Merge pull request #1 from ziejd2/octave_php_separate

Octave php separate

21 files changed, 332 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 
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Entries b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Entries
new file mode 100644
index 00000000..5335ec72
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Entries
@@ -0,0 +1,4 @@
+/log_marg_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/root_gaussian_CPD.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/@root_gaussian_CPD/CVS/Repository b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Repository
new file mode 100644
index 00000000..ff9bf8d4
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/CPDs/Old/@root_gaussian_CPD
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Root b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m
new file mode 100644
index 00000000..4d4e21fc
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m
@@ -0,0 +1,26 @@
+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 (root_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 (ignored).
+
+ncases = length(self_ev);
+
+if ncases==0
+  L = 0;
+  return;
+elseif ncases==1 
+  x = cat(1, self_ev{:});
+  k = length(x);
+  n0 = CPD.prior.n;
+  mu = CPD.prior.mu;
+  alpha = CPD.prior.alpha;
+  beta = CPD.prior.beta;
+  gamma = 2*alpha - k + 1;
+  % Bernardo and Smith p441
+  L = log_student_pdf(x, mu, n0/(n0+1)*0.5*gamma*inv(beta), gamma);
+else
+  error('can''t handle batch data');
+end
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m
new file mode 100644
index 00000000..bd4ffd9e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m
@@ -0,0 +1,74 @@
+function CPD = root_gaussian_CPD(bnet, self, mu, Sigma, mu0, n0, alpha0, beta0)
+% ROOT_GAUSSIAN_CPD Make an unconditional Gaussian distrib.
+%
+% CPD = root_gaussian_CPD(bnet, self, mu, Sigma)
+% This defines the distribution Y ~ N(mu, Sigma),
+% Pass in [] to generate a default random value for a parameter.
+%
+% CPD = root_gaussian_CPD(bnet, self, [], [], mu0, n0, alpha0, beta0)
+% defines a Normal-Wishart prior over the parameters:
+%   P(mu | lambda) = N(mu | mu0, n0*lambda)
+%   P(lambda) = Wishart(lambda | alpha0, beta0)
+% where lambda = inv(Sigma) is the precision matrix of mu.
+% n0 is a scale factor, beta0 is a precision matrix.
+% Pass in [] to generate a default value for a hyperparameter.
+% mu and Sigma will be set to their prior expected values.
+% See "Bayesian Theory", Bernardo and Smith (2000), p441.
+
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  CPD = class(CPD, 'root_gaussian_CPD', generic_CPD(0));
+  return;
+elseif isa(bnet, 'root_gaussian_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+
+
+ns = bnet.node_sizes;
+d = ns(self);
+
+if nargin < 5,
+  prior = [];
+  if isempty(mu), mu = randn(d, 1); end
+  if isempty(Sigma), Sigma = eye(d); end
+else
+  if isempty(mu0), mu0 = zeros(d, 1); end
+  if isempty(n0), n0 = 0.1; end
+  if isempty(alpha0), alpha0 = (d-1)/2 + 1; end % Wishart requires 2 alpha > d-1
+  if isempty(beta0), beta0 = eye(d); end
+  
+  prior.mu = mu0;
+  prior.n = n0;
+  prior.alpha = alpha0;
+  prior.beta = beta0;
+  
+  % set params to their mean
+  mu = prior.mu;
+  Sigma = prior.beta/prior.alpha; % mean of Wishart is E[lambda] = alpha*inv(beta)
+end
+
+CPD.self = self;
+CPD.mu = mu;
+CPD.Sigma = Sigma;
+CPD.prior = prior;
+
+clamped = 0;
+CPD = class(CPD, 'root_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.mu = [];
+CPD.Sigma = [];
+CPD.prior = [];
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/update_params_complete.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/update_params_complete.m
new file mode 100644
index 00000000..7ce58944
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@root_gaussian_CPD/update_params_complete.m
@@ -0,0 +1,29 @@
+function CPD = update_params_complete(CPD, self_ev, pev)
+% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (root_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 (ignored)
+%
+% We update the hyperparams and set the params to the mean of the posterior.
+
+X = cell2num(self_ev);
+[k N] = size(X); % each column is a case
+
+one = ones(N,1);
+xbar = X*one / N; % = mean(X')'
+S = X*(eye(N) - one*one'/N)*X';
+
+n0 = CPD.prior.n;
+nn = 1/(n0 + N);
+mu0 = CPD.prior.mu;
+CPD.prior.mu = nn*(n0*mu0 + N*xbar);
+CPD.prior.alpha = CPD.prior.alpha + 0.5*N;
+CPD.prior.beta = CPD.prior.beta + 0.5*S + 0.5*nn*N*n0*(mu0-xbar)*(mu0-xbar)';
+CPD.prior.n = CPD.prior.n + N;
+
+% set params to their mean
+CPD.mu = CPD.prior.mu;
+% E[Cov] = E inv(n lambda) = 1/(n (alpha-(k+1)/2)) beta
+CPD.Sigma = CPD.prior.beta /(CPD.prior.n * (CPD.prior.alpha - (k+1)/2));
+  
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CPD_to_upot.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CPD_to_upot.m
new file mode 100644
index 00000000..3ce87d0b
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CPD_to_upot.m
@@ -0,0 +1,6 @@
+function pot = CPD_to_upot(CPD, domain)
+% CPD_TO_UPOT Convert a CPD to a utility potential
+% pot = CPD_to_upot(CPD, domain)
+
+sz = CPD.size; % mysize(CPD.CPT);
+pot = upot(domain, sz, CPD.CPT, 0*myones(sz));
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Entries b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Entries
new file mode 100644
index 00000000..02628802
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Entries
@@ -0,0 +1,3 @@
+/CPD_to_upot.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/tabular_chance_node.m/1.1.1.1/Wed May 29 15:59:54 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Repository b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Repository
new file mode 100644
index 00000000..3a232db2
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/CPDs/Old/@tabular_chance_node
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Root b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/tabular_chance_node.m b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/tabular_chance_node.m
new file mode 100644
index 00000000..3476536c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/@tabular_chance_node/tabular_chance_node.m
@@ -0,0 +1,39 @@
+function CPD = tabular_chance_node(sz, CPT)
+% TABULAR_CHANCE_NODE Like tabular_CPD, but simplified
+% CPD = tabular_chance_node(sz, CPT)
+%
+% sz(1:end-1) is the sizes of the parents, sz(end) is the size of this node
+% By default, CPT is a random stochastic matrix.
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  CPD = class(CPD, 'tabular_chance_node');
+  return;
+elseif isa(sz, 'tabular_chance_node')
+  % This might occur if we are copying an object.
+  CPD = sz;
+  return;
+end
+CPD = init_fields;
+
+if nargin < 2,
+  CPT = mk_stochastic(myones(sz)); 
+else
+  CPT = myreshape(CPT, sz);
+end
+
+CPD.CPT = CPT;
+CPD.size = sz;
+
+CPD = class(CPD, 'tabular_chance_node');
+
+%%%%%%%%%%%
+
+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.size = [];
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Entries b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Entries
new file mode 100644
index 00000000..17848105
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Entries
@@ -0,0 +1 @@
+D
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Entries.Log b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Entries.Log
new file mode 100644
index 00000000..ed4a9516
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Entries.Log
@@ -0,0 +1,3 @@
+A D/@linear_gaussian_CPD////
+A D/@root_gaussian_CPD////
+A D/@tabular_chance_node////
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Repository b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Repository
new file mode 100644
index 00000000..cf1b510a
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/CPDs/Old
diff --git a/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Root b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/Old/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt