def pytest_generate_tests(metafunc):
"""
Generate the arguments for test funcs
"""
if "testgraph" in metafunc.funcargnames:
testgraphs = []
# 100 vertex random undirected graph
a = nx.gnp_random_graph(100, 0.1)
for u, v in a.edges_iter():
a.add_weighted_edges_from([(u, v, uniform(0, 100))])
edge_list = []
for u, v, w in a.edges_iter(data = True):
edge_list.append((u, v, w['weight']))
e = iter(edge_list)
deg = sg.wgraph.make_deg(a.order(), e)
e = iter(edge_list)
b = sg.wgraph.make(a.order(), a.size(), e, deg)
# 100 vertex random directed graph
c = nx.gnp_random_graph(100, 0.1, directed = True)
for u, v in c.edges_iter():
c.add_weighted_edges_from([(u, v, uniform(0, 100))])
edge_list = []
for u, v, w in c.edges_iter(data = True):
edge_list.append((u, v, w['weight']))
e = iter(edge_list)
deg = sg.wdigraph.make_deg(c.order(), e)
e = iter(edge_list)
d = sg.wdigraph.make(c.order(), c.size(), e, deg)
testgraphs.append((a, b, c, d))
metafunc.parametrize("testgraph", testgraphs)
def generateGraph(self, max_available_capacity):
"""
"""
OSPF_weights = [2, 4]
# Generate random nodes and edges
graph_tmp = nx.gnp_random_graph(self.n, self.p)
while not nx.is_connected(graph_tmp):
graph_tmp = nx.gnp_random_graph(self.n, self.p)
# Turn it into directed graph
graph = graph_tmp.to_directed()
# Generate random OSPF weights and capacities
taken = []
for (x,y) in graph.edges_iter():
# Generate capacities first
capacity = random.randint(5, max_available_capacity)
graph[x][y]['capacity'] = capacity
# Generate OSPF weights
if (x,y) not in taken:
# Generate random weight
index1 = random.randint(0, 1)
index2 = random.randint(0, 1)
graph[x][y]['weight'] = OSPF_weights[index1]
graph[y][x]['weight'] = OSPF_weights[index2]
taken.append((x,y))
taken.append((y,x))
return graph
def pytest_generate_tests(metafunc):
"""
Generate the arguments for test functions.
"""
if "graph" in metafunc.funcargnames:
wgraphs = []
# 100 vertex random graph
a = nx.gnp_random_graph(100, 0.1)
for e in a.edges_iter(data = True):
e[2]['weight'] = triangular(-2, 2, 0)
deg = sg.wgraph.make_deg(a.order(), create_iter(a.edges_iter(data = True)))
b = sg.wgraph.make(a.order(), a.size(), create_iter(a.edges_iter(data = True)), deg)
wgraphs.append((a, b))
# 100 vertex random graph with parallel edges
a = nx.gnp_random_graph(100, 0.1)
for e in a.edges_iter(data = True):
e[2]['weight'] = triangular(-2, 2, 0)
deg = sg.wgraph.make_deg(a.order(), chain(create_iter(a.edges_iter(data = True)), create_iter(a.edges_iter(data = True))))
b = sg.wgraph.make(a.order(), 2 * a.size(), chain(create_iter(a.edges_iter(data = True)), create_iter(a.edges_iter(data = True))), deg)
wgraphs.append((a, b))
# 100 vertex random graph with overestimated edge count
a = nx.gnp_random_graph(100, 0.1)
for e in a.edges_iter(data = True):
e[2]['weight'] = triangular(-2, 2, 0)
deg = sg.wgraph.make_deg(a.order(), create_iter(a.edges_iter(data = True)))
b = sg.wgraph.make(a.order(), 2 * a.size(), create_iter(a.edges_iter(data = True)), deg)
wgraphs.append((a, b))
metafunc.parametrize("graph", wgraphs)
def vnet_gen(snet, num_standby, fixed_node):
"""
Generate virtual topology
Parameters:
snet: the substrate network topology
num_standby: the number of standby routers requested by a customer
"""
node_list = snet.nodes()
# minimum number of vnodes in a virtual network has to be three, but do
# not need to use all of the virtual nodes excluding the reserved standby
# virtual routers.
# In order to make sure every VN has a VR from a common substrate node, first remove the fixed node from the list
rest_list = copy.deepcopy(node_list)
rest_list.remove(fixed_node)
vnode_in_topo = random.randint(3, len(rest_list) - num_standby)
#vnode_in_topo = random.randint(5, len(rest_list) - num_standby)
#vnode_in_topo = 100
vnet_nodes = random.sample(set(rest_list), vnode_in_topo)
vnet_nodes.append(fixed_node)
rand_vnet = nx.gnp_random_graph(len(vnet_nodes), 0.3)
# Avoid to create VNs with isolatd VRs
check = check_degree(rand_vnet)
while(check != 1):
rand_vnet = nx.gnp_random_graph(len(vnet_nodes), 0.3)
check = check_degree(rand_vnet)
# relabel the nodes in the VN
vnet = conf_vnet(rand_vnet, vnet_nodes)
# remove selfloop edges
vnet.remove_edges_from(vnet.selfloop_edges())
return vnet
def pytest_generate_tests(metafunc):
"""
Generate the arguments for test functions.
"""
if "digraph" in metafunc.funcargnames:
digraphs = []
# 100 vertex random graph
a = nx.gnp_random_graph(100, 0.1, directed=True)
deg = sg.digraph.make_deg(a.order(), a.edges_iter())
b = sg.digraph.make(a.order(), a.size(), a.edges_iter(), deg)
digraphs.append((a, b))
# 100 vertex random graph with parallel edges
a = nx.gnp_random_graph(100, 0.1, directed=True)
deg = sg.digraph.make_deg(a.order(), a.edges() + a.edges())
b = sg.digraph.make(a.order(), 2 * a.size(), a.edges() + a.edges(), deg)
digraphs.append((a, b))
# 100 vertex random graph with overestimated edge count
a = nx.gnp_random_graph(100, 0.1, directed=True)
deg = sg.digraph.make_deg(a.order(), a.edges_iter())
b = sg.digraph.make(a.order(), 2 * a.size(), a.edges_iter(), deg)
digraphs.append((a, b))
metafunc.parametrize("digraph", digraphs)
def pytest_generate_tests(metafunc):
"""
Generate the arguments for test funcs
"""
if "testgraph" in metafunc.funcargnames:
testgraphs = []
# 100 vertex random graph
a = nx.gnp_random_graph(100, 0.1, directed=True)
b = nx.gnp_random_graph(100, 0.1, directed=True)
deg = sg.digraph.make_deg(a.order(), a.edges_iter())
c = sg.digraph.make(a.order(), a.size(), a.edges_iter(), deg)
deg = sg.digraph.make_deg(b.order(), b.edges_iter())
d = sg.digraph.make(b.order(), b.size(), b.edges_iter(), deg)
testgraphs.append((a, b, c, d))
metafunc.parametrize("testgraph", testgraphs)
if "mismatch_graph" in metafunc.funcargnames:
mismatch_graphs = []
a = nx.gnp_random_graph(100, 0.1, directed=True)
b = nx.gnp_random_graph(150, 0.1, directed=True)
deg = sg.digraph.make_deg(a.order(), a.edges_iter())
c = sg.digraph.make(a.order(), a.size(), a.edges_iter(), deg)
deg = sg.digraph.make_deg(b.order(), b.edges_iter())
d = sg.digraph.make(b.order(), b.size(), b.edges_iter(), deg)
mismatch_graphs.append((c, d))
metafunc.parametrize("mismatch_graph", mismatch_graphs)
def generate_random_graph():
if probability_of_graph_construction==0:
k = rand()
else:
k = probability_of_graph_construction
G=NX.gnp_random_graph(number_of_vertices, k)
while not(NX.is_connected(G)):
G=NX.gnp_random_graph(number_of_vertices, k)
k += 0.1
return G
def setUp(self):
self.path = nx.path_graph(7)
self.directed_path = nx.path_graph(7, create_using=nx.DiGraph())
self.cycle = nx.cycle_graph(7)
self.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
self.gnp = nx.gnp_random_graph(30, 0.1)
self.directed_gnp = nx.gnp_random_graph(30, 0.1, directed=True)
self.K20 = nx.complete_graph(20)
self.K10 = nx.complete_graph(10)
self.K5 = nx.complete_graph(5)
self.G_list = [self.path, self.directed_path, self.cycle,
self.directed_cycle, self.gnp, self.directed_gnp, self.K10,
self.K5, self.K20]
def pagerank_vs_hitting_time(num_graphs):
graphs = []
prs = []
hts = []
corrs = []
for i in xrange(num_graphs):
g = nx.gnp_random_graph(NUM_NODES, float(NUM_EDGES) / NUM_NODES,
directed=True)
graphs.append(g)
for e in g.edges_iter():
g[e[0]][e[1]]['weight'] = random.random()
prs.append(tm.pagerank(g))
hts.append(tm.hitting_pagerank(g, 'all'))
corrs.append(stats.spearmanr(prs[i], hts[i])[0])
sys.stdout.write('.')
