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1 function [post,lik,lli] = logistK_eval(beta,x,y)
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2 % [post,lik,lli] = logistK_eval(beta,x,y)
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3 %
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4 % Evaluate logistic regression model.
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5 %
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6 % INPUT
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7 % beta dxk model coefficients (as returned by logistK)
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8 % x dxn matrix of n input column vectors
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9 % [y] kxn vector of class assignments
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10 %
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11 % OUTPUT
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12 % post kxn fitted class posteriors
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13 % lik 1xn vector of sample likelihoods
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14 % lli log likelihood
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15 %
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16 % Let p(i,j) = exp(beta(:,j)'*x(:,i)),
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17 % Class j posterior for observation i is:
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18 % post(j,i) = p(i,j) / (p(i,1) + ... p(i,k))
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19 % The likelihood of observation i given soft class assignments
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20 % y(:,i) is:
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21 % lik(i) = prod(post(:,i).^y(:,i))
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22 % The log-likelihood of the model given the labeled samples is:
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23 % lli = sum(log(lik))
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24 %
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25 % See also logistK.
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26 %
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27 % David Martin <dmartin@eecs.berkeley.edu>
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28 % May 7, 2002
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29
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30 % Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu>
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31 %
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32 % This program is free software; you can redistribute it and/or
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33 % modify it under the terms of the GNU General Public License as
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34 % published by the Free Software Foundation; either version 2 of the
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35 % License, or (at your option) any later version.
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36 %
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37 % This program is distributed in the hope that it will be useful, but
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38 % WITHOUT ANY WARRANTY; without even the implied warranty of
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39 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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40 % General Public License for more details.
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41 %
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42 % You should have received a copy of the GNU General Public License
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43 % along with this program; if not, write to the Free Software
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44 % Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
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45 % 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html.
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46
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47 error(nargchk(2,3,nargin));
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48
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49 % check sizes
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50 if size(beta,1) ~= size(x,1),
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51 error('Inputs beta,x not the same height.');
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52 end
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53 if nargin > 3 & size(y,2) ~= size(x,2),
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54 error('Inputs x,y not the same length.');
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55 end
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56
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57 % get sizes
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58 [d,k] = size(beta);
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59 [d,n] = size(x);
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60
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61 % class posteriors
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62 post = zeros(k,n);
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63 bx = zeros(k,n);
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64 for j = 1:k,
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65 bx(j,:) = beta(:,j)'*x;
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66 end
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67 for j = 1:k,
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68 post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1);
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69 end
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70 clear bx;
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71
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72 % likelihood of each sample
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73 if nargout > 1,
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74 y = y ./ repmat(sum(y,1),k,1); % L1-normalize class assignments
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75 lik = prod(post.^y,1);
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76 end
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77
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78 % total log likelihood
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79 if nargout > 2,
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80 lli = sum(log(lik+eps));
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81 end;
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82
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83 % eof
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