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From: Cem DEMiRKIR <cdemirkir@gm...>  20070516 18:00:09

Thanks for your information, I'm going to try it. Sincerely Cem DEMiRKIR Original Message From: Brendan McCane [mailto:mccane@...] Sent: Tuesday, May 15, 2007 23:56 To: Cem DEMiRKIR Subject: Re: [Vxlusers] Qt4 compatibility Hi Cem, I have recently added a qt4 implementation to the cvs version of VXL. Have you tried that one? On Tue, 20070515 at 20:43 +0300, Cem DEMiRKIR wrote: > Hi everyone > I'd like to know whether there is any person who succeeded in > building vgui library and the examples in MSVS2005 using Qt4. I'd be > grateful to anyone who share his/her knowledge to build and use Qt4 > with VXL VGUI library. > > Sincerely > Cem DEMiRKIR >  >  This SF.net email is sponsored by DB2 Express Download DB2 Express > C  the FREE version of DB2 express and take control of your XML. No > limits. Just data. Click to get it now. > http://sourceforge.net/powerbar/db2/ > _______________________________________________ Vxlusers mailing list > Vxlusers@... > https://lists.sourceforge.net/lists/listinfo/vxlusers  Cheers, Brendan.  Brendan McCane Email: mccane@... Department of Computer Science Phone: +64 3 479 8588/8578. University of Otago Fax: +64 3 479 8529 Box 56, Dunedin, New Zealand. 
From: Mathieu Malaterre <mathieu.malaterre@gm...>  20070516 16:09:06

Hi there, Alright, I think I finally understood how to use the vnl_levenberg_marquardt API to solve my problem. I am trying to fit a model function: a * ( 1.  b * exp( t / c ) ) to a set of 8 discretized signal values (so far nothing exceptional). Here is how I formulated my problem (*). It works quite well when I am *NOT* using the gradient (gradf virtual function). But it behaves incorrectly when I start using it. Could someone please comment on my code, and let me know if there is an obvious error ? thanks for your help, Mathieu PS: Included is a function test_deriv, I was trying to check that the value of my algebraic version of derivatives were consistant with finite difference and AFAIK it is. (*) #include <vnl/vnl_double_3.h> #include <vnl/vnl_least_squares_function.h> #include <vnl/algo/vnl_levenberg_marquardt.h> #include <iostream> static const double signal[] = { 13.5942, 10.2358, 7.54658, 5.39323, 3.66897, 2.28828, 1.18272, 0.297451, }; struct vnl_rosenbrock : public vnl_least_squares_function { static const unsigned int nsignals = sizeof (signal) / sizeof (double); vnl_rosenbrock(bool with_grad): vnl_least_squares_function(3, nsignals, with_grad ? use_gradient : no_gradient) {} // helpers: static double compute(double t, double a, double b, double c) { return a * ( 1.  b * exp( t / c ) ); } static double compute_a(double t, double a, double b, double c) { return ( 1.  b * exp( t / c) ); } static double compute_b(double t, double a, double b, double c) { return a * exp( t / c ); } static double compute_c(double t, double a, double b, double c) { return  t * a * b / (c * c) * exp( t / c ); } void f(vnl_vector<double> const& x, vnl_vector<double>& y) { for (unsigned int i=0; i<nsignals; ++i) { y[i] = compute(i, x(0), x(1), x(2) )  signal[i]; } } void gradf(vnl_vector<double> const& x, vnl_matrix<double> &J) { for (unsigned int i=0; i<nsignals; ++i) { J[0][i] = compute_a(i, x(0), x(1), x(2) ); } for (unsigned int i=0; i<nsignals; ++i) { J[1][i] = compute_b(i, x(0), x(1), x(2) ); } for (unsigned int i=0; i<nsignals; ++i) { J[2][i] = compute_c(i, x(0), x(1), x(2) ); } } }; void test_deriv(const int row = 0) { const double valref = 17.1; const double h = 0.1; vnl_rosenbrock f(false); vnl_matrix<double> myj(3,8); vnl_vector<double> myx(3); myx[0] = myx[1] = myx[2] = valref; f.gradf(myx, myj); std::cout << "my:" << myj[0][row] << std::endl; std::cout << "my:" << myj[1][row] << std::endl; std::cout << "my:" << myj[2][row] << std::endl; vnl_vector<double> myf_h1(8); vnl_vector<double> myf_h2(8); double comp[3]; myx[0] = myx[1] = myx[2] = valref; myx[0] = valref  h; f.f(myx, myf_h1); myx[0] = valref + h; f.f(myx, myf_h2); comp[0] = (myf_h2[row]  myf_h1[row]) / (2 * h); myx[0] = myx[1] = myx[2] = valref; myx[1] = valref  h; f.f(myx, myf_h1); myx[1] = valref + h; f.f(myx, myf_h2); comp[1] = (myf_h2[row]  myf_h1[row]) / (2 * h); myx[0] = myx[1] = myx[2] = valref; myx[2] = valref  h; f.f(myx, myf_h1); myx[2] = valref + h; f.f(myx, myf_h2); comp[2] = (myf_h2[row]  myf_h1[row]) / (2 * h); std::cout << "comp:=" << comp[0] << std::endl; std::cout << "comp:=" << comp[1] << std::endl; std::cout << "comp:=" << comp[2] << std::endl; } int main(int, char *[]) { //test_deriv(3); vnl_rosenbrock f(true); vnl_double_3 x0(1.,1.,1.); vnl_levenberg_marquardt lm(f); vnl_vector<double> x1 = x0.as_vector(); if (f.has_gradient()) lm.minimize_using_gradient(x1); else lm.minimize_without_gradient(x1); lm.diagnose_outcome(std::cout); std::cout << "x1 = " << x1 << std::endl; vnl_double_3 solution(3.26, 5.17, 4.5); double err = (x1  solution).magnitude(); std::cout << "err = " << err << std::endl; return 0; } 
From: Ian Scott <ian.scott@st...>  20070516 09:51:59

