Serialized Form (LBJava 1.2.22 API)

Package edu.illinois.cs.cogcomp.lbjava.classify
- Class edu.illinois.cs.cogcomp.lbjava.classify.Classifier extends Object implements Serializable
  - Serialization Methods
    - readObject
```
private void readObject(ObjectInputStream in)
                 throws IOException,
                        ClassNotFoundException
```
      Special handling during deserialization to ensure that Strings are intern()ed.
      
      Throws:
      
      IOException
      
      ClassNotFoundException
  - Serialized Fields
    - containingPackage
```
String containingPackage
```
      The name of the package containing this classifier.
    - name
```
String name
```
      The name of the classifier usually becomes the identifier of produced features.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteArrayFeature extends DiscretePrimitiveFeature implements Serializable
  - Serialized Fields
    - arrayIndex
```
int arrayIndex
```
      The feature's index in the returned array it is contained in.
    - arrayLength
```
int arrayLength
```
      The size of the returned array this feature is contained in.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteArrayStringFeature extends DiscretePrimitiveStringFeature implements Serializable
  - Serialized Fields
    - arrayIndex
```
int arrayIndex
```
      The feature's index in the returned array it is contained in.
    - arrayLength
```
int arrayLength
```
      The size of the returned array this feature is contained in.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteConjunctiveFeature extends DiscreteFeature implements Serializable
  - Serialized Fields
    - left
```
DiscreteFeature left
```
      One feature argument.
    - right
```
DiscreteFeature right
```
      The other feature argument.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteFeature extends Feature implements Serializable
  - Serialized Fields
    - valueIndex
```
short valueIndex
```
      Index into the set of allowable values corresponding to this value.
    - totalValues
```
short totalValues
```
      The total number of allowable values for this feature.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscretePrimitiveFeature extends DiscreteFeature implements Serializable
  - Serialized Fields
    - identifier
```
ByteString identifier
```
      The identifier string distinguishes this Feature from other Features.
    - value
```
ByteString value
```
      The discrete value is represented as a string.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscretePrimitiveStringFeature extends DiscreteFeature implements Serializable
  - Serialized Fields
    - identifier
```
String identifier
```
      The identifier string distinguishes this Feature from other Features.
    - value
```
String value
```
      The discrete value is represented as a string.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteReferrer extends DiscreteFeature implements Serializable
  - Serialized Fields
    - referent
```
DiscreteFeature referent
```
      The feature being referred to.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteReferringFeature extends DiscreteReferrer implements Serializable
  - Serialized Fields
    - identifier
```
ByteString identifier
```
      The identifier string distinguishes this Feature from other Features.
- Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteReferringStringFeature extends DiscreteReferrer implements Serializable
  - Serialized Fields
    - identifier
```
String identifier
```
      The identifier string distinguishes this Feature from other Features.
- Class edu.illinois.cs.cogcomp.lbjava.classify.Feature extends Object implements Serializable
  - Serialization Methods
    - readObject
```
private void readObject(ObjectInputStream in)
                 throws IOException,
                        ClassNotFoundException
```
      Special handling during deserialization to ensure that Strings are intern()ed.
      
      Throws:
      
