A B C D E F G H I J K L M N O P Q R S T U V W X Y Z _ 

A

active_set - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The set of active examples with 0 < alpha[i] < get_C(i)
active_size - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The number of active examples with 0 < alpha[i] < get_C(i)
add(ClusterExample) - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
 
add(Example) - Method in class it.uniroma2.sag.kelp.data.example.SequenceExample
 
add(SequenceEmission) - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
add(Vector) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Add a vector to this vector
add(float, Vector) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Add a vector multiplied by coeff to this vector
add(float, float, Vector) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Add a vector multiplied by vectorCoeff to this vector multiplied by
add(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
add(float, Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
add(float, float, Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
add(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
add(float, Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
add(float, float, Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
add(int, int, float) - Method in interface it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DeltaMatrix
Insert a value in the matrix
add(int, int, float) - Method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DynamicDeltaMatrix
Deprecated.
Insert a value in the matrix
add(int, int, float) - Method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.StaticDeltaMatrix
Insert a value in the matrix
addAdditionalInformation(String, Object) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
Adds an additional information identified by infoName
addBinaryPrediction(Label, float) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassificationOutput
Sets the score associated to a given class
addBinaryPrediction(Label, float) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
Sets the score associated to a given class
addCount(Example, Prediction) - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
 
addCount(Example, Prediction) - Method in class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
This method should be implemented in the subclasses to update counters useful to compute the performance measure
addCount(Example, Prediction) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
addCount(Example, Prediction) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassSequenceClassificationEvaluator
 
addCount(SequenceExample, SequencePrediction) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassSequenceClassificationEvaluator
This method should be implemented in the subclasses to update counters useful to compute the performance measure
addCount(Example, Prediction) - Method in class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
 
addEdge(Integer, Integer) - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
Adds an edge between the nodes whose IDs are firstNodeId and secondNodeId
addExample(Example) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
 
addExample(Example) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Add an example to the dataset
addExample(float, Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
addExample(float, Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
addExample(float, Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
Adds an example to the model with a given weight.
addExamples(Dataset) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Add all the examples contained in datasetToBeAdded
addInfoToNodes(List<TreeNode>, List<Double>, String) - Static method in class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
A function for adding additional info to tree nodes.
additionalInformation - Variable in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
 
addKernel(float, Kernel) - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
Adds a kernel with a corresponding weight to the linear combination of kernels
addLabel(Label) - Method in class it.uniroma2.sag.kelp.data.example.Example
Adds a label to the example
addNode(Integer, StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
Adds a node whose id is nodeId and whose content is nodeContent
addOutgoingEdge(GraphNode) - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
Adds an outgoing edge from this node to n
addRepresentation(String, Representation) - Method in class it.uniroma2.sag.kelp.data.example.Example
Adds a representation to this example
addRepresentation(String, Representation) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
addSupportVector(SupportVector) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
Adds a support vector NOTE: it does not check whether a support vector with the same instance of the given supportVector is already in the model
addVotedPrediction(Label, float) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
addWordVector(String, Vector) - Method in class it.uniroma2.sag.kelp.wordspace.Wordspace
 
addWordVector(String, Vector) - Method in interface it.uniroma2.sag.kelp.wordspace.WordspaceI
Stores the vector associated to a word.
algoSuffix - Variable in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorization
 
algoSuffix - Variable in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCD
 
algoSuffix - Variable in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinear
 
algoSuffix - Variable in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPA
 
algoSuffix - Variable in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasos
 
algoSuffix - Variable in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCW
 
alpha - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The weight \(\alpha\) of the Support Vectors
alpha - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
alpha_status - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The status of each example
applyPruningToLeftElementOfExamplePair() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
applyPruningToRightElementOfExamplePair() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
avoidOrphans - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 

B

baseKernel - Variable in class it.uniroma2.sag.kelp.kernel.KernelComposition
 
be_shrunk(int, float, float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
 
be_shrunk(int, float, float, float, float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
BEGIN_PAIR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
BEGIN_REPRESENTATION - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
BestPairwiseAlignmentKernel - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = softmax(BK(x_1, y_1) \cdot BK(x_2, y_2) , BK(x_1, y_2) \cdot BK(x_2, y_1))\)
BestPairwiseAlignmentKernel(Kernel, boolean) - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.BestPairwiseAlignmentKernel
Defines a Kernel operating on pairs that applies the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = softmax(BK(x_1, y_1) \cdot BK(x_2, y_2) , BK(x_1, y_2) \cdot BK(x_2, y_1))\)
BestPairwiseAlignmentKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.BestPairwiseAlignmentKernel
 
bestPath() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePrediction
 
bias - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
If bias >= 0, we assume that one additional feature is added to the end of each data instance
bias - Variable in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
 
