diff options
Diffstat (limited to 'report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m')
-rw-r--r-- | report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m | 164 |
1 files changed, 164 insertions, 0 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m new file mode 100644 index 00000000..33419845 --- /dev/null +++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m @@ -0,0 +1,164 @@ +%wsj_lengths = load([ 'wsj_lengths.dat']); +%save([ 'wsj_lengths.mat'],'wsj_lengths'); +load wsj +load wsj_lengths + +figure(1) +clf + +subplot(1,3,1); +hold on + +for i = 2:6 + + 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(500:999,:)); + %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(1,3,2); +hold on + +for i =2:6 + + 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(500:999,:)); + %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 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','Location','NorthWest') +box on +%axis square + + +subplot(1,3,3); +hold on + +for i =2:6 + + b = 10^(i-1) + + disp(['Loading results for b = ' num2str(b) ]); +%%% uncomment these lines if .mat file is not yet generated. %%% + %typecountrecord= load([ 'outputs/typecountrecordwsjgeom0.0.' num2str(b) '.0.dat']); + %typecountrecordmean = mean(typecountrecord(500:999,:)); + %save([ 'outputs/typecountrecordmeanwsjgeom0.0.' num2str(b) '.0.mat'],'typecountrecordmean'); + load([ 'outputs/typecountrecordmeanwsjgeom0.0.' num2str(b) '.0.mat']); + + % plot lines for CRP exact prediction using summation +% [logbins meaneval seval] = logbinmean(counts, crppred_geom(counts,wsj_lengths,b),20,20) +[logbins meaneval seval] = logbinmean(counts, crppred(counts,b),20,20) + plot(log10(logbins),log10(meaneval),'r.'); +%errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'r.'); +% ph = plot(log10(logbins),log10(meaneval),'r'); +% set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5) + + %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','Location','NorthWest') +box on +hold off +%axis square + + |