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view 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
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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);