construct_alarm.m

The BNL toolbox is a set of Matlab functions for defining and estimating the parameters of a Bayesi

function  [bnet,evidence_nodes,partial_evidence_nodes,terminal_merged_nodes,hid_nodes,gausskwadnodes,names,onames,order,inv_o]=...
    construct_alarm(dag,data,names)
%bnet: bnet constructed from bnet software of murphy (struct array)
%evidence_nodes: struct(4): NOT YET IMPLEMENTED: ARE TREATED AS PARTIALLY
    %OBSERVED EVIDENCE NODES
    %evidence_nodes.nodenrs vector of (ordered) nodenrs of nodes that are observed for all cases
    %evidence_nodes.respcat nr of resp categories (not equal to nodesizes,
    %%evidence_nodes.nodesizes 
    %evidence_nodes.data cell array of corresponding columns in dataset  
%partial_evidence_nodes: struct(3): 
    %partial_evidence_nodes.nodenrs vector of (ordered) nodenrs of nodes that are partially observed 
    %partial_evidence_nodes.nodesizes number of resp categories (equals
    %nodesizes in the graph
    %partial_evidence_nodes.data cell array of of corresponding rows in dataset  
%terminal_merged_nodes: struct(5): 
    %terminal_merged_nodes.nodenrs vector of (ordered) nodenrs of terminal (partially) observed nodes 
    %terminal_merged_nodes.nodesizes
    %terminal_merged_nodes.data cell array of corresponding sets of columns in dataset 
    %that are merged into single node 
    %terminal_merged_nodes.nrvars vector of numbers of nodes merged
    %into each (ordered) terminal node
    %terminal_merged_nodes.respcat cell array of nr of respcat of merged
    %nodes
%hid_nodes struct(2)
    %hid_nodes.nodenrs vector of (ordered) nodenrs of nodes that are
    %hidden
    %hid_nodes.nodesizes
%gausskwadnodes: struct(2)
    %gausskwadnodes.nodenrs (ordered) nrs continuous normal hidden nodes that are discretised

    %gausskwadnodes.nodesizes

%the following output is not needed but can be useful during debugging
%names: names of the original order of the nodes
%onames: names for the reordered nodes (reordering is done during the
    %construction of the graphical model)
%order: vector: gives for the ordered node 1...end the corresponding nr of
    %the unordered nodes: onames=names(order)
%inv_o: vector: gives for the unordered node 1...end the corresponding nr of
    %the ordered nodes names=onames(order)

%

%default values
evidence_nodes.nodenrs =[];
evidence_nodes.nodesizes =[];
evidence_nodes.data ={};  
evidence_nodes.respcat =[];
partial_evidence_nodes.nodenrs =[];
partial_evidence_nodes.nodesizes =[];
partial_evidence_nodes.data ={}; 
terminal_merged_nodes.nodenrs =[];
terminal_merged_nodes.nodesizes =[];
terminal_merged_nodes.data ={};
terminal_merged_nodes.nrvars =[];
terminal_merged_nodes.respcat ={};
hid_nodes.nodenrs=[];
hid_nodes.nodesizes=[];
gausskwadnodes.nodenrs=[];
gausskwadnodes.nodesizes=[];


%construct bnet for ESM type data and junction tree using BNT toolbox of K
%Murphy



nr_nodes=length(names);


%%%%%%%%%%%%%%%%%%%%%%%%%%%
%alternatively, specify the adjacency matrix directly 
%%%%%%%%%%%%%%%%%%%%%%%%%%%
order = topological_sort(dag);
adj=dag(order,order);
inv_o=inv_order(order);
onames = names(order);
%define bnet
discrete_nodes = 1:nr_nodes;
partial_evidence_nodes.nodenrs =discrete_nodes;
%node_sizes =max(data')+1;
node_sizes=[2 3 2 2 3 4 4 4 4 3 3 2 2 3 2 2 2 2 3 3 2 3 3 3 3 3 2 2 4  3 3 3 2 4 3 3 4 ];
%node_sizes=node_sizes(order);
partial_evidence_nodes.nodesizes=node_sizes(order);

for i=1:nr_nodes 
    partial_evidence_nodes.data{i}=data(order(i),:);
end


%murphy's
bnet = mk_bnet(adj, partial_evidence_nodes.nodesizes, 'discrete', discrete_nodes, 'names','onames');