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content routing design.m
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content routing design.m
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%%
% Written by "Kais Suleiman" (ksuleiman.weebly.com)
% Notes:
%
% - The contents of this script have been explained in details
% in Chapter 6 of the thesis:
% Kais Suleiman, "Popular Content Distribution in Public
% Transportation Using Artificial Intelligence Techniques.",
% Ph.D. thesis, University of Waterloo, Ontario, Canada, 2019.
% - Notice that the very beginning and the very end of this script
% address the cluster extraction function and operations explained
% in details at the end of Chapter 4 of the aforementioned thesis.
% - Similar to the thesis, the functions to be used throughout
% content distribution tasks are introduced first.
% - Simpler but still similar variable names have been used throughout
% this script instead of the mathematical notations used in the thesis.
% - The assumptions used in the script are the same as those used in
% the thesis including those related to the case study considered
% representing the Grand River Transit bus service offered throughout
% the Region of Waterloo, Ontario, Canada.
% - The following external MATLAB functions have been used throughout
% the script:
% - Patrick Mineault (2020). Unique elements in cell array
% (https://www.mathworks.com/matlabcentral/fileexchange/
% 31718-unique-elements-in-cell-array),
% MATLAB Central File Exchange. Retrieved November 16, 2020.
% - M Sohrabinia (2020). LatLon distance
% (https://www.mathworks.com/matlabcentral/fileexchange/
% 38812-latlon-distance),
% MATLAB Central File Exchange. Retrieved November 16, 2020.
% - Figures and animations are created throughout this script to aid
% in thesis visualizations and other forms of results sharing.
%%
% The extractCluster function:
function [biggest_cluster, cluster_members] = ...
extractCluster(period_start_time, period_end_time, ...
connectivities, time_delta, minimum_contact_duration, ...
maximum_number_of_hops)
for i = 1:size(connectivities{1},1)
for t = period_start_time * 6:period_end_time * 6
if t == period_start_time * 6
period_connectivities{i} = ...
connectivities{t}(i,:); %#ok<*SAGROW>
else
period_connectivities{i} = ...
period_connectivities{i} + ...
connectivities{t}(i,:);
end
end
previous_cluster_members{i} = ...
find(period_connectivities{i} .* time_delta >= ...
minimum_contact_duration);
end
next_cluster_members = previous_cluster_members;
for n = 1:maximum_number_of_hops / 2 - 1
for i = 1:size(connectivities{1},1)
for j = 1:size(previous_cluster_members{i},2);
next_cluster_members{i} = ...
union(next_cluster_members{i}, ...
previous_cluster_members{ ...
previous_cluster_members{i}(1,j)});
end
end
previous_cluster_members = next_cluster_members;
end
[cluster_members,cluster_indices,~] = ...
uniquecell(next_cluster_members);
cluster_sizes = ...
zeros(size(cluster_indices,1),1);
for i = 1:size(cluster_indices,1)
cluster_sizes(i) = ...
size(cluster_members{i},2);
end
biggest_cluster = ...
find(cluster_sizes == max(cluster_sizes));
biggest_cluster = biggest_cluster(1);
end
%%
clear all; %#ok<*CLALL>
close all;
clc;
load('connectivities');
load('modified_data');
load('synthetic_lats');
load('synthetic_lons');
% Extracting the first biggest cluster connectivities:
%
% The first biggest cluster is chosen within the specified
% period start and end times given the first minimum discontinuous
% contact duration. The next biggest clusters are chosen afterwards
% according to the minimum contact durations schedule shown at the
% end of this script.
period_start_time = 16 * 60;
period_end_time = 18 * 60;
time_delta = 10;
minimum_contact_duration = 20 * 60;
maximum_number_of_hops = 20;
[biggest_cluster, cluster_members] = ...
extractCluster(period_start_time, period_end_time, ...
connectivities, time_delta, ...
minimum_contact_duration, maximum_number_of_hops);
for t = 1:(period_end_time * 6 - period_start_time * 6 + 1)
biggest_cluster_connectivities{t} = ...
connectivities{period_start_time * 6 + t - 1} ...
(cluster_members{biggest_cluster}', ...
cluster_members{biggest_cluster}'); ...
end
biggest_cluster_members = ...
cluster_members{biggest_cluster};
save('biggest_cluster_data.mat', ...
'biggest_cluster_connectivities', ...
'biggest_cluster_members', ...
'period_start_time', ...
