```Date: Feb 27, 2013 11:47 AM
Author: Steven Lord
Subject: Re: Alternative solution for NAN

"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 = 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.*snip*3) If you have Statistics Toolbox, do one of these functions do what you want?http://www.mathworks.com/help/stats/multivariate-normal-distribution-1.htmlhttp://www.mathworks.com/help/stats/normal-distribution-1.htmlIf 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 Lordslord@mathworks.comTo contact Technical Support use the Contact Us link on http://www.mathworks.com
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