init

%epidemicControl - This is the main script that initializes the environment,

%performs simulations, and reports comparative results.

%---

% This is part of the DRA Simulator package for Dynamic Resource Allocation 

% strategies aiming to suppress SIS epidemics. See README.md for more 

% details. If you make use of this package, or part of it, please consider 

% citing the paper:

%

%   "A Greedy Approach for Dynamic Control of Diffusion Processes in

%   Networks", K. Scaman, A. Kalogeratos, N Vayatis, IEEE International 

%   Conference on Tools with Artificial Intelligence (ICTAI), 2015.

%

% DRA Simulator is free software: redistribute them and/or modify them 

% under the terms of the GNU General Public License as published by the 

% Free Software Foundation, either version 3 of the License, or (at your 

% option) any later version.

%

% DRA Simulator package is distributed in the hope that it will be useful,

% but WITHOUT ANY WARRANTY; without even the implied warranty of

% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General 

% Public License for more details. You should have received a copy of the 

% GNU General Public License along with DRA Simulator. If not, see 

% <http://www.gnu.org/licenses/>.

%

% Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.

%---

% INITIALIZATION (run only once - to force initialization, clear the environment first)

if (~exist('MATLAB_WORKERS', 'var')) % avoid re-initializations 

    prepareWorkspace();

end

% Initialize the random number generator (RNG) for reproducibility of the results

if (rng_seed >= 0), rng(rng_seed); end

rngInfo = rng;

%% PARAMETER SETUP

% Graph parameters

    graphPars = struct;

    % a. Generate graph

        graphPars.N           = 100;           % the size of graph to generate

        graphPars.graphType = 'erdosRenyi';    % available: 'erdosRenyi', 'preferentialAttachment', 'smallWorld', 'adjacencyMatrix'

        if (strcmp(graphPars.graphType, 'erdosRenyi') == 1)

            graphPars.prob_arete_graph = 0.1;  % only for erdosRenyiGraph() generation

        end

    % b. Use an input graph

    %  - enter 'adjacencyMatrix' in graphPars.graphType

    %  - include the following:

    %  - load or create your graph G 

        %    G = erdosRenyiGraph(graphPars.N, graphPars.prob_arete_graph);   % this is just an example

        %    graphPars.adjacencyMatrix = G;

        %    graphPars.N = size(G,1);

% Infection seeds parameters

    infectionSeedPars      = struct;

    infectionSeedPars.size = 0.9;   % the percentage of initial seeds distributed uniformly at random

% Epidemic parameters

    epidemicPars       = struct;

    epidemicPars.beta  = 3;         % independent infection rate for each edge

    epidemicPars.delta = 1;         % recovery rate for each infected node

    epidemicPars.rho   = 120;       % increase in recovery rate for each treated node

% Control strategy parameters

    btot = 5;                       % number of available treatment resources 

    strategies = createStrategies(btot, 'LRIE');%'RAND', 'LRSR', 'LRIE'); % which strategies to test (baseline, static, dynamic): for more info see the documentation of this function

% Simulation and experimentation parameters

    simulationPars          = struct;

    simulationPars.T        = 2;           % simulation time

    simulationPars.noSlopeT = Inf;         % characteristic time for the non stationarity test

    simulationPars.timeStep = 0.01;        % time step for results

    simulationPars.stateSnapshots = false; % record the infection state and the resource distribution for every change that happens

    numTests = 10;

    % automatically defined

    simulationPars.runInParallel  = (MATLAB_WORKERS > 0);       % use multiple Matlab workers

    simulationPars.consistentRNG  = (rngInfo.Seed == rng_seed); % preserve the consistency of random number generator if the seed is the one given by the user

    if (simulationPars.runInParallel == simulationPars.consistentRNG)

        simulationPars.runInParallel = 0;

        warning('Warning: RNG cannot be consistent when multiple workers are used (simulationPars.runInParallel turned to false)');

    end

%% EPIDEMIC SIMULATION

results = cell(numTests, 1);

tic;

for iTest=1:numTests,

    fprintf('\n=== iTest: %g ==================================================\n', iTest);

    results{iTest} = simulateEpidemic(graphPars, infectionSeedPars, epidemicPars, simulationPars, strategies, 'verbosityLevel', 2);

end

toc;