'period_end_time');
% Visualizing the first biggest cluster at the busiest time:
busiest_time = ...
(period_start_time + period_end_time) / 2 * 6;
title('First biggest cluster @ 5:00 PM','FontSize',18);
hold on
map = imread('map.jpg');
image('CData',map, ...
'XData', ...
[min(modified_data(:,6)) max(modified_data(:,6))], ...
'YData', ...
[min(modified_data(:,5)) max(modified_data(:,5))])
hold on
plot(synthetic_lons(:,busiest_time), ...
synthetic_lats(:,busiest_time), ...
'ko','MarkerFaceColor','y');
hold on
plot(synthetic_lons( ...
biggest_cluster_members,busiest_time), ...
synthetic_lats( ...
biggest_cluster_members,busiest_time), ...
'ko','MarkerFaceColor','r');
hold on
axis([min(modified_data(:,6)) max(modified_data(:,6)) ...
min(modified_data(:,5)) max(modified_data(:,5))]);
ylabel('Latitude','FontSize',18);
xlabel('Longitude','FontSize',18);
hold on
time = ...
sprintf('%02d:%02d',floor(busiest_time / (60 * 6)), ...
floor((busiest_time / (60 * 6) - floor(busiest_time / (60 * 6))) * 60));
text(-80.35,43.575, ...
time,'Color','black','FontSize',20,'FontWeight','bold')
hold on
grid on
saveas(figure(1),'first biggest cluster at 5 pm.fig');
saveas(figure(1),'first biggest cluster at 5 pm.bmp');
%%
% The divideData function:
function initial_node_data_sizes = divideData(number_of_nodes, ...
period_start_time, period_end_time, biggest_cluster_connectivities, ...
numbers_of_data_segments, n)
total_time = period_end_time * 6 - period_start_time * 6 + 1;
for i = 1:number_of_nodes
for t = 1:total_time
if t == 1
initial_node_data_sizes(i) = ...
sum(biggest_cluster_connectivities{t}(i,:));
else
initial_node_data_sizes(i) = initial_node_data_sizes(i) + ...
sum(biggest_cluster_connectivities{t}(i,:));
end
end
end
initial_node_data_sizes = ...
floor(numbers_of_data_segments(n) .* ...
initial_node_data_sizes ./ sum(initial_node_data_sizes));
end
%%
% The allocateData function:
function segment_allocations = ...
allocateData(number_of_nodes, initial_node_data_sizes)
segment_allocations = ...
zeros(number_of_nodes,sum(initial_node_data_sizes));
for i = 1:number_of_nodes
if i == 1
segment_allocations(i,1:initial_node_data_sizes(i)) = ones;
last_segment_allocation = initial_node_data_sizes(i);
else
segment_allocations(i, ...
last_segment_allocation + 1: ...
last_segment_allocation + initial_node_data_sizes(i)) = ones;
last_segment_allocation = ...
last_segment_allocation + initial_node_data_sizes(i);
end
end
end
%%
% The computeFeatures function:
function node_features = computeFeatures(number_of_nodes, ...
biggest_cluster_connectivities, time_index, segment_allocations)
node_features = zeros(number_of_nodes,4);
for i = 1:number_of_nodes
node_features(i,1) = ...
sum(biggest_cluster_connectivities{time_index}(i,:) == 1) ...
/ number_of_nodes;
if node_features(i,1) ~= 0
node_segments = ...
find(segment_allocations(i,:) == 1);
node_neighbors = ...
find(biggest_cluster_connectivities{time_index}(i,:) == 1);
neighbors_neighbors = [];
for j = 1:length(node_neighbors)
neighbor_missing_segments = ...
find(segment_allocations(node_neighbors(j),:) == 0);
if ~ isempty(neighbor_missing_segments)
node_features(i,2) = (node_features(i,2) * (j - 1) + ...
length(node_segments(ismembc(node_segments, ...
sort(neighbor_missing_segments)))) ...
/ length(neighbor_missing_segments)) / j;
end
if j == 1
shared_missing_segments = ...
node_segments(ismembc(node_segments, ...
sort(neighbor_missing_segments)));
else
shared_missing_segments = ...
shared_missing_segments(ismembc(shared_missing_segments, ...
sort(node_segments(ismembc(node_segments, ...
sort(neighbor_missing_segments))))));
end
neighbors_neighbors = [neighbors_neighbors,...
find(biggest_cluster_connectivities{time_index} ...
