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, 35 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m b/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m
new file mode 100644
index 00000000..a2b107bc
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m
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+function [ratio, ratiominus, ratioplus, proba_post, yt] = classification_evaluation(bnet, BDT, class)
+% Computes the classification ratio of a bnet structure on a test dataset BDT
+% [ratio ratiominus ratioplus] = classification_evaluation(bnet, BDT, class)
+% 
+% [ratiominus rationplus] is the 95 percent confident interval.
+% results are in percentage [0 100].
+%
+%  francois.olivier.c.h@gmail.com
+%
+
+    [proba_post,engine] = inference(bnet, mat_to_bnt(BDT), class);
+               [tmp yt] = max(proba_post, [],2);
+                  [N L] = size(BDT);
+                  count = length(find(BDT(class,:)==yt'));
+                  ratio = 100*count/L;
+[ratiominus, ratioplus] = confiance(ratio,L);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function [I, J] = confiance(t, N)
+% Compute the 95 percent confident interval
+%
+% see Y. Bennani and F. Bossaert, 
+%     Predictive neural networks for traffic disturbance detection in the telephone network
+%     In Proceedings of IMACS-CESA 1996, Lille, France.
+
+Z    = 1.96; % this value for the 95 percent confident interval
+T    = t/100;
+tmp  = (Z*Z)/N;
+D    = 1+tmp;
+N1   = T+tmp/2;
+tmp2 = T*(1-T)/N + tmp/(4*N);
+N2   = Z*sqrt(tmp2);
+I    = 100*(N1-N2)/D;
+J    = 100*(N1+N2)/D;