      IOException
      
      ClassNotFoundException
  - Serialized Fields
    - containingPackage
```
String containingPackage
```
      The Java package containing the classifier that produced this feature.
    - generatingClassifier
```
String generatingClassifier
```
      The name of the LBJava classifier that produced this feature.
- Class edu.illinois.cs.cogcomp.lbjava.classify.FeatureVector extends Object implements Serializable
  - Serialized Fields
    - features
```
FVector features
```
      Stores non-label features.
    - labels
```
FVector labels
```
      Stores labels.
    - weight
```
double weight
```
      With this variable, the user can weight the entire vector.
    - realCache
```
FeatureVector realCache
```
      Caches the result of the FeatureVector.makeReal() method.
- Class edu.illinois.cs.cogcomp.lbjava.classify.FeatureVectorReturner extends Classifier implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.classify.LabelVectorReturner extends Classifier implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.classify.MultiValueComparer extends ValueComparer implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealArrayFeature extends RealPrimitiveFeature implements Serializable
  - Serialized Fields
    - arrayIndex
```
int arrayIndex
```
      The feature's index in the returned array it is contained in.
    - arrayLength
```
int arrayLength
```
      The size of the returned array this feature is contained in.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealArrayStringFeature extends RealPrimitiveStringFeature implements Serializable
  - Serialized Fields
    - arrayIndex
```
int arrayIndex
```
      The feature's index in the returned array it is contained in.
    - arrayLength
```
int arrayLength
```
      The size of the returned array this feature is contained in.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealConjunctiveFeature extends RealFeature implements Serializable
  - Serialized Fields
    - left
```
Feature left
```
      One feature argument.
    - right
```
Feature right
```
      The other feature argument.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealFeature extends Feature implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealPrimitiveFeature extends RealFeature implements Serializable
  - Serialized Fields
    - identifier
```
ByteString identifier
```
      The identifier string distinguishes this Feature from other Features.
    - value
```
double value
```
      The real value is represented as a double.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealPrimitiveStringFeature extends RealFeature implements Serializable
  - Serialized Fields
    - identifier
```
String identifier
```
      The identifier string distinguishes this Feature from other Features.
    - value
```
double value
```
      The real value is represented as a double.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealReferrer extends RealFeature implements Serializable
  - Serialized Fields
    - referent
```
RealFeature referent
```
      The feature being referred to.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealReferringFeature extends RealReferrer implements Serializable
  - Serialized Fields
    - identifier
```
ByteString identifier
```
      The identifier string distinguishes this Feature from other Features.
- Class edu.illinois.cs.cogcomp.lbjava.classify.RealReferringStringFeature extends RealReferrer implements Serializable
  - Serialized Fields
    - identifier
```
String identifier
```
      The identifier string distinguishes this Feature from other Features.
- Class edu.illinois.cs.cogcomp.lbjava.classify.ValueComparer extends Classifier implements Serializable
  - Serialized Fields
    - labeler
```
Classifier labeler
```
      The classifier whose value will be compared.
    - value
```
String value
```
      The value to compare with.
Package edu.illinois.cs.cogcomp.lbjava.infer
- Class edu.illinois.cs.cogcomp.lbjava.infer.InferenceNotOptimalException extends Exception implements Serializable
  - Serialized Fields
    - solver
```
edu.illinois.cs.cogcomp.infer.ilp.ILPSolver solver
```
      The ILP algorithm and problem representation that failed.
    - head
```
Object head
```
      The head object of the inference problem.
- Class edu.illinois.cs.cogcomp.lbjava.infer.ParameterizedConstraint extends Classifier implements Serializable
Package edu.illinois.cs.cogcomp.lbjava.learn
- Class edu.illinois.cs.cogcomp.lbjava.learn.AdaBoost extends Learner implements Serializable
  - Serialized Fields
    - weakLearner
```
Learner weakLearner
```
      The weak learning algorithm to be boosted.
    - rounds
```
int rounds
```
      The number of times the weak learner will be called.
    - weakLearners
```
Learner[] weakLearners
```
      Will be filled with trained copies of the weak learner.
    - alpha
```
double[] alpha
```
      Parameters associated with the trained copies of the weak learner.
    - allExamples
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector allExamples
```
      All the examples observed by this learner during training.
    - allowableValues
```
String[] allowableValues
```
      The label producing classifier's allowable values.
