Package cc.mallet.pipe.iterator

Examples of cc.mallet.pipe.iterator.ArrayIterator

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  public void testSpaceSerializable () throws IOException, ClassNotFoundException
  {
    Pipe p = makeSpacePredictionPipe ();
    InstanceList training = new InstanceList (p);
    training.addThruPipe (new ArrayIterator (data));

    MEMM memm = new MEMM (p, null);
    memm.addFullyConnectedStatesForLabels ();
    memm.addStartState();
    memm.setWeightsDimensionAsIn(training);
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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]);
   
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    Pipe p = makeSpacePredictionPipe ();

    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]);
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  public void disabledtestAddOrderNStates ()
  {
    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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       new TokenSequence2FeatureVectorSequence(),
       new PrintInputAndTarget(),
    });
    InstanceList one = new InstanceList (p);
    String[] data = new String[] { "ABCDE", };
    one.addThruPipe (new ArrayIterator (data));
    MEMM crf = new MEMM (p, null);
    crf.addFullyConnectedStatesForLabels();
    crf.setWeightsDimensionAsIn (one);
    MEMMTrainer memmt = new MEMMTrainer (crf);
    MEMMTrainer.MEMMOptimizableByLabelLikelihood mcrf = memmt.getOptimizableMEMM(one);
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  • cc.mallet.extract.test.TestPerDocumentF1Evaluator
  • cc.mallet.fst.tests.TestCRF
  • cc.mallet.fst.tests.TestMEMM
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