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Re: Alternative solution for NAN
Posted:
Feb 27, 2013 11:47 AM


"Carl S." <tkittler@gmail.com> wrote in message news:kgkurb$1t8$1@newscl01ah.mathworks.com... > "Torsten" wrote in message <kgku2t$t2g$1@newscl01ah.mathworks.com>...
*snip*
>> The matrix N you get after the while loop is a scalar multiple of the >> identity matrix and in general has nothing in common with your original >> matrix N. You will have to find out why eig produces NaN values for your >> original matrix N. Are you sure all elements of N are finite ? Best >> wishes >> Torsten. > > Yes, Torsten, they are finite > > My goal is to fit means(mu) and standard deviations(N) to Gaussian shape. > The codes that I wrote above are from the function ; > > function res=MultivariateGaussianPDF(x,mu,N) > while(det(N) == 0)
1) Don't use DET to test for singularity. This matrix:
A = 1e10*eye(400);
has determinant 0 (due to underflow) but it's a scaled identity matrix, which is about as wellbehaved as you can get. If you _must_ test for singularity, check with COND or RCOND.
2) Don't test a floatingpoint number for exact, bitforbit equality unless you need exact, bitforbit equality. Compare with a tolerance instead.
> N=(1e10.*randi(1,size(N)))*eye(size(N)); > end > > [M,d]=size(x); > [U,D]=eig(N); % <=causes NAN problem :((
Show the group a SMALL matrix N with which you can reproduce this behavior.
*snip*
3) If you have Statistics Toolbox, do one of these functions do what you want?
http://www.mathworks.com/help/stats/multivariatenormaldistribution1.html
http://www.mathworks.com/help/stats/normaldistribution1.html
If not, please explain more _in words not code or equations_ specifically what you mean/are trying to do when you say you want to fit means and standard deviations to a Gaussian shape.
 Steve Lord slord@mathworks.com To contact Technical Support use the Contact Us link on http://www.mathworks.com



