preso

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@435 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 99 insertions, 0 deletions

diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_cl.m b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m
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+++ b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m
@@ -0,0 +1,99 @@
+
+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
+
+