# Plot correlations
plt.hist(corrs)
plt.suptitle('Correlation of PageRank and Eigen Hitting Time\n'
'Trials on Erdos-Renyi graphs with %d nodes and prob %0.2f'
% (NUM_NODES, float(NUM_EDGES) / NUM_NODES))
plt.xlabel('Spearman rank-correlation')
plt.ylabel('Number of graphs (independent trials)')
plt.margins(0.07)
plt.show()
def erdoes_3d_plot(number_of_average): N = np.arange(5, 105, 5) X = N P = arange(0, 0.2, 0.005) Y = P X, Y = np.meshgrid(X, Y) Z = X/2 for i in range(len(N)): n = N[i] print n p = 0 for j in range(len(P)): if n > 50 and p > 0.1: Z[j][i] = 100 continue average = 0 for av in range(number_of_average): G = nx.gnp_random_graph(n, p, None, 1) graph = Graph(G) graph.stats() average = average + graph.bow_tie[1] average = average / number_of_average Z[j][i] = average p = p + 0.01 plot_3d(X, Y, Z, '3d_erdoes.png', 'nodes', 'probability of edge creation', 'scc [%]')
def random_weighted_graph(num_nodes, edge_prob, weight_dist='uniform'):
""" Returns a Erdos-Renyi graph with edge weights. """
g = nx.gnp_random_graph(num_nodes, edge_prob, directed=True)
weights = agent_type_rv(weight_dist).rvs(size=g.number_of_edges())
for i, e in enumerate(g.edges_iter()):
g[e[0]][e[1]]['weight'] = weights[i]
return g
def create_graph(N_nodes,p_edge,n_infected,regular = False):
if regular:
k = int(round(p_edge*(N_nodes-1)))
G = nx.random_regular_graph(k,N_nodes)
else:
G = nx.gnp_random_graph(N_nodes,p_edge,directed=False)
return set_graph_strategies(G, n_infected)
def gnp_survey_benchmark(path, n_array, p_array):
with open(path, 'w', newline='\n') as f:
w = csv.writer(f)
w.writerow(['n', 'p = 0.2', 'p = 0.4', 'p = 0.5', 'p = 0.6', 'p = 0.8'])
for n in n_array:
list = [str(n)]
for p in p_array:
print("Starting trial n = " + str(n) + ", p = " + str(p) + ".")
G = nx.gnp_random_graph(n, p)
for (i, j) in G.edges_iter():
G[i][j]['weight'] = 1
print("Calling GH algorithm")
############ TIMING ############
start = time.time()
gh = gomory_hu_tree(G)
end = time.time()
t = end-start
########## END TIMING ##########
list.append(t)
print("Completed trial n = " + str(n) + ", p = " + str(p) + ".")
print("Time = " + str(t))
nx.write_adjlist(G, 'Graph(' + str(n) + "," + str(p) + ").csv")
nx.write_adjlist(gh, 'Gomory_Hu(' + str(n) + "," + str(p) + ").csv")
print()
w.writerow(list)
def test_eigenvector_v_katz_random(self):
G = nx.gnp_random_graph(10,0.5, seed=1234)
l = float(max(eigvals(nx.adjacency_matrix(G).todense())))
e = nx.eigenvector_centrality_numpy(G)
k = nx.katz_centrality_numpy(G, 1.0/l)
for n in G:
assert_almost_equal(e[n], k[n])
def test_random_gnp():
# seeds = [1550709854, 1309423156, 4208992358, 2785630813, 1915069929]
seeds = [12, 13]
for seed in seeds:
G = nx.gnp_random_graph(20, 0.2, seed=seed)
_check_edge_connectivity(G)
def generate_systems(prop_step=5, o_size=20):
""" Generate population of systems
"""
para_range = np.linspace(0, 1, prop_step)
o_range = np.random.uniform(0, 3, o_size)
systs = []
for p in para_range:
systs.append([])
# setup network
graph = nx.gnp_random_graph(20, p)
dim = len(graph.nodes())
Bvec = np.random.uniform(0, 5, size=dim)
for omega in o_range:
# setup dynamical system
system_config = get_base_config(
nx.to_numpy_matrix(graph), Bvec, omega)
systs[-1].append(DW({
'graph': graph,
'system_config': system_config
}))
return systs, para_range
def main():
args = parse_arguments()
# Generate graph for given parameters
if args.cycle:
graph = networkx.cycle_graph(args.vertices)
graph = networkx.path_graph(args.vertices)
elif args.star:
graph = networkx.star_graph(args.vertices)
elif args.wheel:
graph = networkx.wheel_graph(args.vertices)
elif args.ladder:
graph = networkx.ladder_graph(args.vertices)
elif args.fill_rate:
if args.fill_rate > 50.0:
graph = networkx.gnp_random_graph(args.vertices, args.fill_rate / 100.0, None, args.directed)
else:
graph = networkx.fast_gnp_random_graph(args.vertices, args.fill_rate / 100.0, None, args.directed)
else:
graph = networkx.gnm_random_graph(args.vertices, args.edges, None, args.directed)
# Print generated graph
print("{} {}".format(graph.number_of_nodes(), graph.number_of_edges()))
for edge in graph.edges():
print("{} {}".format(edge[0] + 1, edge[1] + 1))
# Export generated graph to .png
if args.image_path:
export_to_png(graph, args.image_path)
def connect_gnp(self,N,p):
"""
Erdos-Renyi (Poisson random) graph G(N,p).