Mathieu Malaterre wrote: > [Hopefully the subject will not be considered as spam...] > > Hello, > > I am getting this weird error when using the vnl_lbfgs: > > > vnl_lbfgs: n = 3, memory = 5, Workspace = 43[ 0.000328064 MB], > ErrorScale = 1, xnorm = 7.59671 > vnl_lbfgs: 1:1.26056 1:53.808 1:0.24621 1:0.244475 1:0.261946 > 1:0.244681 1:0.244425 1:0.244519 1:0.244421 1:0.244466 1:0.244424 > 1:0.244421 1:0.244421 1:0.244421 1:0.244421 1:0.244421 1:0.244421 > 1:0.244421 1:0.244421 1:0.244421 1:0.244421 1:0.244421 1:0.244421 > vnl_lbfgs: ** EEEK ** > done > IFLAG= 1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH > ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR > GRADIENT ARE INCORRECT OR INCORRECT TOLERANCES > 3.20458 5.16978 4.68394 > 1 > Evaluations: 22 > > > As far as I understand INFO=3 means that "MORE THAN 20 FUNCTION > EVALUATIONS WERE REQUIRED AT THE PRESENT ITERATION.". Is there a way > to change this default value ? I assume that you have sensible tolerances, etc. It would seem unlikely that increasing the number of iterations in the line search would solve your problem. 20 iterations should be enough to find a minimum in a 1D problem. It is more likely that your function is behaving badly. However, if you dig into the netlib code, you should be able to find the value and change it. Either way, you really need to instrument your cost function. Print out x, f(x) (and f'(x) if you calculate it) at every evaluation. If it really is failing in a linesearch, then the last 20ish evaluations should have colinear values of x. If you plot the line position against f(x) you should be able to tell what it going on. If you are providing f'(x), it would probably also be a good idea to check the value against a forwarddifferences estimate. Ian. 