Dawn@4: function [] = SVM_Singing( inputFileName )
Dawn@4: 
Dawn@4: cd 'C:\Users\dawn\Dropbox\TestResults'
Dawn@4: 
Dawn@4: DEBUG = 1;
Dawn@4: % output results file name
Dawn@4: % masterFileOutputID = fopen( 'SVM_Singing_HNR.txt', 'a' );
Dawn@4: outputFileName = ['SVM_' inputFileName];
Dawn@4: masterFileOutputID = fopen( outputFileName, 'a' );
Dawn@4: 
Dawn@4: % input results file name
Dawn@4: % inputFileName = 'singingBasicHNRStats.txt';
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, '\n RESULTS FILE NAME: %s\n', inputFileName);
Dawn@4: inputFileID = fopen( inputFileName );
Dawn@4: 
Dawn@4: % noOfArguments = length(varargin);
Dawn@4: 
Dawn@4: % outputFileName = 'individualResults/SVM_Results_';
Dawn@4: % resultsFileName = 'SVM_Results_';
Dawn@4: titleName = '';
Dawn@4: % for i=1 : noOfArguments
Dawn@4: %     titleName = [ titleName varargin{i} '_'];
Dawn@4: %     fprintf( masterFileOutputID, '%s_', varargin{i} );
Dawn@4: % end
Dawn@4: 
Dawn@4: % outputFileName = [ outputFileName titleName ];
Dawn@4: % resultsFileName = [ resultsFileName titleName ];
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, '\t' );
Dawn@4: 
Dawn@4: % outputFileName = [ outputFileName '.txt'];
Dawn@4: % resultsFileName = [ resultsFileName '.txt'];
Dawn@4: % 
Dawn@4: % fileOutputID = fopen( outputFileName, 'w' );
Dawn@4: % fileSVMOutputID = fopen( resultsFileName, 'w' );
Dawn@4: 
Dawn@4: % -------------------- get the data from the results file ---------------
Dawn@4: lineCount = 0;
Dawn@4: fileCount = 0;
Dawn@4: data = [];
Dawn@4: groups = [];
Dawn@4: 
Dawn@4: while( ~(feof(inputFileID)) )
Dawn@4:     
Dawn@4:     outputValues = [];
Dawn@4:     thestr = fgetl(inputFileID);
Dawn@4:     fileCount = fileCount + 1;
Dawn@4: 
Dawn@4:     % determine whether we have a positive or negative sample
Dawn@4:     sampleEmotion( fileCount ) = 'U';
Dawn@4:     if( ~(isempty(strfind(thestr,'pos'))))
Dawn@4:         % sample is positive
Dawn@4:         sampleEmotion( fileCount ) = 'P';
Dawn@4:         groups( fileCount ) = 1;
Dawn@4:     elseif( ~(isempty(strfind(thestr,'neg'))))
Dawn@4:         % sample is negative
Dawn@4:         sampleEmotion( fileCount ) = 'N';
Dawn@4:         groups( fileCount ) = 0;
Dawn@4:     else
Dawn@4:         disp('EEEK!');
Dawn@4:         fileCount = fileCount - 1;
Dawn@4: %         pause;
Dawn@4:     end
Dawn@4: 
Dawn@4:     % determine whether we have a male, female or trans sample
Dawn@4: %     gender( fileCount ) = '?';
Dawn@4: %     if( ~(isempty(strfind(thestr,'fem'))))
Dawn@4: %         % gender is female
Dawn@4: %         gender( fileCount ) = 'F';
Dawn@4: %     elseif( ~(isempty(strfind(thestr,'male'))))
Dawn@4: %         % gender is male
Dawn@4: %         gender( fileCount ) = 'M';
Dawn@4: %     elseif( ~(isempty(strfind(thestr,'trans'))))
Dawn@4: %         % gender is trans
Dawn@4: %         gender( fileCount ) = 'T';
Dawn@4: %     else
Dawn@4: %         disp('EEEK!');
Dawn@4: %         pause;
Dawn@4: %     end
Dawn@4: 
Dawn@4:     if(( ~(isempty(strfind(thestr,'pos')))) || ( ~(isempty(strfind(thestr,'neg')))) )
Dawn@4:         %how many values are in the string?
