本文整理汇总了Python中networkx.fast_gnp_random_graph方法的典型用法代码示例。如果您正苦于以下问题:Python networkx.fast_gnp_random_graph方法的具体用法?Python networkx.fast_gnp_random_graph怎么用?Python networkx.fast_gnp_random_graph使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类networkx
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在下文中一共展示了networkx.fast_gnp_random_graph方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1:
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_ErdősRényi_million_Fast_Gillespie_SIR(self):
print("testing ErdősRényi_million_Fast_Gillespie_SIR")
N = 10 ** 6 # number of individuals
kave = 5 # expected number of partners
G = nx.fast_gnp_random_graph(N, kave / (N - 1)) # Erdős-Rényi graph
rho = 0.005 # initial fraction infected
tau = 0.3 # transmission rate
gamma = 1.0 # recovery rate
t1, S1, I1, R1 = EoN.fast_SIR(G, tau, gamma, rho=rho)
t2, S2, I2, R2 = EoN.Gillespie_SIR(G, tau, gamma, rho=rho)
plt.plot(t1, I1, label='fast_SIR')
plt.plot(t2, I2, label='Gillespie_SIR')
plt.legend()
plt.savefig('test_ErdősRényi_million_Fast_Gillespie_SIR')
示例2: test_fast_nonMarkov_SIR
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_fast_nonMarkov_SIR(self):
def rec_time_fxn_gamma(u):
# gamma(shape, scale = 1.0)
return np.random.gamma(3, 0.5)
def trans_time_fxn(u, v, tau):
if tau > 0:
return np.random.exponential(1. / tau)
else:
return float('Inf')
N = 10 ** 6 # number of individuals
kave = 5 # expected number of partners
G = nx.fast_gnp_random_graph(N, kave / (N - 1)) # Erdős-Rényi graph
tau = 0.3
for cntr in range(10):
t, S, I, R = EoN.fast_nonMarkov_SIR(G, trans_time_fxn=trans_time_fxn,
rec_time_fxn=rec_time_fxn_gamma, trans_time_args=(tau,))
plt.plot(t, R)
plt.savefig('test_fast_nonMarkov_SIR')
示例3: test_quantum_jsd
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_quantum_jsd():
"""Run the above tests again using the collision entropy instead of the
Von Neumann entropy to ensure that all the logic of the JSD implementation
is tested.
"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="JSD is only a metric for 0 ≤ q < 2.")
JSD = distance.QuantumJSD()
G = nx.karate_club_graph()
dist = JSD.dist(G, G, beta=0.1, q=2)
assert np.isclose(dist, 0.0)
G1 = nx.fast_gnp_random_graph(100, 0.3)
G2 = nx.barabasi_albert_graph(100, 5)
dist = JSD.dist(G1, G2, beta=0.1, q=2)
assert dist > 0.0
G1 = nx.barabasi_albert_graph(100, 4)
G2 = nx.fast_gnp_random_graph(100, 0.3)
dist1 = JSD.dist(G1, G2, beta=0.1, q=2)
dist2 = JSD.dist(G2, G1, beta=0.1, q=2)
assert np.isclose(dist1, dist2)
示例4: test_directed_input
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_directed_input():
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="Coercing directed graph to undirected."
)
G = nx.fast_gnp_random_graph(100, 0.3, directed=True)
for label, obj in distance.__dict__.items():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
dist = obj().dist(G, G)
assert np.isclose(dist, 0.0)
G1 = nx.fast_gnp_random_graph(100, 0.3, directed=True)
G2 = nx.fast_gnp_random_graph(100, 0.3, directed=True)
for label, obj in distance.__dict__.items():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
dist1 = obj().dist(G1, G2)
dist2 = obj().dist(G2, G1)
assert np.isclose(dist1, dist2)
for obj in distance.__dict__.values():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
dist = obj().dist(G1, G2)
assert dist > 0.0
示例5: gen_data
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def gen_data(min_num_nodes=20,
max_num_nodes=100,
num_graphs=10,
node_emb_dim=10,
graph_emb_dim=2,
edge_prob=0.5,
seed=123):
"""
Generate synthetic graph data for graph regression, i.e., given node
embedding and graph structure as input, predict a graph embedding
as output.
