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%% COMPUTE AND SHOW METRICS

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

fResults = invertCellStruct(results);   % make a structure 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');

%   - metricName: the name of computed evaluation metric.

%

% Output:

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

%   - sResult: the updated 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 

`./EvalMetric/*`

> functions that compute the evaluation metrics used to asses the quality of the compaired control strategies.

> functions that compute the evaluation metrics used to asses the quality of the compared control strategies.

`./GraphModels/*`

%createStrategies - This function creates one structure storing all the 

%settings for the comtrol strategies that wil be simulated.

%settings for the control strategies that will be simulated.

%

% function strategies = createStrategies (btot, varargin)

%

%        randomly chosen nodes. Note that this is a naive dynamic strategy

%        which decides at random each time an event happens (infection/recovery). 

%     --<Static strategies>--

%     > 'RANDFIXED': it fixes the btot treatments to randomly selected 

%     > 'RANDFIXED': it fixes the btot treatments to be randomly selected 

%       nodes and keep it this way throughout all the simulation.

%     > 'MN': favors the treatment of the nodes with most neighbors (high degree nodes).

%     > 'LN': favors the treatment of the nodes with least neighbors (low degree nodes).

%     > 'MSN' : the treatment resources are given to the btot nodes that

%       have the most susceptible neighbors (i.e. to the most viral nodes)

%     > 'LIN' : the treatment resources are given to the btot nodes that 

%       have the least infected neighbors (i.e. to the most safe nodes)

%       have the least infected neighbors (i.e. to the safest nodes)

%     > 'LRIE': it roughly combines MSN and LIN. This is the optimal greedy

%        control strategy.

%

%dropInMaxEigenValueApprox - An appriximation algorithm for the computation

%dropInMaxEigenValueApprox - An approximation algorithm for the computation

%of a score for each node that represents the drop of in the max eigenvalue 

%observed after removing that node from the graph.

%