runme.m

duke的tutorial on EM的matlab经典源码

% Number of points
N = 300;

% Number of mixture components
K = 3;

% Number of dimensions
D = 2;

% Generate random points from a mixture
[x, pTrue, mTrue, sigmaTrue, classTrue] = makeClusters(N, K, D);

% Assuming that the true value of K is known
K = K;

% See kmeans.m for other calling options
[mKM, classKM, niterKM] = kmeans(x, K);

% See emi.m and em.m for other calling options
[pEM, mEM, sigmaEM, pknEM, niterEM] = emi(x, K);
[dummy, classEM] = colmax(pknEM);

% Display

figure(1)
showClusters(x, mTrue, classTrue);
title('True Clusters')

figure(2)
showClusters(x, mKM, classKM);
if niterKM < 0
    iterKM = 'Max Number of';
else
    iterKM = sprintf('%d', niterKM);
end
title(sprintf('Clusters from %s Iterations of K Means', iterKM))

figure(3)
showClusters(x, mEM, classEM);
if niterEM < 0
    iterEM = 'Max Number of';
else
    iterEM = sprintf('%d', niterEM);
end
title(sprintf('Clusters Inferred from EM Probabilities (%s Iterations)', iterEM))