- 原链接：邻接矩阵转稀疏矩阵
邻接矩阵转稀疏矩阵
Example：
import scipy.sparse as sp
import numpy as np
import torch
adj_matrix = torch.randint(0,2,(4,4))
print(adj_matrix)
# 输出：
# tensor([[1, 1, 0, 0],
# [0, 1, 0, 1],
# [0, 0, 1, 1],
# [1, 0, 0, 0]])
# adj_matrix 是邻接矩阵
tmp_coo = sp.coo_matrix(adj_matrix)
values = tmp_coo.data
indices = np.vstack((tmp_coo.row,tmp_coo.col))
i = torch.LongTensor(indices)
v = torch.LongTensor(values)
edge_index=torch.sparse_coo_tensor(i,v,tmp_coo.shape)
print(edge_index)
# 输出：
#tensor(indices=tensor([[0, 0, 1, 1, 2, 2, 3],
# [0, 1, 1, 3, 2, 3, 0]]),
# values=tensor([1, 1, 1, 1, 1, 1, 1]),
# size=(4, 4), nnz=7, layout=torch.sparse_coo)
torch 矩阵转numpy 矩阵
A = torch.load('adj1.pt')
A = A.numpy()
numpy 矩阵转 scipy 稀疏矩阵
A = sp.coo_matrix(A)
scipy 稀疏矩阵转numpy 矩阵
A.toarray()
将 Scipy Sparse 矩阵转换成 torch sparse 矩阵
def sparse_mx_to_torch_sparse_tensor(sparse_mx):
"""Convert a scipy sparse matrix to a torch sparse tensor."""
sparse_mx = sparse_mx.tocoo().astype(np.float32)
indices = torch.from_numpy(
np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
values = torch.from_numpy(sparse_mx.data)
shape = torch.Size(sparse_mx.shape)
return torch.sparse.FloatTensor(indices, values, shape)
torch sparse矩阵转 torch 稠密矩阵
sparse_adj.to_dense()