(node_neighbors(j),:) == 1)]; %#ok<AGROW>
end
node_features(i,3) = ...
length(shared_missing_segments) / size(segment_allocations,2);
node_features(i,4) = 1 - ...
length(setdiff(neighbors_neighbors,[node_neighbors,i])) ...
/ number_of_nodes;
end
end
end
%%
% The controlRange function:
function [transmitter_neighbors, distances] = controlRange( ...
transmitter_neighbors, synthetic_lats, synthetic_lons, ...
biggest_cluster_members, transmitter, time_index, period_start_time, ...
node_statuses)
distances = zeros(size(transmitter_neighbors,1),1);
for j = 1:length(transmitter_neighbors)
distances(j) = lldistkm( ...
[synthetic_lats( ...
biggest_cluster_members(transmitter),time_index + ...
period_start_time * 6 - 1) ...
synthetic_lons( ...
biggest_cluster_members(transmitter),time_index + ...
period_start_time * 6 - 1)], ...
[synthetic_lats( ...
biggest_cluster_members(transmitter_neighbors(j)),time_index + ...
period_start_time * 6 - 1) ...
synthetic_lons( ...
biggest_cluster_members(transmitter_neighbors(j)),time_index + ...
period_start_time * 6 - 1)]);
end
eliminated_neighbors = [];
for j = 1:length(transmitter_neighbors)
if (node_statuses(transmitter_neighbors(j),1) == 1)|| ...
(node_statuses(transmitter_neighbors(j),3) == 1)
eliminated_neighbors = ...
[eliminated_neighbors, ...
find(distances >= distances(j))]; %#ok<AGROW>
end
transmitter_neighbors(eliminated_neighbors) = [];
distances(eliminated_neighbors) = [];
end
end
%%
% The targetSegments function:
function targeted_segments = targetSegments(segment_allocations, ...
transmitter, transmitter_neighbors, node_statuses)
targeted_segments = [];
transmitter_segments = ...
find(segment_allocations(transmitter,:) == 1);
for j = 1:length(transmitter_neighbors)
if node_statuses(transmitter_neighbors(j),2) == 0
neighbor_missing_segments = ...
find(segment_allocations(transmitter_neighbors(j),:) == 0);
targeted_segments = ...
[targeted_segments, ...
transmitter_segments(ismembc(transmitter_segments, ...
sort(neighbor_missing_segments)))]; %#ok<AGROW>
end
end
targeted_segments = unique(targeted_segments,'stable');
end
%%
% The transmitData function:
function [segment_allocations, actions_history, node_statuses] = ...
transmitData(node_statuses, transmitter, transmitter_neighbors, ...
segment_allocations, targeted_segments, actions_history, time_index, ...
period_start_time, distances, number_of_nodes)
node_statuses(transmitter,1) = 1;
segment_popularities = ...
sum(segment_allocations(transmitter_neighbors( ...
node_statuses(transmitter_neighbors,2) == 0),:),1);
chosen_segment = ...
targeted_segments(segment_popularities(targeted_segments) == ...
min(segment_popularities(targeted_segments)));
chosen_segment = chosen_segment(1);
for k = 1:length(transmitter_neighbors)
if (node_statuses(transmitter_neighbors(k),2) == 0) && ...
(segment_allocations ...
(transmitter_neighbors(k),chosen_segment) == 0)
node_statuses(transmitter_neighbors(k),2:3) = ones;
segment_allocations ...
(transmitter_neighbors(k),chosen_segment) = 1;
actions_history = vertcat(actions_history, ...
[time_index + period_start_time * 6 - 1,transmitter, ...
max(distances) * 1000,chosen_segment, ...
zeros(1,number_of_nodes)]); %#ok<AGROW>
actions_history(end,4 + transmitter_neighbors(k)) = 1;
end
end
node_statuses(transmitter_neighbors,2) = ones;
end
%%
% The followPolicy function:
function [segment_allocations, actions_history, node_statuses] = ...
followPolicy(period_start_time, period_end_time, number_of_nodes, ...
biggest_cluster_connectivities, segment_allocations, omegas, ...
synthetic_lats, synthetic_lons, biggest_cluster_members)
actions_history = [];
total_time = period_end_time * 6 - period_start_time * 6 + 1;
for t = 1:total_time
node_statuses = zeros(number_of_nodes,3);
% node_statuses(:,1) ==> indicate whether nodes are transmitting or not
% node_statuses(:,2) ==> indicate whether nodes are covered or not
% node_statuses(:,3) ==> indicate whether nodes are receiving or not
% Computing node features:
node_features = computeFeatures(number_of_nodes, ...
biggest_cluster_connectivities, t, segment_allocations);
utility_function = ...
node_features * omegas;
[~,transmissions_order] = sortrows(utility_function,-1);
for i = 1:number_of_nodes
transmitter = transmissions_order(i);
if node_statuses(transmitter,2) == 1
continue;
else
transmitter_neighbors = ...
find(biggest_cluster_connectivities{t} ...
