Examples of cc.mallet.types.InstanceList.split()

cc.mallet.types.InstanceList.split()
Shuffles the elements of this list among several smaller lists. @param proportions A list of numbers (not necessarily summing to 1) which,when normalized, correspond to the proportion of elements in each returned sublist. This method (and all the split methods) do not transfer the Instance weights to the resulting InstanceLists. @param r The source of randomness to use in shuffling. @return one InstanceList for each element of proportions

    Pipe p = makeSpacePredictionPipe();
    Pipe p2 = new TestCRF2String();


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new Random(1), new double[] {
        .5, .5 });
    CRF crf = new CRF(p, p2);
    crf.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
    if (testValueAndGradient) {

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    CRF savedCRF;
    File f = new File("TestObject.obj");
    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[] { .5, .5 });
    CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
    crf.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
    crft.setUseSparseWeights(useSparseWeights);
    if (testValueAndGradient) {

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  public void testAddOrderNStates() {
    Pipe p = makeSpacePredictionPipe();


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new java.util.Random(678),
        new double[] { .5, .5 });


    // Compare 3 CRFs trained with addOrderNStates, and make sure
    // that having more features leads to a higher likelihood

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    Pipe p = makeSpacePredictionPipe();
    Pipe p2 = new TestCRF2String();


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[] { .5, .5 });
    CRF crf = new CRF(p, p2);
    crf.addFullyConnectedStatesForLabels();
    crf.setWeightsDimensionAsIn(lists[0], false);
    CRFTrainerByStochasticGradient crft = new CRFTrainerByStochasticGradient(
        crf, 0.0001);

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    Pipe p2 = new TestCRF2String();


    // first do normal training for getting weights
    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[] { .5, .5 });
    CRF crf = new CRF(p, p2);
    crf.addFullyConnectedStatesForLabels();
    crf.setWeightsDimensionAsIn(lists[0], false);
    CRFTrainerByStochasticGradient crft = new CRFTrainerByStochasticGradient(
        crf, 0.0001);

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  public void testTokenAccuracy() {
    Pipe p = makeSpacePredictionPipe();


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new Random(777), new double[] {
        .5, .5 });


    CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
    crf.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);

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    Pipe p = makeSpacePredictionPipe ();
    Pipe p2 = new TestMEMM2String();


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[]{.5, .5});
    MEMM memm = new MEMM(p, p2);
    memm.addFullyConnectedStatesForLabels();
    memm.setWeightsDimensionAsIn(lists[0]);
    
    MEMMTrainer memmt = new MEMMTrainer (memm);

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    MEMM savedCRF;
    File f = new File("TestObject.obj");
    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[]{.5, .5});
    MEMM crf = new MEMM(p.getDataAlphabet(), p.getTargetAlphabet());
    crf.addFullyConnectedStatesForLabels();
    if (useSparseWeights)
      crf.setWeightsDimensionAsIn(lists[0]);
    else

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  {
    Pipe p = makeSpacePredictionPipe ();


    InstanceList instances = new InstanceList (p);
    instances.addThruPipe (new ArrayIterator(data));
    InstanceList[] lists = instances.split (new java.util.Random (678), new double[]{.5, .5});


    // Compare 3 CRFs trained with addOrderNStates, and make sure
    // that having more features leads to a higher likelihood


    MEMM crf1 = new MEMM(p.getDataAlphabet(), p.getTargetAlphabet());

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                Pattern.compile("^\\s*$"), true));
        } else
        {
          Random r = new Random (randomSeedOption.value);
          InstanceList[] trainingLists =
            trainingData.split(
                r, new double[] {trainingFractionOption.value,
                  1-trainingFractionOption.value});
          trainingData = trainingLists[0];
          testData = trainingLists[1];
        }

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