- Class edu.illinois.cs.cogcomp.lbjava.learn.AdaBoost.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - weakLearner
```
Learner weakLearner
```
      The weak learning algorithm to be boosted.
    - rounds
```
int rounds
```
      The number of times the weak learner will be called.
- Class edu.illinois.cs.cogcomp.lbjava.learn.AdaGrad extends Learner implements Serializable
  - Serialized Fields
    - learningRateA
```
double learningRateA
```
    - lossFunctionA
```
String lossFunctionA
```
    - diagonalVector
```
double[] diagonalVector
```
    - weightVector
```
double[] weightVector
```
    - gradientVector
```
double[] gradientVector
```
    - areVectorsInitialized
```
boolean areVectorsInitialized
```
- Class edu.illinois.cs.cogcomp.lbjava.learn.AdaGrad.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - learningRateP
```
double learningRateP
```
    - lossFunctionP
```
String lossFunctionP
```
- Class edu.illinois.cs.cogcomp.lbjava.learn.BiasedRandomWeightVector extends RandomWeightVector implements Serializable
  - Serialized Fields
    - bias
```
double bias
```
      The current bias weight.
- Class edu.illinois.cs.cogcomp.lbjava.learn.BiasedWeightVector extends SparseWeightVector implements Serializable
  - Serialized Fields
    - initialBias
```
double initialBias
```
      The first value for BiasedWeightVector.bias.
    - bias
```
double bias
```
      The current bias weight.
- Class edu.illinois.cs.cogcomp.lbjava.learn.BinaryMIRA extends SparsePerceptron implements Serializable
  - Serialized Fields
    - beta
```
double beta
```
      The user supplied learning algorithm parameter; default BinaryMIRA.defaultBeta. The learning rate changes as a function of beta.
- Class edu.illinois.cs.cogcomp.lbjava.learn.BinaryMIRA.Parameters extends SparsePerceptron.Parameters implements Serializable
  - Serialized Fields
    - beta
```
double beta
```
      The user supplied learning algorithm parameter; default BinaryMIRA.defaultBeta. The learning rate changes as a function of beta.
- Class edu.illinois.cs.cogcomp.lbjava.learn.ChildLexicon extends Lexicon implements Serializable
  - Serialized Fields
    - parents
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.IVector parents
```
      The elements of this vector (which correspond to the features in Lexicon.lexiconInv) serve a dual purpose; first, to indicate by absolute value the number of other features currently stored in this object that have the corresponding feature as a child, and second, to indicate by sign if the corresponding feature has been marked for removal.
    - parentLexicon
```
Lexicon parentLexicon
```
      A reference to the lexicon that uses this lexicon as its child lexicon.
- Class edu.illinois.cs.cogcomp.lbjava.learn.Learner extends Classifier implements Serializable
  - Serialized Fields
    - labeler
```
Classifier labeler
```
      Stores the classifier used to produce labels.
    - extractor
```
Classifier extractor
```
      Stores the classifiers used to produce features.
    - lexicon
```
Lexicon lexicon
```
      Stores the feature Lexicon.
    - labelLexicon
```
Lexicon labelLexicon
```
      Stores the label Lexicon.
    - encoding
```
String encoding
```
      The encoding used by this learner's feature lexicon.
    - predictions
```
FVector predictions
```
      Stores the set of predictions that this learner will choose from when classifying a new example.
    - lcFilePath
```
URL lcFilePath
```
      Caches the location of this learner's offline binary representation.
    - lexFilePath
```
URL lexFilePath
```
      Caches the location of this learner's offline lexicon.
    - readLexiconOnDemand
```
boolean readLexiconOnDemand
```
      Informs this learner that it can and should read its feature lexicon on demand.
    - lossFlag
```
boolean lossFlag
```
      To use a different score function based on the loss, set this flag.
    - candidates
```
int candidates
```
      The number of candidate examples when a global object is passed here.
- Class edu.illinois.cs.cogcomp.lbjava.learn.Learner.Parameters extends Object implements Serializable
  - Serialized Fields
    - rounds
```
int rounds
```
      The number of rounds of training; but wait; this parameter doesn't actually affect the behavior of any learners as the number of training rounds is specified via other mechanisms. Nonetheless, it comes in handy to have it here as a communication vehicle when tuning parameters.
- Class edu.illinois.cs.cogcomp.lbjava.learn.Lexicon extends Object implements Serializable
  - Serialized Fields
    - lexicon
```
Map<K,V> lexicon
```
      The map of features to integer keys.
    - lexiconInv
```
FVector lexiconInv
```
      The inverted map of integer keys to their features.
    - encoding
```
String encoding
```
      The encoding to use for new features added to this lexicon.
    - encodingSet
```
boolean encodingSet
```
      This flag remembers whether Lexicon.encoding has been assigned a value yet or not. Using this flag, we enforce the constraint that once an encoding has been set, it can never be changed. This way, a user will only be capable of using the same lexicon object in two different learners if they have the same encoding. See the implementation of Learner.setLexicon(Lexicon).
    - featureCounts
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.IVector featureCounts