Dawn@4: %         spaces = strfind( thestr, ' ' );
Dawn@4:         spaces = [ strfind( thestr,  sprintf('\t')) strfind( thestr, ' ' )];
Dawn@4:         numberstr = thestr( spaces(1) : end ); % chop off the file name  
Dawn@4:         vars = sscanf( numberstr, '%f', inf );
Dawn@4:         data( fileCount, : ) = vars;
Dawn@4:     end
Dawn@4:         
Dawn@4:     lineCount = lineCount + 1;
Dawn@4:     
Dawn@4: end
Dawn@4: fclose(inputFileID);
Dawn@4: 
Dawn@4: % ------------  apply the SVM classifier  ------------------------
Dawn@4: 
Dawn@4: resultMatrix = [];
Dawn@4: 
Dawn@4: noOfIterations = 100;
Dawn@4: 
Dawn@4: for n = 1:noOfIterations
Dawn@4:     % Randomly select training and test sets, perhaps we should try all and
Dawn@4:     % choose the best?
Dawn@4:     [train, test] = crossvalind('holdOut',groups);
Dawn@4:     cp = classperf(groups);
Dawn@4:     
Dawn@4:     % Use a linear support vector machine classifier
Dawn@4:     svmStruct = svmtrain(data(train,:),groups(train));
Dawn@4:     classes = svmclassify(svmStruct,data(test,:));
Dawn@4:     % See how well the classifier performed
Dawn@4:     classperf(cp,classes,test);
Dawn@4:     numbers = cp.CountingMatrix;
Dawn@4: 
Dawn@4:     resultMatrix (n,:,:) = cp.DiagnosticTable;
Dawn@4: %     
Dawn@4: end
Dawn@4: 
Dawn@4: 
Dawn@4: 
Dawn@4: 
Dawn@4: % for emotion detection give the confusion matrix as 
Dawn@4:     % -----------------------------------------------------------------
Dawn@4:     % positive correctly identified         | positive incorrectly identified (1,2)
Dawn@4:     % negative incorrectly identified (2,1) | negative correctly identified
Dawn@4:     % ------------------------------------------------------------------
Dawn@4:     
Dawn@4: % takes the average of 100 iterations - do we want to take the best?  
Dawn@4: 
Dawn@4: meanResults(1,1) = mean( resultMatrix(:,1,1) );
Dawn@4: meanResults(1,2) = mean( resultMatrix(:,2,1) );
Dawn@4: meanResults(2,1) = mean( resultMatrix(:,1,2) );
Dawn@4: meanResults(2,2) = mean( resultMatrix(:,2,2) );
Dawn@4: 
Dawn@4: meanResults(3,:)=0;
Dawn@4: meanResults(:,3)=0;
Dawn@4: 
Dawn@4: meanResults(3,3) = (meanResults(1,1) + meanResults(2,2));% / sum(sum(meanResults));
Dawn@4: 
Dawn@4: % convert to percentages
Dawn@4: % how many of each sample do we have?
Dawn@4: groupNumbers = unique( groups( test ));
Dawn@4: groupNames = unique( sampleEmotion( test ));
Dawn@4: sampleEmotionTest = sampleEmotion( test );
Dawn@4: % which group is which emotion?
