本文整理汇总了Python中networkx.jaccard_coefficient方法的典型用法代码示例。如果您正苦于以下问题：Python networkx.jaccard_coefficient方法的具体用法？Python networkx.jaccard_coefficient怎么用？Python networkx.jaccard_coefficient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的用法示例。
在下文中一共展示了networkx.jaccard_coefficient方法的6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: __init__
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import jaccard_coefficient [as 别名]
def __init__(self, *hyper_dict, **kwargs):
''' Initialize the JaccardCoefficient class
Args:
d: dimension of the embedding
beta: higher order coefficient
'''
hyper_params = {
'method_name': 'jaccard_coefficient'
}
hyper_params.update(kwargs)
for key in hyper_params.keys():
self.__setattr__('_%s' % key, hyper_params[key])
for dictionary in hyper_dict:
for key in dictionary:
self.__setattr__('_%s' % key, dictionary[key]) 示例2: get_edge_weight
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import jaccard_coefficient [as 别名]
def get_edge_weight(self, i, j):
aa_index = nx.jaccard_coefficient(self._G, [(i, j)])
return six.next(aa_index)[2] 示例3: jaccard_coeff
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import jaccard_coefficient [as 别名]
def jaccard_coeff(self):
"""Computes Jaccard coefficients."""
graph = nx.from_scipy_sparse_matrix(self.adj_matrix)
coeffs = nx.jaccard_coefficient(graph)
return coeffs 示例4: setUp
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import jaccard_coefficient [as 别名]
def setUp(self):
self.func = nx.jaccard_coefficient
self.test = partial(_test_func, predict_func=self.func) 示例5: jaccard_coefficient
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import jaccard_coefficient [as 别名]
def jaccard_coefficient(G, ebunch=None):
r"""Compute the Jaccard coefficient of all node pairs in ebunch.
Jaccard coefficient of nodes `u` and `v` is defined as
.. math::
\frac{|\Gamma(u) \cap \Gamma(v)|}{|\Gamma(u) \cup \Gamma(v)|}
where :math:`\Gamma(u)` denotes the set of neighbors of `u`.
Parameters
----------
G : graph
A NetworkX undirected graph.
ebunch : iterable of node pairs, optional (default = None)
Jaccard coefficient will be computed for each pair of nodes
given in the iterable. The pairs must be given as 2-tuples
(u, v) where u and v are nodes in the graph. If ebunch is None
then all non-existent edges in the graph will be used.
Default value: None.
Returns
-------
piter : iterator
An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
pair of nodes and p is their Jaccard coefficient.
Examples
--------
>>> import networkx as nx
>>> G = nx.complete_graph(5)
>>> preds = nx.jaccard_coefficient(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
... '(%d, %d) -> %.8f' % (u, v, p)
...
'(0, 1) -> 0.60000000'
'(2, 3) -> 0.60000000'
References
----------
.. [1] D. Liben-Nowell, J. Kleinberg.
The Link Prediction Problem for Social Networks (2004).
http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
"""
if ebunch is None:
ebunch = nx.non_edges(G)
def predict(u, v):
cnbors = list(nx.common_neighbors(G, u, v))
union_size = len(set(G[u]) | set(G[v]))
if union_size == 0:
return 0
else:
return len(cnbors) / union_size
return ((u, v, predict(u, v)) for u, v in ebunch) 示例6: jaccard_coefficient
# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import jaccard_coefficient [as 别名]
def jaccard_coefficient(G, ebunch=None):
r"""Compute the Jaccard coefficient of all node pairs in ebunch.
Jaccard coefficient of nodes `u` and `v` is defined as
.. math::
\frac{|\Gamma(u) \cap \Gamma(v)|}{|\Gamma(u) \cup \Gamma(v)|}
where $\Gamma(u)$ denotes the set of neighbors of $u$.
Parameters
----------
G : graph
A NetworkX undirected graph.
ebunch : iterable of node pairs, optional (default = None)
Jaccard coefficient will be computed for each pair of nodes
given in the iterable. The pairs must be given as 2-tuples
(u, v) where u and v are nodes in the graph. If ebunch is None
then all non-existent edges in the graph will be used.
Default value: None.
Returns
-------
piter : iterator
An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
pair of nodes and p is their Jaccard coefficient.
Examples
--------
>>> import networkx as nx
>>> G = nx.complete_graph(5)
>>> preds = nx.jaccard_coefficient(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
... '(%d, %d) -> %.8f' % (u, v, p)
...
'(0, 1) -> 0.60000000'
'(2, 3) -> 0.60000000'
References
----------
.. [1] D. Liben-Nowell, J. Kleinberg.
The Link Prediction Problem for Social Networks (2004).
http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
"""
def predict(u, v):
union_size = len(set(G[u]) | set(G[v]))
if union_size == 0:
return 0
return len(list(nx.common_neighbors(G, u, v))) / union_size
return _apply_prediction(G, predict, ebunch)