"""
# this is kind of a dumb way to do this
self.connect_empty(N)
self.add_edges_from(nx.gnp_random_graph(N,p).edges())
def barabasi_gen(start_size, total_size, m):
"""
Generate a graph according to the process outlined in Barabasi
and Albert, where vertices are added one at a time, and attach to
existing vertices with probability:
p(existing) = deg(existing) / sum(deg(all_vertices))
"""
if m > start_size:
print("m must be >= than the initial number of vertices")
return None
# Start with an Erdos-Renyi random graph?
g = networkx.gnp_random_graph(start_size, p=(m / start_size))
# Need to number each node, so start with:
new_node = start_size + 1
while len(g) < total_size:
# Create the list of other nodes before adding the new node,
# easiest way to do it
others = g.nodes()
# Calculate the probabilities every time, or find a cleverer
# way of updating the values?
total_degree = sum(deg for node, deg in g.degree_iter())
g.add_node(new_node)
while g.degree(new_node) < m:
node_to_check = random.choice(others)
prob = g.degree(node_to_check) / total_degree
dice_roll = random.random()
if dice_roll < prob:
g.add_edge(new_node, node_to_check)
new_node += 1
return g
def test_models_yaml():
'''Creates a random models, saves them in yaml format, loads them from file,
and compares the generated and loaded system models.
'''
for M, init_factory in ((Model, set), (Ts, set),
(Markov, lambda l: dict([(x, 1) for x in l]))):
# generate random model
model = M('Random system model', directed=True, multi=False)
model.g = nx.gnp_random_graph(n=100, p=0.5, seed=1, directed=True)
model.init = init_factory(random.sample(model.g.nodes(), 5))
model.final = set(random.sample(model.g.nodes(), 5))
# save system model to temporary yaml file
f = tempfile.NamedTemporaryFile(mode='w+t', suffix='.yaml',
delete=False)
f.close()
print('Saving', M.__name__, 'system model to', f.name)
model.save(f.name)
# load system model from temporary yaml file
print('Loading', M.__name__, 'system model from', f.name)
model2 = M.load(f.name)
# test that the two system models are equal
assert(model == model2), '{} are not equal!'.format(M.__name__)
# remove temporary file
os.remove(f.name)
def graphFactoryPlanarErdosRenyiGenration(nb_graph,size_graph,graphtype,edgeProba,section='all'):
cpt=0
while cpt <= nb_graph:
G = nx.gnp_random_graph(size_graph,edgeProba)
if graphToCSV(G,graphtype,section,pl.is_planar(G)):
cpt+=1
if cpt%10 == 0:
print(str(cpt)+'/'+str(nb_graph)+' '+str(100*cpt/nb_graph)+'%')
def test_gnp_augmentation():
rng = random.Random(0)
G = nx.gnp_random_graph(30, 0.005, seed=0)
# Randomly make edges available
avail = {(u, v): 1 + rng.random()
for u, v in complement_edges(G)
if rng.random() < .25}
_check_augmentations(G, avail)
def obtain_graph(args):
"""Build a Graph according to command line arguments
Arguments:
- `args`: command line options
"""
if hasattr(args,'gnd') and args.gnd:
n,d = args.gnd
if (n*d)%2 == 1:
raise ValueError("n * d must be even")
G=networkx.random_regular_graph(d,n)
return G
elif hasattr(args,'gnp') and args.gnp:
n,p = args.gnp
G=networkx.gnp_random_graph(n,p)
elif hasattr(args,'gnm') and args.gnm:
n,m = args.gnm
G=networkx.gnm_random_graph(n,m)
elif hasattr(args,'grid') and args.grid:
G=networkx.grid_graph(args.grid)
elif hasattr(args,'torus') and args.torus:
G=networkx.grid_graph(args.torus,periodic=True)
elif hasattr(args,'complete') and args.complete>0:
G=networkx.complete_graph(args.complete)
elif args.graphformat:
G=readGraph(args.input,args.graphformat)
else:
raise RuntimeError("Invalid graph specification on command line")
# Graph modifications
if hasattr(args,'plantclique') and args.plantclique>1:
clique=random.sample(G.nodes(),args.plantclique)
for v,w in combinations(clique,2):
G.add_edge(v,w)
# Output the graph is requested
if hasattr(args,'savegraph') and args.savegraph:
writeGraph(G,
args.savegraph,
args.graphformat,
graph_type='simple')
return G
def draw_random_graph(i):
"""
Draw a random graph with 2**i nodes,
and p=i/(2**i)
"""
g_random = nx.gnp_random_graph(2**i,2*i/(2**i))
nx.draw(g_random,node_size=20)
plt.savefig("./random_graph.svg")
plt.close()
def test_spanner_unweighted_gnp_graph():
"""Test spanner construction on an unweighted gnp graph."""
G = nx.gnp_random_graph(20, 0.4, seed=_seed)
spanner = nx.spanner(G, 4, seed=_seed)
_test_spanner(G, spanner, 4)
spanner = nx.spanner(G, 10, seed=_seed)
_test_spanner(G, spanner, 10)
def setUp(self):
self.Gnp = nx.gnp_random_graph(20, 0.8)
self.Anp = _AntiGraph(nx.complement(self.Gnp))
self.Gd = nx.davis_southern_women_graph()
self.Ad = _AntiGraph(nx.complement(self.Gd))
self.Gk = nx.karate_club_graph()
self.Ak = _AntiGraph(nx.complement(self.Gk))
self.GA = [(self.Gnp, self.Anp),
(self.Gd, self.Ad),
(self.Gk, self.Ak)]
def test_spanner_weighted_gnp_graph():
"""Test spanner construction on an weighted gnp graph."""
G = nx.gnp_random_graph(20, 0.4, seed=_seed)
_assign_random_weights(G, seed=_seed)
spanner = nx.spanner(G, 4, weight='weight', seed=_seed)
_test_spanner(G, spanner, 4, weight='weight')
spanner = nx.spanner(G, 10, weight='weight', seed=_seed)
_test_spanner(G, spanner, 10, weight='weight')
def random_gen(topo_config):
"""
create a random topology
the topo_config is a tuple: (num_nodes, prob)
"""
num_nodes, prob = topo_config
print("Create random topology of {0} with edge generating \
probability of {1}".format(num_nodes, prob))
G = nx.gnp_random_graph(num_nodes, prob)
return G
def ajax_build_graph_view(request, graph_id):
graph = get_object_or_404(Graph, id=graph_id)
size = graph.size
graph_type = graph.graph_type
if graph_type == 'random':
G = nx.gnp_random_graph(size,0.1)
else:
G = nx.scale_free_graph(size)
vega_graph = build_vega_graph(G)
return HttpResponse(vega_graph, mimetype="text/javascript")
def fast_gnp_random_graph(n, p, seed=None):
"""Return a random graph G_{n,p} (Erdős-Rényi graph, binomial graph).
Parameters
----------
n : int
The number of nodes.
p : float
Probability for edge creation.
seed : int, optional
Seed for random number generator (default=None).
Notes
-----
The G_{n,p} graph algorithm chooses each of the [n(n-1)]/2
(undirected) or n(n-1) (directed) possible edges with probability p.
This algorithm is O(n+m) where m is the expected number of
edges m=p*n*(n-1)/2.
It should be faster than gnp_random_graph when p is small and
the expected number of edges is small (sparse graph).
