Interface and Description |
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cc.mallet.optimize.Optimizer.ByBatches |
cc.mallet.types.Vector |
Class and Description |
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cc.mallet.pipe.BranchingPipe |
cc.mallet.types.ChainedInstanceIterator |
cc.mallet.topics.LDA
Use ParallelTopicModel instead.
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cc.mallet.topics.LDAHyper
Use ParallelTopicModel instead, which uses substantially faster data structures even for non-parallel operation.
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cc.mallet.types.Matrix2 |
cc.mallet.grmm.util.PipedIterator |
cc.mallet.pipe.iterator.PipeExtendedIterator |
cc.mallet.pipe.iterator.PipeInputIterator |
cc.mallet.fst.ShallowTransducerTrainer
Use NoopTransducerTrainer instead
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Method and Description |
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cc.mallet.types.InstanceList.add(Object, Object, Object, Object)
Use trainingset.add (new Instance(data,target,name,source)) instead.
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cc.mallet.types.InstanceList.add(Object, Object, Object, Object, double)
Use trainingset.addThruPipe (new Instance(data,target,name,source)) instead.
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cc.mallet.util.Randoms.asJavaRandom() |
cc.mallet.types.MatrixOps.dot(double[], double[])
Use dotProduct()
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cc.mallet.fst.CRF.evaluate(TransducerEvaluator, InstanceList) |
cc.mallet.types.Instance.getProperties() |
cc.mallet.fst.Transducer.TransitionIterator.nextState() |
cc.mallet.types.InstanceList.noisify(double) |
cc.mallet.fst.Transducer.TransitionIterator.numberNext() |
cc.mallet.fst.CRF.predict(InstanceList) |
cc.mallet.fst.HMM.reset() |
cc.mallet.grmm.types.Assignment.restriction(Assignment, VarSet)
marginalize
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cc.mallet.types.InstanceList.sampleWithInstanceWeights(Random) |
cc.mallet.types.Instance.setPropertyList(PropertyList) |
Constructor and Description |
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cc.mallet.util.CommandOption(Class, String, String, Class, boolean, String) |
cc.mallet.types.InstanceList() |
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