Ah, I managed to get rid of the error, as I remembered your suggestion I should take a look on the bevingtonTest. However, the results seem to be wrong, so most likely I made a mistake calculating the derivatives.
https://github.com/cr0w3/ExponentialLeastSquaredProblem.git I uploaded the project on GIT so it's easier to check my code. :) Greetings, Thom On Sat, Sep 12, 2015 at 1:12 PM, Thom Brown <thomas.braunsber...@gmail.com> wrote: > Hey, > > I think I'm almost set up and I'm positive about my jacobian, but I can't > solve two - rather simple? - problems on my own. > > double[] prescribedValues = new double[observedValues.length]; >> for (int i = 0; i < prescribedValues.length; i++) { >> prescribedValues[i] = observedValues[i]; >> } >> > > I don't know if that makes sense. All I do here is to set the target on my > observations. Because I can't think of another way to get observed "F" > values, but I think that's rather correct. > > However: > > >> RealVector startVector = new ArrayRealVector(new double[] { 1.0, 0.0 }); >> startVector.append(new ArrayRealVector(new double[] { 1.0, 0.0 })); >> startVector.append(new ArrayRealVector(new double[] { 1.0, 0.0 })); >> LeastSquaresProblem problem = new LeastSquaresBuilder().start(startVector >> ).model(distanceToCurrentF) >> .target(prescribedValues).lazyEvaluation(false >> ).maxEvaluations(1000).maxIterations(100).build(); > > > this won't work. What I want to do is to set three points each for alpha, > beta and gamma that contain the possible values (0 <= alpha, beta, gamma <= > 1). Apparently equal entries are merged to one? Anyways, I also doubt I can > access my parameters by using: > > Vector3D approx = new Vector3D(params.getEntry(0), params.getEntry(1), >> params.getEntry(2)); > > [...] > > helper.calculateSmoothedObservation(i, approx.getX(), approx.getY(), >> approx.getZ()); > > > at the very beginning of my jacobian function. > calculatedSmoothedObservation(int index, double alpha, double beta, double > gamma) should always update the S, b and I parameters by using the alpha, > beta and gamma values of the optimization. I think I'm not on the right > track here. > > I'm positive that we (okay, you did more than I did here) can solve that > too :P > > Greetings, > Thom >
