diff options
Diffstat (limited to 'report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m')
| -rw-r--r-- | report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m | 117 |
1 files changed, 0 insertions, 117 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m deleted file mode 100644 index 1d07e54c..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m +++ /dev/null @@ -1,117 +0,0 @@ -%wsj_lengths = load([ 'wsj_lengths.dat']); -%save([ 'wsj_lengths.mat'],'wsj_lengths'); -load wsj -load wsj_lengths - -figure(1) -clf - -subplot(1,2,1); -hold on - -for i = 3:6 - - b = 10^(i-1) - - disp(['Loading results for b = ' num2str(b) ]); -%%% uncomment these lines if .mat file is not yet generated. %%% - %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']); - %typecountrecordmean = mean(typecountrecord(:,:)); - %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean'); - load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']); - - % plot lines for CRP exact prediction using summation - [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20); - ph = plot(log10(logbins),log10(predicted),'r'); - set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5) - - % plot lines for CRP Antoniak prediction - [logbins predicted dummy] = logbinmean(counts, antoniakpred(counts,b),20,20); - ph = plot(log10(logbins),log10(predicted),'r'); - set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','--') - - %plot lines for incorrect CRP Antoniak prediction (ACL07) - %[logbins predicted dummy] = logbinmean(counts, noP0pred(counts,b),20,20); - %ph = plot(log10(logbins),log10(predicted),'r'); - %set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle',':') - - % plot lines for CRP Cohn prediction - %[logbins predicted dummy] = logbinmean(counts, cohnpred(counts,b),20,20); - %ph = plot(log10(logbins),log10(predicted),'r'); - %set(ph,'color',[0.2 0.2 1],'linewidth',1.5,'linestyle','.') - - %plot emprical counts with error bars - [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20); - 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:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000])) -set(gca,'xlim',[-0.1 3.5]) -set(gca,'ylim',[-1.1 1.5]) -set(gca,'FontSize',14) -set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ... - '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ... - ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ... - ' ', ' ', ' ', ' '}); -set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ... - '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ... - '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ... - ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ... - ' ', ' ', ' ', ' '}); -%title('Chinese restaurant process adaptor') -ylabel('Mean number of lexical entries') -xlabel('Word frequency (n_w)') -legend('Expectation','Antoniak approx.','Empirical','Location','NorthWest') -box on - - -subplot(1,2,2); -hold on - -for i =3:6 - - b = 10^(i-1) - - disp(['Loading results for b = ' num2str(b) ]); -%%% uncomment these lines if .mat file is not yet generated. %%% - %typecountrecord= load([ 'outputs/typecountrecordwsjpeak0.0.' num2str(b) '.0.dat']); - %typecountrecordmean = mean(typecountrecord(:,:)); - %save([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat'],'typecountrecordmean'); - load([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat']); - - % plot lines for CRP exact prediction using summation - [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20); - ph = plot(log10(logbins),log10(predicted),'r'); - set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5) - - %plot lines for incorrect CRP Antoniak prediction (ACL07) - [logbins predicted dummy] = logbinmean(counts, noP0pred(counts,b),20,20); - ph = plot(log10(logbins),log10(predicted),'r'); - set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','-.') - - %plot emprical counts with error bars - [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20); - 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',[-.1 2.5]) -set(gca,'FontSize',14) -set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ... - '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ... - ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ... - ' ', ' ', ' ', ' '}); -set(gca,'yticklabel', {...%'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ... - '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ... - '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ... - ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ... - ' ', ' ', ' ', ' '}); -%title('Chinese restaurant process adaptor') -ylabel('Mean number of lexical entries') -xlabel('Word frequency (n_w)') -legend('Expectation','GGJ07 approx.','Empirical','Location','NorthWest') -box on -%axis square
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