```
      Counts the number of occurrences of each feature.
    - perClassFeatureCounts
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.IVector2D perClassFeatureCounts
```
      Counts the number of occurrences of each feature on a class-by-class basis.
    - pruneCutoff
```
int pruneCutoff
```
      Features at this index in Lexicon.lexiconInv or higher have been pruned. -1 indicates that no pruning has been done.
    - lexiconChildren
```
ChildLexicon lexiconChildren
```
      Stores features that might appear repeatedly as children of other features, but which are not themselves given indexes in the lexicon.
- Class edu.illinois.cs.cogcomp.lbjava.learn.LinearThresholdUnit extends Learner implements Serializable
  - Serialized Fields
    - learningRate
```
double learningRate
```
      The rate at which weights are updated; default LinearThresholdUnit.defaultLearningRate.
    - weightVector
```
SparseWeightVector weightVector
```
      The LTU's weight vector; default is an empty vector.
    - initialWeight
```
double initialWeight
```
      The weight associated with a feature when first added to the vector; default LinearThresholdUnit.defaultInitialWeight.
    - threshold
```
double threshold
```
      The score is compared against this value to make predictions; default LinearThresholdUnit.defaultThreshold.
    - bias
```
double bias
```
      The bias is stored here rather than as an element of the weight vector.
    - positiveThickness
```
double positiveThickness
```
      The thickness of the hyperplane on the positive side; default LinearThresholdUnit.defaultThickness.
    - negativeThickness
```
double negativeThickness
```
      The thickness of the hyperplane on the negative side; default equal to LinearThresholdUnit.positiveThickness.
    - allowableValues
```
String[] allowableValues
```
      The label producing classifier's allowable values.
- Class edu.illinois.cs.cogcomp.lbjava.learn.LinearThresholdUnit.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - learningRate
```
double learningRate
```
      The rate at which weights are updated; default LinearThresholdUnit.defaultLearningRate.
    - weightVector
```
SparseWeightVector weightVector
```
      The LTU's weight vector; default is an empty vector.
    - initialWeight
```
double initialWeight
```
      The weight associated with a feature when first added to the vector; default LinearThresholdUnit.defaultInitialWeight.
    - threshold
```
double threshold
```
      The score is compared against this value to make predictions; default LinearThresholdUnit.defaultThreshold.
    - thickness
```
double thickness
```
      This thickness will be added to both LinearThresholdUnit.Parameters.positiveThickness and LinearThresholdUnit.Parameters.negativeThickness; default LinearThresholdUnit.defaultThickness.
    - positiveThickness
```
double positiveThickness
```
      The thickness of the hyperplane on the positive side; default 0.
    - negativeThickness
```
double negativeThickness
```
      The thickness of the hyperplane on the negative side; default 0.
- Class edu.illinois.cs.cogcomp.lbjava.learn.MultiLabelLearner extends SparseNetworkLearner implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.MultiLabelLearner.Parameters extends SparseNetworkLearner.Parameters implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.MuxLearner extends Learner implements Serializable
  - Serialized Fields
    - baseLearner
```
Learner baseLearner
```
      Instances of this learning algorithm will be multiplexed; default null.
    - network
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector network
```
      A map from feature values to learners.
    - defaultPrediction
```
String defaultPrediction
```
      This string is returned during testing when the multiplexed Learner doesn't exist; default MuxLearner.defaultDefaultPrediction.
    - defaultFeature
```
Feature defaultFeature
```
      A feature whose value is MuxLearner.defaultPrediction.
- Class edu.illinois.cs.cogcomp.lbjava.learn.MuxLearner.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - baseLearner
```
Learner baseLearner
```
      Instances of this learning algorithm will be multiplexed; default null.
    - defaultPrediction
```
String defaultPrediction
```
      This string is returned during testing when the multiplexed Learner doesn't exist; default MuxLearner.defaultDefaultPrediction.
- Class edu.illinois.cs.cogcomp.lbjava.learn.NaiveBayes extends Learner implements Serializable
  - Serialized Fields
    - smoothing
```
double smoothing
```
      The exponential of this number is used as the conditional probability of a feature that was never observed during training; default NaiveBayes.defaultSmoothing.
    - network
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector network
```
      One NaiveBayes.NaiveBayesVector for each observed prediction value.
- Class edu.illinois.cs.cogcomp.lbjava.learn.NaiveBayes.Count extends Object implements Serializable
  - Serialization Methods
    - readObject
```
private void readObject(ObjectInputStream in)
                 throws IOException,
                        ClassNotFoundException
```
      Special handling during deserialization to ensure that NaiveBayes.Count.updateLog is set to true.
      
