def fit(self, adjacency: Union[sparse.csr_matrix,
np.ndarray]) -> 'Harmonic':
"""Harmonic centrality for connected graphs.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
Returns
-------
self: :class:`Harmonic`
"""
adjacency = check_format(adjacency)
check_square(adjacency)
n = adjacency.shape[0]
indices = np.arange(n)
paths = shortest_path(adjacency, n_jobs=self.n_jobs, indices=indices)
np.fill_diagonal(paths, 1)
inv = (1 / paths)
np.fill_diagonal(inv, 0)
self.scores_ = inv.dot(np.ones(n))
return self
def fit(self, adjacency: Union[sparse.csr_matrix,
np.ndarray]) -> 'Closeness':
"""Closeness centrality for connected graphs.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
Returns
-------
self: :class:`Closeness`
"""
adjacency = check_format(adjacency)
check_square(adjacency)
check_connected(adjacency)
n = adjacency.shape[0]
if self.method == 'exact':
nb_samples = n
indices = np.arange(n)
elif self.method == 'approximate':
nb_samples = min(int(log(n) / self.tol**2), n)
indices = np.random.choice(np.arange(n), nb_samples, replace=False)
else:
raise ValueError(
"Method should be either 'exact' or 'approximate'.")
paths = shortest_path(adjacency, n_jobs=self.n_jobs, indices=indices)
self.scores_ = (
(n - 1) * nb_samples / n) / paths.T.dot(np.ones(nb_samples))
return self
def fit(self, adjacency: Union[sparse.csr_matrix,
np.ndarray]) -> 'Harmonic':
"""
Harmonic centrality for connected graphs.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
Returns
-------
self: :class:`Harmonic`
"""
adjacency = check_format(adjacency)
n = adjacency.shape[0]
if not is_square(adjacency):
raise ValueError(
"The adjacency is not square. Please use 'bipartite2undirected' or "
"'bipartite2directed'.")
indices = np.arange(n)
paths = shortest_path(adjacency, n_jobs=self.n_jobs, indices=indices)
np.fill_diagonal(paths, 1)
inv = (1 / paths)
np.fill_diagonal(inv, 0)
self.scores_ = inv.dot(np.ones(n))
return self
def test_predecessors(self):
adjacency = karate_club()
_, predecessors = shortest_path(adjacency, return_predecessors=True)
self.assertTupleEqual(predecessors.shape, adjacency.shape)
def test_parallel(self):
adjacency = karate_club()
path1 = shortest_path(adjacency)
path2 = shortest_path(adjacency, n_jobs=-1)
self.assertTrue((path1 == path2).all())
def test_parallel(self):
karate = karate_club()
truth = shortest_path(karate)
parallel = shortest_path(karate, n_jobs=None)
self.assertEqual(np.equal(truth, parallel).sum(), 34 ** 2)