(transmitter,:) == 1);
if (all(node_statuses(transmitter_neighbors,2)) == 1) || ...
isempty(transmitter_neighbors)
continue;
else
% Controlling range:
[transmitter_neighbors, distances] = controlRange( ...
transmitter_neighbors, synthetic_lats, synthetic_lons, ...
biggest_cluster_members, transmitter, t, ...
period_start_time, node_statuses);
if isempty(transmitter_neighbors)
continue;
else
% Targeting segments:
targeted_segments = targetSegments(segment_allocations, ...
transmitter, transmitter_neighbors, node_statuses);
if isempty(targeted_segments)
continue;
else
% Transmitting data:
[segment_allocations, actions_history, node_statuses] = ...
transmitData(node_statuses, transmitter, ...
transmitter_neighbors, segment_allocations, ...
targeted_segments, actions_history, t, ...
period_start_time, distances, number_of_nodes);
end
end
end
end
end
end
end
%%
clear all; %#ok<*CLALL>
close all;
clc;
load('biggest_cluster_data');
load('synthetic_lats');
load('synthetic_lons');
number_of_nodes = size(biggest_cluster_connectivities{1},1);
numbers_of_data_segments = 50:50:2000;
segments_number_variation = [];
minimum_segments_exchanged_over_distributed_ratio = 18;
% Estimating the maximum number of data segments to be exchanged
% using the naive-policy under a minimum
% "segments_exchanged_over_distributed_ratio":
for n = 1:length(numbers_of_data_segments)
% Dividing data:
initial_node_data_sizes = divideData(number_of_nodes, ...
period_start_time, period_end_time, biggest_cluster_connectivities, ...
numbers_of_data_segments, n));
% Allocating data:
segment_allocations = ...
allocateData(number_of_nodes, initial_node_data_sizes);
% Specifying the naive policy weights:
omegas = [10;0;0;0];
% Notice that the naive policy is choosing the node with the highest
% number of neighbors normalized by the total number of nodes to start
% transmission first while ignoring the other features.
% Following the specified policy:
[segment_allocations, actions_history, node_statuses] = ...
followPolicy(period_start_time, period_end_time, number_of_nodes, ...
biggest_cluster_connectivities, segment_allocations, omegas, ...
synthetic_lats, synthetic_lons, biggest_cluster_members);
segments_number_variation = vertcat(segments_number_variation, ...
[numbers_of_data_segments(n),sum(initial_node_data_sizes), ...
sum(sum(segment_allocations)) - sum(initial_node_data_sizes)]); %#ok<AGROW>
progress = ...
sprintf('%.0f percent', ...
(n / length(numbers_of_data_segments)) * 100);
clc;
disp(progress);
end
figure(1);
figure_title = ...
sprintf('Number of data segments\ninitially-distributed effect');
title(figure_title,'FontSize',20);
hold on
plot(segments_number_variation(:,1), ...
segments_number_variation(:,3) ./ segments_number_variation(:,2), ...
'-*k','LineWidth',2);
hold on
plot([0;segments_number_variation(:,1)], ...
ones(1,1 + length(numbers_of_data_segments)) .* ...
minimum_segments_exchanged_over_distributed_ratio, ...
'--r','LineWidth',2);
hold on
label = ...
sprintf('Number of data segments\ninitially-distributed');
xlabel(label,'FontSize',20);
hold on
label = ...
sprintf('Segments exchanged\nover initially-distributed ratio');
ylabel(label,'FontSize',20);
hold on
grid on
saveas(figure(1),'segments number variation.fig');
saveas(figure(1),'segments number variation.bmp');
save('segments_number_variation.mat','segments_number_variation');
%%
clear all; %#ok<*CLALL>
close all;
clc;
load('biggest_cluster_data');
load('synthetic_lats');
load('synthetic_lons');
number_of_visualizations = 6;
number_of_iterations = 100;
number_of_nodes = size(biggest_cluster_connectivities{1},1);
number_of_data_segments = 150;
visualizations_data = ...
zeros(number_of_iterations * number_of_visualizations,4 + 1);
% Building search space 3D-visualizations data:
for v = 1:number_of_visualizations
for iteration = 1:number_of_iterations
% Dividing data:
initial_node_data_sizes = divideData(number_of_nodes, ...
period_start_time, period_end_time, biggest_cluster_connectivities, ...
