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, 83 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/examples/test_knn.m b/sourcecodes/bnt-master/SLP/examples/test_knn.m
new file mode 100644
index 00000000..b34a57a0
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/examples/test_knn.m
@@ -0,0 +1,83 @@
+function [ratio, ratiominus, ratioplus, tps]  = test_knn(BD,BDT,class,K)
+% [epsilon, tps]  = test_knn(BD,BDT,class,K)
+%
+% epsilon = good classification rate on BDT,
+% tps = computation time,
+% BD = examples dataset using to find neighbors,
+% BDT = set of examples to class,
+% class = classification attribute number,
+% K = vote on the K nearest neighbor
+%
+% francois.olivier.c.h@gmail.com
+
+[N L]=size(BD);
+Ltest=size(BDT,2);
+fprintf(' test dataset size %d\r\n',Ltest);
+E=setdiff(1:N,class);
+place=0;
+%for k=K
+k=K;
+ place=place+1;
+ good=0;tic
+ for i=1:Ltest
+  %fprintf(' %d\r',i);
+  res=knn(BD(E,:)',BD(class,:)',[1 2],BDT(E,i)',k);
+  if res==BDT(class,i)
+    good=good+1;
+  end
+ end
+ tps(place)=toc;
+ epsilon(1,place)=k;
+ epsilon(2,place)=good/(Ltest);
+%end
+
+ratio = epsilon(2,place);
+[ratiominus ratioplus] = confiance(ratio,L);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%�
+function [ypred]=knn(xapp,yapp,valY,X,k)
+% knn implementation
+%
+% USE : [ypred]=knn(xapp,yapp,valY,X,k)
+%
+% Vincent Guigue 08/01/03
+
+if nargin<4
+  error('too few argumemnts');
+elseif nargin<5
+  k=3;
+else
+  if mod(k,2)==0
+    error('k must be odd');
+  end
+end
+
+if size(xapp,2)~=size(X,2)
+  error('dimension incompatibility');
+end
+ndim = size(xapp,2);
+nptxapp = size(xapp,1);
+nptX = size(X,1);
+
+% distance de X a xapp :
+mat1 =  repmat(xapp, nptX,1);
+%mat21 = reshape(X',1,nptX*ndim)
+mat22 = repmat(X,1,nptxapp)';
+mat2 = reshape(mat22 ,ndim, nptxapp*nptX)';
+distance = mat1 - mat2 ;
+distance = sum(distance.^2,2);
+distance = reshape(distance,nptxapp,nptX);
+[val kppv] = sort(distance,1);
+% bilan sur les k premieres lignes
+kppv = reshape(kppv(1:k,:),k*nptX,1);
+Ykppv = yapp(kppv,1);
+Ykppv = reshape(Ykppv,k,nptX);
+% trouver le plus de reponses identique par colonne
+vote = [];
+for i=1:nptX
+  for j=1:length(valY)
+    vote(j,i)=size(find(Ykppv(:,i)==valY(j)),1);
+  end
+end
+[val ind]=max(vote,[],1);
+ypred = valY(ind);
\ No newline at end of file