1 files changed, 58 insertions, 0 deletions
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m
new file mode 100644
index 00000000..dd039289
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+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m
@@ -0,0 +1,58 @@
+%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]; %same 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
+
+  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(:,:));
+  %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',colors(i-2,:),'linewidth',2);
+
+  % 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',colors(i-2,:),'linewidth',2,'linestyle','--')
+
+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])
+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','Antoniak approximation'};
+legend(labs,'Location','NorthWest')
+box on