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, 63 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/mk_bat_dbn.m b/sourcecodes/bnt-master/BNT/examples/dynamic/mk_bat_dbn.m
new file mode 100644
index 00000000..e4d6f8b9
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/mk_bat_dbn.m
@@ -0,0 +1,63 @@
+function [bnet, names] = mk_bat_dbn()
+% MK_BAT_DBN Make the BAT DBN
+% [bnet, names] = mk_bat_dbn()
+% See
+% - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJCAI 95
+% - Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98.
+
+names = {'LeftClr', 'RightClr', 'LatAct', 'Xdot', 'InLane', 'FwdAct', ...
+      'Ydot', 'Stopped', 'EngStatus', 'FBStatus', ...
+      'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', ...
+      'FYdotDiffSens', 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens', ...
+      'SensorValid', 'FYdotDiff', 'FcloseSlow', 'Fclr', 'BXdot', 'BcloseFast', 'Bclr', 'BYdotDiff'};
+ss = length(names);
+
+intrac = {...
+      'LeftClr', 'LeftClrSens';
+  'RightClr', 'RightClrSens';
+  'LatAct', 'TurnSignalSens'; 'LatAct', 'Xdot';
+  'Xdot', 'XdotSens';
+  'FwdAct', 'Ydot';
+  'Ydot', 'YdotSens'; 'Ydot', 'Stopped';
+  'EngStatus', 'Ydot'; 'EngStatus', 'FYdotDiff'; 'EngStatus', 'Fclr'; 'EngStatus', 'BXdot';
+  'SensorValid', 'XdotSens';   'SensorValid', 'YdotSens';
+  'FYdotDiff', 'FYdotDiffSens'; 'FYdotDiff', 'FcloseSlow';
+  'FcloseSlow', 'FBStatus';
+  'Fclr', 'FclrSens'; 'Fclr', 'FcloseSlow';
+  'BXdot', 'BXdotSens';
+  'Bclr', 'BclrSens'; 'Bclr', 'BXdot'; 'Bclr', 'BcloseFast';
+  'BcloseFast', 'FBStatus';
+  'BYdotDiff', 'BYdotDiffSens'; 'BYdotDiff', 'BcloseFast'};
+[intra, names] = mk_adj_mat(intrac, names, 1);
+
+
+interc = {...
+      'LeftClr', 'LeftClr'; 'LeftClr', 'LatAct';
+  'RightClr', 'RightClr'; 'RightClr', 'LatAct';
+  'LatAct', 'LatAct'; 'LatAct', 'FwdAct';
+  'Xdot', 'Xdot'; 'Xdot', 'InLane';
+  'InLane', 'InLane'; 'InLane', 'LatAct';
+  'FwdAct', 'FwdAct';
+  'Ydot', 'Ydot';
+  'Stopped', 'Stopped';
+  'EngStatus', 'EngStatus';
+  'FBStatus', 'FwdAct'; 'FBStatus', 'LatAct'};
+inter = mk_adj_mat(interc, names, 0);  
+
+obs = {'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', 'FYdotDiffSens', ...
+      'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens'};
+
+for i=1:length(obs)
+  onodes(i) = strmatch(obs{i}, names); %stringmatch(obs{i}, names);
+end
+onodes = sort(onodes);
+
+dnodes = 1:ss; 
+ns = 2*ones(1,ss); % binary nodes
+bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes, 'eclass2', (1:ss)+ss);
+
+% make rnd params
+for i=1:2*ss
+  bnet.CPD{i} = tabular_CPD(bnet, i);
+end
+