NICE_GP_HIK_Core/matlab/plot1dExampleRegression.m

% BRIEF: Small visualization script using the GPHIKRegression
% author: Alexander Freytag
% date: 20-01-2014 (dd-mm-yyyy)

myData = [ 0.1; 0.3; 0.7; 0.8];
% create l1-normalized 'histograms'
myData = cat(2,myData , 1-myData);
myValues = [0.3; 0.0; 1.0; 1.4];


%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% boolean
%interested in time measurements?
b_verboseTime                       = false;

%interested in outputs?
b_verbose                           = false;  

% important for plotting!
b_uncertaintyPredictionForRegression ...
                                    = true; 
b_optimize_noise                    = false;
b_use_quantization                  = false;
b_ils_verbose                       = false;
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% integer
i_nrOfEigenvaluesToConsiderForVarApprox ...
                                    = 2;
i_num_bins                          = 100; % default
i_ils_max_iterations                = 1000; % default
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% double    
d_ils_min_delta                     = 1e-7; % default
d_ils_min_residual                  = 1e-7; % default

% model regularization
d_noise                             = 0.000001; 

% adapt parameter bounds if you are interested in optimization
d_parameter_lower_bound             = 1.0;
d_parameter_upper_bound             = 1.0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% string    
s_ils_method                        = 'CG'; % default

% options: 'none', 'greedy', 'downhillsimplex'
s_optimization_method               = 'none';

% options:  'identity', 'abs', 'absexp'
% with settings above, this equals 'identity'
s_transform                         = 'absexp'; 

% options: 'exact', 'approximate_fine', 'approximate_rough', and 'none'
s_varianceApproximation             = 'exact'; 


% init new GPHIKRegression object
myGPHIKRegression = ...
        GPHIKRegression ( ...
                          'verboseTime',                               b_verboseTime, ...
                          'verbose',                                   b_verbose, ...
                          'uncertaintyPredictionForRegression',        b_uncertaintyPredictionForRegression, ...
                          'optimize_noise',                            b_optimize_noise, ...
                          'use_quantization',                          b_use_quantization, ...
                          'ils_verbose',                               b_ils_verbose, ...
                          ...        
                          'nrOfEigenvaluesToConsiderForVarApprox',     i_nrOfEigenvaluesToConsiderForVarApprox, ...                          
                          'num_bins',                                  i_num_bins, ...                           
                          'ils_max_iterations',                        i_ils_max_iterations, ...                          
                          ...
                          'ils_min_delta',                             d_ils_min_delta, ...
                          'ils_min_residual',                          d_ils_min_residual, ...
                          'noise',                                     d_noise, ...        
                          'parameter_lower_bound',                     d_parameter_lower_bound, ...
                          'parameter_upper_bound',                     d_parameter_upper_bound, ...
                          ...
                          'ils_method',                                s_ils_method, ...
                          'optimization_method',                       s_optimization_method, ...   
                          'transform',                                 s_transform, ...
                          'varianceApproximation',                     s_varianceApproximation ...
        );
    

%% run train method
myGPHIKRegression.train( sparse(myData), myValues );


%% evaluate model on test data

myDataTest = 0:0.01:1;
% create l1-normalized 'histograms'
myDataTest = cat(1, myDataTest, 1-myDataTest)';


scores = zeros(size(myDataTest,1),1);
uncertainties = zeros(size(myDataTest,1),1);
for i=1:size(myDataTest,1)
    example = sparse(myDataTest(i,:));
    [ scores(i), uncertainties(i)] = myGPHIKRegression.estimate( example );
end

%% plot results

% create figure and set title
classificationFig = figure;
set ( classificationFig, 'name', 'Regression with GPHIK');

hold on;

%#initialize x array
x=0:0.01:1;

%#create first curve
uncLower=scores-uncertainties;
%#create second curve
uncUpper=scores+uncertainties;


%#create polygon-like x values for plotting
X=[x,fliplr(x)];
%# concatenate y-values accordingly
Y=[uncLower',fliplr(uncUpper')]; 
%#plot filled area for predictive variance ( aka regression uncertainty )
fill(X,Y,'y');                  

% plot mean values
plot ( x,scores, ...
       'LineStyle', '--', ...
       'LineWidth', 2, ...
       'Color', 'r', ...
       'Marker','none', ...
       'MarkerSize',1, ...
       'MarkerEdgeColor','r', ...
       'MarkerFaceColor',[0.5,0.5,0.5] ...
       );

% plot training data
plot ( myData(:,1), myValues, ...
       'LineStyle', 'none', ...
       'LineWidth', 3, ...
       'Marker','o', ...
       'MarkerSize',6, ...
       'MarkerEdgeColor','b', ...
       'MarkerFaceColor',[0.5,0.5,0.5] ...
       );

xlabel('1st Input dimension');
ylabel('Regression score');   

i_fontSizeAxis = 16;
set(get(gca,'XLabel'), 'FontSize', i_fontSizeAxis);
set(get(gca,'YLabel'), 'FontSize', i_fontSizeAxis);

   

%% clean up and delete object
myGPHIKRegression.delete();

clear ( 'myGPHIKRegression' );