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

"Carl S." <> wrote in message
> "Torsten" wrote in message <kgku2t$t2g$>...


>> 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

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
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