List of computeInverseGraph() Examples

Examples of computeInverseGraph()

statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()
Obtains the graph that can be used in calls of {@link #checkFanoutInconsistency(AbstractLearnerGraph,boolean,LearnerGraph,CmpVertex,int)} and many others.Returns an inverse when predictForward is true and graph otherwise. @param graph what to compute an inverse of @param predictForward whether to invert @return either an inverse or the original graph

Examples of statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()

    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<Label,MarkovOutcome> outgoing_labels_probabilities=m.predictTransitionsFromState(markovMatrix,m.computeInverseGraph(graph2, true),true,graph2.findVertex("B"),graph2.getCache().getAlphabet(),Arrays.asList(new Label[]{}),m.getChunkLen(),null);
    Assert.assertEquals(2,outgoing_labels_probabilities.size());
    Assert.assertEquals(MarkovOutcome.negative,outgoing_labels_probabilities.get(lblU));
    Assert.assertEquals(MarkovOutcome.positive,outgoing_labels_probabilities.get(lblB));
  }

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Examples of statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()

    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<Label,MarkovOutcome> outgoing_labels_probabilities=m.predictTransitionsFromState(markovMatrix,m.computeInverseGraph(graph2, true),true,graph2.findVertex("B"),graph2.getCache().getAlphabet(),Arrays.asList(new Label[]{}),m.getChunkLen(),null);
    Assert.assertEquals(2,outgoing_labels_probabilities.size());
    Assert.assertEquals(MarkovOutcome.negative,outgoing_labels_probabilities.get(lblU));
    Assert.assertEquals(MarkovOutcome.positive,outgoing_labels_probabilities.get(lblB));
  }

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Examples of statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()

    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);


    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-d->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<Label,MarkovOutcome> outgoing_labels_probabilities=m.predictTransitionsFromState(markovMatrix,m.computeInverseGraph(graph2, true),true,graph2.findVertex("B"),graph2.getCache().getAlphabet(),Arrays.asList(new Label[]{lblA}),m.getChunkLen(),null);
    Assert.assertEquals(2,outgoing_labels_probabilities.size());
    Assert.assertEquals(MarkovOutcome.negative,outgoing_labels_probabilities.get(lblU));
    Assert.assertEquals(MarkovOutcome.positive,outgoing_labels_probabilities.get(lblB));
  }

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Examples of statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()

    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-d->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Helper.checkForCorrectException(new whatToRun() {
      @Override
      public void run() throws NumberFormatException
      {
        m.predictTransitionsFromState(markovMatrix,m.computeInverseGraph(graph2, true),true,graph2.findVertex("B"),graph2.getCache().getAlphabet(),Arrays.asList(new Label[]{lblA,lblB}),m.getChunkLen(),null);
      }
    }, IllegalArgumentException.class, "supplied path");
  }


  @Test

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Examples of statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()

    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);


    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-d->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<Label,MarkovOutcome> outgoing_labels_probabilities=m.predictTransitionsFromState(markovMatrix,m.computeInverseGraph(graph2, true),true,graph2.findVertex("B"),graph2.getCache().getAlphabet(),Arrays.asList(new Label[]{lblA}),m.getChunkLen(),null);
    Assert.assertEquals(2,outgoing_labels_probabilities.size());
    Assert.assertEquals(MarkovOutcome.negative,outgoing_labels_probabilities.get(lblU));
    Assert.assertEquals(MarkovOutcome.positive,outgoing_labels_probabilities.get(lblB));
  }

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Examples of statechum.analysis.learning.MarkovUniversalLearner.computeInverseGraph()

    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-d->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Helper.checkForCorrectException(new whatToRun() {
      @Override
      public void run() throws NumberFormatException
      {
        m.predictTransitionsFromState(markovMatrix,m.computeInverseGraph(graph2, true),true,graph2.findVertex("B"),graph2.getCache().getAlphabet(),Arrays.asList(new Label[]{lblA,lblB}),m.getChunkLen(),null);
      }
    }, IllegalArgumentException.class, "supplied path");
  }


  @Test

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