|
1234567891011121314151617181920212223242526272829303132333435363738 |
- from triacontagon.data import Data
- from triacontagon.sampling import negative_sample_adj_mat, \
- negative_sample_data
- from triacontagon.decode import dedicom_decoder
- import torch
-
-
- def test_negative_sample_adj_mat_01():
- adj_mat = torch.tensor([
- [0, 1, 0, 1, 0],
- [0, 0, 0, 0, 1],
- [1, 1, 0, 0, 0],
- [0, 0, 1, 0, 1],
- [0, 1, 0, 0, 0]
- ])
-
- print('adj_mat:', adj_mat)
-
- adj_mat_neg = negative_sample_adj_mat(adj_mat)
-
- print('adj_mat_neg:', adj_mat_neg.to_dense())
-
-
- def test_negative_sample_data_01():
- d = Data()
- d.add_vertex_type('Gene', 5)
-
- d.add_edge_type('Gene-Gene', 0, 0, [
- torch.tensor([
- [0, 1, 0, 1, 0],
- [0, 0, 0, 0, 1],
- [1, 1, 0, 0, 0],
- [0, 0, 1, 0, 1],
- [0, 1, 0, 0, 0]
- ], dtype=torch.float).to_sparse()
- ], dedicom_decoder)
-
- d_neg = negative_sample_data(d)
|