ToolLooCrossVal

Leave One Out Cross Validation with Nearest Neighbor Classifier
>
> @param FeatureMatrix: features (dimension iNumFeatures x iNumObservations)
> @param ClassIdx: vector with class indices (length iNumObservations, starting from 0)
>
> @retval Acc overall accuracy after Cross-Validation
 ======================================================================

0001 %Leave One Out Cross Validation with Nearest Neighbor Classifier
0002 %>
0003 %> @param FeatureMatrix: features (dimension iNumFeatures x iNumObservations)
0004 %> @param ClassIdx: vector with class indices (length iNumObservations, starting from 0)
0005 %>
0006 %> @retval Acc overall accuracy after Cross-Validation
0007 % ======================================================================
0008 function [Acc, conf_mat] = ToolLooCrossVal(FeatureMatrix, ClassIdx)
0009  
0010     % initialize
0011     TP = 0;
0012     
0013     conf_mat = zeros(length(unique(ClassIdx)));
0014     
0015     % loop over observations
0016     for o = 1:size(FeatureMatrix, 2)
0017         % remove current observation from 'training set'
0018         v_train = [FeatureMatrix(:, 1:o-1) FeatureMatrix(:, o+1:end)]';
0019         C_train = [ClassIdx(1:o-1) ClassIdx(:, o+1:end)]';
0020         
0021         % compute result of Nearest Neighbor Classifier given the traindata
0022         res = ToolSimpleKnn(FeatureMatrix(:, o)', v_train, C_train, 1);
0023         
0024         conf_mat(ClassIdx(o)+1, res+1) = conf_mat(ClassIdx(o)+1, res+1) + 1;     
0025         
0026         % if result is correct increment number of true positives
0027         if (res == ClassIdx(o))
0028             TP = TP+1;
0029         end
0030     end
0031  
0032     % compute overall (micro) accuracy
0033     Acc = TP / length(ClassIdx);
0034 end