Daniel@0: function bnet = mk_ideker_bnet(CPD_type, p)
Daniel@0: % MK_IDEKER_BNET Make the Bayes net in the PSB'00 paper by Ideker, Thorsson and Karp.
Daniel@0: %
Daniel@0: % BNET = MK_IDEKER_BNET uses the boolean functions specified in the paper 
Daniel@0: % "Discovery of regulatory interactions through perturbation: inference and experimental design",
Daniel@0: % Pacific Symp. on Biocomputing, 2000.
Daniel@0: % 
Daniel@0: % BNET = MK_IDEKER_BNET('root') uses the above boolean functions, but puts a uniform
Daniel@0: % distribution on the root nodes.
Daniel@0: %
Daniel@0: % BNET = MK_IDEKER_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...)
Daniel@0: % distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform.
Daniel@0: % 
Daniel@0: % BNET = MK_IDEKER_BNET('bool') makes each CPT a random boolean function.
Daniel@0: %
Daniel@0: % BNET = MK_IDEKER_BNET('orig') is the same as MK_IDEKER_BNET.
Daniel@0: 
Daniel@0: 
Daniel@0: if nargin == 0
Daniel@0:   CPD_type = 'orig';
Daniel@0: end
Daniel@0: 
Daniel@0: n = 4;
Daniel@0: dag = zeros(n);
Daniel@0: dag(1,3)=1;
Daniel@0: dag(2,[3 4])=1;
Daniel@0: dag(3,4)=1;
Daniel@0: ns = 2*ones(1,n);
Daniel@0: bnet = mk_bnet(dag, ns);
Daniel@0: 
Daniel@0: switch CPD_type
Daniel@0:  case 'orig',
Daniel@0:   bnet.CPD{1} = tabular_CPD(bnet, 1, [0 1]);
Daniel@0:   bnet.CPD{2} = tabular_CPD(bnet, 2, [0 1]);
Daniel@0:   bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
Daniel@0:   bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
Daniel@0:  case 'root',
Daniel@0:   bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]);
Daniel@0:   bnet.CPD{2} = tabular_CPD(bnet, 2, [0.5 0.5]);
Daniel@0:   bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
Daniel@0:   bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
Daniel@0:  case 'bool',
Daniel@0:   for i=1:n
Daniel@0:     bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd');
Daniel@0:   end
Daniel@0:  case 'cpt',
Daniel@0:   for i=1:n
Daniel@0:     bnet.CPD{i} = tabular_CPD(bnet, i, p);
Daniel@0:   end
Daniel@0:  otherwise,
Daniel@0:   error(['unknown type ' CPD_type]);
Daniel@0: end