Page 195 - Computational Statistics Handbook with MATLAB

182 Computational Statistics Handbook with MATLAB

Example 5.28
In this example, we use the iris data to illustrate the grand tour. First we
load up the data and set up some preliminaries.
% This is for the iris data.
load iris
% Put data into one matrix.
x = [setosa;virginica;versicolor];
% Set up vector of frequencies.
th = sqrt([2 3]);
% Set up other constants.
[n,d] = size(x);
% This is a small irrational number:
delt = eps*10^14;
% Do the tour for some specified time steps.
maxit = 1000;
cof = sqrt(2/d);
% Set up storage space for projection vectors.
a = zeros(d,1);
b = zeros(d,1);
z = zeros(n,2);
We now do some preliminary plotting, just to get the handles we need to use
MATLAB’s Handle Graphics for plotting. This enables us to update the
points that are plotted rather than replotting the entire figure.
% Get an initial plot, so the tour can be implemented
% using Handle Graphics.
Hlin1 = plot(z(1:50,1),z(1:50,2),'ro');
set(gcf,'backingstore','off')
set(gca,'Drawmode','fast')
hold on
Hlin2 = plot(z(51:100,1),z(51:100,2),'go');
Hlin3 = plot(z(101:150,1),z(101:150,2),'bo');
hold off
axis equal
axis vis3d
axis off
Now we do the actual pseudo grand tour, where we use a maximum number
of iterations given by maxit.
for t = 0:delt:(delt*maxit)
% Find the transformation vectors.
for j = 1:d/2
a(2*(j-1)+1) = cof*sin(th(j)*t);
a(2*j) = cof*cos(th(j)*t);
b(2*(j-1)+1) = cof*cos(th(j)*t);

© 2002 by Chapman & Hall/CRC