www.pudn.com > gpml.rar > binaryLaplaceGP.m, change:2007-06-26,size:3071b

function varargout = binaryLaplaceGP(hyper, covfunc, lik, varargin) % binaryLaplaceGP - Laplace's approximation for binary Gaussian process % classification. Two modes are possible: training or testing: if no test % cases are supplied, then the approximate negative log marginal likelihood % and its partial derivatives wrt the hyperparameters is computed; this mode is % used to fit the hyperparameters. If test cases are given, then the test set % predictive probabilities are returned. The program is flexible in allowing % several different likelihood functions and a multitude of covariance % functions. % % usage: [nlZ, dnlZ ] = binaryLaplaceGP(hyper, covfunc, lik, x, y); % or: [p, mu, s2, nlZ] = binaryLaplaceGP(hyper, covfunc, lik, x, y, xstar); % % where: % % hyper is a (column) vector of hyperparameters % covfunc is the name of the covariance function (see below) % lik is the name of the likelihood function (see below) % x is a n by D matrix of training inputs % y is a (column) vector (of size n) of binary +1/-1 targets % xstar is a nn by D matrix of test inputs % nlZ is the returned value of the negative log marginal likelihood % dnlZ is a (column) vector of partial derivatives of the negative % log marginal likelihood wrt each log hyperparameter % p is a (column) vector (of length nn) of predictive probabilities % mu is a (column) vector (of length nn) of predictive latent means % s2 is a (column) vector (of length nn) of predictive latent variances % % The length of the vector of log hyperparameters depends on the covariance % function, as specified by the "covfunc" input to the function, specifying the % name of a covariance function. A number of different covariance function are % implemented, and it is not difficult to add new ones. See "help covFunctions" % for the details. % % The shape of the likelihood function is given by the "lik" input to the % function, specifying the name of the likelihood function. The two implemented % likelihood functions are: % % logistic the logistic function: 1/(1+exp(-x)) % cumGauss the cumulative Gaussian (error function) % % The function can conveniently be used with the "minimize" function to train % a Gaussian process, eg: % % [hyper, fX, i] = minimize(hyper, 'binaryLaplaceGP', length, 'covSEiso', % 'logistic', x, y); % % Copyright (c) 2004, 2005, 2006, 2007 by Carl Edward Rasmussen, 2007-02-19. if nargin<5 || nargin>6 disp('Usage: [nlZ, dnlZ ] = binaryLaplaceGP(hyper, covfunc, lik, x, y);') disp(' or: [p, mu, s2, nlZ] = binaryLaplaceGP(hyper, covfunc, lik, x, y, xstar);') return end % Note, this function is just a wrapper provided for backward compatibility, % the functionality is now provided by the more general binaryGP function. varargout = cell(nargout, 1); % allocate the right number of output arguments [varargout{:}] = binaryGP(hyper, 'approxLA', covfunc, lik, varargin{:});