internal static DiscreteTimeMarkovChain Create()
{
// Just a simple DTMC
// 0⟶1⟲
var markovChain = new DiscreteTimeMarkovChain(ModelCapacityByMemorySize.Tiny);
markovChain.StateFormulaLabels = new string[] { Label1Formula.Label, Label2Formula.Label };
markovChain.StateRewardRetrieverLabels = new string[] { };
markovChain.StartWithInitialDistribution();
markovChain.AddInitialTransition(0, 1.0);
markovChain.FinishInitialDistribution();
markovChain.SetStateLabeling(1, new StateFormulaSet(new[] { true, false }));
markovChain.StartWithNewDistribution(1);
markovChain.AddTransition(1, 1.0);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(0, new StateFormulaSet(new[] { false, true }));
markovChain.StartWithNewDistribution(0);
markovChain.AddTransition(1, 1.0);
markovChain.FinishDistribution();
//markovChain.ProbabilityMatrix.OptimizeAndSeal();
return(markovChain);
}
internal static DiscreteTimeMarkovChain Create()
{
// A DTMC for \phi Until \psi (or in this case Label1 U Label2)
// 0⟶0.1⟼1⟲
// 0.2⟼2⟼3⟲
// 0.7⟼4↗
// \psi in 3. \phi in 0,2
var markovChain = new DiscreteTimeMarkovChain(ModelCapacityByMemorySize.Tiny);
markovChain.StateFormulaLabels = new string[] { Label1Formula.Label, Label2Formula.Label };
markovChain.StateRewardRetrieverLabels = new string[] { };
markovChain.StartWithInitialDistribution();
markovChain.AddInitialTransition(0, 1.0);
markovChain.FinishInitialDistribution();
markovChain.SetStateLabeling(0, new StateFormulaSet(new[] { true, false }));
markovChain.StartWithNewDistribution(0);
markovChain.AddTransition(1, 0.1);
markovChain.AddTransition(2, 0.2);
markovChain.AddTransition(4, 0.7);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(1, new StateFormulaSet(new[] { false, false }));
markovChain.StartWithNewDistribution(1);
markovChain.AddTransition(1, 1.0);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(2, new StateFormulaSet(new[] { true, false }));
markovChain.StartWithNewDistribution(2);
markovChain.AddTransition(3, 1.0);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(3, new StateFormulaSet(new[] { false, true }));
markovChain.StartWithNewDistribution(3);
markovChain.AddTransition(3, 1.0);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(4, new StateFormulaSet(new[] { false, false }));
markovChain.StartWithNewDistribution(4);
markovChain.AddTransition(3, 1.0);
markovChain.FinishDistribution();
//markovChain.ProbabilityMatrix.OptimizeAndSeal();
return(markovChain);
}
internal static DiscreteTimeMarkovChain Create()
{
// Transformed LabeledTransitionMarkovChain.Example4
// 0⟶0.6⟼1⟼0.9⟲
// 0.01⇢3
// 0.09⇢4
// 0.3⟼2⟼0.9⇢1
// 0.01⇢3
// 0.09⇢4
// 0.1⟼4⟼3⟲
var markovChain = new DiscreteTimeMarkovChain(ModelCapacityByMemorySize.Tiny);
markovChain.StateFormulaLabels = new string[] { Label1Formula.Label, Label2Formula.Label };
markovChain.StateRewardRetrieverLabels = new string[] { };
markovChain.StartWithInitialDistribution();
markovChain.AddInitialTransition(0, 1.0);
markovChain.FinishInitialDistribution();
markovChain.SetStateLabeling(0, new StateFormulaSet(new[] { false, false })); // state 1(-) of LabeledTransitionMarkovChainExamples.Example4
markovChain.StartWithNewDistribution(0);
markovChain.AddTransition(1, 0.6);
markovChain.AddTransition(2, 0.3);
markovChain.AddTransition(4, 0.1);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(1, new StateFormulaSet(new[] { false, false })); // state 2(-) of LabeledTransitionMarkovChainExamples.Example4
markovChain.StartWithNewDistribution(1);
markovChain.AddTransition(1, 0.9);
markovChain.AddTransition(3, 0.01);
markovChain.AddTransition(4, 0.09);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(2, new StateFormulaSet(new[] { true, false })); // state 2(lab1) of LabeledTransitionMarkovChainExamples.Example4
markovChain.StartWithNewDistribution(2);
markovChain.AddTransition(1, 0.9);
markovChain.AddTransition(3, 0.01);
markovChain.AddTransition(4, 0.09);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(3, new StateFormulaSet(new[] { false, false })); // state 3(-) of LabeledTransitionMarkovChainExamples.Example4
markovChain.StartWithNewDistribution(3);
markovChain.AddTransition(3, 1.0);
markovChain.FinishDistribution();
markovChain.SetStateLabeling(4, new StateFormulaSet(new[] { false, true })); // state 3(lab2) of LabeledTransitionMarkovChainExamples.Example4
markovChain.StartWithNewDistribution(4);
markovChain.AddTransition(3, 1.0);
markovChain.FinishDistribution();
//markovChain.ProbabilityMatrix.OptimizeAndSeal();
return(markovChain);
}