%
% An extension of example 9.2
% This illustrates the 1-D histogram using relative frequency and frequency.
% 
% Written by:
% -- 
% John L. Weatherwax                2005-08-14
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

close all; drawnow; 
rehash; 
clc; 

tit = {}; fn = {}; 

% Generate some standard normal data.
X(:,1) = randn(400,1);
tit{1} = 'standard normal data'; 
fn{1}  = 'standard_normal'; 

% Generate some uniform data.
tmp = 2.4*rand(398,1) - 1.2;
% Add some outliers (two) to this data.
X(:,2) = [tmp; [-2.9 2.9]'];
tit{2} = 'uniform data with two outliers'; 
fn{2}  = 'uniform'; 

% Generate data from three different normals: 
tmp1 = randn(300,1)*.5;
tmp2 = randn(50,1)*.4-2;
tmp3 = randn(50,1)*.4+2;
X(:,3) = [tmp1; tmp2; tmp3];
tit{3} = 'a mixture of three gaussians';
fn{3}  = 'mixture'; 

if( 0 ) 
  % plot various box plots using this set of toy data
  
  % This is from the Statistics Toolbox:
  figure,boxplot(X,0,[],1,10); title('boxplot');
  
  % We can get the histplot. This function is 
  % included with this text.
  boxp(X,'hp'); title('histplot');
  
  % Let's see what these look like using the box-percentile plot.
  boxprct(X)
  title( 'box percentile plot' );
else
  for ii=1:3,
    figure; histfit(X(:,ii)); title( tit{ii} ); 
    axis( [ -3, +3, 0, 90 ] ); 
    saveas( gcf, ['../../WriteUp/Graphics/Chapter9/prob_9_6_',fn{ii}], 'epsc' ); 
  end
end