%% COMPUTE AND SHOW METRICS

fResults = invertCellStruct(results);   % make a strcture with proper format for exploring the results

fResults = applyMetric(fResults, @(s) 100 * double(s.numInfected) / graphPars.N, 'percentageInfected');

fResults = applyMetric(fResults, @(s) 100 * double(s.numInfected(end)) / graphPars.N, 'finalPercentageInfected');

fResults = applyMetric(fResults, @(s) sum(s.numInfected) * simulationPars.timeStep, 'AUC');

fResults = applyMetric(fResults, @(s) timeOfExtinction(s), 'timeExtinction');

fResults = applyMetric(fResults, @(s) double(s.numInfected(end)), 'finalNumInfected');

%% SHOW OUTPUT RESULTS & PLOTS

showMeasures(fResults, 'AUC', 'timeExtinction', 'finalPercentageInfected');

plotCurves(fResults, 'percentageInfected', 'title', 'Comparison of resource allocation strategies', 'xlabel', 'time', 'ylabel', 'percentage of infected nodes');

%simulationResult - This function applies a function that computes an 

%evaluation metric on the simulation result and store it as a field in the 

%results' structure.

%

% function sResult = applyMetric (simulationResult, ...

%                                 metricFunction, metricName)

%

% Input:

%   - sResult: a structure with the simulation results.

%   - metricFunction: a function handler that computes an evaluation metric

%     on the recorded simulation results. This is defined through the call.

%   - metricName: the name of computed evaluation metric.

%

% Output:

%   - sResult: the udated structure with the simulation results.

%---

% This is part of the DRA Simulator package for Dynamic Resource Allocation

% strategies aiming to suppress SIS epidemics. See the comments header 

% of the main script DRAsimulator.m and the README.md for more details.

% This package is distributed under GNU GPL v.3 or later.

%

% Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.

%---

function sResult = applyMetric (sResult, metricFunction, metricName)

sResult = structfun(@(strategy) addMetricToStrategy(strategy), sResult, 'UniformOutput', false);

function strategy = addMetricToStrategy(strategy)

    if (iscell(strategy))

        strategy = cellfun(@(test) addMetricValue(test, metricFunction(test), metricName), strategy, 'UniformOutput', false);

    else

        strategy = addMetricValue(strategy, metricFunction(strategy), metricName);

    end

end

function strategy = addMetricValue(strategy, value, name)

    strategy.(name) = value;

end

end

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%computeMeanAndStd - This is a helper function which is called by the 

%showMeasures(). It computes mean and std values for given fields of the 

%structure with the simulation results.

%

% function meanAndStd = computeMeanAndStd (fRresults, fieldName)

%

% Input:

%   - fRresults: a structure with the simulation results.

%   - fieldName: the field to compute the values (should be a name of an

%     evaluation metric existing in the structure).

%

% Output:

%   - meanAndStd: the resulting values in a structure.

%---

% This is part of the DRA Simulator package for Dynamic Resource Allocation

% strategies aiming to suppress SIS epidemics. See the comments header 

% of the main script DRAsimulator.m and the README.md for more details.

% This package is distributed under GNU GPL v.3 or later.

%

% Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.

%---

function meanAndStd = computeMeanAndStd (fRresults, fieldName)

valuesStruct = structfun(@(s) cellfun(@(t) t.(fieldName)(end), s), fRresults, 'UniformOutput', false);

meanAndStd   = structfun(@(v) createStruct(v), valuesStruct, 'UniformOutput', false);

function meanAndStd = createStruct(values)

    meanAndStd      = struct;

    meanAndStd.mean = mean(values(values ~= Inf));

    meanAndStd.std  = std(values(values ~= Inf));

end

end

%smallWorldGraph - Shows a selection of evaluation metrics stored in the 

%simulation results structure.

%

% function showMeasures (fRresults, varargin)

%

% Input:

%   - fRresults: a structure with the simulation results.

%   - varargin : a list of names of the evaluation metrics to show.

%---

% This is part of the DRA Simulator package for Dynamic Resource Allocation

% strategies aiming to suppress SIS epidemics. See the comments header 

% of the main script DRAsimulator.m and the README.md for more details.

% This package is distributed under GNU GPL v.3 or later.

%

% Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.

%---

function showMeasures (fRresults, varargin)

methodNames = fieldnames(fRresults);

numMeasures = length(varargin);

numMethods  = length(methodNames);

measures = cell(numMeasures, 1);

for j=1:numMeasures

    measure = computeMeanAndStd(fRresults, varargin{j});

    measures{j} = structfun(@(strategy) strategy.mean, measure);

end

fprintf('-----------------------\n Comparing strategies\n-----------------------\n');

for i=1:numMethods

    for j=1:numMeasures

        measure = measures{j};

        fprintf('%s: %10.2f (%2.2f), ', varargin{j}, measure(i), measure(i) / min(measure));

    end

    fprintf('-- [%20s]\n', methodNames{i});

end

end

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