      Throws:
      
      IOException
      
      ClassNotFoundException
  - Serialized Fields
    - count
```
double count
```
      The accumulated value.
- Class edu.illinois.cs.cogcomp.lbjava.learn.NaiveBayes.NaiveBayesVector extends SparseWeightVector implements Serializable
  - Serialized Fields
    - counts
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector counts
```
      The counts in the vector indexed by their Lexicon key.
    - priorCount
```
NaiveBayes.Count priorCount
```
      The prior count is the number of times either scaledAdd method has been called.
- Class edu.illinois.cs.cogcomp.lbjava.learn.NaiveBayes.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - smoothing
```
double smoothing
```
      The exponential of this number is used as the conditional probability of a feature that was never observed during training; default NaiveBayes.defaultSmoothing.
- Class edu.illinois.cs.cogcomp.lbjava.learn.PassiveAggressive extends LinearThresholdUnit implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.PassiveAggressive.Parameters extends LinearThresholdUnit.Parameters implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.RandomWeightVector extends SparseWeightVector implements Serializable
  - Serialized Fields
    - stddev
```
double stddev
```
      The random numbers that are generated by this class are Gaussian with mean 0 and standard deviation defined by this variable.
    - instanceNumber
```
int instanceNumber
```
      Remembers the instance number of this instance.
    - random
```
Random random
```
      The random number generator for this instance.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseAveragedPerceptron extends SparsePerceptron implements Serializable
  - Serialized Fields
    - awv
```
SparseAveragedPerceptron.AveragedWeightVector awv
```
      Holds the same reference as LinearThresholdUnit.weightVector casted to SparseAveragedPerceptron.AveragedWeightVector.
    - averagedBias
```
double averagedBias
```
      Keeps the extra information necessary to compute the averaged bias.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseAveragedPerceptron.AveragedWeightVector extends SparseWeightVector implements Serializable
  - Serialized Fields
    - averagedWeights
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.DVector averagedWeights
```
      Together with SparseWeightVector.weights, this vector provides enough information to reconstruct the average of all weight vectors arrived at during the course of learning.
    - examples
```
int examples
```
      Counts the total number of training examples this vector has seen.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseAveragedPerceptron.Parameters extends SparsePerceptron.Parameters implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseConfidenceWeighted extends LinearThresholdUnit implements Serializable
  - Serialized Fields
    - confidence
```
double confidence
```
      The confidence parameter as described above; default SparseConfidenceWeighted.defaultConfidence.
    - initialVariance
```
double initialVariance
```
      The strictly positive initial variance of the parameters; default SparseConfidenceWeighted.defaultInitialVariance.
    - variances
```
SparseWeightVector variances
```
      The inverses of the current variances of the parameters.
    - variancesBias
```
double variancesBias
```
      The bias element of the SparseConfidenceWeighted.variances vector.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseConfidenceWeighted.Parameters extends LinearThresholdUnit.Parameters implements Serializable
  - Serialized Fields
    - confidence
```
double confidence
```
      The confidence parameter as described above; default SparseConfidenceWeighted.defaultConfidence.
    - initialVariance
```
double initialVariance
```
      The strictly positive initial variance of the parameters; default SparseConfidenceWeighted.defaultInitialVariance.
    - variances
```
SparseWeightVector variances
```
      The current variances of the parameters; default LinearThresholdUnit.defaultWeightVector.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseMIRA extends Learner implements Serializable
  - Serialized Fields
    - network
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector network
```
      A map from labels to the weight vector corresponding to that label.
    - conjunctiveLabels
```
boolean conjunctiveLabels
```
      Whether or not this learner's labeler produces conjunctive features.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseMIRA.Parameters extends Learner.Parameters implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseNetworkLearner extends Learner implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - baseLTU
```
LinearThresholdUnit baseLTU
```
      The underlying algorithm used to learn each class separately as a binary classifier; default SparseNetworkLearner.defaultBaseLTU.
    - network
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector network
```
      A collection of the linear threshold units used to learn each label, indexed by the label.
    - numExamples
```
int numExamples
```
      The total number of examples in the training data, or 0 if unknown.
    - numFeatures
```
int numFeatures
```
      The total number of distinct features in the training data, or 0 if unknown.
    - conjunctiveLabels
```
boolean conjunctiveLabels
```