N.B.: modification to other tasks like node classification is straightforward
A single graph in your dataset should contin:
X: Node embedding, numpy array, shape N X D where N is # nodes
A: Graph structure, numpy array, shape N X N X E where E is # edge types
Y: Graph embedding, numpy array, shape N X O
"""
npr = np.random.RandomState(seed)
N = npr.randint(min_num_nodes, high=max_num_nodes+1, size=num_graphs)
data = []
for ii in range(num_graphs):
data_dict = {}
data_dict['X'] = npr.randn(N[ii], node_emb_dim)
# we assume # edge type = 1, but you can easily extend it to be more than 1
data_dict['A'] = np.expand_dims(
nx.to_numpy_matrix(
nx.fast_gnp_random_graph(N[ii], edge_prob, seed=npr.randint(1000))),
axis=2)
data_dict['Y'] = npr.randn(1, graph_emb_dim)
data += [data_dict]
return data
示例6: get_gnp_generator
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def get_gnp_generator(args):
n = args.syn_gnp_n
p = (2 * np.log(n) / n) if args.syn_gnp_p == 0. else args.syn_gnp_p
def _gen(seed):
return nx.fast_gnp_random_graph(n, p, seed, True)
return _gen
示例7: addChaos
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def addChaos(di_graphs, k):
anomaly_time_steps = sorted(random.sample(range(len(di_graphs)), k))
for t in anomaly_time_steps:
n = di_graphs[t].number_of_nodes()
e = di_graphs[t].number_of_edges()
di_graphs[t] = nx.fast_gnp_random_graph(n, e / float(n * (n - 1)),
seed=None, directed=False)
di_graphs[t] = di_graphs[t].to_directed()
return di_graphs, anomaly_time_steps
示例8: random_k_edge_connected_graph
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def random_k_edge_connected_graph(size, k, p=.1, rng=None):
"""
Super hacky way of getting a random k-connected graph
Example:
>>> # ENABLE_DOCTEST
>>> import plottool_ibeis as pt
>>> from ibeis.algo.graph.nx_utils import * # NOQA
>>> size, k, p = 25, 3, .1
>>> rng = ut.ensure_rng(0)
>>> gs = []
>>> for x in range(4):
>>> G = random_k_edge_connected_graph(size, k, p, rng)
>>> gs.append(G)
>>> ut.quit_if_noshow()
>>> pnum_ = pt.make_pnum_nextgen(nRows=2, nSubplots=len(gs))
>>> fnum = 1
>>> for g in gs:
>>> pt.show_nx(g, fnum=fnum, pnum=pnum_())
"""
import sys
for count in it.count(0):
seed = None if rng is None else rng.randint(sys.maxsize)
# Randomly generate a graph
g = nx.fast_gnp_random_graph(size, p, seed=seed)
conn = nx.edge_connectivity(g)
# If it has exactly the desired connectivity we are one
if conn == k:
break
# If it has more, then we regenerate the graph with fewer edges
elif conn > k:
p = p / 2
# If it has less then we add a small set of edges to get there
elif conn < k:
# p = 2 * p - p ** 2
# if count == 2:
aug_edges = list(k_edge_augmentation(g, k))
g.add_edges_from(aug_edges)
break
return g
示例9: sample
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def sample(self):
return nx.fast_gnp_random_graph(
self.num_nodes, self.p, directed=self.directed)
示例10: ER_graph_generation
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def ER_graph_generation(N, kave):
return nx.fast_gnp_random_graph(N, kave/(N-1.))
示例11: test_discrete_SIR
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_discrete_SIR(self):
print('testing discrete_SIR')
passed = True
G = nx.fast_gnp_random_graph(1000, 0.004)
def test_trans_fxn(u, v):
'''
Transmissions occur if one odd and one even. So it is basically bipartite.
