def _construct_edge_list_from_distance(X, k_neigh):
"""
construct edge_list (numpy array) from kNN distance for single modality
:param X -> numpy array: feature
:param k_neigh -> int: # of neighbors
:return: N * k_neigh numpy array
"""
dis = cos_dis(X)
dis = torch.Tensor(dis)
_, k_idx = dis.topk(k_neigh, dim=-1, largest=False)
return k_idx.numpy()
def construct_H_with_KNN(X, k_neig=10, is_probH=False, m_prob=1):
"""
init multi-scale hypergraph Vertex-Edge matrix from original node feature matrix
:param X: N_object x feature_number
:param K_neigs: the number of neighbor expansion
:param is_probH: prob Vertex-Edge matrix or binary
:param m_prob: prob
:return: N_object x N_hyperedge
"""
if len(X.shape) != 2:
X = X.reshape(-1, X.shape[-1])
dis_mat = cos_dis(X)
H = construct_H_with_KNN_from_distance(dis_mat, k_neig, is_probH, m_prob)
return H
def construct_H_with_KNN(X, K_neigs=[10], is_probH=False, m_prob=1):
"""
init multi-scale hypergraph Vertex-Edge matrix from original node feature matrix
:param X: N_object x feature_number
:param K_neigs: the number of neighbor expansion
:param is_probH: prob Vertex-Edge matrix or binary
:param m_prob: prob
:return: N_object x N_hyperedge
"""
if len(X.shape) != 2:
X = X.reshape(-1, X.shape[-1])
if type(K_neigs) == int:
K_neigs = [K_neigs]
dis_mat = cos_dis(X)
H = None
for k_neig in K_neigs:
H_tmp = construct_H_with_KNN_from_distance(dis_mat, k_neig, is_probH, m_prob)
H = hyperedge_concat(H, H_tmp)
return H