rvmRegPred.m

function [y, sigma, p] = rvmRegPred(model, X, t)
% Compute RVM regression model reponse y = w'*X+w0 and likelihood 
% Input:
%   model: trained model structure
%   X: d x n testing data
%   t (optional): 1 x n testing response
% Output:
%   y: 1 x n prediction
%   sigma: variance
%   p: 1 x n likelihood of t
% Written by Mo Chen (sth4nth@gmail.com).
index = model.index;
w = model.w;
w0 = model.w0;

X = X(index,:);
y = w'*X+w0;
%% probability prediction
if nargout > 1
    beta = model.beta;
    U = model.U;        % 3.54
    Xo = bsxfun(@minus,X,model.xbar);
    XU = U'\Xo;
    sigma = sqrt((1+dot(XU,XU,1))/beta);   %3.59
end

if nargin == 3 && nargout == 3
    p = exp(-0.5*(((t-y)./sigma).^2+log(2*pi))-log(sigma));
end