See Also
--------
gnp_random_graph
References
----------
.. [1] Batagelj and Brandes, "Efficient generation of large random networks",
Phys. Rev. E, 71, 036113, 2005.
"""
G=empty_graph(n)
G.name="fast_gnp_random_graph(%s,%s)"%(n,p)
if not seed is None:
random.seed(seed)
if p<=0 or p>=1:
return nx.gnp_random_graph(n,p)
v=1 # Nodes in graph are from 0,n-1 (this is the second node index).
w=-1
lp=math.log(1.0-p)
while v<n:
lr=math.log(1.0-random.random())
w=w+1+int(lr/lp)
while w>=v and v<n:
w=w-v
v=v+1
if v<n:
G.add_edge(v,w)
return G
def fast_gnp_random_graph(n, p, seed=None, directed=False):
"""Returns a `G_{n,p}` random graph, also known as an Erdős-Rényi graph or
a binomial graph.
Parameters
----------
n : int
The number of nodes.
p : float
Probability for edge creation.
seed : int, optional
Seed for random number generator (default=None).
directed : bool, optional (default=False)
If ``True``, this function returns a directed graph.
Notes
-----
The `G_{n,p}` graph algorithm chooses each of the `[n (n - 1)] / 2`
(undirected) or `n (n - 1)` (directed) possible edges with probability `p`.
This algorithm runs in `O(n + m)` time, where `m` is the expected number of
edges, which equals `p n (n - 1) / 2`. This should be faster than
:func:`gnp_random_graph` when `p` is small and the expected number of edges
is small (that is, the graph is sparse).
See Also
--------
gnp_random_graph
References
----------
.. [1] Vladimir Batagelj and Ulrik Brandes,
"Efficient generation of large random networks",
Phys. Rev. E, 71, 036113, 2005.
"""
G = empty_graph(n)
G.name = "fast_gnp_random_graph(%s,%s)" % (n, p)
if not seed is None:
random.seed(seed)
if p <= 0 or p >= 1:
return nx.gnp_random_graph(n, p, directed=directed)
w = -1
lp = math.log(1.0 - p)
if directed:
G = nx.DiGraph(G)
# Nodes in graph are from 0,n-1 (start with v as the first node index).
v = 0
while v < n:
lr = math.log(1.0 - random.random())
w = w + 1 + int(lr / lp)
if v == w: # avoid self loops
w = w + 1
while w >= n and v < n:
w = w - n
v = v + 1
if v == w: # avoid self loops
w = w + 1
if v < n:
G.add_edge(v, w)
else:
# Nodes in graph are from 0,n-1 (start with v as the second node index).
v = 1
while v < n:
lr = math.log(1.0 - random.random())
w = w + 1 + int(lr / lp)
while w >= v and v < n:
w = w - v
v = v + 1
if v < n:
G.add_edge(v, w)
return G
def generate(self,
meanfriends=5,
sdfriends=5,
frienddist="uni",
connectdist="watstro"):
#generates object and the f network
for a in range(self.size):
self.gf.add_node(a, obj=Agent(a))
if connectdist == "CStyle":
for a in range(self.size):
#
#self.gf.add_node(a,obj=______object_____)
#
tar = ["r"]
friends = []
for b in list(self.gf[a]):
friends.append(b)
for c in list(self.gf[b]):
tar.append(c)
if frienddist == "uni":
#numf=rng.uniform()
#numf=numf*meanfriends//1+5
numf = 5
#numf=15-len(friends)
#if numf <0:
# numf=1
numf = int(numf)
if connectdist == "CStyle":
for aa in range(numf):
nex = rng.choice(tar)
if nex == "r" or int(nex) in friends + ["r", a]:
while nex in friends + ["r", a] or int(
nex) in friends + ["r", a]:
nex = int(rng.choice(range(self.size)))
nex = int(nex)
self.gf.add_edge(a, int(nex))
tar = tar + list(self.gf[nex])
friends.append(nex)
if len(self.gf[a]) < 5:
print(a)
print(friends)
print(self.gf[a])
print(" \n")
elif connectdist == "randomunif":
#notperfect
numf = 10
connect = {k: [] for k in range(self.size)}
li = []
for a in range(self.size - 1):
it = 0
while len(connect[a]) < numf and it < 100:
it += 1
r = rng.choice(range(a + 1, self.size))
if len(connect[r]) < 10 or r in connect[a]:
#print(a,r)
li.append([int(a), int(r)])
connect[a].append(r)
connect[r].append(a)
print(a, it, len(connect[a]))
print(len(li))
for b, c in li:
self.gf.add_edge(b, c)
#gnm_random_graph(n, m, seed=None, directed=False)
#connected_watts_strogatz_graph(n, k, p[, ...])
#if len(self.gf[a])<5:
# print(a)
# print(friends)
# print(self.gf[a])
#print(" \n")
elif connectdist == "randomunif2":
al = []
numf = 10
for a in range(numf):
al = al + list(range(self.size))
con = []
al2 = al
rng.shuffle(al2)
it = 0
while it < 10000:
it += 1
if len(al2) >= 1:
cand = al2[:2]
if cand[0] != cand[
1] and cand not in con and cand[::-1] not in con:
con.append(frozenset(al2[:2]))
al2 = al2[2:]
else:
rng.shuffle(al2)
else:
break
print(con, len(con), len(set(con)), it)
for b, c in con:
self.gf.add_edge(b, c)
elif connectdist == "prederd":
n = self.size
k = 10 / (n - 1)
te = nx.gnp_random_graph(n, k)
self.gf.add_edges_from(te.edges)
elif connectdist == "watstro":
n = self.size
p = 0.05
k = 10
te = nx.connected_watts_strogatz_graph(n, k, p, 100)
self.gf.add_edges_from(te.edges)
elif connectdist == "bara":
n = self.size
p = 0.05
k = 5
te = nx.barabasi_albert_graph(n, k)
self.gf.add_edges_from(te.edges)
elif connectdist == "pow":
n = self.size
#k=min(n/10,5)
k = 4
p = 0.05
te = nx.powerlaw_cluster_graph(n, k, p)
self.gf.add_edges_from(te.edges)
elif connectdist == "full":
n = self.size
e = []
for a in range(n - 1):
for b in range(a + 1, n):
e.append(set([a, b]))
self.gf.add_edges_from(e)
#karate_club_graph()
elif connectdist == "star":
n = self.size
e = []
for a in range(n):
e.append([a, (n + 1) % n])
self.gf.add_edges_from(e)
elif connectdist == "circle":
n = self.size
e = []
for a in range(n):
e.append([a, (a + 1) % n])
self.gf.add_edges_from(e)
#karate_club_graph()
else:
raise Exception("ERROR: UNVALID GENERATE KEY")
self.agentsid = self.gf.nodes
for a in self.agentsid:
self.agents.append(self.getobj(a))
friendsinstances = []
for friend in self.friendsof(a):
temp = self.getobj(friend)
friendsinstances.append(temp)
self.getobj(a).define_friends(friendsinstances)
self.pos = nx.spring_layout(self.gf)
import networkx as nx
import matplotlib.pyplot as plt
def modularidad(g, atributo):
A = nx.adjacency_matrix(g)
B = 0
m = len(g.edges())
k = []
name = []
for key, value in g.degree():
k.append(value)
name.append(key)
for i in range(len(k)):
for j in range(len(k)):
if g.node[name[i]][atributo] == g.node[name[j]][atributo]:
