66 lines
2.1 KiB
Matlab
66 lines
2.1 KiB
Matlab
%% Reset everything
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clear all;
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clc;
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close all;
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addpath('helpers');
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%% Configure the benchmark
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% The name of the algorithms in the final plots
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names = { '6pt'; 'ge (8pt)'; '17pt'};
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% The main experiment parameters
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min_outlier_fraction = 0.05;
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max_outlier_fraction = 0.45;
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outlier_fraction_step = 0.05;
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%% Run the benchmark
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%prepare the overall result arrays
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number_outlier_fraction_levels = round((max_outlier_fraction - min_outlier_fraction) / outlier_fraction_step + 1);
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num_algorithms = size(names,1);
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mean_number_iterations = zeros(num_algorithms,number_outlier_fraction_levels);
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mean_execution_times = zeros(num_algorithms,number_outlier_fraction_levels);
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outlier_fraction_levels = zeros(1,number_outlier_fraction_levels);
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%Run the experiment
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for n=1:number_outlier_fraction_levels
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outlier_fraction = (n - 1) * outlier_fraction_step + min_outlier_fraction;
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outlier_fraction_levels(1,n) = outlier_fraction;
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display(['Analyzing outlier fraction level: ' num2str(outlier_fraction)])
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clear number_iterations
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clear execution_times
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temp_file_name1 = ['number_iterations_' num2str(outlier_fraction) '.mat'];
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temp_file_name2 = ['execution_times_' num2str(outlier_fraction) '.mat'];
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load(temp_file_name1)
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load(temp_file_name2)
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%Now compute the mean and median value of the error for each algorithm
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for a=1:num_algorithms
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mean_number_iterations(a,n) = mean(number_iterations(a,:));
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mean_execution_times(a,n) = mean(execution_times(a,:));
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end
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end
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%% Plot the results
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figure(1)
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plot(outlier_fraction_levels,mean_number_iterations,'LineWidth',2)
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legend(names,'Location','NorthWest')
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xlabel('outlier fraction')
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ylabel('mean number iterations')
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axis([0.05 0.25 0 1500])
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grid on
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figure(2)
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hold on
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plot(outlier_fraction_levels(1,1:6),mean_execution_times(1,1:6),'LineWidth',2)
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plot(outlier_fraction_levels,mean_execution_times(2:3,:),'LineWidth',2)
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legend(names,'Location','NorthWest')
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xlabel('outlier fraction')
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ylabel('mean execution time [s]')
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axis([0.05 0.45 0 40])
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grid on |