diff toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/test_ho_inf_enginge.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/test_ho_inf_enginge.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,87 @@
+function [engine,engine2] = test_ho_inf_enginge(order,T)
+
+assert(order >= 1)
+% Model a SISO system, i. e. all node are one-dimensional
+% The nodes are numbered as follows
+% u(t) = 1 input
+% y(t) = 2 model output
+% z(t) = 3 noise
+% q(t) = 4 observed output = noise + model output
+
+ns = [1 1 1 1];
+
+% Model a linear system, i.e. there are no discrete nodes
+dn = [];
+
+% Modeling of connections within a time slice
+intra = zeros(4);
+intra(2,4) = 1; % Connection y(t) -> q(t)
+intra(3,4) = 1; % Connection z(t) -> q(t)
+
+% Connections to the next time slice
+inter = zeros(4,4,order);
+inter(1,2,1) = 1; % u(t) -> y(t+1);
+inter(2,2,1) = 1; %y(t) -> y(t+1);
+inter(3,3,1) = 1; %z(t) -> z(t+1);
+
+if order >= 2
+    inter(1,2,2) = 1; % u(t) -> y(t+2);
+    inter(2,2,2) = 1; % y(t) -> y(t+2);
+end
+
+for i = 3: order
+    inter(:,:,i) = inter(:,:,i-1); %u(t) -> y(t+i) y(t) -> y(t) +i
+end;
+
+
+% Compution of a higer order Markov Model
+bnet = mk_higher_order_dbn(intra,inter,ns,'discrete',dn);
+bnet2 = mk_dbn(intra,inter(:,:,1),ns,'discrete',dn)
+
+
+%Calculation of the number of nodes with different parameters
+%There is one input and one output nodes  2
+%There are two different disturbance node 2
+%There are order +1 nodes for y           1 + order
+numOfNodes = 5 + order; 
+
+% First input node
+bnet.CPD{1} = gaussian_CPD(bnet,1,'mean',0);
+bnet2.CPD{1} = gaussian_CPD(bnet,1,'mean',0);
+% Modeled output
+bnet.CPD{2} = gaussian_CPD(bnet,2,'mean',0);
+bnet2.CPD{2} = gaussian_CPD(bnet,2,'mean',0);
+%Disturbance
+bnet.CPD{3} = gaussian_CPD(bnet,3,'mean',0);
+bnet2.CPD{3} = gaussian_CPD(bnet,3,'mean',0);
+
+%Qutput
+bnet.CPD{4} = gaussian_CPD(bnet,4,'mean',0);
+bnet2.CPD{4} = gaussian_CPD(bnet,4,'mean',0);
+
+
+%Output node in the second time-slice
+%Remember that node number 6 is an example for 
+%the fifth equivalence class
+bnet.CPD{5} = gaussian_CPD(bnet,6,'mean',0);
+bnet2.CPD{5} = gaussian_CPD(bnet,6,'mean',0);
+
+%Disturbance node in the second time slice
+bnet.CPD{6} = gaussian_CPD(bnet,7,'mean',0);
+bnet2.CPD{6} = gaussian_CPD(bnet,7,'mean',0);
+
+% Modeling of the remaining nodes for y
+for i = 7:numOfNodes
+    bnet.CPD{i} = gaussian_CPD(bnet,(i - 6)*4 + 7,'mean',0);
+end
+
+% Generation of the inference engine
+engine = dv_unrolled_dbn_inf_engine(bnet,T);
+engine2 = jtree_unrolled_dbn_inf_engine(bnet,T);
+
+
+
+
+
+
+