[number_of_data_segments], 1));
% Allocating data:
segment_allocations = ...
allocateData(number_of_nodes, initial_node_data_sizes);
% Specifying visualization slice policy weights:
if v == 1
omegas = randi([-10,10],4,1);
omegas([1,2],1) = 0;
end
if v == 2
omegas = randi([-10,10],4,1);
omegas([1,3],1) = 0;
end
if v == 3
omegas = randi([-10,10],4,1);
omegas([1,4],1) = 0;
end
if v == 4
omegas = randi([-10,10],4,1);
omegas([2,3],1) = 0;
end
if v == 5
omegas = randi([-10,10],4,1);
omegas([2,4],1) = 0;
end
if v == 6
omegas = randi([-10,10],4,1);
omegas([3,4],1) = 0;
end
% Following the specified policy:
[segment_allocations, actions_history, node_statuses] = ...
followPolicy(period_start_time, period_end_time, number_of_nodes, ...
biggest_cluster_connectivities, segment_allocations, omegas, ...
synthetic_lats, synthetic_lons, biggest_cluster_members);
visualizations_data((v - 1) * number_of_iterations + iteration,:) = ...
[omegas',length(actions_history)];
progress = ...
sprintf('%.0f percent', ...
((v - 1) * number_of_iterations + iteration) / ...
(number_of_visualizations * number_of_iterations) * 100);
clc;
disp(progress);
end
end
save('visualizations_data.mat','visualizations_data');
%%
clear all; %#ok<*CLALL>
close all;
clc;
load('visualizations_data');
% Visualizing search space 3D-data:
figure(1);
title('Search space with \omega_{1} & \omega_{2} set to 0', ...
'FontSize',18);
hold on;
xlabel('\omega_{3}','FontSize',18,'FontWeight','Bold');
hold on;
ylabel('\omega_{4}','FontSize',18,'FontWeight','Bold');
hold on;
v = 1;
x = ...
visualizations_data((v - 1) * 100 + 1:v * 100,3);
y = ...
visualizations_data((v - 1) * 100 + 1:v * 100,4);
z = ...
visualizations_data((v - 1) * 100 + 1:v * 100,5);
tri = delaunay(x,y);
trisurf(tri,x,y,z,'linestyle','none')
shading interp
view(2)
colorbar
saveas(figure(1),'search space with zero omega 1 and 2.fig');
saveas(figure(1),'search space with zero omega 1 and 2.bmp');
figure(2);
title('Search space with \omega_{1} & \omega_{3} set to 0', ...
'FontSize',18);
hold on;
xlabel('\omega_{2}','FontSize',18,'FontWeight','Bold');
hold on;
ylabel('\omega_{4}','FontSize',18,'FontWeight','Bold');
hold on;
v = 2;
x = ...
visualizations_data((v - 1) * 100 + 1:v * 100,2);
y = ...
visualizations_data((v - 1) * 100 + 1:v * 100,4);
z = ...
visualizations_data((v - 1) * 100 + 1:v * 100,5);
tri = delaunay(x,y);
trisurf(tri,x,y,z,'linestyle','none')
shading interp
view(2)
colorbar
saveas(figure(2),'search space with zero omega 1 and 3.fig');
saveas(figure(2),'search space with zero omega 1 and 3.bmp');
figure(3);
title('Search space with \omega_{1} & \omega_{4} set to 0', ...
'FontSize',18);
hold on;
xlabel('\omega_{2}','FontSize',18,'FontWeight','Bold');
hold on;
ylabel('\omega_{3}','FontSize',18,'FontWeight','Bold');
hold on;
v = 3;
x = ...
visualizations_data((v - 1) * 100 + 1:v * 100,2);
y = ...
visualizations_data((v - 1) * 100 + 1:v * 100,3);
z = ...
visualizations_data((v - 1) * 100 + 1:v * 100,5);
tri = delaunay(x,y);
trisurf(tri,x,y,z,'linestyle','none')
shading interp
view(2)
colorbar
saveas(figure(3),'search space with zero omega 1 and 4.fig');
saveas(figure(3),'search space with zero omega 1 and 4.bmp');
figure(4);
title('Search space with \omega_{2} & \omega_{3} set to 0', ...
