diff options
Diffstat (limited to 'report/pyp_clustering/acl09-short/code/wsjplots_cl.m')
-rw-r--r-- | report/pyp_clustering/acl09-short/code/wsjplots_cl.m | 99 |
1 files changed, 0 insertions, 99 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_cl.m b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m deleted file mode 100644 index eed41846..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_cl.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 - - |