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1 function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type, method)
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2 % MIXGAUSS_INIT Initial parameter estimates for a mixture of Gaussians
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3 % function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type. method)
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4 %
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5 % INPUTS:
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6 % data(:,t) is the t'th example
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7 % M = num. mixture components
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8 % cov_type = 'full', 'diag' or 'spherical'
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9 % method = 'rnd' (choose centers randomly from data) or 'kmeans' (needs netlab)
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10 %
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11 % OUTPUTS:
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12 % mu(:,k)
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13 % Sigma(:,:,k)
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14 % weights(k)
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15
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16 if nargin < 4, method = 'kmeans'; end
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17
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18 [d T] = size(data);
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19 data = reshape(data, d, T); % in case it is data(:, t, sequence_num)
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20
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21 switch method
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22 case 'rnd',
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23 C = cov(data');
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24 Sigma = repmat(diag(diag(C))*0.5, [1 1 M]);
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25 % Initialize each mean to a random data point
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26 indices = randperm(T);
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27 mu = data(:,indices(1:M));
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28 weights = normalise(ones(M,1));
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29 case 'kmeans',
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30 mix = gmm(d, M, cov_type);
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31 options = foptions;
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32 max_iter = 5;
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33 options(1) = -1; % be quiet!
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34 options(14) = max_iter;
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35 mix = gmminit(mix, data', options);
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36 mu = reshape(mix.centres', [d M]);
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37 weights = mix.priors(:);
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38 for m=1:M
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39 switch cov_type
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40 case 'diag',
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41 Sigma(:,:,m) = diag(mix.covars(m,:));
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42 case 'full',
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43 Sigma(:,:,m) = mix.covars(:,:,m);
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44 case 'spherical',
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45 Sigma(:,:,m) = mix.covars(m) * eye(d);
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46 end
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47 end
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48 end
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49
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