'FontSize',18);
hold on;
xlabel('\omega_{1}','FontSize',18,'FontWeight','Bold');
hold on;
ylabel('\omega_{4}','FontSize',18,'FontWeight','Bold');
hold on;
v = 4;
x = ...
visualizations_data((v - 1) * 100 + 1:v * 100,1);
y = ...
visualizations_data((v - 1) * 100 + 1:v * 100,4);
z = ...
visualizations_data((v - 1) * 100 + 1:v * 100,5);
tri = delaunay(x,y);
trisurf(tri,x,y,z,'linestyle','none')
shading interp
view(2)
colorbar
saveas(figure(4),'search space with zero omega 2 and 3.fig');
saveas(figure(4),'search space with zero omega 2 and 3.bmp');
figure(5);
title('Search space with \omega_{2} & \omega_{4} set to 0', ...
'FontSize',18);
hold on;
xlabel('\omega_{1}','FontSize',18,'FontWeight','Bold');
hold on;
ylabel('\omega_{3}','FontSize',18,'FontWeight','Bold');
hold on;
v = 5;
x = ...
visualizations_data((v - 1) * 100 + 1:v * 100,1);
y = ...
visualizations_data((v - 1) * 100 + 1:v * 100,3);
z = ...
visualizations_data((v - 1) * 100 + 1:v * 100,5);
tri = delaunay(x,y);
trisurf(tri,x,y,z,'linestyle','none')
shading interp
view(2)
colorbar
saveas(figure(5),'search space with zero omega 2 and 4.fig');
saveas(figure(5),'search space with zero omega 2 and 4.bmp');
figure(6);
title('Search space with \omega_{3} & \omega_{4} set to 0', ...
'FontSize',18);
hold on;
xlabel('\omega_{1}','FontSize',18,'FontWeight','Bold');
hold on;
ylabel('\omega_{2}','FontSize',18,'FontWeight','Bold');
hold on;
v = 6;
x = ...
visualizations_data((v - 1) * 100 + 1:v * 100,1);
y = ...
visualizations_data((v - 1) * 100 + 1:v * 100,2);
z = ...
visualizations_data((v - 1) * 100 + 1:v * 100,5);
tri = delaunay(x,y);
trisurf(tri,x,y,z,'linestyle','none')
shading interp
view(2)
colorbar
saveas(figure(6),'search space with zero omega 3 and 4.fig');
saveas(figure(6),'search space with zero omega 3 and 4.bmp');
%%
clear all; %#ok<*CLALL>
close all;
clc;
load('biggest_cluster_data');
load('synthetic_lats');
load('synthetic_lons');
number_of_iterations = 100;
number_of_nodes = size(biggest_cluster_connectivities{1},1);
number_of_data_segments = 150;
regression_data = zeros(number_of_iterations,4 + 1);
% Generating initial regression data:
for iteration = 1:number_of_iterations
% Dividing data:
initial_node_data_sizes = divideData(number_of_nodes, ...
period_start_time, period_end_time, biggest_cluster_connectivities, ...
[number_of_data_segments], 1));
% Allocating data:
segment_allocations = ...
allocateData(number_of_nodes, initial_node_data_sizes);
% Specifying the random policy weights:
omegas = randi([-10,10],4,1);
% Following the specified policy:
[segment_allocations, actions_history, node_statuses] = ...
followPolicy(period_start_time, period_end_time, number_of_nodes, ...
biggest_cluster_connectivities, segment_allocations, omegas, ...
synthetic_lats, synthetic_lons, biggest_cluster_members);
regression_data(iteration,:) = ...
[omegas',length(actions_history)];
progress = ...
sprintf('%.0f percent',iteration / number_of_iterations * 100);
clc;
disp(progress);
end
save('regression_data.mat','regression_data');
%%
clear all; %#ok<*CLALL>
close all;
clc;
load('biggest_cluster_data');
load('regression_data');
load('synthetic_lats');
load('synthetic_lons');
number_of_iterations = 900;
number_of_random_points = 1000;
number_of_nodes = size(biggest_cluster_connectivities{1},1);