1 files changed, 0 insertions, 99 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots2.m b/report/pyp_clustering/acl09-short/code/wsjplots2.m
deleted file mode 100644
index eed41846..00000000
--- a/report/pyp_clustering/acl09-short/code/wsjplots2.m
+++ /dev/null
@@ -1,99 +0,0 @@
-
-load wsj
-
-figure(1)
-clf 
-subplot(1,2,2)
-hold on
-
-for i = 1:9
-  a = i/10;
-  [logbins predicted dummy] = logbinmean(counts,counts.^a,20,20);
-  ph = plot(log10(logbins),log10(predicted),'k');
-  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
-end
-
-for i = 1:9
-  a = i/10;  
-  disp(['Loading results for a = ' num2str(a) ]);
-
-  typecountrecord= load([ 'typecountrecordwsjflat' num2str(a) '.1.0.dat']);
-  
-  typecountrecordmean = mean(typecountrecord(500:1000,:));
-  
-  save([ 'typecountrecordmeanwsjflat' num2str(a) '.1.0.mat'],'typecountrecordmean');
-  
-  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
-  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
-  drawnow
-end
-
-
-
-
-[logbins meanval seval] = logbinmean(counts,counts,20,20)
-[logbins predicted dummy] = logbinmean(counts,counts,20,20)
-ph = plot(log10(logbins),log10(predicted),'r');
-hold on
-errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
-
-set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
-
-set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
-set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
-set(gca,'xlim',[-0.1 3.5])
-set(gca,'ylim',[-0.1 3.5])
-set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
-		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
-		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
-		    ' ', ' ', ' ', ' '});
-set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
-		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
-		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
-		    ' ', ' ', ' ', ' '});
-
-title('Pitman-Yor process adaptor')
-ylabel('Mean number of lexical entries')
-xlabel('Word frequency (n_w)')
-box on
-
-subplot(1,2,1)
-
-for i = 1:5
-
-  b = 10^(i-1)
-
-  disp(['Loading results for b = ' num2str(b) ]);
-  typecountrecord= load([ 'typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
-
-  typecountrecordmean = mean(typecountrecord(500:1000,:));
-  save([ 'typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
-  
-  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
-  [logbins predicted dummy] = logbinmean(counts,crppred(counts,b),20,20)
-%  errorbar(log10(logbins),meanval,seval,'k.');
-  hold on
-  ph = plot(log10(logbins),log10(predicted),'r');
-  %  ph = plot(log10(logbins),predicted,'r');
-  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
-  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
-end
-
-set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
-set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
-set(gca,'xlim',[-0.1 3.5])
-set(gca,'ylim',[-0.1 1.5])
-set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
-		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
-		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
-		    ' ', ' ', ' ', ' '});
-set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
-		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
-		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
-		    ' ', ' ', ' ', ' '});
-title('Chinese restaurant process adaptor')
-ylabel('Mean number of lexical entries')
-xlabel('Word frequency (n_w)')
-box on
-
-