def _rebuild_covariance_matrix(self, covariance):
"""Rebuild the covariance matrix from its parameter values.
This method follows the steps:
* Rebuild a square matrix out of a triangular one.
* Add the missing half of the matrix by adding its transposed and
then removing the duplicated diagonal values.
* ensure the matrix is positive definite
Args:
covariance (list):
covariance values after unflattening model parameters.
Result:
list[list[float]]:
Symmetric positive semi-definite matrix.
"""
covariance = np.array(square_matrix(covariance))
covariance = (covariance + covariance.T -
(np.identity(covariance.shape[0]) * covariance))
if not check_matrix_symmetric_positive_definite(covariance):
covariance = make_positive_definite(covariance)
return covariance.tolist()
def test_check_matrix_symmetric_positive_definite():
"""Test check matrix numpy."""
# Run
matrix = np.array([[0.5, 0.5], [0.5, 0.5]])
result = check_matrix_symmetric_positive_definite(matrix)
# Asserts
assert result
def test_check_matrix_symmetric_positive_definite_np_error():
"""Test check matrix numpy raise error."""
# Run
matrix = np.array([[-1, 0], [0, 0]])
result = check_matrix_symmetric_positive_definite(matrix)
# Asserts
assert not result
def test_check_matrix_symmetric_positive_definite_shape_error():
"""Test check matrix shape error."""
# Run
matrix = np.array([])
result = check_matrix_symmetric_positive_definite(matrix)
# Asserts
assert not result
def _prepare_sampled_covariance(self, covariance):
"""Prepare a covariance matrix.
Args:
covariance (list):
covariance after unflattening model parameters.
Result:
list[list]:
symmetric Positive semi-definite matrix.
"""
covariance = np.array(square_matrix(covariance))
covariance = (covariance + covariance.T -
(np.identity(covariance.shape[0]) * covariance))
if not check_matrix_symmetric_positive_definite(covariance):
covariance = make_positive_definite(covariance)
return covariance.tolist()