      Whether or not this learner's labeler produces conjunctive features.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseNetworkLearner.Parameters extends Learner.Parameters implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - baseLTU
```
LinearThresholdUnit baseLTU
```
      The underlying algorithm used to learn each class separately as a binary classifier; default SparseNetworkLearner.defaultBaseLTU.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparsePerceptron extends LinearThresholdUnit implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparsePerceptron.Parameters extends LinearThresholdUnit.Parameters implements Serializable
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseWeightVector extends Object implements Serializable
  - Serialized Fields
    - weights
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.DVector weights
```
      The weights in the vector indexed by their Lexicon key.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseWinnow extends LinearThresholdUnit implements Serializable
  - Serialized Fields
    - beta
```
double beta
```
      The rate at which weights are demoted; default equal to 1 / LinearThresholdUnit.learningRate.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SparseWinnow.Parameters extends LinearThresholdUnit.Parameters implements Serializable
  - Serialized Fields
    - beta
```
double beta
```
      The rate at which weights are demoted; default equal to 1 / LinearThresholdUnit.learningRate.
- Class edu.illinois.cs.cogcomp.lbjava.learn.StochasticGradientDescent extends Learner implements Serializable
  - Serialized Fields
    - weightVector
```
SparseWeightVector weightVector
```
      The hypothesis vector; default StochasticGradientDescent.defaultWeightVector.
    - bias
```
double bias
```
      The bias is stored here rather than as an element of the weight vector.
    - learningRate
```
double learningRate
```
      The rate at which weights are updated; default StochasticGradientDescent.defaultLearningRate.
- Class edu.illinois.cs.cogcomp.lbjava.learn.StochasticGradientDescent.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - weightVector
```
SparseWeightVector weightVector
```
      The hypothesis vector; default StochasticGradientDescent.defaultWeightVector.
    - learningRate
```
double learningRate
```
      The rate at which weights are updated; default StochasticGradientDescent.defaultLearningRate.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SupportVectorMachine extends Learner implements Serializable
  - Serialized Fields
    - warningPrinted
```
boolean warningPrinted
```
      Keeps track of whether the doneLearning() warning message has been printed.
    - solverType
```
String solverType
```
      The type of solver; default SupportVectorMachine.defaultSolverType unless there are more than 2 labels observed in the training data, in which case "MCSVM_CS" becomes the default. Note that if you are doing multi-class classification, you can still override the "MCSVM_CS" default to use another solver type.
      Possible values:
      
      "L2_LR" = L2-regularized logistic regression;
      "L2LOSS_SVM_DUAL" = L2-loss support vector machines (dual);
      "L2LOSS_SVM" = L2-loss support vector machines (primal);
      "L1LOSS_SVM_DUAL" = L1-loss support vector machines (dual);
      "MCSVM_CS" = multi-class support vector machines by Crammer and Singer
    - C
```
double C
```
      The cost parameter C; default SupportVectorMachine.defaultC
    - epsilon
```
double epsilon
```
      The tolerance of termination criterion; default SupportVectorMachine.defaultEpsilon.
    - bias
```
double bias
```
      If SupportVectorMachine.bias >= 0, an instance vector x becomes [x; bias]; otherwise, if SupportVectorMachine.bias < 0, no bias term is added.
    - biasFeatures
```
int biasFeatures
```
      The number of bias features; there are either 0 or 1 of them.
    - displayLL
```
boolean displayLL
```
      Controls if liblinear-related messages are output
    - numClasses
```
int numClasses
```
      The number of unique class labels seen during training.
    - numFeatures
```
int numFeatures
```
      The number of unique features seen during training.
    - conjunctiveLabels
```
boolean conjunctiveLabels
```
      Whether or not this learner's labeler produces conjunctive features.
    - weights
```
double[] weights
```
      An array of weights representing the weight vector learned after training with liblinear.
    - allLabels
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.IVector allLabels
```
      The array of example labels
    - allExamples
```
edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector allExamples
```
      The array of example vectors.
    - allowableValues
```
String[] allowableValues
```
      The label producing classifier's allowable values.
    - newLabelLexicon
```
Lexicon newLabelLexicon
```
      Created during SupportVectorMachine.doneLearning() in case the training examples observed by SupportVectorMachine.learn(int[],double[],int[],double[]) are only a subset of a larger, pre-extracted set. If this is not the case, it will simply be a duplicate reference to Learner.labelLexicon.
- Class edu.illinois.cs.cogcomp.lbjava.learn.SupportVectorMachine.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - solverType
```
String solverType
```
      The type of solver; default SupportVectorMachine.defaultSolverType.
      Possible values:
      