'''
if (u + v) % 2 == 0:
return False
else:
return True
sim = EoN.discrete_SIR(G, test_transmission=test_trans_fxn, args=(), initial_infecteds=[0, 2, 4, 6, 8, 10],
return_full_data=True)
# by initial condition and transmission rule, infection generations alternate parity.
for node in G:
if 'I' in sim.node_history(node)[1]:
idx = sim.node_history(node)[1].index('I')
if (node + sim.node_history(node)[0][idx]) % 2 == 1: # should always be False
print('Error', node, sim.node_history(node))
passed = False
print('number infected', sim.R()[-1])
if not passed:
print('failed')
else:
print('passed')
assert passed
示例12: test_basic_discrete_SIR
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_basic_discrete_SIR(self):
print('testing basic_discrete_SIR, percolation_based_discrete_SIR, and EBCM_discrete_from_graph')
plt.clf()
N = 1000000
initial_count = 5000
G = nx.fast_gnp_random_graph(N, 4. / N)
p = 0.4
sim = EoN.basic_discrete_SIR(G, p, initial_infecteds=range(initial_count), return_full_data=True)
t, S, I, R = sim.summary()
sim2 = EoN.percolation_based_discrete_SIR(G, p, initial_infecteds=range(initial_count), return_full_data=True)
t2, S2, I2, R2 = sim2.summary()
t3, S3, I3, R3 = EoN.EBCM_discrete_from_graph(G, p, rho=float(initial_count) / N)
t4, S4, I4, R4 = EoN.EBCM_discrete_from_graph(G, p, initial_infecteds=range(initial_count))
print(t)
print(S)
print(I)
print(R)
print(t[0:4], S[0:4], I[0:4], R[0:4])
print(t2[0:4], S2[0:4], I2[0:4], R2[0:4])
print(t3[0:4], S3[0:4], I3[0:4], R3[0:4])
print(t4[0:4], S4[0:4], I4[0:4], R4[0:4])
plt.plot(t, S, label='basic sim', alpha=0.3)
plt.plot(t, I, alpha=0.3)
plt.plot(t, R, alpha=0.3)
plt.plot(t2, S2, '--', label='percolation based', alpha=0.3)
plt.plot(t2, I2, '--', alpha=0.3)
plt.plot(t2, R2, '--', alpha=0.3)
plt.plot(t3, S3, '-.', label='Discrete EBCM', alpha=0.3)
plt.plot(t3, I3, '-.', alpha=0.3)
plt.plot(t3, R3, '-.', alpha=0.3)
plt.plot(t4, S4, ':', label='Discrete EBCM 2', alpha=0.3)
plt.plot(t4, I4, ':', alpha=0.3)
plt.plot(t4, R4, ':', alpha=0.3)
plt.legend(loc='upper right')
filename = 'basic_discrete_SIR_test'
plt.savefig(filename)
print("check {} for good match".format(filename))
示例13: test_estimate_SIR_prob_size
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_estimate_SIR_prob_size(self):
print('testing estimate_SIR_prob_size')
N = 1000000
G = nx.fast_gnp_random_graph(N, 5. / N)
for p in scipy.linspace(0.1, 0.5, 5):
P, A = EoN.estimate_SIR_prob_size(G, p)
gamma = 1.
tau = p * gamma / (1 - p)
P2, A2 = EoN.estimate_directed_SIR_prob_size(G, tau, 1.0)
t, S, I, R = EoN.EBCM_discrete_from_graph(G, p)
print("should all be approximately the same: ", R[-1] / G.order(), A, A2)
示例14: test_different_graphs
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_different_graphs():
""" The distance between two different graphs must be nonzero."""
## NOTE: This test is not totally rigorous. For example, two different
## networks may have the same eigenvalues, thus a method that compares
## their eigenvalues would result in distance 0. However, this is very
## unlikely in the constructed case, so we rely on it for now.
G1 = nx.fast_gnp_random_graph(100, 0.3)
G2 = nx.barabasi_albert_graph(100, 5)
for obj in distance.__dict__.values():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
dist = obj().dist(G1, G2)
assert dist > 0.0
示例15: test_symmetry
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import fast_gnp_random_graph [as 别名]
def test_symmetry():
"""The distance between two graphs must be symmetric."""
G1 = nx.barabasi_albert_graph(100, 4)
G2 = nx.fast_gnp_random_graph(100, 0.3)
for label, obj in distance.__dict__.items():
if isinstance(obj, type) and BaseDistance in obj.__bases__:
dist1 = obj().dist(G1, G2)
dist2 = obj().dist(G2, G1)
assert np.isclose(dist1, dist2)