remove report

1 files changed, 0 insertions, 74 deletions

diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl.m
deleted file mode 100644
index 50582e7f..00000000
--- a/report/pyp_clustering/acl09-short/code/wsjplots_acl.m
+++ /dev/null
@@ -1,74 +0,0 @@
-%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