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1 function mix = gmmunpak(mix, p)
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2 %GMMUNPAK Separates a vector of Gaussian mixture model parameters into its components.
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3 %
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4 % Description
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5 % MIX = GMMUNPAK(MIX, P) takes a GMM data structure MIX and a single
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6 % row vector of parameters P and returns a mixture data structure
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7 % identical to the input MIX, except that the mixing coefficients
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8 % PRIORS, centres CENTRES and covariances COVARS (and, for PPCA, the
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9 % lambdas and U (PCA sub-spaces)) are all set to the corresponding
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10 % elements of P.
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11 %
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12 % See also
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13 % GMM, GMMPAK
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14 %
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15
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16 % Copyright (c) Ian T Nabney (1996-2001)
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17
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18 errstring = consist(mix, 'gmm');
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19 if ~errstring
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20 error(errstring);
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21 end
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22 if mix.nwts ~= length(p)
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23 error('Invalid weight vector length')
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24 end
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25
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26 mark1 = mix.ncentres;
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27 mark2 = mark1 + mix.ncentres*mix.nin;
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28
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29 mix.priors = reshape(p(1:mark1), 1, mix.ncentres);
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30 mix.centres = reshape(p(mark1 + 1:mark2), mix.ncentres, mix.nin);
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31 switch mix.covar_type
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32 case 'spherical'
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33 mark3 = mix.ncentres*(2 + mix.nin);
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34 mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);
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35 case 'diag'
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36 mark3 = mix.ncentres*(1 + mix.nin + mix.nin);
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37 mix.covars = reshape(p(mark2 + 1:mark3), mix.ncentres, mix.nin);
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38 case 'full'
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39 mark3 = mix.ncentres*(1 + mix.nin + mix.nin*mix.nin);
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40 mix.covars = reshape(p(mark2 + 1:mark3), mix.nin, mix.nin, ...
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41 mix.ncentres);
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42 case 'ppca'
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43 mark3 = mix.ncentres*(2 + mix.nin);
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44 mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);
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45 % Now also extract k and eigenspaces
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46 mark4 = mark3 + mix.ncentres*mix.ppca_dim;
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47 mix.lambda = reshape(p(mark3 + 1:mark4), mix.ncentres, ...
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48 mix.ppca_dim);
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49 mix.U = reshape(p(mark4 + 1:end), mix.nin, mix.ppca_dim, ...
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50 mix.ncentres);
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51 otherwise
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52 error(['Unknown covariance type ', mix.covar_type]);
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53 end
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54
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