B += A[i, j] - k[i] * k[j] / (2 * m)
return float(B) / (2 * m)
if __name__ == '__main__':
g = nx.gnp_random_graph(10, 0.3)
for node, attrDict in dict(g.nodes()).items():
attrDict['gender'] = 'a' if (node % 2 == 0) else 'b'
colores = [('blue' if gender == 'a' else 'red')
for gender in nx.get_node_attributes(g, "gender").values()]
plt.figure()
nx.draw(g, node_color=colores)
print(modularidad(g, 'gender'))
def test_dominating_set():
G = nx.gnp_random_graph(100, 0.1)
D = nx.dominating_set(G)
assert nx.is_dominating_set(G, D)
D = nx.dominating_set(G, start_with=0)
assert nx.is_dominating_set(G, D)
posS[p][1] += 0.04 #offset 0.07
nx.draw_networkx_labels(condensation_graph_relabeled, posS)
# plt.savefig('foo3.png')
### ΔΙΣΥΝΔΕΣΙΜΟΤΗΤΑ ΜΗ ΚΑΤΕΥΘΥΝΟΜΕΝΩΝ ΓΡΑΦΩΝ
# G = nx.barbell_graph(4,2)
#
#
# # G=nx.Graph()
# # G.add_edges_from([(0,1),(1,2),(2,0),(3,4)])
# # G.add_node(5)
# # pos={0:(0,0),1:(0,1),2:(0.5,1),3:(0.5,0),4:(1,0),5:(0.75,0.5)}
#
#
G = nx.gnp_random_graph(20, 0.1)
# G = nx.gnp_random_graph(15,0.04)
# G = nx.gnm_random_graph(15,10)
# # G = nx.barabasi_albert_graph(50,2)
# # G = nx.newman_watts_strogatz_graph(20,2,0.9)
# # G.add_node(55)
# # G.add_edges_from([(50,51),(51,52),(52,50),(53,54)])
#
# # G = nx.erdos_renyi_graph(15,0.06)
# pos=nx.spring_layout(G,k=0.15,iterations=10)
# # pos=nx.graphviz_layout(G)
# # pos=layout(G)
##### ΑΝΑΖΗΤΗΣΗ
number_of_biconnected_components = 2
def test_result_gnp_20_0_95_4(self):
assert (calc_and_compare(NX.gnp_random_graph(20, 0.95, 11)))
def erdos_renyi_lap(
G
): #ER build graphs based on individual proteins, so number of nodes and probability of those edges being created
lap_test = []
lap_test1 = []
lap_test2 = []
N = nx.number_of_nodes(G)
E = nx.number_of_edges(G)
prob_edges = ((
(N * (N - 1)) / 2) / E) / 100 #divide by 100 float not percentage
#print prob_edges
'''start_time = time.time()
for i in range(500):
er_graph = nx.gnp_random_graph(N,prob_edges,seed=None) #number of nodes, probability for edge creation
lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0])-lap
eigenvalues,eigenvectors = scipy.sparse.linalg.eigsh(lap,k=2)
lap_sum = (eigenvalues[1]-eigenvalues[0])
lap_test1.append(lap_sum)
print lap_test1
print ("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
for i in range(500):
er_graph = nx.fast_gnp_random_graph(N,prob_edges,seed=None) #number of nodes, probability for edge creation
lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0])-lap
eigenvalues,eigenvectors = scipy.sparse.linalg.eigsh(lap,k=2)
lap_sum = (eigenvalues[1]-eigenvalues[0])
lap_test2.append(lap_sum)
print lap_test2
print ("--- %s seconds ---" % (time.time() - start_time))'''
start_time = time.time()
for i in range(500):
er_graph = nx.erdos_renyi_graph(
N, prob_edges,
seed=None) #number of nodes, probability for edge creation
lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0]) - lap
eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(lap, k=2)
lap_sum = (eigenvalues[1] - eigenvalues[0])
lap_test.append(lap_sum)
print lap_test
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
for i in range(500):
er_graph = nx.erdos_renyi_graph(
N, prob_edges,
seed=None) #number of nodes, probability for edge creation
lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0]) - lap
eigenvalues, eigenvectors = np.linalg.eigh(lap)
lap_sum = (eigenvalues[1] - eigenvalues[0])
lap_test1.append(eigenvalues)
print lap_test1
print("--- %s seconds ---" % (time.time() - start_time))
# using IGRAPH
start_time = time.time()
for i in range(500):
er_graph = Graph.Erdos_Renyi(
n=N, p=prob_edges, directed=True,
loops=False) #number of nodes, probability for edge creation
lap = er_graph.laplacian(normalized=True)
e = np.linalg.eigvals(lap)
lap_test2.append(e)
print lap_test2
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
for i in range(500):
er_graph = nx.gnp_random_graph(
N, prob_edges,
seed=None) #number of nodes, probability for edge creation
'''lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0])-lap
eigenvalues,eigenvectors = scipy.sparse.linalg.eigsh(lap,k=2)
lap_sum = (eigenvalues[1]-eigenvalues[0])
lap_test1.append(lap_sum)'''
print er_graph
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
for i in range(500):
er_graph = nx.fast_gnp_random_graph(
N, prob_edges,
seed=None) #number of nodes, probability for edge creation
'''lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0])-lap
eigenvalues,eigenvectors = scipy.sparse.linalg.eigsh(lap,k=2)
lap_sum = (eigenvalues[1]-eigenvalues[0])
lap_test2.append(lap_sum)'''
print er_graph
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
for i in range(500):
er_graph = nx.erdos_renyi_graph(
N, prob_edges,
seed=None) #number of nodes, probability for edge creation
'''lap = nx.normalized_laplacian_matrix(er_graph, weight='weight')
lap = np.eye(lap.shape[0])-lap
eigenvalues,eigenvectors = scipy.sparse.linalg.eigsh(lap,k=2)
lap_sum = (eigenvalues[1]-eigenvalues[0])
lap_test.append(lap_sum)'''
print er_graph
print("--- %s seconds ---" % (time.time() - start_time))
def __init__(self, init_type, **kwargs): #init_type: {Directly, Random, From File, Random Tree, Random Chimera}
if (init_type == "Directly"):
if 'Const' in kwargs.keys():
Const = kwargs['Const']
else:
Const = 0.0
if 'Pot' in kwargs.keys():
Pot = kwargs['Pot']
else:
Pot = np.zeros((1, len(kwargs['Inter'])))
(self.Inter, self.Pot, self.Const) = (kwargs['Inter'], Pot, Const)
elif (init_type == "Random"):
if 'n' in kwargs.keys():
n = kwargs['n']
else:
print("Should specify number of vertices n=")
if 'p' in kwargs.keys():
p = kwargs['p']
else:
print("Should specify the probability of an edge p=")
if 'seed' in kwargs.keys():
seed = kwargs['seed']
else:
seed = None
G = nx.gnp_random_graph(n, p, seed)
A = csr_matrix.todense(nx.adjacency_matrix(G))
(self.Inter, self.Pot, self.Const) = (Laplacian(A)/4, np.zeros((n)), 0)
elif (init_type == "From File"):
if 'filename' in kwargs.keys():
filename = kwargs['filename']
else:
print("You should specify the filename!")
