function net = bcm(gpnet)% bcm - Bayesian Committee Machine%% Synopsis:%   net = bcm(gpnet)%   % Arguments:%   gpnet: A Gaussian process template for BCM modules, as output by Netlab's%       function gp.m. Each module of the BCM will inherit its initial%       parameters from gpnet.%   % Returns:%   net: Structure describing the BCM%   % Description:%   The Bayesian Committee Machine (BCM) is an approximation method for%   large-scale Gaussian process regression. The training data is split%   into a number of blocks, for which individual Gaussian process%   predictors ("modules") are trained. The prediction of a BCM is a%   weighted combination of the predictions of individual modules on the%   test data. Also, test data is processed in blocks, which leads to%   improved performance.%   The code here is a wrapper routine for Gaussian process routines%   provided by the Netlab toolbox. Netlab is thus required for this code%   to run.%% Examples:%   Building a BCM for 7-dimensional input, where each module is a GP%   with squared-exponential kernel:%       gpnet = gp(7, 'sqexp');%       net = bcm(gpnet);%   Equip the BCM with its training data, split up into modules of size%   500: %       net = bcminit(net, Xtrain, Ytrain, 500);%   Fit each module's hyperparameters, and pre-compute a few matrices:%       net = bcmtrain(net, 'individual');%       net = bcmprepare(net);%   For increased performance: cluster the training data beforehand (10%   clusters in the example below) then assign clusters to modules:%       options = [1 1e-5 1e-4 0 0 0 0 0 0 0 0 0 0 30];%       r = randperm(size(Xtrain,1));%       [centres,opt,post] = kmeans(Xtrain(r(1:10)),Xtrain,options);%       [m,assignment] = max(post,[],2);%       net = bcminit(net, Xtrain, Ytrain, assignment);%       net = bcmprepare(net);%   Now can do prediction:%       [pred, errorBar] = bcmfwd(net, Xtest, 400);%   % See also: bcminit,bcmprepare,bcmtrain,bcmfwd,bcmerr,bcmgrad,bcmpak,bcmunpak% % Author(s): Anton Schwaighofer, Nov 2004% $Id: bcm.m,v 1.1 2004/11/18 21:18:24 anton Exp $error(nargchk(1, 1, nargin));net = struct('type', 'bcm', 'gpnet', gpnet);net.nin = gpnet.nin;net.nout = 1;net.module = [];net.invPrior = {};net.weights = {};