additive_kernel.m

%load dependenices
run('~/Software/gpml/gpml-matlab-v3.4-2013-11-11/startup.m');

% params
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
restart=true;
filename='hypers22-Jan-2015.mat';
maxiters=20;
npoints=3500;
nsparse=2000;

%load data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
X=dlmread('descriptors.dat',' ',0,0);
ens=dlmread('energies.dat',' ',0,0);
y=ens-mean(ens);


%subsample points
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
rVec = randperm(size(y,1));
y1 = y(rVec(1:npoints),:);
X1=X(rVec(1:npoints),:);

%choose sparse inducing points
Xind=X(rVec(1:nsparse),:);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


% init hypers
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if restart
  load(filename,'-mat','hyp')
else
  theta=0.1.*ones(size(X1,2),1);
  sigma=0.1;
  delta=2.0*std(y);
  hyp.lik = log(sigma);
  covNeighb = [theta',theta',delta,delta];
  covFull = [theta',delta];
  hyp.cov = log([covNeighb,covFull]');
end

init_hypers=hyp

likfunc = @likGauss;
inffunc = @infFITC;
cneighb = {'covADD',{[1,2],'covSEiso'}};
cfull={'covSEard'};
covfunc= {@covFITC,{'covSum',{cneighb,cfull}} , Xind};


%feval(covfunc{:})
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% optimise
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if maxiters > 1
  hyp = minimize(hyp, @gp, -maxiters, inffunc, [], covfunc, likfunc, X1, y1);
  %hyp = minimize_lbfgsb(hyp, @gp, -maxiters, @inffunc, [], cofunc, likfunc, X1, y1);
  newfilename = strcat('hypers',date,'.mat');
  save(newfilename,'init_hypers','hyp');
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[m s2] = gp(hyp, inffunc, [], covfunc, likfunc, X1, y1, X);


dlmwrite('gp_mtl.dat',[y m],' ');