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@@ -8,21 +8,86 @@ myData = cat(2,myData , 1-myData);
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myValues = [0.3; 0.0; 1.0; 1.4];
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% boolean
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+%interested in time measurements?
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+b_verboseTime = false;
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+
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+%interested in outputs?
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+b_verbose = false;
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+
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+% important for plotting!
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+b_uncertaintyPredictionForRegression ...
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+ = true;
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+b_optimize_noise = false;
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+b_use_quantization = false;
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+b_ils_verbose = false;
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+%
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% integer
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+i_nrOfEigenvaluesToConsiderForVarApprox ...
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+ = 2;
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+i_num_bins = 100; % default
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+i_ils_max_iterations = 1000; % default
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% double
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+d_ils_min_delta = 1e-7; % default
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+d_ils_min_residual = 1e-7; % default
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+
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+% model regularization
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+d_noise = 0.000001;
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+
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+% adapt parameter bounds if you are interested in optimization
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+d_parameter_lower_bound = 1.0;
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+d_parameter_upper_bound = 1.0;
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% string
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+s_ils_method = 'CG'; % default
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+
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+% options: 'none', 'greedy', 'downhillsimplex'
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+s_optimization_method = 'downhillsimplex';
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+
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+% options: 'identity', 'abs', 'absexp'
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+% with settings above, this equals 'identity'
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+s_transform = 'absexp';
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+
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+% options: 'exact', 'approximate_fine', 'approximate_rough', and 'none'
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+s_varianceApproximation = 'exact';
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+
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+
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% init new GPHIKRegression object
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-myGPHIKRegression = GPHIKRegression ( 'verbose', 'false', ...
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- 'optimization_method', 'none', ...
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- 'varianceApproximation', 'exact',...
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- 'nrOfEigenvaluesToConsiderForVarApprox',1,...
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- 'uncertaintyPredictionForRegression', true, ...
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- 'noise', 0.000001 ...
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- );
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-
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- %'varianceApproximation', 'approximate_fine',...
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- %'varianceApproximation', 'exact',...
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-
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-% run train method
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+myGPHIKRegression = ...
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+ GPHIKRegression ( ...
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+ 'verboseTime', b_verboseTime, ...
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+ 'verbose', b_verbose, ...
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+ 'uncertaintyPredictionForRegression', b_uncertaintyPredictionForRegression, ...
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+ 'optimize_noise', b_optimize_noise, ...
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+ 'use_quantization', b_use_quantization, ...
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+ 'ils_verbose', b_ils_verbose, ...
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+ ...
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+ 'nrOfEigenvaluesToConsiderForVarApprox', i_nrOfEigenvaluesToConsiderForVarApprox, ...
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+ 'num_bins', i_num_bins, ...
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+ 'ils_max_iterations', i_ils_max_iterations, ...
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+ ...
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+ 'ils_min_delta', d_ils_min_delta, ...
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+ 'ils_min_residual', d_ils_min_residual, ...
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+ 'noise', d_noise, ...
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+ 'parameter_lower_bound', d_parameter_lower_bound, ...
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+ 'parameter_upper_bound', d_parameter_upper_bound, ...
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+ ...
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+ 'ils_method', s_ils_method, ...
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+ 'optimization_method', s_optimization_method, ...
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+ 'transform', s_transform, ...
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+ 'varianceApproximation', s_varianceApproximation ...
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+ );
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+
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+
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+%% run train method
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myGPHIKRegression.train( myData, myValues );
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+
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+%% evaluate model on test data
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+
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myDataTest = 0:0.01:1;
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% create l1-normalized 'histograms'
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myDataTest = cat(1, myDataTest, 1-myDataTest)';
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@@ -35,6 +100,7 @@ for i=1:size(myDataTest,1)
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[ scores(i), uncertainties(i)] = myGPHIKRegression.estimate( example );
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end
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+%% plot results
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% create figure and set title
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classificationFig = figure;
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@@ -88,7 +154,7 @@ set(get(gca,'YLabel'), 'FontSize', i_fontSizeAxis);
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-% clean up and delete object
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+%% clean up and delete object
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myGPHIKRegression.delete();
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clear ( 'myGPHIKRegression' );
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Alexander Freytag