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- import numpy as np
- import pytest
- 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
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