BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 112 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMtools/bipartiteMatchingDemo.m b/sourcecodes/bnt-master/KPMtools/bipartiteMatchingDemo.m
new file mode 100644
index 00000000..112547be
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMtools/bipartiteMatchingDemo.m
@@ -0,0 +1,112 @@
+% Consider matching sources to detections
+
+%  s1 d2  
+%         s2 d3
+%  d1
+
+%a  = bipartiteMatchingHungarian([52;0.01])
+
+% sources(:,i) = [x y] coords
+sources = [0.1 0.7; 0.6 0.4]';
+detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]';
+dst = sqdist(sources, detections);
+
+% a = [2 3] which means s1-d2, s2-d3
+a = bipartiteMatchingHungarian(dst);
+a2 = bipartiteMatchingIntProg(dst);
+assert(isequal(a(:),a2(:)))
+
+
+figure(1); clf
+bipartiteMatchingDemoPlot(sources, detections, a)
+
+
+
+
+%%%% Flip roles of sources and detections
+
+%dst  = dst';
+dst = sqdist(detections, sources);
+% a = [0 1 2] which means d1-0, d2-s1, d3-s2
+a = bipartiteMatchingHungarian(dst);
+
+a2 = bipartiteMatchingIntProg(dst);
+assert(isequal(a(:),a2(:)))
+
+figure(2); clf
+bipartiteMatchingDemoPlot(detections, sources, a) % swapped args
+
+
+
+
+%%%%%%%%%% Move s1 nearer to d1
+%  d2  
+%         s2 d3
+%  s1 d1
+
+sources = [0.1 0.3; 0.6 0.4]';
+detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]';
+dst = sqdist(sources, detections);
+
+% a = [2 3] which means s1-d2, s2-d3
+a = bipartiteMatchingHungarian(dst);
+[a2, ass] = bipartiteMatchingIntProg(dst);
+assert(isequal(a(:),a2(:)))
+
+
+figure(3); clf
+bipartiteMatchingDemoPlot(sources, detections, a)
+
+
+
+%%%%%%%%%%
+
+% Use random points
+
+% Generate 2D data from a mixture of 2 Gaussians (from netlab demgmm1)
+randn('state', 0); rand('state', 0);
+gmix = gmm(2, 2, 'spherical');
+ndat1 = 10; ndat2 = 10; ndata = ndat1+ndat2;
+%gmix.centres =  [0.3 0.3; 0.7 0.7]; 
+%gmix.covars = [0.01 0.01];
+gmix.centres =  [0.5 0.5; 0.5 0.5];
+gmix.covars = [0.1 0.01];
+[x, label] = gmmsamp(gmix, ndata);
+
+ndx = find(label==1);
+sources = x(ndx,:)';
+ndx = find(label==2);
+detections = x(ndx,:)';
+dst = sqdist(sources, detections);
+
+[a, ass] = bipartiteMatchingIntProg(dst);
+[a2] = bipartiteMatchingHungarian(dst);
+assert(isequal(a(:), a2(:)))
+
+figure(4); clf
+bipartiteMatchingDemoPlot(sources, detections, a)
+
+% only match 80% of points
+p1 = size(sources, 2);
+p2 = size(detections, 2);
+nmatch = ceil(0.8*min(p1,p2));
+a2 = bipartiteMatchingIntProg(dst, nmatch);
+figure(5); clf
+bipartiteMatchingDemoPlot(sources, detections, a2)
+
+
+%%% swap roles
+
+ndx = find(label==2);
+sources = x(ndx,:)';
+ndx = find(label==1);
+detections = x(ndx,:)';
+dst = sqdist(sources, detections);
+
+% only match 80% of points
+p1 = size(sources, 2);
+p2 = size(detections, 2);
+nmatch = ceil(0.8*min(p1,p2));
+a2 = bipartiteMatchingIntProg(dst, nmatch);
+figure(6); clf
+bipartiteMatchingDemoPlot(sources, detections, a2)