import os
name, extension = os.path.splitext(filename)
if (extension == '.json'):
(self.Inter, self.Pot, self.Const) = BQPJSON(filename)
self.Inter = 1*self.Inter
self.Pot = 1*self.Pot
self.Const = 1*self.Const
#elif (file_extension == '.mat'):
#retrieve a dense graph from .mat file
#elif (file_extension == '.sparse'):
#retrieve a sparse graph from .sparse file
else:
print("Wrong File Extension")
elif (init_type == "Random Chimera"):
import dwave_networkx as dnx
G = dnx.chimera_graph(kwargs['M'], kwargs['N'], kwargs['L'])
A = csr_matrix.todense(nx.adjacency_matrix(G))
(self.Inter, self.Pot, self.Const) = (Laplacian(A)/4, np.zeros((n)), 0)
elif (init_type == "Random Tree"):
if 'seed' in kwargs.keys():
seed = kwargs['seed']
else:
seed = None
if 'n' in kwargs.keys():
n = kwargs['n']
else:
n = random.randint(10, 100)
G = nx.random_tree(n, seed)
A = csr_matrix.todense(nx.adjacency_matrix(G))
(self.Inter, self.Pot, self.Const) = (Laplacian(A)/4, np.zeros((n)), 0)
import matplotlib.pyplot as plt
import seaborn as sns
import random
import numpy as np
n = 2
m = 10
numberOfneb = 1000
result = [[0 for i in range(m)] for j in range(n)]
numOfIterations = 0
for iz in range(10):
print("iteraion i = ", iz, " % ", (iz + 1) / 10)
result[0][iz] = (iz + 1) / 10
gr2 = nx.gnp_random_graph(numberOfneb, (iz + 1) / 10,
seed=354,
directed=False)
'''nx.draw(gr2, with_labels=True)'''
pos = nx.spring_layout(gr2)
labels = {}
for x in range(len(gr2)):
labels[x] = random.choice(['A', 'B', 'C'])
'''print("Labels :", labels)'''
neighbors = gr2.edges
'''print("the neighbors: ", neighbors)'''
m2 = 3
votes = [[0 for i in range(m2)] for j in range(numberOfneb)]
votes2 = [[0 for i in range(m2)] for j in range(numberOfneb)]
continue
in_flow = 0
out_flow = 0
for u in graph.predecessors(v):
in_flow += graph.edges[u, v]["flow"]
for w in graph.successors(v):
out_flow += graph.edges[v, w]["flow"]
if in_flow != out_flow:
return False
return True
if __name__ == "__main__":
import random
for i in range(100):
G = nx.gnp_random_graph(100, 0.5, directed=True)
DAG = nx.DiGraph([(u, v, {
'capacity': random.randint(0, 10)
}) for (u, v) in G.edges() if u < v])
#visualize.visualize_graph(DAG, weight="capacity", filename="1.png")
graph = compute_blocking_flow(DAG, 0, 99, 150)
# l = list(graph.edges(data=True))
# for u, v, attr in l:
# if attr["flow"] == 0:
# graph.remove_edge(u, v)
# for u in graph.nodes:
# pass
# # print(graph.nodes[u]["excess"])
# visualize.visualize_graph(graph, weight="flow", filename="2.png")
def random_graph():
return nx.gnp_random_graph(rd.randint(1, 100), rd.random(), None,
rd.choice([True, False]))
def update_item_graph(self, i, kk):
Sinv1 = np.linalg.inv(self.S[i])
theta1 = np.dot(Sinv1, self.b[i])
C0 = set(nx.node_connected_component(self.GI, kk))
# H = GI.subgraph(C0)
A = [a for a in self.GI.neighbors(kk)]
N0 = []
N = [[] for l in range(len(A))]
num_users = len(self.S)
for j in range(num_users):
if j == i:
continue
if invertible(self.S[j]):
Sinv = np.linalg.inv(self.S[j])
theta = np.dot(Sinv, self.b[j])
if np.abs(np.dot(theta - theta1,
self.items[kk, :])) < self.alpha * (
fracT(self.T[i]) + fracT(self.T[j])):
N0.append(j)
for a in range(len(A)):
l = A[a]
if np.abs(np.dot(theta - theta1,
self.items[l, :])) < self.alpha * (
fracT(self.T[i]) + fracT(self.T[j])):
N[a].append(j)
else:
N0.append(j)
for a in range(len(A)):
N[a].append(j)
N0 = set(N0)
for a in range(len(A)):
l = A[a]
if l == kk:
continue
N[a] = set(N[a])
if N0 != N[a]:
self.GI.remove_edge(kk, l)
# H.remove_edge(kk, l)
C = set(nx.node_connected_component(self.GI, kk))
if C != C0:
c = self.i_ind[kk]
self.G[c] = nx.gnp_random_graph(num_users,
edge_probability(num_users))
self.i_clusters[c] = [l for l in C]
C0 = C0 - C
while len(C0) > 0:
l = next(iter(C0))
C1 = set(nx.node_connected_component(self.GI, l))
c1 = self.find_available_index()
self.G[c1] = nx.gnp_random_graph(num_users,
edge_probability(num_users))
self.i_clusters[c1] = [l for l in C1]
for l1 in C1:
self.i_ind[l1] = c1
C0 = C0 - C1
return
def nxg_gnp_random(directed):
return nx.gnp_random_graph(100, 0.3, seed=1234, directed=directed)
# -*- coding: utf-8 -*- """ Created on Sat Apr 11 10:04:39 2020 @author: bijuangalees """ import networkx as nx #G=nx.barbell_graph(5,3) #G=nx.complete_graph(4) #G=nx.cycle_graph(5) #G=nx.ladder_graph(5) #G=nx.path_graph(6) #G=nx.star_graph(1000) #G=nx.wheel_graph(9) G = nx.gnp_random_graph(5, 0.5) nx.draw(G)
def test_random_gnp():
G = nx.gnp_random_graph(100, 0.1)
_check_separating_sets(G)
def generate_graph(N):
G = nx.gnp_random_graph(N, p_edge)
nx.write_gml(G, graph_file_path)
return G
def fast_gnp_random_graph(n, p, seed=None, directed=False):
"""Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph or
a binomial graph.
Parameters
----------
n : int
The number of nodes.
p : float
Probability for edge creation.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
directed : bool, optional (default=False)
If True, this function returns a directed graph.
Notes
-----
The $G_{n,p}$ graph algorithm chooses each of the $[n (n - 1)] / 2$
(undirected) or $n (n - 1)$ (directed) possible edges with probability $p$.
This algorithm [1]_ runs in $O(n + m)$ time, where `m` is the expected number of
edges, which equals $p n (n - 1) / 2$. This should be faster than
:func:`gnp_random_graph` when $p$ is small and the expected number of edges
is small (that is, the graph is sparse).
See Also
--------
gnp_random_graph
References
----------
.. [1] Vladimir Batagelj and Ulrik Brandes,
"Efficient generation of large random networks",
Phys. Rev. E, 71, 036113, 2005.
