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1 function CPD = set_fields(CPD, varargin)
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2 % SET_PARAMS Set the parameters (fields) for a gaussian_CPD object
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3 % CPD = set_params(CPD, name/value pairs)
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4 %
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5 % The following optional arguments can be specified in the form of name/value pairs:
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6 %
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7 % mean - mu(:,i) is the mean given Q=i
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8 % cov - Sigma(:,:,i) is the covariance given Q=i
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9 % weights - W(:,:,i) is the regression matrix given Q=i
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10 % cov_type - if 'diag', Sigma(:,:,i) is diagonal
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11 % tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i
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12 % clamp_mean - if 1, we do not adjust mu(:,i) during learning
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13 % clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning
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14 % clamp_weights - if 1, we do not adjust W(:,:,i) during learning
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15 % clamp - if 1, we do not adjust any params
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16 % cov_prior_weight - weight given to I prior for estimating Sigma
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17 % cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0]
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18 %
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19 % e.g., CPD = set_params(CPD, 'mean', [0;0])
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20
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21 args = varargin;
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22 nargs = length(args);
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23 for i=1:2:nargs
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24 switch args{i},
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25 case 'mean', CPD.mean = args{i+1};
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26 case 'cov', CPD.cov = args{i+1};
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27 case 'weights', CPD.weights = args{i+1};
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28 case 'cov_type', CPD.cov_type = args{i+1};
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29 %case 'tied_cov', CPD.tied_cov = strcmp(args{i+1}, 'yes');
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30 case 'tied_cov', CPD.tied_cov = args{i+1};
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31 case 'clamp_mean', CPD.clamped_mean = args{i+1};
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32 case 'clamp_cov', CPD.clamped_cov = args{i+1};
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33 case 'clamp_weights', CPD.clamped_weights = args{i+1};
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34 case 'clamp', clamp = args{i+1};
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35 CPD.clamped_mean = clamp;
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36 CPD.clamped_cov = clamp;
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37 CPD.clamped_weights = clamp;
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38 case 'cov_prior_weight', CPD.cov_prior_weight = args{i+1};
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39 case 'cov_prior_entropic', CPD.cov_prior_entropic = args{i+1};
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40 otherwise,
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41 error(['invalid argument name ' args{i}]);
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42 end
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43 end
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