27 lines
792 B
Python
27 lines
792 B
Python
|
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.).astype(dtype, copy=False)
|
||
|
|
|
||
|
|
assert min_pos(X) == np.finfo(dtype).max
|