def generate_random_uniform(N, T, G_method, seed=1234):
rng = np.random.RandomState(seed)
N_ = 4 if N >= 5 else N - 1
switcher = {
'complete': nx.complete_graph(N),
'path': nx.path_graph(N),
'cycle': nx.cycle_graph(N),
'regular': nx.random_regular_graph(N_, N, seed=rng),
'wheel': nx.wheel_graph(N),
'tree': nx.random_tree(N, seed=rng),
'chordal': nx.Graph(nx.chordal_cycle_graph(N)),
}
G = switcher.get(G_method)
player_list = []
for n in range(N):
p = Player(x=rng.uniform(3, -3, T),
sm=13.5,
s0=0,
ram=5,
ec=0.9,
ed=0.9)
player_list.append(p)
buying_price = np.ones(T) * 3.0
selling_price = np.ones(T) * 1.0
game = Game(player_list, buying_price, selling_price, G)
game.graphtype = G_method
game.seed = seed
return game
def expanders_graphs():
print("Margulis - Gabber - Galil Graph")
MGGG = nx.margulis_gabber_galil_graph(n=3)
draw_graph(MGGG)
print("Chordal - Cycle Graph")
MGGG = nx.chordal_cycle_graph(8)
draw_graph(MGGG)
def make_graph(g_name, n):
switcher = {
"path": nx.path_graph(n),
"complete": nx.complete_graph(n),
"binomial tree": nx.binomial_tree(n),
"circular ladder": nx.circular_ladder_graph(n),
"cycle": nx.cycle_graph(n),
"dorogovtsev": nx.dorogovtsev_goltsev_mendes_graph(n),
"ladder": nx.ladder_graph(n),
"star": nx.star_graph(n),
"wheel": nx.wheel_graph(n),
"margulis gabber galil": nx.margulis_gabber_galil_graph(n),
"chordal cycle": nx.chordal_cycle_graph(n),
"hypercube": nx.hypercube_graph(n),
"mycielski": nx.mycielski_graph(n)
}
return switcher.get(g_name)
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
import pytest
import os
from allegedb import ORM
import networkx as nx
scalefreestart = nx.MultiDiGraph(name='scale_free_graph_5')
scalefreestart.add_edges_from([(0, 1), (1, 2), (2, 0)])
testgraphs = [
nx.chvatal_graph(),
nx.scale_free_graph(5, create_using=scalefreestart),
nx.chordal_cycle_graph(
5, create_using=nx.MultiGraph(name='chordal_cycle_graph_5')),
]
# have to name it after creation because it clears the create_using
path_graph_9 = nx.path_graph(9)
path_graph_9.name = 'path_graph_9'
testgraphs.append(path_graph_9)
@pytest.fixture
def db():
name = 'allegedb_load_test.db'
if os.path.exists(name):
os.remove(name)
with ORM('sqlite:///' + name) as orm:
for graph in testgraphs:
{
nx.Graph: orm.new_graph,
nx.DiGraph: orm.new_digraph,
nx.MultiGraph: orm.new_multigraph,
n = 4
#def graph_caller(self, graph_name):
switcher = {
"path": nx.path_graph(n),
"complete": nx.complete_graph(n),
"binomial tree": nx.binomial_tree(n),
"circular ladder": nx.circular_ladder_graph(n),
"cycle": nx.cycle_graph(n),
"dorogovtsev": nx.dorogovtsev_goltsev_mendes_graph(n),
"ladder": nx.ladder_graph(n),
"star": nx.star_graph(n),
"wheel": nx.wheel_graph(n),
"margulis gabber galil": nx.margulis_gabber_galil_graph(n),
"chordal cycle": nx.chordal_cycle_graph(n),
"hypercube": nx.hypercube_graph(n),
"mycielski": nx.mycielski_graph(n)
}
# return switcher.get(graph_name,"Invalid choice")
graph_name = input("Enter a graph name to generate\n")
print(graph_name)
#G = graph_caller(graph_name)
G = switcher.get(graph_name)
for k in range(3,11):
for x in range(3,11):
#greedy_coloring = nx.coloring.greedy_color(G, strategy='largest_first')