Dawn@4: thisGroupNumber = groupNumbers(1);
Dawn@4: thisGroup = find( groups( test ) == thisGroupNumber );
Dawn@4: thisGroupName = unique( sampleEmotionTest( thisGroup ));
Dawn@4: 
Dawn@4: thatGroupNumber = groupNumbers(2);
Dawn@4: thatGroup = find( groups( test ) == thatGroupNumber );
Dawn@4: thatGroupName = unique( sampleEmotionTest( thatGroup ));
Dawn@4: 
Dawn@4: if(length( thisGroupName ) ~= 1 )
Dawn@4:     disp('ARGH!');
Dawn@4:     pause;
Dawn@4: end
Dawn@4: 
Dawn@4: thisGroupNumberOfSamples = length( thisGroup );
Dawn@4: thatGroupNumberOfSamples = length( thatGroup );
Dawn@4: 
Dawn@4: if( thisGroupName == 'P' )
Dawn@4:     %swap all the variables ready for checking
Dawn@4:     temp = thisGroupNumberOfSamples;
Dawn@4:     thisGroupNumberOfSamples = thatGroupNumberOfSamples;
Dawn@4:     thatGroupNumberOfSamples = temp;
Dawn@4:     
Dawn@4:     temp = thisGroupName;
Dawn@4:     thisGroupName = thatGroupName
Dawn@4:     thatGroupName = temp;
Dawn@4:     disp('CHECK ME!');
Dawn@4: end
Dawn@4: 
Dawn@4: if( thisGroupName == 'N' )
Dawn@4:      % group 0 is negative
Dawn@4:     if( sum( meanResults(1,:) ) == thisGroupNumberOfSamples )
Dawn@4:         %if the elements in the first row add up to the number of negative
Dawn@4:         %samples, then swap the rows because we want the top row to be the
Dawn@4:         %results for the positive samples
Dawn@4:         temp(:,1) = meanResults(1:2,2);
Dawn@4:         temp(:,2) = meanResults(1:2,1);
Dawn@4:         temp2(1,:) = temp(2,:);
Dawn@4:         temp2(2,:) = temp(1,:);
Dawn@4:         
Dawn@4:         meanResults(1:2,1) = temp2(:,1);
Dawn@4:         meanResults(1:2,2) = temp2(:,2);
Dawn@4: 
Dawn@4:         % check the number of positive samples
Dawn@4:         if(( sum( meanResults(1,:) ) == thatGroupNumberOfSamples ) ...
Dawn@4:             && ( thatGroupName == 'P' ) )
Dawn@4:             % row 1 is positive
Dawn@4:             disp('matrix correct');
Dawn@4:         else
Dawn@4:             disp('ARGH!');
Dawn@4:             pause;
Dawn@4:         end
Dawn@4: 
Dawn@4:     elseif( sum( meanResults(2,:) ) == thisGroupNumberOfSamples  )
Dawn@4: 
Dawn@4:         % the elements in the second row add up to the number of negative
Dawn@4:         % samples, so the matrix is the correct way around
Dawn@4: 
Dawn@4:         % check the number of positive samples
Dawn@4:         if(( sum( meanResults(1,:) ) == thatGroupNumberOfSamples ) ...
Dawn@4:             && ( thatGroupName == 'P' ) )
Dawn@4:             % row 0 is positive
Dawn@4:             disp('matrix correct');
Dawn@4:         else
Dawn@4:             disp('ARGH!');
Dawn@4:             pause;
Dawn@4:         end
Dawn@4:     end
Dawn@4: end
Dawn@4: 
Dawn@4: % calculate the percentages
Dawn@4: numberOfSamples = sum(sum( meanResults(1:2,1:2)));
Dawn@4: percentageResults = meanResults;
Dawn@4: percentageResults(1,1) = meanResults(1,1) / numberOfSamples;
Dawn@4: percentageResults(1,2) = meanResults(1,2) / numberOfSamples;
Dawn@4: percentageResults(2,1) = meanResults(2,1) / numberOfSamples;
Dawn@4: percentageResults(2,2) = meanResults(2,2) / numberOfSamples;
Dawn@4: percentageResults(3,3) = meanResults(3,3) / numberOfSamples;
Dawn@4: 
Dawn@4: percentageResults = percentageResults * 100
Dawn@4: 
Dawn@4: confusionMatrix = percentageResults;
Dawn@4:  fprintf( masterFileOutputID, '\n %f \t %f \n %f \t %f \n %f \t %f \t %f \n', confusionMatrix(1,1), confusionMatrix(1,2), confusionMatrix(2,1), confusionMatrix(2,2), 0, 0, confusionMatrix(3,3));
Dawn@4: 
Dawn@4:  % print latex results to the screen
Dawn@4: str1 = sprintf(' & %2.2f & %2.2f &  \\\\', percentageResults(1,1), percentageResults(1,2) );
Dawn@4: disp(str1);
Dawn@4: str1 = sprintf(' & %2.2f & %2.2f &  \\\\', percentageResults(2,1), percentageResults(2,2) );
Dawn@4: disp(str1);
Dawn@4: str1 = sprintf(' & & & %2.2f \\\\',percentageResults(3,3) );
Dawn@4: disp(str1);
Dawn@4:  
Dawn@4: fprintf( masterFileOutputID, '\n' );
Dawn@4: fclose( masterFileOutputID );
Dawn@4: 
Dawn@4: end
Dawn@4: 
Dawn@4: