{

    LearnerGraph graph = buildLearnerGraph("A-a->A-a1->B-a2->C-a3->D-c1->E-c2->F-c3->G-c4->H-c5->A / B-b1->A / C-b2->A / D-b3->A","test_generatePathThatEndsAtInitialState4",config,converter);

    final RandomPathGenerator generator = new RandomPathGenerator(graph,new Random(0),8,null);

    generator.setWalksShouldLeadToInitialState();

     

      @Override

      public int getLength() {

        return 2;

      }

  {

    LearnerGraph graph = buildLearnerGraph("F-a->F","test_generationOfRejectsNotEnteringInit1",config,converter);

    final RandomPathGenerator generator = new RandomPathGenerator(graph,new Random(0),8,null);

    generator.setWalksShouldLeadToInitialState();

     

      @Override

      public int getLength() {

        return 2;

      }

    final RandomPathGenerator generator = new RandomPathGenerator(graph,new Random(0),8,null);

    generator.setWalksShouldLeadToInitialState();

    checkForCorrectException(new whatToRun() { @Override public void run() {

   

       

        @Override

        public int getLength() {

          return 2;

        }

  {

    LearnerGraph graph = buildLearnerGraph("F-a->F-b->G-a->F","test_generationOfRejectsNotEnteringInit2",config,converter);

    final RandomPathGenerator generator = new RandomPathGenerator(graph,new Random(0),8,null);

    generator.setWalksShouldLeadToInitialState();

     

      @Override

      public int getLength() {

        return 2;

      }

  {

    LearnerGraph graph = buildLearnerGraph("F-a->F-b->G-a->H-a->F","test_generationOfRejectsNotEnteringInit3",config,converter);

    final RandomPathGenerator generator = new RandomPathGenerator(graph,new Random(0),8,null);

    generator.setWalksShouldLeadToInitialState();

     

      @Override

      public int getLength() {

        return 2;

      }

      // try learning the same machine using a random generator selector passed as a parameter.

      LearnerGraph pta = new LearnerGraph(config);

      RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(trainingSample),5,null);

      final int tracesToGenerate = makeEven(traceQuantity);

                 

          @Override

          public int getLength() {

            return (int)(traceLengthMultiplier*states*tracesAlphabet);

          }

 

         LearnerGraph pta = new LearnerGraph(config);

        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);

        final int tracesToGenerate = makeEven(traceQuantity);

                   

            @Override

            public int getLength() {

              return (int) traceLengthMultiplier*alphabet*states;

            }

 

        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,

        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.

        final int tracesToGenerate = makeEven(traceQuantity);

        final Random rnd = new Random(seed*31+attempt);

                   

            @Override

            public int getLength() {

              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;

            }

 

        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,

        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.

        final int tracesToGenerate = makeEven(states*traceQuantity);

        final Random rnd = new Random(seed*31+attempt);

                   

            @Override

            public int getLength() {

              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;

            }

 

        // The total number of elements in test sequences (alphabet*states*traceQuantity) will be distributed around (random(pathLength)+1). The total size of PTA is a product of these two.

        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,

        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.

        final int tracesToGenerate = makeEven(traceQuantity);

                   

            @Override

            public int getLength() {

              return 2*states*alphabet;//(rnd.nextInt(pathLength)+1)*lengthMultiplier;

            }