1 files changed, 0 insertions, 74 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.m
deleted file mode 100644
index 8d570b7a..00000000
--- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.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
-
-%colors = [0 0 0; 0 0 1; 1 0 0; 0 1 0]; %pure black, red, blue, green
-colors = [0 0 0; 1 .4 .2; .4 .4 1; 0 .7 .5]; %similar but less garish
-%colors = [0 0 0; .6 .4 .4; .9 .6 .6; 1 .8 .8]; %shades of pink
-%colors = [0 0 0; .3 .3 1; .4 .8 1; .5 1 .8]; %blue/green
-
-for i = 3:6
-  col = colors(i-2,:);
-  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));
-  set(ph,'color',col,'linewidth',2);
-
-  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);
-  ph = plot(log10(logbins),log10(meanval));
-  %set(ph,'color',col,'linestyle','o','markerfacecolor',col,'markersize',8);
-  set(ph,'color',col,'linestyle','o','linewidth',2,'markersize',8);
-  %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);
-  ph = plot(log10(logbins),log10(meanval));
-  %set(ph,'color',col,'linestyle','^','markerfacecolor',col,'markersize',8);
-  set(ph,'color',col,'linestyle','^','linewidth',2,'markersize',8);
-  %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 2.5])
-set(gca,'FontSize',16)
-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 (tables)')
-xlabel('Word frequency (n_w)')
-labs = {'Expectation','Empirical, fixed base','Empirical, inferred base'};
-legend(labs,'Location','NorthWest')
-box on