"Carl S." <firstname.lastname@example.org> wrote in message news:email@example.com... > "Torsten" wrote in message <firstname.lastname@example.org>...
>> 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 = 1e-10*eye(400);
has determinant 0 (due to underflow) but it's a scaled identity matrix, which is about as well-behaved as you can get. If you _must_ test for singularity, check with COND or RCOND.
2) Don't test a floating-point number for exact, bit-for-bit equality unless you need exact, bit-for-bit equality. Compare with a tolerance instead.
> N=(1e-10.*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.
3) If you have Statistics Toolbox, do one of these functions do what you want?