building symmetric adjacency matrix in utils

[marigoold]

learn-to-cluster/utils/adjacency.py

Lines 20 to 24 in 3b83458

	def build_symmetric_adj(adj, self_loop=True):
	adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
	if self_loop:
	adj = adj + sp.eye(adj.shape[0])
	return adj

The original adjacency matrix built from faiss is not symmetric, so what is the purpose to convert it to symmetric matrix? And why do you implement it in this way?

[marigoold]

learn-to-cluster/utils/adjacency.py

Lines 8 to 17 in 3b83458

	def row_normalize(mx):
	"""Row-normalize sparse matrix"""
	rowsum = np.array(mx.sum(1))
	# if rowsum <= 0, keep its previous value
	rowsum[rowsum <= 0] = 1
	r_inv = np.power(rowsum, -1).flatten()
	r_inv[np.isinf(r_inv)] = 0.
	r_mat_inv = sp.diags(r_inv)
	mx = r_mat_inv.dot(mx)
	return mx

By the way, what is the purpose of row_normalize, and # if rowsum <= 0, keep its previous value?