private void CreateRandomVariables(Func <bool> hazard, Dictionary <string, Func <bool> > states, IList <Fault> faults)
{
var usedFaults = faults ?? _model.Faults;
var usedStates = states ?? new Dictionary <string, Func <bool> >();
var randomVariableCreator = new RandomVariableFactory(_model);
_hazardVar = randomVariableCreator.FromState(hazard, "H");
_states = usedStates.Select(state => randomVariableCreator.FromState(state.Value, state.Key)).ToList();
_faultVars = randomVariableCreator.FromFaults(usedFaults);
_mcsVars = randomVariableCreator.FromDccaLimitedByFaults(hazard, _faultVars);
}
public string GenerateSimulationData(ICollection <FaultRandomVariable> faults, ICollection <McsRandomVariable> mcs,
ICollection <BooleanRandomVariable> states, BooleanRandomVariable hazard, int numberOfSimulations)
{
_faults = faults;
_mcs = mcs;
_hazard = hazard;
_states = states;
_numberOfSimulations = numberOfSimulations;
Console.Out.WriteLine("Create simulation data...");
var allVariables = AllRandomVariables();
var allFormulas = CreateAllFormulas();
SafetySharpProbabilisticSimulator.Configuration.UseOptionProbabilitiesInSimulation = _config.UseRealProbabilitiesForSimulation;
var simulator = new SafetySharpProbabilisticSimulator(_model, allFormulas.Values.ToArray());
using (var w = new StreamWriter(DataPath))
{
w.WriteLine(string.Join(",", allVariables.Select(randomVariable => randomVariable.Name)));
for (var currentStep = 0; currentStep < _numberOfSimulations; currentStep++)
{
if (_numberOfSimulations > 100 && currentStep % (_numberOfSimulations / 100) == 0)
{
Console.Out.WriteLine($"{(double)currentStep / _numberOfSimulations:P0} done.");
}
simulator.SimulateSteps(_stepBounds);
var results = new bool[allVariables.Count];
for (var varIndex = 0; varIndex < allVariables.Count; varIndex++)
{
var currentVariable = allVariables[varIndex];
// cut sets cannot be checked for a given state, so check the occurence of its faults
if (currentVariable is McsRandomVariable)
{
var cutSet = (McsRandomVariable)currentVariable;
results[varIndex] =
cutSet.FaultVariables.All(fault => simulator.GetCountOfSatisfiedOnTrace(allFormulas[fault]) > 0);
}
// check the occurence of the random variable
else
{
results[varIndex] = simulator.GetCountOfSatisfiedOnTrace(allFormulas[currentVariable]) > 0;
}
}
w.WriteLine(string.Join(",", results.Select(res => res ? 'T' : 'F')));
}
w.Flush();
}
return(DataPath);
}
/// <summary>
/// Learn the best fitting DAG structure and probability distributions by using simulation data
/// </summary>
public BayesianNetwork LearnBayesianNetwork(ICollection <FaultRandomVariable> faults, ICollection <McsRandomVariable> mcs,
ICollection <BooleanRandomVariable> states, BooleanRandomVariable hazard)
{
_faults = faults;
_mcs = mcs;
_hazard = hazard;
_states = states;
Console.Out.WriteLine("Generate data for reducing model domain...");
CreateWhiteAndBlacklist();
Console.Out.WriteLine("Call R script for learning the network...");
var jsonResult = CallRLearningScript();
var result = JsonConvert.DeserializeObject <BayesianNetworkResult>(jsonResult);
return(ToBayesianNetwork(result));
}
/// <summary>
/// Identifies as many conditional independencies in the model as possible.
/// </summary>
public DualKeyDictionary <RandomVariable, ICollection <ISet <RandomVariable> > > FindIndependencies(IList <FaultRandomVariable> faults, ICollection <McsRandomVariable> minimalCriticalSets, IList <BooleanRandomVariable> states, BooleanRandomVariable hazard)
{
_hazard = hazard;
_faults = faults;
_minimalCriticalSets = new HashSet <McsRandomVariable>(minimalCriticalSets);
_states = states;
_conditionalIndependencies = new DualKeyDictionary <RandomVariable, ICollection <ISet <RandomVariable> > >();
var variablesForCalculation = GetVariablesForCalculation();
CalculatePairIndependencies(variablesForCalculation);
if (_config.UseDccaResultsForLearning && _minimalCriticalSets.Count > 0)
{
GenerateDccaIndependencies();
CalculateFurtherDccaIndependencies();
}
return(_conditionalIndependencies);
}
/// <summary>
/// Use the structure of the DCCA to improve the missing orientations in the current DAG pattern.
/// </summary>
public DagPattern <RandomVariable> UseDccaForOrientations(ICollection <McsRandomVariable> minimalCriticalSets, ICollection <FaultRandomVariable> faults, BooleanRandomVariable hazard)
{
foreach (var minimalCriticalSet in minimalCriticalSets)
{
_dag.OrientUndirectedEdge(minimalCriticalSet, hazard);
foreach (var fault in minimalCriticalSet.FaultVariables)
{
_dag.OrientUndirectedEdge(fault, minimalCriticalSet);
}
}
Console.Out.WriteLine("After using DCCA to orient edges:");
_dag.ExportToGraphviz();
OrientRemainingEdges(false);
Console.Out.WriteLine("After orienting remaining edges after DCCA:");
_dag.ExportToGraphviz();
return(_dag);
}