      "L2_LR" = L2-regularized logistic regression;
      "L2LOSS_SVM_DUAL" = L2-loss support vector machines (dual);
      "L2LOSS_SVM" = L2-loss support vector machines (primal);
      "L1LOSS_SVM_DUAL" = L1-loss support vector machines (dual);
      "MCSVM_CS" = multi-class support vector machines by Crammer and Singer
    - C
```
double C
```
      The cost parameter C; default SupportVectorMachine.defaultC
    - epsilon
```
double epsilon
```
      The tolerance of termination criterion; default SupportVectorMachine.defaultEpsilon.
    - bias
```
double bias
```
      If SupportVectorMachine.bias >= 0, an instance vector x becomes [x; bias]; otherwise, if SupportVectorMachine.bias < 0, no bias term is added.
    - displayLL
```
boolean displayLL
```
      Determines if liblinear-related output should be displayed; default false
- Class edu.illinois.cs.cogcomp.lbjava.learn.WekaWrapper extends Learner implements Serializable
  - Serialized Fields
    - attributeString
```
String attributeString
```
      A string encoding of the attributes used by this learner.
    - baseClassifier
```
weka.classifiers.Classifier baseClassifier
```
      Stores the instance of the WEKA classifier which we are training; default is weka.classifiers.bayes.NaiveBayes.
    - freshClassifier
```
weka.classifiers.Classifier freshClassifier
```
      Stores a fresh instance of the WEKA classifier for the purposes of forgetting.
    - attributeInfo
```
weka.core.FastVector attributeInfo
```
      Information about the features this learner takes as input is parsed from an attribute string and stored here. This information is crucial in the task of interfacing with the WEKA algorithms, and must be present before the Learner.learn(Object) method can be called.
      Here is an example of a valid attribute string: nom_SimpleLabel_1,2,3,:str_First:nom_Second_a,b,c,d,r,t,:num_Third:
      nom stands for "Nominal", i.e. the feature SimpleLabel was declared as discrete, and had the value list {"1","2","3"}.
      str stands for "Stirng", i.e. the feature First was declared to be discrete, but was not provided with a value list. When using the WekaWrapper, it is best to provide value lists whenever possible, because very few WEKA classifiers can handle string attributes.
      num stands for "Numerical", i.e. the feature Third was declared to be real.
    - instances
```
weka.core.Instances instances
```
      The main collection of Instance objects.
    - trained
```
boolean trained
```
      Indicates whether the WekaWrapper.doneLearning() method has been called and the WekaWrapper.forget() method has not yet been called.
    - allowableValues
```
String[] allowableValues
```
      The label producing classifier's allowable values.
- Class edu.illinois.cs.cogcomp.lbjava.learn.WekaWrapper.Parameters extends Learner.Parameters implements Serializable
  - Serialized Fields
    - baseClassifier
```
weka.classifiers.Classifier baseClassifier
```
      Stores the instance of the WEKA classifier which we are training; default WekaWrapper.defaultBaseClassifier.
    - attributeString
```
String attributeString
```
      A string encoding of the return types of each of the feature extractors in use; default WekaWrapper.defaultAttributeString.
Package edu.illinois.cs.cogcomp.lbjava.parse
- Class edu.illinois.cs.cogcomp.lbjava.parse.LinkedChild extends Object implements Serializable
  - Serialized Fields
    - parent
```
LinkedVector parent
```
      A link to this child's parent.
    - previous
```
LinkedChild previous
```
      A link to the previous child in the parent vector.
    - next
```
LinkedChild next
```
      A link to the next child in the parent vector.
    - start
```
int start
```
      The offset into the raw data input file at which this child starts.
    - end
```
int end
```
      The offset into the raw data input file at which this child ends.
    - label
```
String label
```
      Space for a label for this linked child.
- Class edu.illinois.cs.cogcomp.lbjava.parse.LinkedVector extends LinkedChild implements Serializable
  - Serialized Fields
    - children
```
Vector<E> children
```
      The linked vector is simply represented as a vector of children.
Package edu.illinois.cs.cogcomp.lbjava.util
- Class edu.illinois.cs.cogcomp.lbjava.util.FVector extends Object implements Serializable
  - Serialized Fields
    - vector
```
Feature[] vector
```
      The elements of the vector.
    - size
```
int size
```
      The number of elements in the vector.