BinaryClassificationEvaluator - Class in it.uniroma2.sag.kelp.utils.evaluation
This is an instance of an Evaluator.
BinaryClassificationEvaluator(Label) - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
 
BinaryClassifier - Class in it.uniroma2.sag.kelp.predictionfunction.classifier
It is a generic binary classification function that can be learned with a machine learning algorithm It learns a binary concept.
BinaryClassifier() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
BinaryCSvmClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm
It implements the C-SVM learning algorithm discussed in [CC Chang & CJ Lin, 2011].
BinaryCSvmClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
BinaryCSvmClassification(Kernel, Label, float, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
BinaryCSvmClassification(Kernel, Label, float, float, boolean) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
BinaryKernelMachineClassifier - Class in it.uniroma2.sag.kelp.predictionfunction.classifier
It is a kernel-base binary classifier
BinaryKernelMachineClassifier() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
 
BinaryKernelMachineClassifier(BinaryKernelMachineModel, Label) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
 
BinaryKernelMachineModel - Class in it.uniroma2.sag.kelp.predictionfunction.model
It is the model for a binary kernel machine consisting of an implicit hyperplane in the Reproducing Kernel Hilbert Space.
BinaryKernelMachineModel(Kernel) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
BinaryKernelMachineModel() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
BinaryLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is a learning algorithm that has to learn a concept associated to a single label.
BinaryLinearClassifier - Class in it.uniroma2.sag.kelp.predictionfunction.classifier
It linear binary classifier operating directly on an explicit vector space
BinaryLinearClassifier() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
 
BinaryLinearModel - Class in it.uniroma2.sag.kelp.predictionfunction.model
It is the model for a binary linear method consisting of an explicit hyperplane
BinaryLinearModel() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
BinaryMarginClassifierOutput - Class in it.uniroma2.sag.kelp.predictionfunction.classifier
It is the output provided by binary margin classifiers like the ones trained with SVM or perceptron based learning algorithms.
BinaryMarginClassifierOutput(Label, float) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
 
BinaryModel - Class in it.uniroma2.sag.kelp.predictionfunction.model
It is the model for a binary method consisting on a hyperplane
BinaryModel() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
 
BinaryNuSvmClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm
It implements the \(\nu\)-SVM learning algorithm discussed in [CC Chang & CJ Lin, 2011].
BinaryNuSvmClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
BinaryNuSvmClassification(Kernel, Label, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
budget - Variable in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
BudgetedLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
It is binary kernel-based online learning method that binds the number of support vector to a fix number (i.e.
BudgetedLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
BudgetedPassiveAggressiveClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
Online Passive-Aggressive on a budget reference: Zhuang Wang and Slobodan Vucetic Online Passive-Aggressive Algorithms on a Budget
BudgetedPassiveAggressiveClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
BudgetedPassiveAggressiveClassification(int, Kernel, float, float, BudgetedPassiveAggressiveClassification.DeletingPolicy, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
BudgetedPassiveAggressiveClassification(int, Kernel, float, boolean, BudgetedPassiveAggressiveClassification.DeletingPolicy, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
BudgetedPassiveAggressiveClassification.DeletingPolicy - Enum in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
It is the updating policy applied when the budget is full.

C

C - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
c - Variable in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
calculate_r() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
calculate_rho() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
 
calculate_rho() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
calculate_rho() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
calculateDistance(Example, Cluster) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
Estimate the distance of an example from the centroid
calculateVector(Example) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
It derives an array of doubles containing the linearized representation
checkConsistency(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
checkRowValidity() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
 
checkThresholdCondition(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
ClassificationDemo - Class in it.uniroma2.sag.kelp.examples.main
 
ClassificationDemo() - Constructor for class it.uniroma2.sag.kelp.examples.main.ClassificationDemo
 
ClassificationLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm.classification
It is a generic Machine Learning algorithm for Classification tasks
ClassificationOutput - Interface in it.uniroma2.sag.kelp.predictionfunction.classifier
It is a generic output provided by a classifier
classifier - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
The classifier to be returned
classifier - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
classifier - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
classifier - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
Classifier - Interface in it.uniroma2.sag.kelp.predictionfunction.classifier
It is a generic classifier, i.e.
Classify - Class in it.uniroma2.sag.kelp.main
 
Classify() - Constructor for class it.uniroma2.sag.kelp.main.Classify
 
classStats - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
clear() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
This function clear the set of object inside the cluster
clear() - Method in interface it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DeltaMatrix
Clear the delta matrix
clear() - Method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DynamicDeltaMatrix
Deprecated.
Clear the delta matrix
clear() - Method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.StaticDeltaMatrix
Clear the delta matrix
clear() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster
 
clear() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Clear all the counters for a new processing.
clear() - Method in class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
This method should reset the state of the evaluator
clear() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Clear all the counters for a new processing.
clear() - Method in class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
 
clone() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
close() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
Closes the reading buffer
close() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetWriter
 
close() - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetReader
Closes the reading buffer
close() - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetWriter
Close the output file
Cluster - Class in it.uniroma2.sag.kelp.data.clustering
It is the instance of a Cluster, intended as a set of objects, instantiated as Examples, grouped together according to a measure of similarity.
Cluster() - Constructor for class it.uniroma2.sag.kelp.data.clustering.Cluster
The cluster is initialized without any label
Cluster(String) - Constructor for class it.uniroma2.sag.kelp.data.clustering.Cluster
The cluster is initialized and labeled
cluster(Dataset) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.clustering.ClusteringAlgorithm
It starts the clustering process exploiting the provided dataset
cluster(Dataset, ExampleSelector) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.clustering.ClusteringAlgorithm
It starts the clustering process exploiting the provided dataset
cluster(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
cluster(Dataset, ExampleSelector) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
cluster(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
cluster(Dataset, ExampleSelector) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
ClusterExample - Class in it.uniroma2.sag.kelp.data.clustering
 
ClusterExample(Example, float) - Constructor for class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
ClusterExample() - Constructor for class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
ClusterExampleTypeResolver - Class in it.uniroma2.sag.kelp.data.clustering
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of ClusterExamples
ClusterExampleTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
ClusteringAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm.clustering
It is a generic Clustering algorithm
ClusteringEvaluator - Class in it.uniroma2.sag.kelp.utils.evaluation
Implements Evaluation methods for clustering algorithms.
ClusteringEvaluator() - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.ClusteringEvaluator
 
ClusterList - Class in it.uniroma2.sag.kelp.data.clustering
 
ClusterList() - Constructor for class it.uniroma2.sag.kelp.data.clustering.ClusterList
 
ClusterTypeResolver - Class in it.uniroma2.sag.kelp.data.clustering
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of Clusters
ClusterTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
cn - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The regularization parameter of negative examples
COMMENT_REPRESENTATION_NAME - Static variable in class it.uniroma2.sag.kelp.data.dataset.LibsvmDatasetReader
 
COMMENT_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.dataset.LibsvmDatasetReader
 
compareTo(ClusterExample) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
compareTo(SequencePath) - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
compareTo(SequenceEmission) - Method in class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
compareTo(StringLabel) - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
 
compareTo(NodeDistance) - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
compareTo(char[], char[]) - Static method in class it.uniroma2.sag.kelp.kernel.tree.TreeKernelUtils
 
