List of generateRandomPosNeg() Examples

Examples of generateRandomPosNeg()

statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()
Generates random positive and negative paths. Data added is split into a number of parts, with a specific number of sequences per chunk. @param numberPerChunk number of sequences per chunk. @param chunks the number of chunks to generate.

Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

      for(int attempt=0;attempt<2;++attempt)
      {// try learning the same machine a few times
        LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        final int tracesToGenerate = makeEven(traceQuantity);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return (int)(traceLengthMultiplier*states*tracesAlphabet);
            }

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Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

        // 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);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return 2*states*alphabet;
            }

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Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()


    final int attempt=0;
    LearnerGraph pta = new LearnerGraph(config);
    RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
    final int tracesToGenerate = PairQualityLearner.makeEven(traceQuantity);
    generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                  
        @Override
        public int getLength() {
          return (int)(traceLengthMultiplier*states*alphabet);

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Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

        // 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);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return 2*states*alphabet;//(rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }

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Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

      
      // 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);
      generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                  
          @Override
          public int getLength() {
            return (int)(traceLengthMultiplier*states*tracesAlphabet);
          }

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Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()


         LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        final int tracesToGenerate = makeEven(traceQuantity);


        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return (int) traceLengthMultiplier*alphabet*states;
            }

View Full Code Here

Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

        // 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);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }

View Full Code Here

Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

        // 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(states*traceQuantity);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }

View Full Code Here

Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        // 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);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return 2*states*alphabet;//(rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }

View Full Code Here

Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator.generateRandomPosNeg()

        // 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(states*traceQuantity);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                    
            @Override
            public int getLength() {
              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }

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