Package statechum.analysis.learning.observers

Examples of statechum.analysis.learning.observers.Test_LearnerComparator


    //
    // It is worth noting that since the above was written GD has been modified to generate
    // an identical graph by relabelling states. If this one is utilised, the outcomes
    // of learning should be identical.
   
    new Test_LearnerComparator(learner2,simulator,!evalData.config.isIgnoreVertexAttributesInLogReplay()).learnMachine(initial.plus, initial.minus);
    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
      VertexID.comparisonKind = ComparisonKind.COMPARISON_NORM;// reset this one if needed.
  }
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      e.printStackTrace();
    }
    //WMethod.checkM(initial.graph, learner2.getLearner().init(initial.plus, initial.minus));
    
     */
    new Test_LearnerComparator(learner2.getLearner(),simulator).learnMachine(initial.plus, initial.minus);
  }
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    //
    // It is worth noting that since the above was written GD has been modified to generate
    // an identical graph by relabelling states. If this one is utilised, the outcomes
    // of learning should be identical.
   
    new Test_LearnerComparator(learner2,simulator,!evalData.config.isIgnoreVertexAttributesInLogReplay()).learnMachine(initial.plus, initial.minus);
    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
      VertexID.comparisonKind = ComparisonKind.COMPARISON_NORM;// reset this one if needed.
    inputStream.close();
  }
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    // where A and C respectively are winners.
    // GD comparison of the two graphs determines that we need to remove the part
    // of the first graph starting with a19 transition and just loop a19 around B.
    // This way, states B and D are preserved and the outcome of reconstruction is  B-a19->B-a5->D
    // which is bisimular to the expected graph but state names are different.
    new Test_LearnerComparator(learner2,simulator,!evalData.config.isIgnoreVertexAttributesInLogReplay()).learnMachine(initial.plus, initial.minus);
    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
      VertexID.comparisonKind = ComparisonKind.COMPARISON_NORM;// reset this one if needed.
  }
View Full Code Here

    //
    // It is worth noting that since the above was written GD has been modified to generate
    // an identical graph by relabelling states. If this one is utilised, the outcomes
    // of learning should be identical.
   
    new Test_LearnerComparator(learner2,simulator,!evalData.config.isIgnoreVertexAttributesInLogReplay()).learnMachine(initial.plus, initial.minus);
    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
      VertexID.comparisonKind = ComparisonKind.COMPARISON_NORM;// reset this one if needed.
  }
View Full Code Here

    //
    // It is worth noting that since the above was written GD has been modified to generate
    // an identical graph by relabelling states. If this one is utilised, the outcomes
    // of learning should be identical.
   
    new Test_LearnerComparator(learner2,simulator,!evalData.config.isIgnoreVertexAttributesInLogReplay()).learnMachine(initial.plus, initial.minus);
    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
      VertexID.comparisonKind = ComparisonKind.COMPARISON_NORM;// reset this one if needed.
    inputStream.close();
  }
View Full Code Here

    //
    // It is worth noting that since the above was written GD has been modified to generate
    // an identical graph by relabelling states. If this one is utilised, the outcomes
    // of learning should be identical.
   
    new Test_LearnerComparator(learner2,simulator,!evalData.config.isIgnoreVertexAttributesInLogReplay()).learnMachine(initial.plus, initial.minus);
    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
      VertexID.comparisonKind = ComparisonKind.COMPARISON_NORM;// reset this one if needed.
    inputStream.close();
  }
View Full Code Here

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