comparison Code/Classifiers/kmeans_Singing.m @ 4:92ca03a8fa99 tip

Update to ICASSP 2013 benchmark

+function [] = kmeans_Singing( inputFileName )
+
+cd 'C:\Users\dawn\Dropbox\TestResults'
+inputFileName
+
+DEBUG = 1;
+% output results file name
+outputFileName = ['kmeans_' inputFileName];
+masterFileOutputID = fopen( outputFileName, 'a' );
+
+fprintf( masterFileOutputID, '\n RESULTS FILE NAME: %s\n', inputFileName);
+inputFileID = fopen( inputFileName );
+
+titleName = '';
+
+fprintf( masterFileOutputID, '\t' );
+
+% -------------------- get the data from the results file ---------------
+
+lineCount = 0;
+fileCount = 0;
+data = [];
+while( ~(feof(inputFileID)) )
+    
+    outputValues = [];
+    thestr = fgetl(inputFileID);
+    fileCount = fileCount + 1;
+
+    % determine whether we have a positive or negative sample
+    sampleEmotion( fileCount ) = 'U';
+    if( ~(isempty(strfind(thestr,'pos'))))
+        % sample is positive
+        sampleEmotion( fileCount ) = 'P';
+    elseif( ~(isempty(strfind(thestr,'neg'))))
+        % sample is negative
+        sampleEmotion( fileCount ) = 'N';
+    else
+        disp('EEEK!');
+        fileCount = fileCount - 1;
+%         pause;
+    end
+
+    % determine whether we have a male, female or trans sample
+%     gender( fileCount ) = '?';
+%     if( ~(isempty(strfind(thestr,'fem'))))
+%         % gender is female
+%         gender( fileCount ) = 'F';
+%     elseif( ~(isempty(strfind(thestr,'male'))))
+%         % gender is male
+%         gender( fileCount ) = 'M';
+%     elseif( ~(isempty(strfind(thestr,'trans'))))
+%         % gender is trans
+%         gender( fileCount ) = 'T';
+%     else
+%         disp('EEEK!');
+%         pause;
+%     end
+
+    if(( ~(isempty(strfind(thestr,'pos')))) || ( ~(isempty(strfind(thestr,'neg')))) )
+        %how many values are in the string?
+%         spaces = strfind( thestr, ' ' );
+        spaces = [ strfind( thestr,  sprintf('\t')) strfind( thestr, ' ' )];
+        numberstr = thestr( spaces(1) : end ); % chop off the file name  
+        vars = sscanf( numberstr, '%f', inf );
+        data( fileCount, : ) = vars;
+    end
+    lineCount = lineCount + 1;
+    
+end
+fclose(inputFileID);
+
+% ------------  apply the k-means classifier  ------------------------
+
+noOfClusters = 2; % we are only trying to identify positive and negative emotions
+
+
+[idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
+    'start', 'sample', 'Distance', 'cityblock');
+
+%display results grouped by emotion
+fprintf( masterFileOutputID, '\n Emotion grouping \n');
+fprintf( masterFileOutputID, 'cityblock \n');
+[ groupStats, groupNames ] = processKMeansResults( 'cityblock', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG );
+[ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'cityblock' );
+
+
+fprintf( masterFileOutputID, 'sqEuclidean \n');
+[idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
+    'start', 'sample', 'Distance', 'sqEuclidean');
+
+[ groupStats, groupNames ] = processKMeansResults( 'sqEuclidean', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG );
+[ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'sqEuclidean' );
+
+
+fprintf( masterFileOutputID, 'cosine \n');
+[idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
+    'start', 'sample', 'Distance', 'cosine');
+
+[ groupStats, groupNames ] = processKMeansResults( 'cosine', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG );
+[ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'cosine' );
+
+
+fprintf( masterFileOutputID, 'correlation \n');
+[idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
+    'start', 'sample', 'Distance', 'correlation');
+
+[ groupStats, groupNames ] = processKMeansResults( 'correlation', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG );
+[ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'correlation' );
+
+% --------------------------------------------------------------------
+
+% display results grouped by gender
+% fprintf( masterFileOutputID, '\n Gender grouping \n');
+% noOfClusters = 3;
+% 
+% [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
+%     'start', 'sample', 'Distance', 'cityblock');
+% 
+% fprintf( masterFileOutputID, 'cityblock \n');
+% [ groupStats, groupNames ] = processKMeansResults( 'cityblock', idx, gender, masterFileOutputID, titleName, DEBUG );
+% [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID );
+% 
+% [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
+%     'start', 'sample', 'Distance', 'sqEuclidean');
+% 
+% fprintf( masterFileOutputID, 'sqEuclidean \n');
+% [ groupStats, groupNames ] = processKMeansResults( 'sqEuclidean', idx, gender, masterFileOutputID, titleName, DEBUG );
+% [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID );
+
+fprintf( masterFileOutputID, '\n' );
+fclose( masterFileOutputID );
+
+end
+
+