis_gabber_galil_graph(35)
elif 18 == opt:
graph = nx.chordal_cycle_graph(data_num)
elif 19 == opt:
graph = nx.fast_gnp_random_graph(data_num, random.random())
elif 20 == opt: # jump eigen value
graph = nx.gnp_random_graph(data_num, random.random())
elif 21 == opt: # disconnected graph
graph = nx.dense_gnm_random_graph(data_num, data_num / 2)
elif 22 == opt: # disconnected graph
graph = nx.gnm_random_graph(data_num, data_num / 2)
elif 23 == opt:
graph = nx.erdos_renyi_graph(data_num, data_num / 2)
elif 24 == opt:
graph = nx.binomial_graph(data_num, data_num / 2)
elif 25 == opt:
graph = nx.newman_watts_strogatz_graph(data_num, 5,
random.random())
elif 26 == opt:
graph = nx.watts_strogatz_graph(data_num, 5, random.random())
elif 26 == opt: # smooth eigen
graph = nx.connected_watts_strogatz_graph(data_num, 5,
random.random())
elif 27 == opt: # smooth eigen
graph = nx.random_regular_graph(5, data_num)
elif 28 == opt: # smooth eigen
graph = nx.barabasi_albert_graph(data_num, 5)
elif 29 == opt: # smooth eigen
graph = nx.powerlaw_cluster_graph(data_num, 5, random.random())
elif 30 == opt: # smooth eigen
graph = nx.duplication_divergence_graph(data_num, random.random())
elif 31 == opt:
p = random.random()
q = random.random()
graph = nx.random_lobster(data_num, p, q)
elif 32 == opt:
p = random.random()
q = random.random()
k = random.random()
graph = nx.random_shell_graph([(data_num / 3, 50, p),
(data_num / 3, 40, q),
(data_num / 3, 30, k)])
elif 33 == opt: # smooth eigen
top_num = int(random.random() * data_num)
bottom_num = data_num - top_num
graph = nx.k_random_intersection_graph(top_num, bottom_num, 3)
elif 34 == opt:
graph = nx.random_geometric_graph(data_num, .1)
elif 35 == opt:
graph = nx.waxman_graph(data_num)
elif 36 == opt:
graph = nx.geographical_threshold_graph(data_num, .5)
elif 37 == opt:
top_num = int(random.random() * data_num)
bottom_num = data_num - top_num
graph = nx.uniform_random_intersection_graph(
top_num, bottom_num, .5)
elif 39 == opt:
graph = nx.navigable_small_world_graph(data_num)
elif 40 == opt:
graph = nx.random_powerlaw_tree(data_num, tries=200)
elif 41 == opt:
graph = nx.karate_club_graph()
elif 42 == opt:
graph = nx.davis_southern_women_graph()
elif 43 == opt:
graph = nx.florentine_families_graph()
elif 44 == opt:
graph = nx.complete_multipartite_graph(data_num, data_num,
data_num)
# OPT 1
# norm_lap = nx.normalized_laplacian_matrix(graph).toarray()
# OPT 2: renormalized
# pk.dump(graph, open('tmp/g.pk', 'wb'))
# plot_graph(graph, label)
# note difference: normalized laplacian and normalzation by eigenvalue
norm_lap, eigval, eigvec = normalize_lap(graph)
return graph, norm_lap, eigval, eigvec
def test_random_intersection_graph(self):
G=nx.uniform_random_intersection_graph(10,5,0.5)
assert_equal(len(G),10)
greatest_y_aristaGiant = sns.lineplot(data=dtb_giant, x="greatest_component", y="arista_number", hue="vertex_number")
greatest_y_aristaGiant.get_figure().savefig("../data/GG/greatest_y_aristaGG.png")
#Grafo extra: URIG
plist = [0] * 1000
rclist = [0] * 1000
vlist = [0] * 1000
vtimer = [0] * 1000
vertex = 0
for k in range(10):
vertex = vertex +100
for item in range(20):
vlist[k * item] = vertex
p = item / 20
dtb = nx.uniform_random_intersection_graph(vertex, 20, p)
ts = time.time()
rc = nx.number_connected_components(dtb)
plist[k * item] = p
rclist[k * item] = rc
vtimer[k * item] = time.time() -ts
dtb_cave = pd.DataFrame({"p": plist, "componentes": rclist, "vertices": vlist, "computation_time" : vtimer})
sns.pairplot(dtb_cave, hue="vertices", diag_kind='hist')
graficosGiant.savefig("../data/URIG/graficosURIG.png")
