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1 function [initState, transmat, mu, Sigma] = gausshmm_train_observed(obsData, hiddenData, ...
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2 nstates, varargin)
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3 % GAUSSHMM_TRAIN_OBSERVED Estimate params of HMM with Gaussian output from fully observed sequences
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4 % [initState, transmat, mu, Sigma] = gausshmm_train_observed(obsData, hiddenData, nstates,...)
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5 %
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6 % INPUT
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7 % If all sequences have the same length
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8 % obsData(:,t,ex)
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9 % hiddenData(ex,t) - must be ROW vector if only one sequence
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10 % If sequences have different lengths, we use cell arrays
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11 % obsData{ex}(:,t)
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12 % hiddenData{ex}(t)
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13 %
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14 % Optional argumnets
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15 % dirichletPriorWeight - for smoothing transition matrix counts
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16 %
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17 % Optional parameters from mixgauss_Mstep:
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18 % 'cov_type' - 'full', 'diag' or 'spherical' ['full']
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19 % 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0]
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20 % 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ]
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21 % 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ]
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22 % 'cov_prior' - Lambda_i, added to YY(:,:,i) [0.01*eye(d,d,Q)]
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23 %
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24 % Output
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25 % mu(:,q)
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26 % Sigma(:,:,q)
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27
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28 [dirichletPriorWeight, other] = process_options(...
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29 varargin, 'dirichletPriorWeight', 0);
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30
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31 [transmat, initState] = transmat_train_observed(hiddenData, nstates, ...
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32 'dirichletPriorWeight', dirichletPriorWeight);
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33
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34 % convert to obsData(:,t*nex)
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35 if ~iscell(obsData)
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36 [D T Nex] = size(obsData);
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37 obsData = reshape(obsData, D, T*Nex);
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38 else
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39 obsData = cat(2, obsData{:});
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40 hiddenData = cat(2,hiddenData{:});
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41 end
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42 [mu, Sigma] = condgaussTrainObserved(obsData, hiddenData(:), nstates, varargin{:});
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43
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44
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