BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 161 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/gaussian_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/gaussian_CPD.m
new file mode 100644
index 00000000..de519218
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/gaussian_CPD.m
@@ -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 = [];