Coverage for NeuralTSNE/NeuralTSNE/Utils/Preprocessing/Normalizers/tests/test_normalizers.py: 100%

1import pytest 

2import torch 

3 

4from NeuralTSNE.Utils.Preprocessing.Normalizers import normalize_columns 

5 

6 

7@pytest.mark.parametrize( 

8 "input, expected", 

9 [ 

10 ([1, 2, 10], [0.0, 1 / 9, 1.0]), 

11 ([[5, 6, 7], [9, 3, 6]], [[0.0, 1.0, 1.0], [1.0, 0.0, 0.0]]), 

12 ( 

13 [[2, 7, 9], [4, 3, 5], [8, 1, 6]], 

14 [[0.0, 1.0, 1.0], [2 / 6, 2 / 6, 0.0], [1.0, 0.0, 1 / 4]], 

15 ), 

16 ], 

17) 

18def test_normalize_columns(input, expected): 

19 tensor = torch.tensor(input) 

20 expected = torch.tensor(expected) 

21 result = normalize_columns(tensor) 

22 assert torch.allclose(result, expected)