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, 156 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/ho1.m b/sourcecodes/bnt-master/BNT/examples/dynamic/ho1.m
new file mode 100644
index 00000000..b6825ce1
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/ho1.m
@@ -0,0 +1,156 @@
+function ho1()
+
+% Example of how to create a higher order DBN
+% Written by Rainer Deventer <deventer@informatik.uni-erlangen.de> 3/28/03
+
+bnet = createBNetNL();
+
+%%%%%%%%%%%%
+
+
+function bnet = createBNetNL(varargin)
+     % Generate a Bayesian network, which is able to model nonlinearities at
+% the input. The only input is the order of the dynamic system. If this 
+% parameter is missing, the an order of two is assumed
+if nargin > 0 
+    order = varargin{1}
+else
+    order = 2;
+end
+
+ss = 6; % For each time slice the following nodes are modeled
+        % ud(t_k) Discrete node, which decides whether saturation is reached.
+        %         Node number 2
+        % uv(t_k) Visible input node with node number  2
+        % uh(t_k) Hidden  input node with node number 3     
+        % y(t_k)  Modeled output, Number 4
+        % z(t_k)  Disturbing variable, number 5
+        % q(t_k), number6 6
+
+intra = zeros(ss,ss);
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Within each timeslice ud(t_k) is connected with uv(t_k) and uh(t_k)    %
+% This part is used to model saturation                                  %
+% A connection from  uv(t_k) to uh(t_k) is omitted                       %
+% Additionally   y(t_k) is connected with q(t_k). To model the disturbing%
+% value z(t_k) is connected with q(t_k).                                 %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+intra(1,2:3) = 1; % Connections ud(t_k) -> uv(t_k) and ud(t_k) -> uh(t_k)
+intra(4:5,6) = 1; % Connectios  y(t_k)  -> q(t_k)  and z(t_k)  -> q(t_k) 
+
+
+  
+inter = zeros(ss,ss,order);
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% The Markov assumption is not met as connections from time slice t to t+2 %
+% exist.                                                                   %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+for i = 1:order
+    if i == 1
+        inter(1,1,i) = 1; %Connect the discrete nodes. This is necessary to improve
+                          %the disturbing reaction
+        inter(3,4,i) = 1; %Connect uh(t_{k-1}) with y(t_k)
+        inter(4,4,i) = 1; %Connect y(t_{k-1})  with y(t_k)    
+        inter(5,5,i) = 1; %Connect z(t_{k-1})  with z(t_k)
+    else
+        inter(3,4,i) = 1; %Connect uh(t_{k-i}) with y(t_k)
+        inter(4,4,i) = 1; %Connect  y(t_{k-i}) with y(t_k)
+    end
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Define the dimensions of the discrete nodes. Node 1 has two states     %
+% 1 = lower saturation reached                                           %
+% 2 = Upper saturation reached                                           %
+% Values in between are model by probabilities between 0 and 1           %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+node_sizes = ones(1,ss);
+node_sizes(1) = 2;
+dnodes = [1];
+
+eclass = [1:6;7 2:3 8 9 6;7 2:3 10 11 6];
+bnet = mk_higher_order_dbn(intra,inter,node_sizes,...
+                           'discrete',dnodes,...
+                           'eclass',eclass);
+
+cov_high = 400;
+cov_low  = 0.01;
+weight1 = randn(1,1);
+weight2 = randn(1,1);
+weight3 = randn(1,1);
+weight4 = randn(1,1);
+
+numOfNodes = 5 + order;
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Nodes of the first time-slice   %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Discrete input node, 
+bnet.CPD{1} = tabular_CPD(bnet,1,'CPT',[1/2 1/2],'adjustable',0);
+
+
+% Modeled visible input
+bnet.CPD{2} = gaussian_CPD(bnet,2,'mean',[0 10],'clamp_mean',1,...
+                            'cov',[10 10],'clamp_cov',1);
+
+% Modeled hidden input
+bnet.CPD{3} = gaussian_CPD(bnet,3,'mean',[0, 10],'clamp_mean',1,...
+			          'cov',[0.1 0.1],'clamp_cov',1);
+
+% Modeled output in the first timeslice, thus there are no parents
+% Usuallz the output nodes get a low covariance. But in the first
+% time-slice a prediction of the output is not possible due to 
+% missing information
+bnet.CPD{4} = gaussian_CPD(bnet,4,'mean',0,'clamp_mean',1,...
+			          'cov',cov_high,'clamp_cov',1);
+
+%Disturbance
+bnet.CPD{5} = gaussian_CPD(bnet,5,'mean',0,...
+                                  'cov',[4],...
+                                  'clamp_mean',1,...
+                                  'clamp_cov',1);
+
+%Observed output. 
+bnet.CPD{6} = gaussian_CPD(bnet,6,'mean',0,...
+                                  'clamp_mean',1,...
+                                  'cov',cov_low,'clamp_cov',1,...
+                                  'weights',[1 1],'clamp_weights',1);
+
+% Discrete node at second time slice
+bnet.CPD{7} = tabular_CPD(bnet,7,'CPT',[0.6 0.4 0.4 0.6],'adjustable',0);
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Node for the model output %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+bnet.CPD{8} = gaussian_CPD(bnet,10,'mean',0,...
+				   'cov',cov_high,...
+				   'clamp_mean',1,...
+			           'clamp_cov',1);
+%                                   'weights',[0.0791 0.9578]);
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Node for the disturbance %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%                        
+bnet.CPD{9} = gaussian_CPD(bnet,11,'mean',0,'clamp_mean',1,...
+                                   'cov',[4],'clamp_cov',1,...
+                                   'weights',[1],'clamp_weights',1);
+                                   
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Node for the model output %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+bnet.CPD{10} = gaussian_CPD(bnet,16,'mean',0,'clamp_mean',1,...
+                                    'cov',cov_low,'clamp_cov',1);
+%                                   'weights',[0.0188 -0.0067 0.0791 0.9578]);
+
+
+
+    
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Node for the disturbance %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%                        
+bnet.CPD{11} = gaussian_CPD(bnet,17,'mean',0,'clamp_mean',1,...
+                                           'cov',[0.2],'clamp_cov',1,...
+                                           'weights',[1],'clamp_weights',1);
+
+
+
+