"""
G = empty_graph(n)
if p <= 0 or p >= 1:
return nx.gnp_random_graph(n, p, seed=seed, directed=directed)
w = -1
lp = math.log(1.0 - p)
if directed:
G = nx.DiGraph(G)
# Nodes in graph are from 0,n-1 (start with v as the first node index).
v = 0
while v < n:
lr = math.log(1.0 - seed.random())
w = w + 1 + int(lr / lp)
if v == w: # avoid self loops
w = w + 1
while v < n <= w:
w = w - n
v = v + 1
if v == w: # avoid self loops
w = w + 1
if v < n:
G.add_edge(v, w)
else:
# Nodes in graph are from 0,n-1 (start with v as the second node index).
v = 1
while v < n:
lr = math.log(1.0 - seed.random())
w = w + 1 + int(lr / lp)
while w >= v and v < n:
w = w - v
v = v + 1
if v < n:
G.add_edge(v, w)
return G
def test_random_gnp_directed():
# seeds = [3894723670, 500186844, 267231174, 2181982262, 1116750056]
seeds = [2181982262]
for seed in seeds:
G = nx.gnp_random_graph(20, 0.2, directed=True, seed=seed)
_check_edge_connectivity(G)
bt_sc = p.map(
_betmap,
zip([G] * num_chunks, [True] * num_chunks, [None] * num_chunks,
node_chunks))
# Reduce the partial solutions
bt_c = bt_sc[0]
for bt in bt_sc[1:]:
for n in bt:
bt_c[n] += bt[n]
return bt_c
if __name__ == "__main__":
G_ba = nx.barabasi_albert_graph(1000, 3)
G_er = nx.gnp_random_graph(1000, 0.01)
G_ws = nx.connected_watts_strogatz_graph(1000, 4, 0.1)
for G in [G_ba, G_er, G_ws]:
print("")
print("Computing betweenness centrality for:")
print(nx.info(G))
print("\tParallel version")
start = time.time()
bt = betweenness_centrality_parallel(G)
print("\t\tTime: %.4F" % (time.time() - start))
print("\t\tBetweenness centrality for node 0: %.5f" % (bt[0]))
print("\tNon-Parallel version")
start = time.time()
bt = nx.betweenness_centrality(G)
print("\t\tTime: %.4F seconds" % (time.time() - start))
print("\t\tBetweenness centrality for node 0: %.5f" % (bt[0]))
def get_edge_array(node_count, node_start):
edges = []
for edge in nx.gnp_random_graph(node_count, 0.1).edges():
edges.append([edge[0] + node_start, edge[1] + node_start])
return np.array(edges)
Returns
-------
adj_iter : iterator
An iterator of (node, adjacency set) for all nodes in
the graph.
"""
for n in self.adj:
yield (n, set(self.adj) - set(self.adj[n]) - set([n]))
if __name__ == '__main__':
# Build several pairs of graphs, a regular graph
# and the AntiGraph of it's complement, which behaves
# as if it were the original graph.
Gnp = nx.gnp_random_graph(20, 0.8)
Anp = AntiGraph(nx.complement(Gnp))
Gd = nx.davis_southern_women_graph()
Ad = AntiGraph(nx.complement(Gd))
Gk = nx.karate_club_graph()
Ak = AntiGraph(nx.complement(Gk))
pairs = [(Gnp, Anp), (Gd, Ad), (Gk, Ak)]
# test connected components
for G, A in pairs:
gc = [set(c) for c in nx.connected_components(G)]
ac = [set(c) for c in nx.connected_components(A)]
for comp in ac:
assert comp in gc
# test biconnected components
for G, A in pairs:
gc = [set(c) for c in nx.biconnected_components(G)]
def RandomGNP(n, p, seed=None, fast=True, algorithm='Sage'):
r"""
Returns a random graph on `n` nodes. Each edge is inserted independently
with probability `p`.
INPUT:
- ``n`` -- number of nodes of the graph
- ``p`` -- probability of an edge
- ``seed`` -- integer seed for random number generator (default=None).
- ``fast`` -- boolean set to True (default) to use the algorithm with
time complexity in `O(n+m)` proposed in [BatBra2005]_. It is designed
for generating large sparse graphs. It is faster than other algorithms for
*LARGE* instances (try it to know whether it is useful for you).
- ``algorithm`` -- By default (```algorithm='Sage'``), this function uses the
algorithm implemented in ```sage.graphs.graph_generators_pyx.pyx``. When
``algorithm='networkx'``, this function calls the NetworkX function
``fast_gnp_random_graph``, unless ``fast=False``, then
``gnp_random_graph``. Try them to know which algorithm is the best for
you. The ``fast`` parameter is not taken into account by the 'Sage'
algorithm so far.
REFERENCES:
.. [ErdRen1959] P. Erdos and A. Renyi. On Random Graphs, Publ.
Math. 6, 290 (1959).
.. [Gilbert1959] E. N. Gilbert. Random Graphs, Ann. Math. Stat.,
30, 1141 (1959).
.. [BatBra2005] V. Batagelj and U. Brandes. Efficient generation of
large random networks. Phys. Rev. E, 71, 036113, 2005.
PLOTTING: When plotting, this graph will use the default spring-layout
algorithm, unless a position dictionary is specified.
EXAMPLES: We show the edge list of a random graph on 6 nodes with
probability `p = .4`::
sage: set_random_seed(0)
sage: graphs.RandomGNP(6, .4).edges(labels=False)
[(0, 1), (0, 5), (1, 2), (2, 4), (3, 4), (3, 5), (4, 5)]
We plot a random graph on 12 nodes with probability `p = .71`::
sage: gnp = graphs.RandomGNP(12,.71)
sage: gnp.show() # long time
We view many random graphs using a graphics array::
sage: g = []
sage: j = []
sage: for i in range(9):
....: k = graphs.RandomGNP(i+3,.43)
....: g.append(k)
sage: for i in range(3):
....: n = []
....: for m in range(3):
....: n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....: j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time
sage: graphs.RandomGNP(4,1)
Complete graph: Graph on 4 vertices
TESTS::
sage: graphs.RandomGNP(50,.2,algorithm=50)
Traceback (most recent call last):
...
ValueError: 'algorithm' must be equal to 'networkx' or to 'Sage'.
sage: set_random_seed(0)
sage: graphs.RandomGNP(50,.2, algorithm="Sage").size()
243
sage: graphs.RandomGNP(50,.2, algorithm="networkx").size()
258
"""
if n < 0:
raise ValueError("The number of nodes must be positive or null.")
if 0.0 > p or 1.0 < p:
raise ValueError("The probability p must be in [0..1].")
if seed is None:
seed = current_randstate().long_seed()
if p == 1:
from sage.graphs.generators.basic import CompleteGraph
return CompleteGraph(n)
if algorithm == 'networkx':
import networkx
if fast:
G = networkx.fast_gnp_random_graph(n, p, seed=seed)
else:
G = networkx.gnp_random_graph(n, p, seed=seed)
return Graph(G)
elif algorithm in ['Sage', 'sage']:
# We use the Sage generator
from sage.graphs.graph_generators_pyx import RandomGNP as sageGNP
return sageGNP(n, p)
else:
raise ValueError(
"'algorithm' must be equal to 'networkx' or to 'Sage'.")
