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authorChris Dyer <cdyer@cs.cmu.edu>2010-12-22 08:58:07 -0600
committerChris Dyer <cdyer@cs.cmu.edu>2010-12-22 08:58:07 -0600
commitb5ca2bd7001a385594af8dc4b9206399c679f8c5 (patch)
tree332cb09a27f783760532e688e14bde21a128bb2b /report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m
parent86805dcb8aaaa716fdc73725ad41e411be53f6a6 (diff)
remove report
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diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m
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-%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
-
-