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%% Reset everything
clear all;
clc;
close all;
addpath('helpers');
%% Configure the benchmark
% The algorithms we want to test
algorithms = [ 6; 8; 17 ];
% The name of the algorithms in the final plots
names = { '6pt'; 'ge (8pt)'; '17pt'};
% The main experiment parameters
min_outlier_fraction = 0.05;%0.05;
max_outlier_fraction = 0.25;
outlier_fraction_step = 0.025;
p = 0.99;
%% Run the benchmark
%prepare the overall result arrays
number_outlier_fraction_levels = round((max_outlier_fraction - min_outlier_fraction) / outlier_fraction_step + 1);
num_algorithms = size(algorithms,1);
expected_number_iterations = zeros(num_algorithms,number_outlier_fraction_levels);
outlier_fraction_levels = zeros(1,number_outlier_fraction_levels);
%Run the experiment
for n=1:number_outlier_fraction_levels
outlier_fraction = (n - 1) * outlier_fraction_step + min_outlier_fraction;
outlier_fraction_levels(1,n) = outlier_fraction;
display(['Analyzing outlier fraction level: ' num2str(outlier_fraction)])
%Now compute the mean and median value of the error for each algorithm
for a=1:num_algorithms
expected_number_iterations(a,n) = log(1-p)/log(1-(1-outlier_fraction)^(algorithms(a,1)));
end
end
%% Plot the results
figure(1)
plot(outlier_fraction_levels,expected_number_iterations,'LineWidth',2)
legend(names,'Location','NorthWest')
xlabel('outlier fraction')
ylabel('expected number iterations')
grid on
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