From fd519b0e45c857b266814994ba8c1421f508e522 Mon Sep 17 00:00:00 2001
From: redpony <redpony@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Tue, 27 Jul 2010 16:13:19 +0000
Subject: preso

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@435 ec762483-ff6d-05da-a07a-a48fb63a330f
---
 .../pyp_clustering/acl09-short/code/wsjplots_cl.m  | 99 ++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 report/pyp_clustering/acl09-short/code/wsjplots_cl.m

(limited to 'report/pyp_clustering/acl09-short/code/wsjplots_cl.m')

diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_cl.m b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m
new file mode 100644
index 00000000..eed41846
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m
@@ -0,0 +1,99 @@
+
+load wsj
+
+figure(1)
+clf 
+subplot(1,2,2)
+hold on
+
+for i = 1:9
+  a = i/10;
+  [logbins predicted dummy] = logbinmean(counts,counts.^a,20,20);
+  ph = plot(log10(logbins),log10(predicted),'k');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+end
+
+for i = 1:9
+  a = i/10;  
+  disp(['Loading results for a = ' num2str(a) ]);
+
+  typecountrecord= load([ 'typecountrecordwsjflat' num2str(a) '.1.0.dat']);
+  
+  typecountrecordmean = mean(typecountrecord(500:1000,:));
+  
+  save([ 'typecountrecordmeanwsjflat' num2str(a) '.1.0.mat'],'typecountrecordmean');
+  
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+  drawnow
+end
+
+
+
+
+[logbins meanval seval] = logbinmean(counts,counts,20,20)
+[logbins predicted dummy] = logbinmean(counts,counts,20,20)
+ph = plot(log10(logbins),log10(predicted),'r');
+hold on
+errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+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',[-0.1 3.5])
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+
+title('Pitman-Yor process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+box on
+
+subplot(1,2,1)
+
+for i = 1:5
+
+  b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  typecountrecord= load([ 'typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+
+  typecountrecordmean = mean(typecountrecord(500:1000,:));
+  save([ 'typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
+  [logbins predicted dummy] = logbinmean(counts,crppred(counts,b),20,20)
+%  errorbar(log10(logbins),meanval,seval,'k.');
+  hold on
+  ph = plot(log10(logbins),log10(predicted),'r');
+  %  ph = plot(log10(logbins),predicted,'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+  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',[-0.1 1.5])
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+box on
+
+
-- 
cgit v1.2.3