private double GetScore(Goal goal, IPropagator propagator, HashSet <string> selection)
{
double maxSatRate;
var r = _model.Resolutions().Where(x => selection.Contains(x.ResolvingGoalIdentifier));
// Integrate the selection
foreach (var resolution in r)
{
integrator.Integrate(resolution);
}
var satRate = (DoubleSatisfactionRate)propagator.GetESR(goal);
//var all_goals_satisfied = (satRate.SatisfactionRate > goal.RDS);
//if (all_goals_satisfied) {
var cost = selection.Count();
maxSatRate = satRate.SatisfactionRate;
foreach (var resolution in r)
{
integrator.Remove(resolution);
}
return(maxSatRate);
}
/// <summary>
/// Returns the minimal cost ensuring the goals respective RDSs. Returns -1 if no
/// selection satisfy the RDS.
/// </summary>
/// <param name="goals">The goals</param>
/// <returns>The minimal cost.</returns>
public double GetMinimalCost(HashSet <Goal> goals, IPropagator propagator)
{
var timer = Stopwatch.StartNew();
int minCost = -1;
var sr = new Dictionary <string, DoubleSatisfactionRate>();
bool all_goals_satisfied = true;
foreach (var goal in goals)
{
sr.Add(goal.Identifier, (DoubleSatisfactionRate)propagator.GetESR(goal));
all_goals_satisfied &= (sr[goal.Identifier].SatisfactionRate > goal.RDS);
}
if (all_goals_satisfied)
{
return(0);
}
int count = 0;
int tested_count = 0;
int safe_count = 0;
var countermeasure_goals = _model.Resolutions().Select(x => x.ResolvingGoalIdentifier).Distinct();
foreach (var cg in GetAllCombinations(countermeasure_goals.ToList()))
{
count++;
var cost = cg.Count();
//if (minCost >= 0 && cost > minCost) continue;
tested_count++;
var r = _model.Resolutions().Where(x => cg.Contains(x.ResolvingGoalIdentifier));
foreach (var resolution in r)
{
integrator.Integrate(resolution);
}
sr = new Dictionary <string, DoubleSatisfactionRate>();
all_goals_satisfied = true;
foreach (var goal in goals)
{
sr.Add(goal.Identifier, (DoubleSatisfactionRate)propagator.GetESR(goal));
all_goals_satisfied &= (sr[goal.Identifier].SatisfactionRate > goal.RDS);
}
if (all_goals_satisfied)
{
safe_count++;
stats.MaxSafeCost = Math.Max(stats.MaxSafeCost, cost);
if (minCost == -1 || cost < minCost)
{
minCost = cost;
}
}
Console.WriteLine("Selection: "
+ string.Join(",", r.Select(x => x.ResolvingGoalIdentifier))
+ " cost: " + cost
+ " " + string.Join(" ", goals.Select(x => x.Identifier + ": " + sr[x.Identifier].SatisfactionRate)));
foreach (var resolution in r)
{
integrator.Remove(resolution);
}
}
timer.Stop();
stats.TimeToComputeMinimalCost = timer.Elapsed;
stats.NbResolvingGoals = countermeasure_goals.Count();
stats.NbSelections = count;
stats.NbTestedSelections = tested_count;
stats.NbSafeSelections = safe_count;
return(minCost);
}
public IEnumerable <OptimalSelection> GetOptimalSelections(double minCost, HashSet <Goal> goals, IPropagator propagator)
{
var timer = Stopwatch.StartNew();
var optimalSelections = new List <OptimalSelection> ();
int tested_count = 0;
var bestSR = new Dictionary <string, double>();
var sr = new Dictionary <string, DoubleSatisfactionRate>();
foreach (var goal in goals)
{
bestSR.Add(goal.Identifier, 0);
}
var countermeasure_goals = _model.Resolutions().Select(x => x.ResolvingGoalIdentifier).Distinct();
foreach (var cg in GetAllCombinations(countermeasure_goals.ToList()))
{
var cost = cg.Count();
if (minCost > 0 && cost > minCost)
{
continue;
}
tested_count++;
var activeResolutions = _model.Resolutions().Where(x => cg.Contains(x.ResolvingGoalIdentifier));
foreach (var resolution in activeResolutions)
{
integrator.Integrate(resolution);
}
sr = new Dictionary <string, DoubleSatisfactionRate>();
var all_goals_satisfied = true;
foreach (var goal in goals)
{
sr.Add(goal.Identifier, (DoubleSatisfactionRate)propagator.GetESR(goal));
all_goals_satisfied &= (sr[goal.Identifier].SatisfactionRate > goal.RDS);
}
if (all_goals_satisfied)
{
foreach (var goal in goals)
{
double satisfactionRate = sr[goal.Identifier].SatisfactionRate;
if (satisfactionRate > bestSR[goal.Identifier] + EPSILON)
{
bestSR[goal.Identifier] = satisfactionRate;
optimalSelections = new List <OptimalSelection>();
optimalSelections.Add(new OptimalSelection(activeResolutions, cost, satisfactionRate, new string[] {}));
}
else if (Math.Abs(satisfactionRate - bestSR[goal.Identifier]) < EPSILON)
{
optimalSelections.Add(new OptimalSelection(activeResolutions, cost, satisfactionRate, new string[] {}));
}
}
}
foreach (var resolution in activeResolutions)
{
integrator.Remove(resolution);
}
}
timer.Stop();
stats.TimeToComputeSelection = timer.Elapsed;
stats.NbTestedSelectionsForOptimality = tested_count;
return(optimalSelections);
}