def test_works_when_different(self):
"""Taking abs value of everything would lead to false
positives."""
a = np.array([[1, -1], [2, 2]])
b = np.array([[-1, -1], [2, 2]])
with self.assertRaises(AssertionError):
npt.assert_equal(*_normalize_signs(a, b))
def test_works_when_different(self):
"""Taking abs value of everything would lead to false
positives."""
a = np.array([[1, -1],
[2, 2]])
b = np.array([[-1, -1],
[2, 2]])
with self.assertRaises(AssertionError):
npt.assert_equal(*_normalize_signs(a, b))
def test_harder(self):
"""We don't want a value that might be negative due to
floating point inaccuracies to make a call to allclose in the
result to be off."""
a = np.array([[-1e-15, 1], [5, 2]])
b = np.array([[1e-15, 1], [5, 2]])
# Clearly a and b would refer to the same "column
# eigenvectors" but a slopppy implementation of
# _normalize_signs could change the sign of column 0 and make a
# comparison fail
npt.assert_almost_equal(*_normalize_signs(a, b))
def test_harder(self):
"""We don't want a value that might be negative due to
floating point inaccuracies to make a call to allclose in the
result to be off."""
a = np.array([[-1e-15, 1],
[5, 2]])
b = np.array([[1e-15, 1],
[5, 2]])
# Clearly a and b would refer to the same "column
# eigenvectors" but a slopppy implementation of
# _normalize_signs could change the sign of column 0 and make a
# comparison fail
npt.assert_almost_equal(*_normalize_signs(a, b))
def test_shapes_and_nonarray_input(self):
with self.assertRaises(ValueError):
_normalize_signs([[1, 2], [3, 5]], [[1, 2]])
def test_column_almost_zero(self):
a = np.array([[1e-15, 3],
[-2e-14, -6]])
b = np.array([[0, 3],
[-1e-15, -6]])
npt.assert_almost_equal(*_normalize_signs(a, b))
def test_column_zeros(self):
a = np.array([[0, 1],
[0, 2]])
b = np.array([[0, -1],
[0, -2]])
npt.assert_equal(*_normalize_signs(a, b))
def test_hard(self):
a = np.array([[0, 1],
[1, 2]])
b = np.array([[0, 1],
[-1, 2]])
npt.assert_equal(*_normalize_signs(a, b))
def test_zeros(self):
a = np.array([[0, 3],
[0, -1]])
b = np.array([[0, -3],
[0, 1]])
npt.assert_equal(*_normalize_signs(a, b))
def test_easy_already_equal(self):
a = np.array([[1, -2],
[3, 1]])
b = a.copy()
npt.assert_equal(*_normalize_signs(a, b))
def test_easy_different(self):
a = np.array([[1, 2],
[3, -1]])
b = np.array([[-1, 2],
[-3, -1]])
npt.assert_equal(*_normalize_signs(a, b))
