adapmixdec.m

最新的模式识别分类工具箱,希望对朋友们有用!

% This uses "my" mixture decomposition software
% (Vittorio)
%

% generate a unit circle
clear;

NPOINTS=50;
angles = 0:(2*pi/NPOINTS):2*pi;
cir=[cos(angles)
  sin(angles) ];

ncolors = 7;
colors = ['b','r','g','k','m','c','y'];

nmarks = 12;
marks  = ['.','h','d','p','h','^','v','*','s','<','>','o'];

serv  = 13;
getServerData;

inputdim=2;
ncentres=2;

select=[1 6];
options = foptions;
datos=[data(:,select)];
options(14) = 5;
options(1)  = 1;      % Prints out error values.
options(14) = 100;      % number of iterations.

mix = mgmmem( datos, options);

figure(1),clf;

plot(datos(:,1),datos(:,2),'.'), hold on;

ncentres = mix.ncentres;
for i=1:ncentres
  plot(mix.centres(i,1),mix.centres(i,2),'r*');
end

ax = axis;

% plot the lines
for i=1:ncentres
  C = mix.covars(:,:,i);
  [V D] = eig(C);
  b = -mix.centres(i,:)*V(:,1);

  z=(datos - ones(size(datos,1),1)*mix.centres(i,:))';
  intermediate=inv(C)*z;

  for j=1:size(datos,1)
    probability(i,j) = z(:,j)'*intermediate(:,j);
  end

  ellipsoid = V*sqrt(D)*cir + mix.centres(i,:)'*ones(1,size(cir,2));
  plot(ellipsoid(1,:),ellipsoid(2,:),'r');

  % axis of least error
  x0 = mix.centres(i,1) - sqrt(D(1,1))*V(1,1);
  y0 = mix.centres(i,2) - sqrt(D(1,1))*V(2,1);
  x1 = mix.centres(i,1) + sqrt(D(1,1))*V(1,1);
  y1 = mix.centres(i,2) + sqrt(D(1,1))*V(2,1);
  line([x0 x1],[y0 y1]);
  
  % axis of mos error
  x0 = mix.centres(i,1) - sqrt(D(2,2))*V(1,2);
  y0 = mix.centres(i,2) - sqrt(D(2,2))*V(2,2);
  x1 = mix.centres(i,1) + sqrt(D(2,2))*V(1,2);
  y1 = mix.centres(i,2) + sqrt(D(2,2))*V(2,2);
  line([x0 x1],[y0 y1]);
end

axis(ax);
figure(1),orient tall;
print -dpsc fig1.ps

% now relabel the data

[Y,labels] = min(abs(probability));

figure(2),clf;
for i=1:ncentres
  subset = find(labels==i);
  plot(datos(subset,1),datos(subset,2),[marks(floor(i/nmarks)+1) colors(mod(i,ncolors)+1)]), hold on;
end

xlabel(labdat(select(1),:));
ylabel(labdat(select(2),:));
orient tall;


print -dpsc fig2.ps

figure(3),clf;
tobeplotted=[1 4 5 10];
for j=1:length(tobeplotted);
  subplot(length(tobeplotted),1,j);
  for i=1:ncentres
    subset = find(labels==i);
    plot(time(subset),data(subset,tobeplotted(j)),[marks(floor(i/nmarks)+1) colors(mod(i,ncolors)+1)]); hold on;
  end
  xlabel('time');
  ylabel(labdat(tobeplotted(j),:));
end
orient tall;
print -dpsc fig3.ps;

figure(4),clf;
tobeplotted=[7 8 2 3];
for j=1:length(tobeplotted);
  subplot(length(tobeplotted),1,j);
  for i=1:ncentres
    subset = find(labels==i);
    plot(time(subset),data(subset,tobeplotted(j)),[marks(floor(i/nmarks)+1) colors(mod(i,ncolors)+1)]); hold on;
  end
  xlabel('time');
  ylabel(labdat(tobeplotted(j),:));
end
orient tall;
print -dpsc fig4.ps

figure(5),clf;
tobeplotted=[6 16 11 12];
for j=1:length(tobeplotted);
  subplot(length(tobeplotted),1,j);
  for i=1:ncentres
    subset = find(labels==i);
    plot(time(subset),data(subset,tobeplotted(j)),[marks(floor(i/nmarks)+1) ...
	  colors(mod(i,ncolors)+1)]); hold on;

  end
  xlabel('time');
  ylabel(labdat(tobeplotted(j),:));
end
orient tall;
print -dpsc fig5.ps