www.pudn.com > FitFunc.zip > plot_mix_gaussian.m

function plot_mix_gaussian( u,sig,prob,X ) 
%
% plot_mix_gaussian - plot the samples and the estimation. 
%                     for 1D distribution, plot the normalized histogram and the 1D distribution function        
%                     for 2D distribution, plot the samples and the contour of FWHM
%                     for mD distribution (m>2) - do not plot nothing.
%
% format:   plot_mix_gaussian( u,sig,prob,X )
%
% input:    u       - mean of each gaussian in the distribution. 1xM vector or 2xM matrix. 
%                     each gaussian mean is stored in a separate column. 
%           sig     - for 1D distribution -> standard deviation of each gaussian in the distribution 
%                     each gaussian mean is stored in a separate column -> a 1xM vector 
%                     for 2D distribution -> covariance matrix for each gaussian in the distribution 
%                     2x2xM matrix, the 3rd dimension is the gaussians index, 
%                     the 1st and 2nd dimensions are the covariance matrix 
%           prob    - probability of each gaussian in the distribution to create the current sample. 
%                     this is a 1xM vector 
%           X       - the samples, 1xN vector or 2xN matrix, depends on the dimension of the  
%                     distribution (i.e. 1D or 2D)
%
% output:   to the graphic current axis.
%
%
%
 
% check input 
if (nargin<3) 
    error( 'plot_mix_gaussian - insufficient input parameters' ); 
end 
if ~exist( 'X' ) 
    X = []; 
end 
 
% constants 
nbins = 200; 
 
% check the size of the input to determin if it's 1D or 2D distribution 
if (size(u,1)==2) & (size(u,2)==size(prob,2)) 
    plot_mix_gaussian_2d( u,sig,prob,X ); 
else 
    plot_mix_gaussian_1d( u,sig,prob,X,nbins ); 
end 
drawnow; 
 
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
%%                              Inner function implementation 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
 
function plot_mix_gaussian_1d( u,sig,prob,X,nbins ) 
% plot normalized histogram and normalized distribution on top of it 
 
% constant 
points = 1000; 
 
if ~isempty( X ) 
    [n,x]   = hist( X,nbins );              % calc the histogram 
    dx      = x(2)-x(1);                    % calc a single bin width 
    n       = n / sum( n*dx );              % normalize histogram to have area of 1 
    bar( x,n,'hist' );                      % plot normalized histogram 
    xlim( [x(1)-dx/2,x(end)+dx/2] );        % make sure that the axis is squeezed to it's limits 
    g       = gca; 
    c       = [1 0 0];                      % choose the red color 
    x_c     = linspace( x(1)-dx/2,x(end)+dx/2,points ); 
else 
    g       = gca; 
    X       = xlim( g );                    % get the axis limits 
    c       = [0 1 0];                      % choose the green color 
    x_c     = linspace( X(1),X(2),points ); 
end 
 
% plot the distribution 
for m = 1:length(prob) 
    y   = prob(m) / sqrt( 2*pi*sig(m)^2 ) * exp( -((x_c-u(m)).^2)/(2*sig(m)^2) ); 
    line( x_c,y,'color',c,'parent',g,'linewidth',2 ); 
end 
shg; 
drawnow; 
 
% ---------------------------------------------------------------------------------------- 
 
function plot_mix_gaussian_2d( u,covar,prob,X ) 
% plot 2D samples and distribution data on top of it 
 
if ~isempty( X ) 
    if size(X,2)