1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
|
import pytest
import numpy as np
from sklearn.utils._testing import assert_allclose
from sklearn.utils.arrayfuncs import min_pos
def test_min_pos():
# Check that min_pos returns a positive value and that it's consistent
# between float and double
X = np.random.RandomState(0).randn(100)
min_double = min_pos(X)
min_float = min_pos(X.astype(np.float32))
assert_allclose(min_double, min_float)
assert min_double >= 0
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_min_pos_no_positive(dtype):
# Check that the return value of min_pos is the maximum representable
# value of the input dtype when all input elements are <= 0 (#19328)
X = np.full(100, -1.0).astype(dtype, copy=False)
assert min_pos(X) == np.finfo(dtype).max
|
