annotate toolboxes/FullBNT-1.0.7/netlabKPM/kmeans_demo.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function kmeans_demo()
wolffd@0 2
wolffd@0 3 % Generate T points from K=5 1D clusters, and try to recover the cluster
wolffd@0 4 % centers using k-means.
wolffd@0 5 % Requires BNT, netlab and the matlab stats toolbox v4.
wolffd@0 6
wolffd@0 7 K = 5;
wolffd@0 8 ndim = 1;
wolffd@0 9 true_centers = 1:K;
wolffd@0 10 sigma = 1e-6;
wolffd@0 11 T = 100;
wolffd@0 12 % data(t,:) is the t'th data point
wolffd@0 13 data = zeros(T, ndim);
wolffd@0 14 % ndx(t) = i means the t'th data point is sample from cluster i
wolffd@0 15 %ndx = sample_discrete(normalise(ones(1,K)));
wolffd@0 16 ndx = [1*ones(1,20) 2*ones(1,20) 3*ones(1,20) 4*ones(1,20) 5*ones(1,20)];
wolffd@0 17 for t=1:T
wolffd@0 18 data(t) = sample_gaussian(true_centers(ndx(t)), sigma, 1);
wolffd@0 19 end
wolffd@0 20 plot(1:T, data, 'x')
wolffd@0 21
wolffd@0 22
wolffd@0 23
wolffd@0 24 % set the centers randomly from Gauss(0)
wolffd@0 25 mix = gmm(ndim, K, 'spherical');
wolffd@0 26 h = plot_centers_as_lines(mix, [], T);
wolffd@0 27
wolffd@0 28 if 0
wolffd@0 29 % Place initial centers at K data points chosen at random, but add some noise
wolffd@0 30 choose_ndx = randperm(T);
wolffd@0 31 choose_ndx = choose_ndx(1:K);
wolffd@0 32 init_centers = data(choose_ndx) + sample_gaussian(0, 0.1, K);
wolffd@0 33 mix.centres = init_centers;
wolffd@0 34 h = plot_centers_as_lines(mix, h, T);
wolffd@0 35 end
wolffd@0 36
wolffd@0 37 if 0
wolffd@0 38 % update centers using netlab k-means
wolffd@0 39 options = foptions;
wolffd@0 40 niter = 10;
wolffd@0 41 options(14) = niter;
wolffd@0 42 mix = gmminit(mix, data, options);
wolffd@0 43 h = plot_centers_as_lines(mix, h, T);
wolffd@0 44 end
wolffd@0 45
wolffd@0 46 % use matlab stats toolbox k-means with multiple restarts
wolffd@0 47 nrestarts = 5;
wolffd@0 48 [idx, centers] = kmeans(data, K, 'replicates', nrestarts, ...
wolffd@0 49 'emptyAction', 'singleton', 'display', 'iter');
wolffd@0 50 mix.centres = centers;
wolffd@0 51 h = plot_centers_as_lines(mix, h, T);
wolffd@0 52
wolffd@0 53 % fine tune with EM; compute covariances of each cluster
wolffd@0 54 options = foptions;
wolffd@0 55 niter = 20;
wolffd@0 56 options(1) = 1; % display cost fn at each iter
wolffd@0 57 options(14) = niter;
wolffd@0 58 mix = gmmem(mix, data, options);
wolffd@0 59 h = plot_centers_as_lines(mix, h, T);
wolffd@0 60
wolffd@0 61 %%%%%%%%%
wolffd@0 62 function h = plot_centers_as_lines(mix, h, T)
wolffd@0 63
wolffd@0 64 K = mix.ncentres;
wolffd@0 65 hold on
wolffd@0 66 if isempty(h)
wolffd@0 67 for k=1:K
wolffd@0 68 h(k)=line([0 T], [mix.centres(k) mix.centres(k)]);
wolffd@0 69 end
wolffd@0 70 else
wolffd@0 71 for k=1:K
wolffd@0 72 set(h(k), 'xdata', [0 T], 'ydata', [mix.centres(k) mix.centres(k)]);
wolffd@0 73 end
wolffd@0 74 end
wolffd@0 75 hold off
wolffd@0 76