def test__get_nearest_correlation_matrix_invalid(self):
"""Test ``_get_nearest_correlation_matrix`` with a non psd input.
If the matrix is not positive semi-definite, modify it to make it PSD.
Input:
- matrix which is not positive semi-definite.
Expected Output:
- modified matrix which is positive semi-definite.
"""
# Run
not_psd_matrix = np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, -1],
])
output = GaussianCopula._get_nearest_correlation_matrix(not_psd_matrix)
# Assert
expected = [
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
]
assert expected == output.tolist()
not_psd_eigenvalues = scipy.linalg.eigh(not_psd_matrix)[0]
output_eigenvalues = scipy.linalg.eigh(output)[0]
assert (not_psd_eigenvalues < 0).any()
assert (output_eigenvalues >= 0).all()
def test__get_nearest_correlation_matrix_valid(self):
"""Test ``_get_nearest_correlation_matrix`` with a psd input.
If the matrix is positive semi-definite, do nothing.
Input:
- matrix which is positive semi-definite.
Expected Output:
- the input, unmodified.
"""
# Run
correlation_matrix = np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
])
output = GaussianCopula._get_nearest_correlation_matrix(
correlation_matrix)
# Assert
expected = [
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
]
assert expected == output.tolist()
assert output is correlation_matrix
