def recurse_concat(
x: sparse.csr_matrix,
vectors_e: sparse.csr_matrix,
seq: list,
maxlen: int = 128,
top_n: int = 1,
scale: float = 1.25,
idx2idx_canon: dict = None,
) -> None:
if len(seq) < maxlen:
matrix_dist = metrics.pairwise.cosine_distances(x, vectors_e)
ranks = [np.argsort(distances) for distances in matrix_dist]
assert len(ranks) == 1
rank = ranks[0]
seen = set(seq)
count = 0
for idx in rank:
if count == top_n:
break
idx_canon = idx if idx2idx_canon is None else idx2idx_canon[idx]
if idx_canon not in seen:
e = vectors_e[idx] / (scale**len(seq))
new = x.maximum(e)
seq.append(idx_canon)
count += 1
recurse_concat(new, vectors_e, seq)
def directed2undirected(adjacency: sparse.csr_matrix,
weighted: bool = True) -> sparse.csr_matrix:
if weighted:
return sparse.csr_matrix(adjacency + adjacency.T)
else:
return adjacency.maximum(adjacency.T)