Serialized Form

Package edu.illinois.cs.cogcomp.lbjava.classify

Class edu.illinois.cs.cogcomp.lbjava.classify.Classifier extends Object implements Serializable

Serialization Methods

readObject

Serialized Fields

containingPackage

name

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteArrayFeature extends DiscretePrimitiveFeature implements Serializable

Serialized Fields

arrayIndex

arrayLength

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteArrayStringFeature extends DiscretePrimitiveStringFeature implements Serializable

Serialized Fields

arrayIndex

arrayLength

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteConjunctiveFeature extends DiscreteFeature implements Serializable

Serialized Fields

left

right

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteFeature extends Feature implements Serializable

Serialized Fields

valueIndex

totalValues

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscretePrimitiveFeature extends DiscreteFeature implements Serializable

Serialized Fields

identifier

value

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscretePrimitiveStringFeature extends DiscreteFeature implements Serializable

Serialized Fields

identifier

value

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteReferrer extends DiscreteFeature implements Serializable

Serialized Fields

referent

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteReferringFeature extends DiscreteReferrer implements Serializable

Serialized Fields

identifier

Class edu.illinois.cs.cogcomp.lbjava.classify.DiscreteReferringStringFeature extends DiscreteReferrer implements Serializable

Serialized Fields

identifier

Class edu.illinois.cs.cogcomp.lbjava.classify.Feature extends Object implements Serializable

Serialization Methods

readObject

Serialized Fields

containingPackage

generatingClassifier

Class edu.illinois.cs.cogcomp.lbjava.classify.FeatureVector extends Object implements Serializable

Serialized Fields

features

labels

weight

realCache

Class edu.illinois.cs.cogcomp.lbjava.classify.FeatureVectorReturner extends Classifier implements Serializable

Class edu.illinois.cs.cogcomp.lbjava.classify.LabelVectorReturner extends Classifier implements Serializable

Class edu.illinois.cs.cogcomp.lbjava.classify.MultiValueComparer extends ValueComparer implements Serializable

Class edu.illinois.cs.cogcomp.lbjava.classify.RealArrayFeature extends RealPrimitiveFeature implements Serializable

Serialized Fields

arrayIndex

arrayLength

Class edu.illinois.cs.cogcomp.lbjava.classify.RealArrayStringFeature extends RealPrimitiveStringFeature implements Serializable

Serialized Fields

arrayIndex

arrayLength

Class edu.illinois.cs.cogcomp.lbjava.classify.RealConjunctiveFeature extends RealFeature implements Serializable

Serialized Fields

left

right

Class edu.illinois.cs.cogcomp.lbjava.classify.RealFeature extends Feature implements Serializable

Class edu.illinois.cs.cogcomp.lbjava.classify.RealPrimitiveFeature extends RealFeature implements Serializable

Serialized Fields

identifier

value

Class edu.illinois.cs.cogcomp.lbjava.classify.RealPrimitiveStringFeature extends RealFeature implements Serializable

Serialized Fields

identifier

value

Class edu.illinois.cs.cogcomp.lbjava.classify.RealReferrer extends RealFeature implements Serializable

Serialized Fields

referent