def fit(self, adjacency: Union[sparse.csr_matrix, np.ndarray]) -> 'LouvainHierarchy':
"""Fit algorithm to data.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
Returns
-------
self: :class:`LouvainHierarchy`
"""
adjacency = check_format(adjacency)
check_square(adjacency)
tree = self._recursive_louvain(adjacency, self.depth)
dendrogram, _ = get_dendrogram(tree)
dendrogram = np.array(dendrogram)
dendrogram[:, 2] -= min(dendrogram[:, 2])
self.dendrogram_ = reorder_dendrogram(dendrogram)
return self
def fit(
self, input_matrix: Union[sparse.csr_matrix,
np.ndarray]) -> 'LouvainHierarchy':
"""Fit algorithm to data.
Parameters
----------
input_matrix :
Adjacency matrix or biadjacency matrix of the graph.
Returns
-------
self: :class:`LouvainHierarchy`
"""
self._init_vars()
adjacency, self.bipartite = get_adjacency(input_matrix)
tree = self._recursive_louvain(adjacency, self.depth)
dendrogram, _ = get_dendrogram(tree)
dendrogram = np.array(dendrogram)
dendrogram[:, 2] -= min(dendrogram[:, 2])
self.dendrogram_ = reorder_dendrogram(dendrogram)
if self.bipartite:
self._split_vars(input_matrix.shape)
return self