diff options
Diffstat (limited to 'sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD')
6 files changed, 141 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Entries b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Entries new file mode 100644 index 00000000..96e99049 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Entries @@ -0,0 +1,4 @@ +/linear_gaussian_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_params_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +D diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Repository b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Repository new file mode 100644 index 00000000..ac2255d4 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Repository @@ -0,0 +1 @@ +FullBNT/BNT/CPDs/Old/@linear_gaussian_CPD diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Root b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Root new file mode 100644 index 00000000..f3bd14a6 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/CVS/Root @@ -0,0 +1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m new file mode 100644 index 00000000..55076c4b --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m @@ -0,0 +1,87 @@ +function CPD = linear_gaussian_CPD(bnet, self, theta, sigma, theta0, n0, alpha0, beta0) +% LINEAR_GAUSSIAN_CPD Make a linear Gaussian distrib. +% +% CPD = linear_gaussian_CPD(bnet, self, theta, lambda) +% This defines the distribution P(Y|X) = N(y | theta'*x, sigma), +% where y (self) is a scalar, theta is a regression vector, and sigma is the variance. +% Pass in [] to generate a default random value for a parameter. +% +% CPD = linear_gaussian_CPD(bnet, self, [], [], theta0, n0, alpha0, beta0) +% defines a Normal-Gamma prior over the parameters: +% P(theta | lambda) = N(theta | theta0, n0*lambda) +% P(lambda) = Gamma(lambda | alpha0, beta0) +% where lambda = 1/sigma is the precision for y. +% n0 is a precision matrix, beta0 is a scale factor. +% Pass in [] to generate a default value for a hyperparameter. +% theta and sigma will be set to their prior expected values. +% See "Bayesian Theory", Bernardo and Smith (2000), p442. + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'linear_gaussian_CPD', generic_CPD(0)); + return; +elseif isa(bnet, 'linear_gaussian_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); +d = sum(ns(ps)); +assert(ns(self)==1); + + +if nargin < 5, + prior = []; + if isempty(theta), theta = randn(d, 1); end + if isempty(sigma), sigma = 1; end +else + + %if isempty(theta0), theta0 = zeros(d, 1); end + %if isempty(n0), n0 = 0.1*eye(d); end + %if isempty(alpha0), alpha0 = 0.1; end + %if isempty(beta0), beta0 = 0.1; end + + % use non-informative priors + if isempty(theta0), theta0 = zeros(d, 1); end + if isempty(n0), n0 = 0.001*ones(d); end + if isempty(alpha0), alpha0 = -d/2 + 0.001; end + if isempty(beta0), beta0 = 0.001; end + + prior.theta = theta0; + prior.n = n0; + prior.alpha = alpha0; + prior.beta = beta0; + + % set params to their mean + theta = prior.theta; + %sigma = prior.beta/prior.alpha; % mean of Gamma is E[lambda] = alpha/beta +end + + +CPD.self = self; +CPD.theta = theta; +CPD.sigma = sigma; +CPD.prior = prior; + + +clamped = 0; +CPD = class(CPD, 'linear_gaussian_CPD', generic_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.theta = []; +CPD.sigma = []; +CPD.prior = []; diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m new file mode 100644 index 00000000..3d06244f --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m @@ -0,0 +1,23 @@ +function L = log_marg_prob_node(CPD, self_ev, pev) +% LOG_MARG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m)) for node i (linear_gaussian) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% This differs from log_prob_node because we integrate out the parameters. +% self_ev{m} is the evidence on this node in case m. +% pev{i,m} is the evidence on the i'th parent in case m +% We assume there is <= 1 case. + +ncases = length(self_ev); + +if ncases==0 + L = 0; + return; +elseif ncases==1 + y = self_ev{1}; + x = cat(1, pev{:}); % column vector + f = 1-x'*inv(x*x' + CPD.prior.n)*x; + alpha = CPD.prior.alpha; + L = log_student_pdf(y, x'*CPD.prior.theta, f*alpha/CPD.prior.beta, 2*alpha); +else + error('can''t handle batch data'); +end diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m new file mode 100644 index 00000000..dbe8d5da --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m @@ -0,0 +1,25 @@ +function CPD = update_params_complete(CPD, self_ev, pev) +% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (linear_gaussian) +% CPD = update_params_complete(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m. +% pev{i,m} is the evidence on the i'th parent in case m +% +% We update the hyperparams and set the params to the mean of the posterior. + +y = cat(1, self_ev{:}); +X = cell2num(pev)'; +[N k] = size(X); % each row is a case + +n0 = CPD.prior.n; +th0 = CPD.prior.theta; +CPD.prior.theta = inv(n0 + X'*X)*(n0*th0 + X'*y); +thn = CPD.prior.theta; +CPD.prior.beta = CPD.prior.beta + 0.5*(y-X*thn)'*y + 0.5*(th0-thn)'*n0*th0; +CPD.prior.alpha = CPD.prior.alpha + 0.5*N; +CPD.prior.n = CPD.prior.n + X'*X; + + +% set params to their mean +CPD.theta = CPD.prior.theta; +%CPD.sigma = CPD.prior.beta/CPD.prior.alpha; % mean of Gamma is E[lambda] = alpha/beta |