CompositionalNodeSimilarity - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional
This class implements a specific node similarity that computes the similarity between compositional nodes, by applying the "sum" operator.
CompositionalNodeSimilarity() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
Default constructor, used only for JSON serialization/deserialization purposes.
CompositionalNodeSimilarity(WordspaceI) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarity(WordspaceI, boolean, boolean) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityDilation - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation
This class implements a specific node similarity that computes the similarity between compositional nodes, by applying the "dilation" operator.
CompositionalNodeSimilarityDilation() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
Default constructor, used only for JSON serialization/deserialization purposes.
CompositionalNodeSimilarityDilation(WordspaceI) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityDilation(WordspaceI, boolean, boolean) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityProduct - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product
This class implements a specific node similarity that computes the similarity between compositional nodes, by applying the "prod" operator, i.e.
CompositionalNodeSimilarityProduct() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
Default constructor, used only for JSON serialization/deserialization purposes.
CompositionalNodeSimilarityProduct(WordspaceI) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityProduct(WordspaceI, boolean, boolean) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilaritySum - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum
This class implements a specific node similarity that computes the similarity between compositional nodes, by applying the "sum" operator.
CompositionalNodeSimilaritySum() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
Default constructor, used only for JSON serialization/deserialization purposes.
CompositionalNodeSimilaritySum(WordspaceI) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilaritySum(WordspaceI, boolean, boolean) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
Constructor that enable to specify the wordspace path.
CompositionalStructureElement - Class in it.uniroma2.sag.kelp.data.representation.structure
CompositionalStructureElement represents a compositional node.
CompositionalStructureElement() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
CompositionalStructureElement(String, LexicalStructureElement, LexicalStructureElement) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
compute() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
 
compute() - Method in class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
This method is intented to force the computation of the performance measure.
compute() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
compute() - Method in class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
 
computed - Variable in class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
 
computeTotalNumberOfNodes(SimpleDataset, String) - Static method in class it.uniroma2.sag.kelp.examples.demo.pruning.TreePruningDemo
 
computeWeight(Example, float, float, float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
concatenateVectors(Example, List<String>, List<Float>) - Static method in class it.uniroma2.sag.kelp.data.manipulator.VectorConcatenationManipulator
Returns a SparseVector corresponding to the concatenation of the vectors in example identified with representationsToBeMerged Each vector is scaled with respect to the corresponding scaling factor in weights
concatenateVectors(Example, List<String>, List<Float>, String) - Static method in class it.uniroma2.sag.kelp.data.manipulator.VectorConcatenationManipulator
Add a new representation identified with combinationName corresponding to the concatenation of the vectors in example identified with representationsToBeMerged Each vector is scaled with respect to the corresponding scaling factor in weights
containsAdditionalInfo(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
Verifies whether this element contains the additional information identified by infoName
ContentBasedTreeNodeFilter - Class in it.uniroma2.sag.kelp.data.representation.tree.node.filter
This implementation of TreeNodeFilter selects only treeNode containing a StructureElement interesting w.r.t.
ContentBasedTreeNodeFilter(StructureElementFilter) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.filter.ContentBasedTreeNodeFilter
Constructor for ContentBasedTreeNodeFilter
ContentBasedTreeNodeSimilarity - Class in it.uniroma2.sag.kelp.data.representation.tree.node.similarity
Evaluates the similarity between two TreeNodes comparing their StructureElements using a StructureElementSimilarityI
ContentBasedTreeNodeSimilarity(StructureElementSimilarityI) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.similarity.ContentBasedTreeNodeSimilarity
Constructor
copyVector() - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Returns a copy of this vector.
copyVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
copyVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
correct - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
cp - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The regularization parameter of positive examples
cp - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
createInputStream(String) - Static method in class it.uniroma2.sag.kelp.utils.FileUtils
Creates an InputStream for reading a file.
createMaxChildrenPrunerObject(int, String) - Static method in class it.uniroma2.sag.kelp.examples.demo.pruning.TreePruningDemo
 
createMaxNumberOfLeavesPrunerObject(int, String) - Static method in class it.uniroma2.sag.kelp.examples.demo.pruning.TreePruningDemo
 
createOutputStream(String) - Static method in class it.uniroma2.sag.kelp.utils.FileUtils
Creates an OutputStream for reading a file.
CsvDatasetReader - Class in it.uniroma2.sag.kelp.data.dataset
A utility class to read dataset in the csv format.
CsvDatasetReader(String, String, boolean, CsvDatasetReader.LabelPosition) - Constructor for class it.uniroma2.sag.kelp.data.dataset.CsvDatasetReader
Constructor for reading dataset in csv format.
CsvDatasetReader(String, String, boolean, CsvDatasetReader.LabelPosition, StringLabel) - Constructor for class it.uniroma2.sag.kelp.data.dataset.CsvDatasetReader
Constructor for reading dataset in csv format for regression tasks (the regression value is assumed to be in the first column).
CsvDatasetReader.LabelPosition - Enum in it.uniroma2.sag.kelp.data.dataset
 