dydt[i] = s[0] + n_W[i] * W[0]
dydt[i + N] = s[1] + n_W[i] * W[1]
dydt[i + 2 * N] = s[2] + n_W[i] * W[2]
return dydt
#Parametros
N = int(input("N= "))
ti = int(input("ti= "))
tf = int(input("tf= "))
s = [ti, tf]
mu = float(input("mu= "))
delta = float(input("delta= "))
p = float(input("p= "))
G = nx.gnp_random_graph(N, p)
A = nx.adjacency_matrix(G).A
D = np.zeros(N)
for i in range(N):
S = 0
for j in range(N):
S = S + A[i, j]
D[i] = S
#Frequencias naturais
W = np.random.normal(mu, delta, (3, N))
w = np.zeros((3, N))
n_W = np.zeros(N)
for i in range(N):
H = np.zeros((3, 3))
nodecount = g2.vertices.count()
print("Graph has " + str(nodecount) + " vertices.")
out = filename.split("/")[-1]
print("Writing distribution to file " + out + ".csv")
distrib.toPandas().to_csv(out + ".csv")
# Otherwise, generate some random graphs.
else:
print("Generating random graphs.")
vschema = StructType([StructField("id", IntegerType())])
eschema = StructType(
[StructField("src", IntegerType()),
StructField("dst", IntegerType())])
gnp1 = nx.gnp_random_graph(100, 0.05, seed=1234)
gnp2 = nx.gnp_random_graph(2000, 0.01, seed=5130303)
gnm1 = nx.gnm_random_graph(100, 1000, seed=27695)
gnm2 = nx.gnm_random_graph(1000, 100000, seed=9999)
todo = {"gnp1": gnp1, "gnp2": gnp2, "gnm1": gnm1, "gnm2": gnm2}
for gx in todo:
print("Processing graph " + gx)
v = sqlContext.createDataFrame(sc.parallelize(todo[gx].nodes()),
vschema)
e = sqlContext.createDataFrame(sc.parallelize(todo[gx].edges()),
eschema)
g = simple(GraphFrame(v, e))
distrib = degreedist(g)
print("Writing distribution to file " + gx + ".csv")
distrib.toPandas().to_csv(gx + ".csv")
#!/usr/bin/env python
"""
===========
Degree Rank
===========
Random graph from given degree sequence.
Draw degree rank plot and graph with matplotlib.
"""
# Author: Aric Hagberg <*****@*****.**>
import networkx as nx
import matplotlib.pyplot as plt
G = nx.gnp_random_graph(100, 0.02)
degree_sequence = sorted([d for n, d in G.degree()], reverse=True)
# print "Degree sequence", degree_sequence
dmax = max(degree_sequence)
plt.loglog(degree_sequence, 'b-', marker='o')
plt.title("Degree rank plot")
plt.ylabel("degree")
plt.xlabel("rank")
# draw graph in inset
plt.axes([0.45, 0.45, 0.45, 0.45])
Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0])
pos = nx.spring_layout(Gcc)
plt.axis('off')
nx.draw_networkx_nodes(Gcc, pos, node_size=20)
nx.draw_networkx_edges(Gcc, pos, alpha=0.4)
Returns
-------
adj_iter : iterator
An iterator of (node, adjacency set) for all nodes in
the graph.
"""
for n in self.adj:
yield (n, set(self.adj) - set(self.adj[n]) - set([n]))
# Build several pairs of graphs, a regular graph
# and the AntiGraph of it's complement, which behaves
# as if it were the original graph.
Gnp = nx.gnp_random_graph(20, 0.8, seed=42)
Anp = AntiGraph(nx.complement(Gnp))
Gd = nx.davis_southern_women_graph()
Ad = AntiGraph(nx.complement(Gd))
Gk = nx.karate_club_graph()
Ak = AntiGraph(nx.complement(Gk))
pairs = [(Gnp, Anp), (Gd, Ad), (Gk, Ak)]
# test connected components
for G, A in pairs:
gc = [set(c) for c in nx.connected_components(G)]
ac = [set(c) for c in nx.connected_components(A)]
for comp in ac:
assert comp in gc
# test biconnected components
for G, A in pairs:
gc = [set(c) for c in nx.biconnected_components(G)]
def test_result_gnp_50_0_5_4(self):
assert (calc_and_compare(NX.gnp_random_graph(50, 0.5, 4)))
def fast_gnp_random_graph(n, p, seed=None, directed=False):
"""Return a random graph G_{n,p} (Erdős-Rényi graph, binomial graph).
Parameters
----------
n : int
The number of nodes.
p : float
Probability for edge creation.
seed : int, optional
Seed for random number generator (default=None).
directed : bool, optional (default=False)
If True return a directed graph
Notes
-----
The G_{n,p} graph algorithm chooses each of the [n(n-1)]/2
(undirected) or n(n-1) (directed) possible edges with probability p.
This algorithm is O(n+m) where m is the expected number of
edges m=p*n*(n-1)/2.
It should be faster than gnp_random_graph when p is small and
the expected number of edges is small (sparse graph).
See Also
--------
gnp_random_graph
References
----------
.. [1] Vladimir Batagelj and Ulrik Brandes,
"Efficient generation of large random networks",
Phys. Rev. E, 71, 036113, 2005.
"""
G = empty_graph(n)
G.name="fast_gnp_random_graph(%s,%s)"%(n,p)
if not seed is None:
random.seed(seed)
if p <= 0 or p >= 1:
return nx.gnp_random_graph(n,p,directed=directed)
w = -1
lp = math.log(1.0 - p)
if directed:
G = nx.DiGraph(G)
# Nodes in graph are from 0,n-1 (start with v as the first node index).
v = 0
while v < n:
lr = math.log(1.0 - random.random())
w = w + 1 + int(lr/lp)
if v == w: # avoid self loops
w = w + 1
while w >= n and v < n:
w = w - n
v = v + 1
if v == w: # avoid self loops
w = w + 1
if v < n:
G.add_edge(v, w)
else:
# Nodes in graph are from 0,n-1 (start with v as the second node index).
v = 1
while v < n:
lr = math.log(1.0 - random.random())
w = w + 1 + int(lr/lp)
while w >= v and v < n:
w = w - v
v = v + 1
if v < n:
G.add_edge(v, w)
return G
if v1 == t: return (cost, path)
for c, v2 in g.get(v1, ()):
if v2 in seen: continue
prev = mins.get(v2, None)
next = cost + c
if prev is None or next < prev:
mins[v2] = next
heappush(q, (next, v2, path))
return float("inf"), None
# initialize the graph
n = NODE_COUNT
p = EDGE_PROBABILITY
G = nx.gnp_random_graph(n, p, directed=True)
edgelist = []
# gives all nodes weights
for i in G.edges:
G[i[0]][i[1]]["weight"] = 1 + random.randint(0, 20)
edgelist.append((i[0], i[1], G[i[0]][i[1]]["weight"]))
print(edgelist)
# initial graph
pos = nx.spring_layout(G)
nx.draw(G, pos, node_color='blue', with_labels=False, node_size=250)
# select two random nodes as long as they
from DBK import DBK
import networkx as nx
import random
def maximum_clique_exact_solve_np_hard(G):
max_clique_number = nx.graph_clique_number(G)
cliques = nx.find_cliques(G)
for cl in cliques:
if len(cl) == max_clique_number:
return cl
for i in range(500):
G = nx.gnp_random_graph(random.randint(66, 100),
random.uniform(0.01, 0.99))
print(len(G))
solution = DBK(G, 65, maximum_clique_exact_solve_np_hard)
assert len(solution) == nx.graph_clique_number(G)
