diff toolboxes/FullBNT-1.0.7/bnt/general/mk_higher_order_dbn.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/general/mk_higher_order_dbn.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,181 @@
+function bnet = mk_higher_order_dbn(intra, inter, node_sizes, varargin)
+% MK_DBN Make a Dynamic Bayesian Network.
+%
+% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs
+% from i in slice t to j in slice t iff intra(i,j) = 1, and 
+% from i in slice t to j in slice t+1 iff inter(i,j) = 1,
+% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1.
+% node_sizes(i) is the number of values node i can take on.
+% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
+% See also mk_bnet.
+%
+% Optional arguments [default in brackets]
+% 'discrete' - list of discrete nodes [1:n]
+% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ]
+% 'eclass1' - equiv class for slice 1 [1:n]
+% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1]
+%    equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j},
+%    i.e., nodes i and j have tied parameters.
+% 'intra1' - topology of first slice, if different from others
+% 'names' - a cell array of strings to be associated with nodes 1:n [{}]
+%    This creates an associative array, so you write e.g.
+%     'evidence(bnet.names{'bar'}) = 42' instead of  'evidence(2} = 42' 
+%     assuming names = { 'foo', 'bar', ...}.
+%    
+% For backwards compatibility with BNT2, arguments can also be specified as follows
+%   bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1)
+%
+% After calling this function, you must specify the parameters (conditional probability
+% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc.
+
+
+n = length(intra);
+ss = n;
+bnet.nnodes_per_slice = ss;
+bnet.intra = intra;
+bnet.inter = inter;
+bnet.intra1 = intra;
+
+% As this method is used to generate a higher order Markov Model
+% also connect from time slice t - i -> t with i > 1 has to be 
+% taken into account.
+
+%inter should be a three dimensional array where inter(:,:,i)
+%describes the connections from time-slice t - i to t.  
+[rows,columns,order] = size(inter);
+assert(rows    == n);
+assert(columns == n);
+dag = zeros((order + 1)*n);
+
+i = 0;
+while i <= order
+    j = i;
+    while j <= order
+        if j == i
+            dag(1 + i*n:(i+1)*n,1+i*n:(i+1)*n) = intra;
+        else
+            dag(1+i*n:(i+1)*n,1+j*n:(j+1)*n) = inter(:,:,j - i);
+        end
+        j = j + 1;
+    end;
+    i = i + 1;
+end;
+
+bnet.dag = dag;
+bnet.names = {};
+
+directed = 1;
+if ~acyclic(dag,directed)
+  error('graph must be acyclic')
+end
+
+% Calculation of the equivalence classes
+bnet.eclass1 = 1:n;
+bnet.eclass = zeros(order + 1,ss);
+bnet.eclass(1,:) = 1:n;
+for i = 1:order
+    bnet.eclass(i+1,:) = bnet.eclass(i,:);
+    for j = 1:ss 
+        if(isequal(parents(dag,(i-1)*n+j)+ss,parents(dag,(i*n + j))))
+	   %fprintf('%d has isomorphic parents, eclass %d \n',j,bnet.eclass(i,j))
+        else
+	   bnet.eclass(i + 1,j) = max(bnet.eclass(i+1,:))+1;
+	   %fprintf('%d has non isomorphic parents, eclass %d \n',j,bnet.eclass(i,j))  
+	end;
+    end;
+end;
+bnet.eclass1 = 1:n;
+
+% To be compatible with whe rest of the code 
+bnet.eclass2 = bnet.eclass(2,:);
+
+dnodes = 1:n;
+bnet.observed = [];
+
+if nargin >= 4
+  args = varargin;
+  nargs = length(args);
+  if ~isstr(args{1})
+    if nargs >= 1 dnodes = args{1}; end
+    if nargs >= 2 bnet.eclass1 = args{2}; bnet.eclass(1,:) = args{2}; end
+    if nargs >= 3 bnet.eclass2 = args{3}; bnet.eclass(2,:) = args{2}; end
+    if nargs >= 4 bnet.intra1 = args{4}; end
+  else
+    for i=1:2:nargs
+      switch args{i},
+       case 'discrete', dnodes = args{i+1}; 
+       case 'observed', bnet.observed = args{i+1}; 
+       case 'eclass1',  bnet.eclass1 = args{i+1}; bnet.eclass(1,:) = args{i+1}; 
+       case 'eclass2',  bnet.eclass2 = args{i+1}; bnet.eclass(2,:) = args{i+1};
+       case 'eclass',   bnet.eclass = args{i+1};  
+       case 'intra1',  bnet.intra1 = args{i+1}; 
+       %case 'ar_hmm',  bnet.ar_hmm = args{i+1};  % should check topology
+       case 'names',  bnet.names = assocarray(args{i+1}, num2cell(1:n)); 
+       otherwise,  
+	error(['invalid argument name ' args{i}]);       
+      end
+    end
+  end
+end
+
+bnet.observed = sort(bnet.observed); % for comparing sets
+ns = node_sizes;
+bnet.node_sizes_slice = ns(:)';
+bnet.node_sizes = repmat(ns(:),1,order + 1);
+
+cnodes = mysetdiff(1:n, dnodes);
+bnet.dnodes_slice = dnodes;
+bnet.cnodes_slice = cnodes;
+bnet.dnodes = dnodes;
+bnet.cnodes = cnodes;
+% To adapt the function to higher order Markov models include dnodes for more 
+% time slices
+for i = 1:order
+    bnet.dnodes = [bnet.dnodes dnodes+i*n];
+    bnet.cnodes = [bnet.cnodes cnodes+i*n];
+end
+
+% Generieren einer Matrix, deren i-te Spalte die Aequivalenzklassen
+% der i-ten Zeitscheibe enthaelt. 
+bnet.equiv_class = [bnet.eclass(1,:)]';
+for i = 2:(order + 1)
+    bnet.equiv_class = [bnet.equiv_class   bnet.eclass(i,:)'];
+end
+
+bnet.CPD = cell(1,max(bnet.equiv_class(:)));
+
+ss = n;
+onodes = bnet.observed;
+hnodes = mysetdiff(1:ss, onodes);
+bnet.hidden_bitv = zeros(1,(order + 1)*ss);
+for i = 0:order
+    bnet.hidden_bitv(hnodes +i*ss) = 1;
+end;
+
+bnet.parents = cell(1, (order + 1)*ss);
+for i=1:(order + 1)*ss
+  bnet.parents{i} = parents(bnet.dag, i);
+end
+
+bnet.auto_regressive = zeros(1,ss);
+% ar(i)=1 means (observed) node i depends on i in the  previous slice
+for o=bnet.observed(:)'
+  if any(bnet.parents{o+ss} <= ss)
+    bnet.auto_regressive(o) = 1;
+  end
+end
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