/**
* Get the covariance matrix of unbound estimated parameters.
*
* @param problem estimation problem
* @return covariance matrix
* @exception EstimationException if the covariance matrix cannot be computed (singular problem)
*/
public double[][] getCovariances(EstimationProblem problem) throws EstimationException {
// set up the jacobian
updateJacobian();
// compute transpose(J).J, avoiding building big intermediate matrices
final int n = problem.getMeasurements().length;
final int m = problem.getUnboundParameters().length;
final int max = m * n;
double[][] jTj = new double[m][m];
for (int i = 0; i < m; ++i) {
for (int j = i; j < m; ++j) {
double sum = 0;
for (int k = 0; k < max; k += m) {
sum += jacobian[k + i] * jacobian[k + j];
}
jTj[i][j] = sum;
jTj[j][i] = sum;
}
}
try {
// compute the covariances matrix
RealMatrix inverse =
new LUDecompositionImpl(MatrixUtils.createRealMatrix(jTj)).getSolver().getInverse();
return inverse.getData();
} catch (InvalidMatrixException ime) {
throw new EstimationException("unable to compute covariances: singular problem");
}
}
