def _optimize(self, n_nodes, adjacency_norm, probs_out, probs_in):
"""One local optimization pass of the Louvain algorithm
Parameters
----------
n_nodes :
the number of nodes in the adjacency
adjacency_norm :
the norm of the adjacency
probs_out :
the array of degrees of the adjacency
probs_in :
the array of degrees of the transpose of the adjacency
Returns
-------
labels :
the communities of each node after optimization
pass_increase :
the increase in modularity gained after optimization
"""
node_probs_in = probs_in
node_probs_out = probs_out
adjacency = 0.5 * directed2undirected(adjacency_norm)
self_loops = adjacency.diagonal()
indptr: np.ndarray = adjacency.indptr
indices: np.ndarray = adjacency.indices
data: np.ndarray = adjacency.data
return fit_core(self.resolution, self.tol, n_nodes, node_probs_out,
node_probs_in, self_loops, data, indices, indptr)
def _optimize(self, adjacency_norm, probs_ou, probs_in):
"""One local optimization pass of the Louvain algorithm
Parameters
----------
adjacency_norm :
the norm of the adjacency
probs_ou :
the array of degrees of the adjacency
probs_in :
the array of degrees of the transpose of the adjacency
Returns
-------
labels :
the communities of each node after optimization
pass_increase :
the increase in modularity gained after optimization
"""
node_probs_in = probs_in.astype(np.float32)
node_probs_ou = probs_ou.astype(np.float32)
adjacency = 0.5 * directed2undirected(adjacency_norm)
self_loops = adjacency.diagonal().astype(np.float32)
indptr: np.ndarray = adjacency.indptr.astype(np.int32)
indices: np.ndarray = adjacency.indices.astype(np.int32)
data: np.ndarray = adjacency.data.astype(np.float32)
return fit_core(self.resolution, self.tol, node_probs_ou,
node_probs_in, self_loops, data, indices, indptr)