Mauch MIREX 2010

function CPD = gmux_CPD(bnet, self, varargin)

% GMUX_CPD Make a Gaussian multiplexer node

%

% CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD,

% except we assume there is exactly one discrete parent (call it M)

% which is used to select which cts parent to pass through to the output.

% i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W(m)*x(m) + mu(m), Sigma(m))

% where Y represents this node, and the Xi's are the cts parents.

% All the Xi must have the same size, and the num values for M must be K.

%

% Currently the params for this kind of CPD cannot be learned.

%

% Optional arguments [ default in brackets ]

%

% mean       - mu(:,i) is the mean given M=i [ zeros(Y,K) ]

% cov        - Sigma(:,:,i) is the covariance given M=i [ repmat(1*eye(Y,Y), [1 1 K]) ]

% weights    - W(:,:,i) is the regression matrix given M=i [ randn(Y,X,K) ]

if nargin==0

  % This occurs if we are trying to load an object from a file.

  CPD = init_fields;

  clamp = 0;

  CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp));

  return;

elseif isa(bnet, 'gmux_CPD')

  % This might occur if we are copying an object.

  CPD = bnet;

  return;

end

CPD = init_fields;

CPD = class(CPD, 'gmux_CPD', generic_CPD(1));

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);

if length(CPD.dps) ~= 1

  error('gmux must have exactly 1 discrete parent')

end

ss = fam_sz(end);

cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps))

if ~all(fam_sz(CPD.cps) == cpsz)

  error('all cts parents must have same size')

end

dpsz = fam_sz(CPD.dps);

if dpsz ~= length(cps)

  error(['the arity of the mux node is ' num2str(dpsz) ...

	 ' but there are ' num2str(length(cps)) ' cts parents']);

end

% set default params

%CPD.mean = zeros(ss, 1);

%CPD.cov = eye(ss);

%CPD.weights = randn(ss, cpsz);

CPD.mean = zeros(ss, dpsz);

CPD.cov = 1*repmat(eye(ss), [1 1 dpsz]);    

CPD.weights = randn(ss, cpsz, dpsz);

args = varargin;

nargs = length(args);

for i=1:2:nargs

  switch args{i},

   case 'mean',        CPD.mean = args{i+1}; 

   case 'cov',         CPD.cov = args{i+1}; 

   case 'weights',    CPD.weights = args{i+1}; 

   otherwise,  

    error(['invalid argument name ' args{i}]);

  end

end

%%%%%%%%%%%

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 = [];