comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/tabular_CPD.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function CPD = tabular_CPD(bnet, self, varargin)
+% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT)
+%
+% CPD = tabular_CPD(bnet, node) creates a random CPT.
+%
+% The following arguments can be specified [default in brackets]
+%
+% CPT - specifies the params ['rnd']
+%   - T means use table T; it will be reshaped to the size of node's family.
+%   - 'rnd' creates rnd params (drawn from uniform)
+%   - 'unif' creates a uniform distribution
+%   - 'leftright' only transitions from i to i/i+1 are allowed, for each non-self parent context.
+%       The non-self parents are all parents except oldself.
+% selfprob - The prob of transition from i to i if CPT = 'leftright' [0.1]
+% old_self - id of the node corresponding to self in the previous slice [self-ss]
+% adjustable - 0 means don't adjust the parameters during learning [1]
+% prior_type - defines type of prior ['none']
+%  - 'none' means do ML estimation
+%  - 'dirichlet' means add pseudo-counts to every cell
+%  - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand)
+% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1]
+% dirichlet_type - defines the type of Dirichlet prior ['BDeu']
+%  - 'unif' means put dirichlet_weight in every cell
+%  - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell
+%    where r = self_sz and q = prod(parent_sz) (see Heckerman)
+% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0]
+%
+% e.g., tabular_CPD(bnet, i, 'CPT', T)
+% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif')
+%
+% REFERENCES
+% M. Brand - "Structure learning in conditional probability models via an entropic  prior
+%   and parameter extinction", Neural Computation 11 (1999): 1155--1182
+% M. Brand - "Pattern discovery via entropy minimization" [covers annealing]
+%   AI & Statistics 1999. Equation numbers refer to this paper, which is available from
+%   www.merl.com/reports/docs/TR98-21.pdf
+% D. Heckerman, D. Geiger and M. Chickering, 
+%   "Learning Bayesian networks: the combination of knowledge and statistical data",
+%   Microsoft Research Tech Report, 1994
+
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, []));
+  return;
+elseif isa(bnet, 'tabular_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+
+ns = bnet.node_sizes;
+ps = parents(bnet.dag, self);
+fam_sz = ns([ps self]);
+CPD.sizes = fam_sz;
+CPD.leftright = 0;
+
+% set defaults
+CPD.CPT = mk_stochastic(myrand(fam_sz));
+CPD.adjustable = 1;
+CPD.prior_type = 'none';
+dirichlet_type = 'BDeu';
+dirichlet_weight = 1;
+CPD.trim = 0;
+selfprob = 0.1;
+
+% extract optional args
+args = varargin;
+% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT)
+if ~isempty(args) & ~isstr(args{1})
+  CPD.CPT = myreshape(args{1}, fam_sz);
+  args = [];
+end
+
+% if old_self is specified, read in the value before CPT is created
+old_self = []; 
+for i=1:2:length(args)
+  switch args{i},
+   case 'old_self', old_self = args{i+1};
+  end
+end
+
+for i=1:2:length(args)
+  switch args{i},
+   case 'CPT',
+    T = args{i+1};
+    if ischar(T)
+      switch T
+       case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz));
+       case 'rnd',  CPD.CPT = mk_stochastic(myrand(fam_sz));
+       case 'leftright', 
+	% we just initialise the CPT to leftright - this structure will
+	% be maintained by EM, assuming we don't use a prior...
+	CPD.leftright = 1;
+	if isempty(old_self) % we assume the network is a DBN
+	  ss = bnet.nnodes_per_slice;
+	  old_self = self-ss;
+	end
+	other_ps = mysetdiff(ps, old_self);
+	Qps = prod(ns(other_ps));
+	Q = ns(self);
+	p = selfprob;
+	LR = mk_leftright_transmat(Q, p);
+	transprob = repmat(reshape(LR, [1 Q Q]), [Qps 1 1]); % transprob(k,i,j)
+	transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j)
+	CPD.CPT = myreshape(transprob, fam_sz);
+       otherwise,   error(['invalid CPT ' T]);       
+      end
+    else
+      CPD.CPT = myreshape(T, fam_sz);
+    end
+    
+   case 'prior_type', CPD.prior_type = args{i+1};
+   case 'dirichlet_type', dirichlet_type = args{i+1};
+   case 'dirichlet_weight', dirichlet_weight = args{i+1};
+   case 'adjustable', CPD.adjustable = args{i+1};
+   case 'clamped', CPD.adjustable = ~args{i+1};
+   case 'trim', CPD.trim = args{i+1};
+   case 'old_self', noop = 1; % already read in
+   otherwise, error(['invalid argument name: ' args{i}]);       
+  end
+end
+
+switch CPD.prior_type
+ case 'dirichlet',
+  switch dirichlet_type
+   case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz);
+   case 'BDeu',  CPD.dirichlet = dirichlet_weight * mk_stochastic(myones(fam_sz));
+   otherwise, error(['invalid dirichlet_type ' dirichlet_type])
+  end
+ case {'entropic', 'none'}
+  CPD.dirichlet = [];
+ otherwise, error(['invalid prior_type ' prior_type])
+end
+
+  
+
+% fields to do with learning
+if ~CPD.adjustable
+  CPD.counts = [];
+  CPD.nparams = 0;
+  CPD.nsamples = [];
+else
+  CPD.counts = zeros(size(CPD.CPT));
+  psz = fam_sz(1:end-1);
+  ss = fam_sz(end);
+  if CPD.leftright
+    % For each of the Qps contexts, we specify Q elements on the diagoanl
+    CPD.nparams = Qps * Q;
+  else
+    % sum-to-1 constraint reduces the effective arity of the node by 1
+    CPD.nparams = prod([psz ss-1]);
+  end
+  CPD.nsamples = 0;
+end
+
+fam_sz = CPD.sizes;
+psz = prod(fam_sz(1:end-1));
+ssz = fam_sz(end);
+CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading
+
+CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz));
+
+
+%%%%%%%%%%%
+
+function CPD = init_fields()
+% This ensures we define the fields in the same order 
+% no matter whether we load an object from a file,
+% or create it from scratch. (Matlab requires this.)
+
+CPD.CPT = [];
+CPD.sizes = [];
+CPD.prior_type = [];
+CPD.dirichlet = [];
+CPD.adjustable = [];
+CPD.counts = [];
+CPD.nparams = [];
+CPD.nsamples = [];
+CPD.trim = [];
+CPD.trimmed_trans = [];
+CPD.leftright = [];
+
+