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Diffstat (limited to 'report/pyp_clustering/acl09-short/code/wsjplots_acl.m')
| -rw-r--r-- | report/pyp_clustering/acl09-short/code/wsjplots_acl.m | 74 |
1 files changed, 74 insertions, 0 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl.m new file mode 100644 index 00000000..50582e7f --- /dev/null +++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl.m @@ -0,0 +1,74 @@ +%wsj_lengths = load([ 'wsj_lengths.dat']); +%save([ 'wsj_lengths.mat'],'wsj_lengths'); +load wsj +load wsj_lengths + +figure(1) +clf + +hold on + +for i = 3:6 + + b = 10^(i-1) + + % 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 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','.') + + 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 emprical counts with error bars + [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20); + plot(log10(logbins),log10(meanval),'k*'); + %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.'); + + 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 emprical counts with error bars + [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20); + plot(log10(logbins),log10(meanval),'ko'); + %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'ko'); + +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 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','Antoniak approx.','Empirical, fixed base','Empirical, inferred base','Location','NorthWest') +box on |