diff other/evalResults.m @ 1:995546d09284

add gensim notebook and matlab scripts

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/other/evalResults.m	Tue Jan 24 17:44:45 2017 +0000
@@ -0,0 +1,174 @@
+
+load('Adobe.mat')
+load('Results1Percent.mat')
+%%
+datamap = featuredata(end).IdxVar;
+reduceData = Data(:,datamap);
+reduceLabels = Labels(datamap);
+%%
+reduceFeatures = FeatureNames(datamap);
+
+%%
+load('Results1Percent.mat')
+
+%%
+reduceFeatures = featuredata(1).FeatureNamesRanked;
+
+dataToUseSize = 500;
+dataToUse = ceil(rand(dataToUseSize,1)*size(reduceData,1))';
+
+dMap = pdist(reduceData(dataToUse,:));
+clusterMethod = 'ward';
+% 'average'     Unweighted average distance (UPGMA)
+% 'centroid'	Centroid distance (UPGMC), appropriate for Euclidean distances only
+% 'complete'	Furthest distance
+% 'median'      Weighted center of mass distance (WPGMC), appropriate for Euclidean distances only
+% 'single'      Shortest distance
+% 'ward'        Inner squared distance (minimum variance algorithm), appropriate for Euclidean distances only
+% 'weighted'	Weighted average distance (WPGMA)
+
+dl = linkage(dMap, clusterMethod);
+dendrogram(dl)
+% figure; imagesc(squareform(dMap_sp))
+% title('euclidian self similarity');
+
+%%
+incon_sp = inconsistent(dl)
+
+
+%%
+% Use all data
+
+dMapAll = pdist(reduceData);
+clusterMethod = 'ward';
+% 'average'     Unweighted average distance (UPGMA)
+% 'centroid'	Centroid distance (UPGMC), 
+%                     appropriate for Euclidean distances only
+% 'complete'	Furthest distance
+% 'median'      Weighted center of mass distance (WPGMC),
+%                     appropriate for Euclidean distances only
+% 'single'      Shortest distance
+% 'ward'        Inner squared distance (minimum variance algorithm), 
+%                   appropriate for Euclidean distances only
+% 'weighted'	Weighted average distance (WPGMA)
+
+dl_all = linkage(dMapAll, clusterMethod);
+% [~,T] = dendrogram(dl_all,0)
+
+%%
+% print filelist for each cluster
+
+numClusters = 25;
+fnames = cell(1,numClusters);
+[~,T] = dendrogram(dl_all,numClusters);
+for i = 1:numClusters
+    numFiles = sum(T==i);
+    fnames{i} = Filenames(find(T==i));
+end
+
+%%
+% makeCSV for Weka
+% format 
+
+feats = reduceData;
+
+% csvOut = mat2cell(feats,ones(size(feats,1),1), ones(size(feats,2),1))
+csvOut = num2cell(feats);
+csvOut = [csvOut, num2cell(T)];
+% size(csvOut)
+% size([FeatureNames(datamap)', {'Class'}])
+csvOut = [[FeatureNames(datamap)', {'Class'}]; csvOut];
+
+%%
+% fnames to CSV
+
+maxLen = size(fnames,2);
+
+for i = 1:maxLen
+    depth = size(fnames{i},1);
+    for ii = 1:depth
+        csvOut(i,ii) = fnames{i}(ii);
+    end
+end
+
+printString = '';
+for i = 1:maxLen
+    printString = [printString ' %s, '];
+end
+
+fid = fopen('junk.csv','w');
+fprintf(fid,[printString '\n'],csvOut{1:end,:});
+% fprintf(fid,'%f, %f, %f\n',c{2:end,:})
+fclose(fid) ;
+% dlmwrite('test.csv', csvOut, '-append') ;
+
+%%
+T = cluster(dl_sp,'cutoff',1.3);
+figure; plot(T);
+
+
+
+%%
+
+
+T = cluster(dl_sp,'maxclust',2);
+plot(T)
+%%
+T = cluster(dl_sp,'maxclust',3);
+plot(T)
+%%
+T = cluster(dl_sp,'maxclust',4);
+plot(T)
+T = cluster(dl_sp,'maxclust',5);
+plot(T)
+T = cluster(dl_sp,'maxclust',6);
+plot(T)
+T = cluster(dl_sp,'maxclust',7);
+plot(T)
+T = cluster(dl_sp,'maxclust',8);
+plot(T)
+T = cluster(dl_sp,'maxclust',9);
+plot(T)
+%%
+T = cluster(dl_sp,'maxclust',10);
+plot(T)
+%%
+T = cluster(dl_sp,'maxclust',100);
+plot(T)
+%%
+median(T)
+
+
+T = cluster(dl_sp,'maxclust',1000);
+median(T)
+
+
+plot(T)
+csvwrite('dataOutput',reduceData);
+
+
+
+
+
+
+
+
+
+
+
+
+% dMap_euc = pdist(reduceData);
+% dMap_cos = pdist(reduceData,'cos');
+% dMap_cos = pdist(reduceData,'cosine');
+% dl_euc = linkage(dMap_euc);
+% dl_cos = linkage(dMap_cos);
+% % dl_sp
+% dl_sp(10,:)
+% dl_sp(1:10,:)
+% sprintf('%f', dl_sp(1:10,:))
+% dl_sp(1:10,:)
+% format short g
+% dl_sp(1:10,:)
+% plot(dl_sp(:))
+% plot(dl_sp(:,3))
+% incon_sp = inconsistent(dl_sp)