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demoABO7.m
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demoABO7.m
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% A script to test ABO
% Hart3 Function 3D
% @author: favour@nyikosa.com 15/MAY/2017
% clc
% close all
% clear
rng('default')
%---------------------------- Gaussian Process Model ---------------------------
settings = getDefaultGPMetadataGPML();
settings.hyp_opt_mode = 2;
settings.hyp_bounds_set = 1;
settings.hyp_opt_mode_nres = 20;
settings.hyp_lb = log([ 0.001, 0.001, 0.001, 0.001, 0.001 ]);
settings.hyp_ub = log([ 0.001, 1.000, 1.000, 4.000, 5.000 ]);
gpModel = { {'infGaussLik'},{'meanZero'} , ...
{'covSEard'}, {'likGauss'} };
hyperparameters.mean = [];
l = 0.5;
sf = 1.0;
hyperparameters.cov = log([l; l; l; sf]);
sn = 0.01;
hyperparameters.lik = log(sn);
%---------------------------------- ABO ----------------------------------------
max_t_train = 0.1;
max_t_test = 1.0;
settings.abo = 1.0;
settings.current_time_abo = 1.0;
settings.initial_time_tag = max_t_train;
settings.time_delta = 0.02;
settings.final_time_tag = max_t_test;
%----------------------------------- Data --------------------------------------
title_ = 'Hart 3';
n_test = 100;
n_train = 5;
dim = 3;
[xt, yt] = getInitialHart3FunctionDataABO(n_train, max_t_train);
[xt, yt] = orderData(xt, yt);
[xs, ys] = getInitialHart3FunctionDataABO(n_test, max_t_test);
%--------------------------- BO settings ----------------------------------
lb_ = [0, 0, 0];
ub_ = [1, 1, 1];
lb = lb_; %[0, 0];
ub = ub_; %[1, 1];
x0 = [0.5, 0.5, 0.5];
iters = 50;
settings.xt = xt;
settings.yt = yt;
settings.gpModel = gpModel;
settings.hyp = hyperparameters;
%settings = getDefaultBOSettingsEL_ABO(x0, iters, settings);
%settings = getDefaultBOSettingsEL(x0, iters, settings);
%settings = getDefaultBOSettingsLCB_ABO(x0, iters, settings);
settings = getDefaultBOSettingsLCB(x0, iters, settings);
%settings = getDefaultBOSettingsMinMean_ABO(x0, iters, settings);
%settings = getDefaultBOSettingsMinMean(x0, iters, settings);
settings.acq_opt_mode = 9;
settings.acq_opt_mode_nres = 5;
settings.tolX = eps;
settings.tolObjFunc = eps;
settings.acq_bounds_set = 1;
settings.acq_lb = lb;
settings.acq_ub = ub;
settings.acq_lb_ = lb_;
settings.acq_ub_ = ub_;
settings.true_func = @(x) hart3_func(x);
settings.true_func_bulk = @(x) hart3_func_bulk(x);
settings.streamlined = 0;
settings.closePointsMax = 0;
settings.animateBO = 0;
settings.animatePerformance = 0;
settings.finalStepMinfunc = 0; % perform minfunc after using a global method
settings.mcmc = 0;
settings.standardized = 0;
settings.abo = 1;
%settings.nit = -500;
settings.streamlined = 0;
settings.optimiseForTime = 0;
settings.burnInIterations = 5;
settings.num_grid_points = 1500;
% flexible acquisition
settings.flex_acq = 0;
% get proposals from latin hypercube
% num_points = 10;
% dim = settings.dimensionality;
% xs = getInitialInputFunctionData(num_points,dim,lb,ub);
% settings.xs = xs;
settings.windowing = 0;
settings.window = 4;
settings.resetting = 0;
settings.alg = 'ABO';
[xopt, fopt, m_] = doBayesOpt(settings);
allX = m_.allX;
allY = m_.allX;
original_xt = m_.original_xt;
original_yt = m_.original_yt;
traceX = m_.traceX;
traceFunc = m_.traceFunc;
%traceFopt_true = m_.traceFopt_true;
%traceXopt_true = m_.traceXopt_true;
% timeLengthscales = m_.timeLengthscales;
% dists = m_.distanceToFOpt;
% iters = m_.iterations;
percent_ = .1;
total_iters = (max_t_test - max_t_train) ./ settings.time_delta ;
num_burn = round( percent_ .* total_iters );
num_burn = 5;
timeLengthscales = m_.timeLengthscales(num_burn+1:end);
dists = m_.distanceToFOpt(num_burn+1:end);
[b, e, B, E] = calculateMeasuresABO( traceFunc(num_burn+1:end)', dists' );
% save('ABO_7.mat', 'traceX', 'traceFunc', 'traceXopt_true', 'traceFopt_true', ...
% 'timeLengthscales', 'dists', 'iters', 'allX', 'allY', 'original_xt', ...
% 'original_yt');