def fit(self, adjacency: Union[sparse.csr_matrix, np.ndarray], seeds: Union[np.ndarray, dict] = None) \
-> 'Propagation':
"""Node classification by label propagation.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
seeds :
Seed nodes. Can be a dict {node: label} or an array where "-1" means no label.
Returns
-------
self: :class:`Propagation`
"""
adjacency = check_format(adjacency)
n = adjacency.shape[0]
index_seed, index_remain, labels_seed = self._instanciate_vars(
adjacency, seeds)
if self.node_order == 'random':
np.random.shuffle(index_remain)
elif self.node_order == 'decreasing':
index = np.argsort(-adjacency.T.dot(np.ones(n))).astype(np.int32)
index_remain = index[index_remain]
elif self.node_order == 'increasing':
index = np.argsort(adjacency.T.dot(np.ones(n))).astype(np.int32)
index_remain = index[index_remain]
labels = -np.ones(n, dtype=np.int32)
labels[index_seed] = labels_seed
labels_remain = np.zeros_like(index_remain, dtype=np.int32)
indptr = adjacency.indptr.astype(np.int32)
indices = adjacency.indices.astype(np.int32)
if self.weighted:
data = adjacency.data.astype(np.float32)
else:
data = np.ones(n, dtype=np.float32)
t = 0
while t < self.n_iter and not np.array_equal(labels_remain,
labels[index_remain]):
t += 1
labels_remain = labels[index_remain].copy()
labels = np.asarray(
vote_update(indptr, indices, data, labels, index_remain))
membership = membership_matrix(labels)
membership = normalize(adjacency.dot(membership))
self.labels_ = labels
self.membership_ = membership
return self
def fit(self, adjacency: Union[sparse.csr_matrix, np.ndarray], seeds: Union[np.ndarray, dict]) \
-> 'Propagation':
"""Node classification by label propagation.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
seeds :
Seed nodes. Can be a dict {node: label} or an array where "-1" means no label.
Returns
-------
self: :class:`Propagation`
"""
adjacency = check_format(adjacency)
n = adjacency.shape[0]
index_seed, index_remain, labels_seed = self._instanciate_vars(
adjacency, seeds)
labels = -np.ones(n, dtype=np.int32)
labels[index_seed] = labels_seed
labels_remain = np.zeros_like(index_remain, dtype=np.int32)
indptr = adjacency.indptr.astype(np.int32)
indices = adjacency.indices.astype(np.int32)
t = 0
while t < self.n_iter and not np.array_equal(labels_remain,
labels[index_remain]):
t += 1
labels_remain = labels[index_remain].copy()
labels = vote_update(indptr, indices, labels, index_remain)
membership = membership_matrix(labels)
membership = normalize(adjacency.dot(membership))
self.labels_ = labels
self.membership_ = membership
return self