本文整理汇总了Python中networkx.katz_centrality方法的典型用法代码示例。如果您正苦于以下问题：Python networkx.katz_centrality方法的具体用法？Python networkx.katz_centrality怎么用？Python networkx.katz_centrality使用的例子？那么恭喜您, 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的用法示例。
在下文中一共展示了networkx.katz_centrality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: _calc_graph_func
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def _calc_graph_func(p):
con, times_chunk, graph_func = p
vals = []
now = time.time()
for run, t in enumerate(times_chunk):
utils.time_to_go(now, run, len(times_chunk), 10)
con_t = con[:, :, t]
g = nx.from_numpy_matrix(con_t)
if graph_func == 'closeness_centrality':
x = nx.closeness_centrality(g)
elif graph_func == 'degree_centrality':
x = nx.degree_centrality(g)
elif graph_func == 'eigenvector_centrality':
x = nx.eigenvector_centrality(g, max_iter=10000)
elif graph_func == 'katz_centrality':
x = nx.katz_centrality(g, max_iter=100000)
else:
raise Exception('Wrong graph func!')
vals.append([x[k] for k in range(len(x))])
vals = np.array(vals)
return vals, times_chunk 示例2: __init__
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def __init__(self, method='degree', analyzer=NltkNormalizer().split_and_normalize):
self.analyze = analyzer
self.method = method
self.methods_on_digraph = {'hits', 'pagerank', 'katz'}
self._get_scores = {'degree': nx.degree, 'betweenness': nx.betweenness_centrality,
'pagerank': nx.pagerank_scipy, 'hits': self._hits, 'closeness': nx.closeness_centrality,
'katz': nx.katz_centrality}[method]
# Add a new value when a new vocabulary item is seen
self.vocabulary = defaultdict()
self.vocabulary.default_factory = self.vocabulary.__len__ 示例3: test_K5
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_K5(self):
"""Katz centrality: K5"""
G = networkx.complete_graph(5)
alpha = 0.1
b = networkx.katz_centrality(G, alpha)
v = math.sqrt(1 / 5.0)
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = networkx.katz_centrality(G, alpha, nstart=nstart)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n]) 示例4: test_P3
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_P3(self):
"""Katz centrality: P3"""
alpha = 0.1
G = networkx.path_graph(3)
b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
2: 0.5598852584152162}
b = networkx.katz_centrality(G, alpha)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4) 示例5: test_maxiter
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_maxiter(self):
alpha = 0.1
G = networkx.path_graph(3)
max_iter = 0
try:
b = networkx.katz_centrality(G, alpha, max_iter=max_iter)
except networkx.NetworkXError as e:
assert str(max_iter) in e.args[0], "max_iter value not in error msg"
raise # So that the decorater sees the exception. 示例6: test_beta_as_scalar
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_beta_as_scalar(self):
alpha = 0.1
beta = 0.1
b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
2: 0.5598852584152162}
G = networkx.path_graph(3)
b = networkx.katz_centrality(G, alpha, beta)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4) 示例7: test_beta_as_dict
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_beta_as_dict(self):
alpha = 0.1
beta = {0: 1.0, 1: 1.0, 2: 1.0}
b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
2: 0.5598852584152162}
G = networkx.path_graph(3)
b = networkx.katz_centrality(G, alpha, beta)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4) 示例8: test_multigraph
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_multigraph(self):
e = networkx.katz_centrality(networkx.MultiGraph(), 0.1) 示例9: test_empty
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_empty(self):
e = networkx.katz_centrality(networkx.Graph(), 0.1)
assert_equal(e, {}) 示例10: test_bad_beta
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_bad_beta(self):
G = networkx.Graph([(0,1)])
beta = {0:77}
e = networkx.katz_centrality(G, 0.1,beta=beta) 示例11: test_bad_beta_numbe
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_bad_beta_numbe(self):
G = networkx.Graph([(0,1)])
e = networkx.katz_centrality(G, 0.1,beta='foo') 示例12: test_K5_unweighted
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_K5_unweighted(self):
"""Katz centrality: K5"""
G = networkx.complete_graph(5)
alpha = 0.1
b = networkx.katz_centrality(G, alpha, weight=None)
v = math.sqrt(1 / 5.0)
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = networkx.eigenvector_centrality_numpy(G)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=3) 示例13: test_katz_centrality_weighted
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_katz_centrality_weighted(self):
G = self.G
alpha = self.G.alpha
p = networkx.katz_centrality(G, alpha)
for (a, b) in zip(list(p.values()), self.G.evc):
assert_almost_equal(a, b) 示例14: test_katz_centrality_unweighted
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_katz_centrality_unweighted(self):
G = self.H
alpha = self.H.alpha
p = networkx.katz_centrality(G, alpha)
for (a, b) in zip(list(p.values()), self.G.evc):
assert_almost_equal(a, b) 示例15: test_K5
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import katz_centrality [as 别名]
def test_K5(self):
"""Katz centrality: K5"""
G = nx.complete_graph(5)
alpha = 0.1
b = nx.katz_centrality(G, alpha)
v = math.sqrt(1 / 5.0)
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.katz_centrality(G, alpha, nstart=nstart)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])