diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/gaussian_CPD.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/CPDs/@gaussian_CPD/gaussian_CPD.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,161 @@
+function CPD = gaussian_CPD(bnet, self, varargin)
+% GAUSSIAN_CPD Make a conditional linear Gaussian distrib.
+%
+% CPD = gaussian_CPD(bnet, node, ...) will create a CPD with random parameters,
+% where node is the number of a node in this equivalence class.
+
+% To define this CPD precisely, call the continuous (cts) parents (if any) X,
+% the discrete parents (if any) Q, and this node Y. Then the distribution on Y is:
+% - no parents: Y ~ N(mu, Sigma)
+% - cts parents : Y|X=x ~ N(mu + W x, Sigma)
+% - discrete parents: Y|Q=i ~ N(mu(i), Sigma(i))
+% - cts and discrete parents: Y|X=x,Q=i ~ N(mu(i) + W(i) x, Sigma(i))
+%
+% The list below gives optional arguments [default value in brackets].
+% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y) and Q = prod(ns(Q)).)
+% Parameters will be reshaped to the right size if necessary.
+%
+% mean       - mu(:,i) is the mean given Q=i [ randn(Y,Q) ]
+% cov        - Sigma(:,:,i) is the covariance given Q=i [ repmat(100*eye(Y,Y), [1 1 Q]) ]
+% weights    - W(:,:,i) is the regression matrix given Q=i [ randn(Y,X,Q) ]
+% cov_type   - if 'diag', Sigma(:,:,i) is diagonal [ 'full' ]
+% tied_cov   - if 1, we constrain Sigma(:,:,i) to be the same for all i [0]
+% clamp_mean - if 1, we do not adjust mu(:,i) during learning [0]
+% clamp_cov  - if 1, we do not adjust Sigma(:,:,i) during learning [0]
+% clamp_weights - if 1, we do not adjust W(:,:,i) during learning [0]
+% cov_prior_weight - weight given to I prior for estimating Sigma [0.01]
+% cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0]
+%
+% e.g., CPD = gaussian_CPD(bnet, i, 'mean', [0; 0], 'clamp_mean', 1)
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  clamp = 0;
+  CPD = class(CPD, 'gaussian_CPD', generic_CPD(clamp));
+  return;
+elseif isa(bnet, 'gaussian_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+ 
+CPD = class(CPD, 'gaussian_CPD', generic_CPD(0));
+
+args = varargin;
+ns = bnet.node_sizes;
+ps = parents(bnet.dag, self);
+dps = myintersect(ps, bnet.dnodes);
+cps = myintersect(ps, bnet.cnodes);
+fam_sz = ns([ps self]);
+
+CPD.self = self;
+CPD.sizes = fam_sz;
+
+% Figure out which (if any) of the parents are discrete, and which cts, and how big they are
+% dps = discrete parents, cps = cts parents
+CPD.cps = find_equiv_posns(cps, ps); % cts parent index
+CPD.dps = find_equiv_posns(dps, ps);
+ss = fam_sz(end);
+psz = fam_sz(1:end-1);
+dpsz = prod(psz(CPD.dps));
+cpsz = sum(psz(CPD.cps));
+
+% set default params
+CPD.mean = randn(ss, dpsz);
+CPD.cov = 100*repmat(eye(ss), [1 1 dpsz]);    
+CPD.weights = randn(ss, cpsz, dpsz);
+CPD.cov_type = 'full';
+CPD.tied_cov = 0;
+CPD.clamped_mean = 0;
+CPD.clamped_cov = 0;
+CPD.clamped_weights = 0;
+CPD.cov_prior_weight = 0.01;
+CPD.cov_prior_entropic = 0;
+nargs = length(args);
+if nargs > 0
+  CPD = set_fields(CPD, args{:});
+end
+
+% Make sure the matrices have 1 dimension per discrete parent.
+% Bug fix due to Xuejing Sun 3/6/01
+CPD.mean = myreshape(CPD.mean, [ss ns(dps)]);
+CPD.cov = myreshape(CPD.cov, [ss ss ns(dps)]);
+CPD.weights = myreshape(CPD.weights, [ss cpsz ns(dps)]);
+
+% Precompute indices into block structured  matrices
+% to speed up CPD_to_lambda_msg and CPD_to_pi
+cpsizes = CPD.sizes(CPD.cps);
+CPD.cps_block_ndx = cell(1, length(cps));
+for i=1:length(cps)
+  CPD.cps_block_ndx{i} = block(i, cpsizes);
+end
+
+%%%%%%%%%%% 
+% Learning stuff
+
+% expected sufficient statistics 
+CPD.Wsum = zeros(dpsz,1);
+CPD.WYsum = zeros(ss, dpsz);
+CPD.WXsum = zeros(cpsz, dpsz);
+CPD.WYYsum = zeros(ss, ss, dpsz);
+CPD.WXXsum = zeros(cpsz, cpsz, dpsz);
+CPD.WXYsum = zeros(cpsz, ss, dpsz);
+
+% For BIC
+CPD.nsamples = 0;
+switch CPD.cov_type
+ case 'full',
+  % since symmetric 
+    %ncov_params = ss*(ss-1)/2; 
+    ncov_params = ss*(ss+1)/2; 
+  case 'diag',
+    ncov_params = ss;
+  otherwise
+    error(['unrecognized cov_type ' cov_type]);
+end
+% params = weights + mean + cov
+if CPD.tied_cov
+  CPD.nparams = ss*cpsz*dpsz + ss*dpsz + ncov_params;
+else
+  CPD.nparams = ss*cpsz*dpsz + ss*dpsz + dpsz*ncov_params;
+end
+
+% for speeding up maximize_params
+CPD.useC = exist('rep_mult');
+
+clamped = CPD.clamped_mean & CPD.clamped_cov & CPD.clamped_weights;
+CPD = set_clamped(CPD, clamped);
+
+%%%%%%%%%%%
+
+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.self = [];
+CPD.sizes = [];
+CPD.cps = [];
+CPD.dps = [];
+CPD.mean = [];
+CPD.cov = [];
+CPD.weights = [];
+CPD.clamped_mean = [];
+CPD.clamped_cov = [];
+CPD.clamped_weights = [];
+CPD.cov_type = [];
+CPD.tied_cov = [];
+CPD.Wsum = [];
+CPD.WYsum = [];
+CPD.WXsum = [];
+CPD.WYYsum = [];
+CPD.WXXsum = [];
+CPD.WXYsum = [];
+CPD.nsamples = [];
+CPD.nparams = [];            
+CPD.cov_prior_weight = [];
+CPD.cov_prior_entropic = [];
+CPD.useC = [];
+CPD.cps_block_ndx = [];