# How to suppress this error?

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Jakob Sievers on 25 Sep 2020
Commented: Jakob Sievers on 25 Sep 2020
Hi there
I am receiving the following error message:
Warning: Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN.
> In mpower>integerMpower (line 80)
In ^ (line 49)
In gaussfit (line 111)
The line in question is:
a = (F2)^(-1)*F'*(y-f0) + a0;
I wish to suppress, not solve, this problem and so I added the following to the beginning of my script, and yet still receive the error message. What am I doing wrong?
warning('off','MATLAB:singularMatrix')
Jakob Sievers on 25 Sep 2020
I just had a look and the file (which fits a gaussian curve) is from 2012, so it might indeed be overdue for an update:
function [sigma, mu,error] = gaussfit( x, y, sigma0, mu0,runmode,idix)
% [sigma, mu] = gaussfit( x, y, sigma0, mu0 )
% Fits a guassian probability density function into (x,y) points using iterative
% LMS method. Gaussian p.d.f is given by:
% y = 1/(sqrt(2*pi)*sigma)*exp( -(x - mu)^2 / (2*sigma^2))
% The results are much better than minimazing logarithmic residuals
%
% INPUT:
% sigma0 - initial value of sigma (optional)
% mu0 - initial value of mean (optional)
%
% OUTPUT:
% sigma - optimal value of standard deviation
% mu - optimal value of mean
%
% REMARKS:
% The function does not always converge in which case try to use initial
% values sigma0, mu0. Check also if the data is properly scaled, i.e. p.d.f
% should approx. sum up to 1
%
% VERSION: 23.02.2012
%
% EXAMPLE USAGE:
% x = -10:1:10;
% s = 2;
% m = 3;
% y = 1/(sqrt(2*pi)* s ) * exp( - (x-m).^2 / (2*s^2)) + 0.02*randn( 1, 21 );
% [sigma,mu] = gaussfit( x, y )
% xp = -10:0.1:10;
% yp = 1/(sqrt(2*pi)* sigma ) * exp( - (xp-mu).^2 / (2*sigma^2));
% plot( x, y, 'o', xp, yp, '-' );
warning off;
warning('off','MATLAB:singularMatrix')
error=0; %no error
% Maximum number of iterations
Nmax = 50;
if nargin==4
runmode=1; %output warnings!
end
if( length( x ) ~= length( y ))
fprintf( 'x and y should be of equal length\n\r' );
exit;
end
n = length( x );
x = reshape( x, n, 1 );
y = reshape( y, n, 1 );
%sort according to x
X = [x,y];
X = sortrows( X );
x = X(:,1);
y = X(:,2);
%Checking if the data is normalized
dx = diff( x );
dy = 0.5*(y(1:length(y)-1) + y(2:length(y)));
s = sum( dx .* dy );
if( s > 1.5 | s < 0.5 )
fprintf( 'Data is not normalized! The pdf sums to: %f. Normalizing...\n\r', s );
error=1;
y = y ./ s;
end
X = zeros( n, 3 );
X(:,1) = 1;
X(:,2) = x;
X(:,3) = (x.*x);
% try to estimate mean mu from the location of the maximum
[ymax,index]=max(y);
mu = x(index);
% estimate sigma
sigma = 1/(sqrt(2*pi)*ymax);
if( nargin == 3 )
sigma = sigma0;
end
if( nargin == 4 )
mu = mu0;
end
%xp = linspace( min(x), max(x) );
% iterations
ii=0;
while ii<Nmax
ii=ii+1;
% yp = 1/(sqrt(2*pi)*sigma) * exp( -(xp - mu).^2 / (2*sigma^2));
% plot( x, y, 'o', xp, yp, '-' );
dfdsigma = -1/(sqrt(2*pi)*sigma^2)*exp(-((x-mu).^2) / (2*sigma^2));
dfdsigma = dfdsigma + 1/(sqrt(2*pi)*sigma).*exp(-((x-mu).^2) / (2*sigma^2)).*((x-mu).^2/sigma^3);
dfdmu = 1/(sqrt(2*pi)*sigma)*exp(-((x-mu).^2)/(2*sigma^2)).*(x-mu)/(sigma^2);
F = [ dfdsigma dfdmu ];
a0 = [sigma;mu];
f0 = 1/(sqrt(2*pi)*sigma).*exp( -(x-mu).^2 /(2*sigma^2));
if any(isnan(F(:)))
error=1;
ii=Nmax;
else
F2=F'*F;
if rank(F2)<min(size(F))
error=1;
ii=Nmax;
else
a = (F2)^(-1)*F'*(y-f0) + a0;
sigma = a(1);
mu = a(2);
if( sigma < 0 )
sigma = abs( sigma );
if runmode==1
fprintf( 'Instability detected! Rerun with initial values sigma0 and mu0! \r' );
fprintf( 'Check if your data is properly scaled! p.d.f should approx. sum up to 1 \r' );
end
error=1;
ii=Nmax;
end
end
end
end

the cyclist on 25 Sep 2020
Two thoughts:
First: Are you certain you have the correct warning? After you run your code that gives the warning, what is the output of
w = warning('query','last')
(Your warning does look like it matches the one you are turning off, so I'm guessing that's not the issue.)
Second: I believe that turning off warnings only persists for the session. Does this warning happen during the same session in which you turned it off?
Jakob Sievers on 25 Sep 2020
The above solution worked for my needs. Thanks for your attention though :)

Jakob Sievers on 25 Sep 2020
This solution worked for me. Thanks!