How to suppress this error?

53 vues (au cours des 30 derniers jours)
Jakob Sievers
Jakob Sievers le 25 Sep 2020
Réponse apportée : Real User le 21 Mar 2024 à 14:57
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')
  4 commentaires
Jakob Sievers
Jakob Sievers le 25 Sep 2020
This is not a script I originally wrote so I couldn't say. It just worked for my purpose. Though if you have a better way, not to mention a faster way, of doing it I am all ears. My process involves using this equation quite often and would love to shave some time off of it if possible.
Jakob Sievers
Jakob Sievers le 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

Connectez-vous pour commenter.

Réponses (2)

the cyclist
the cyclist le 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?
  1 commentaire
Jakob Sievers
Jakob Sievers le 25 Sep 2020
The above solution worked for my needs. Thanks for your attention though :)

Connectez-vous pour commenter.


Real User
Real User le 21 Mar 2024 à 14:57
warning('off','MATLAB:nearlySingularMatrix')
But the cyclist's answer works for any error (that's how I found this).

Catégories

En savoir plus sur Logical dans Help Center et File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by