D

Dataset - Interface in it.uniroma2.sag.kelp.data.dataset
Dataset is a set of Examples
DatasetReader - Class in it.uniroma2.sag.kelp.data.dataset
A utility class to read dataset in the platform format.
DatasetReader(String) - Constructor for class it.uniroma2.sag.kelp.data.dataset.DatasetReader
 
DatasetWriter - Class in it.uniroma2.sag.kelp.data.dataset
 
DatasetWriter(String) - Constructor for class it.uniroma2.sag.kelp.data.dataset.DatasetWriter
 
DCDLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.dcd
Implements Dual Coordinate Descent (DCD) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification.
DCDLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
DCDLearningAlgorithm(Label, double, double, DCDLoss, boolean, int, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
DCDLearningAlgorithm(double, double, DCDLoss, boolean, int, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
DCDLearningAlgorithm(double, double, int, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
This constructor uses the L2 loss and ignores the bias of the hyper-plane
DCDLoss - Enum in it.uniroma2.sag.kelp.learningalgorithm.classification.dcd
 
DEFAULT_BEAM_SIZE - Static variable in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
DEFAULT_ENRICHMENT_NAME - Static variable in class it.uniroma2.sag.kelp.data.manipulator.LexicalStructureElementManipulator
 
DEFAULT_MAX_EMISSION_CAND - Static variable in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
DEFAULT_PREFIX - Static variable in class it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger
 
defaultWeightValue - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
DELIMITER - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
DeltaMatrix - Interface in it.uniroma2.sag.kelp.kernel.tree.deltamatrix
Sparse Delta Matrix
DenseVector - Class in it.uniroma2.sag.kelp.data.representation.vector
Dense Feature Vector.
DenseVector() - Constructor for class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Empty constructor necessary for making RepresentationFactory support this implementation.
DenseVector(double[]) - Constructor for class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Initializing constructor.
DenseVector(DenseMatrix64F) - Constructor for class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Initializing constructor.
describe() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
 
describe() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
Describe what the elements of the tree-pruner will do once the manipulator is invoked.
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodePruner
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodeToBePrunedCheckerAbstractClass
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeIfLeaf
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLeafNumber
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeNumberOfChildren
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
describe() - Method in interface it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelector
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllChildren
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllDescendants
 
describe() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllLeaves
 
diff(DenseVector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
DirectedGraphRepresentation - Class in it.uniroma2.sag.kelp.data.representation.graph
This class implements a directed Graph representation.
DirectedGraphRepresentation() - Constructor for class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
DirectKernel<T extends Representation> - Class in it.uniroma2.sag.kelp.kernel
It is a kernel that operates exploiting directly on a specific representation.
DirectKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.DirectKernel
Initializes a kernel operating directly on a specific representation identified by representationIdentifier
DirectKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.DirectKernel
 
disableCache() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Disables the kernel cache
dist - Variable in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
distance - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
do_shrinking() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
 
do_shrinking() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
do_shrinking() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Apply the shrinking step
doShrinking - Static variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
A boolean value to apply shrinking
duplicate() - Method in class it.uniroma2.sag.kelp.data.example.Example
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
This method will duplicate the current Learning algorithm
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
This method will duplicate the current Learning algorithm
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
This method will duplicate the current Learning algorithm
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
duplicate() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
duplicate() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
duplicate() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
 
duplicate() - Method in class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
Returns a new instance of this Evaluator
duplicate() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
duplicate() - Method in class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
 
DynamicDeltaMatrix - Class in it.uniroma2.sag.kelp.kernel.tree.deltamatrix
Deprecated.
DynamicDeltaMatrix() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DynamicDeltaMatrix
Deprecated.
 
DynamicIndexKernelCache - Class in it.uniroma2.sag.kelp.kernel.cache
Deprecated.
DynamicIndexKernelCache(int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
Initializes a DynamicIndexKernelCache that can contain all the possible pairwise kernel computations between up to examplesToStore examples
DynamicIndexKernelCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
 
DynamicIndexSquaredNormCache - Class in it.uniroma2.sag.kelp.kernel.cache
 
DynamicIndexSquaredNormCache(int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
Initializes a DynamicIndexSquaredNormCache that can contain all up to examplesToStore norms
DynamicIndexSquaredNormCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
 

E

EDGE_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
edgesToString() - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
 
END_PAIR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
END_REPRESENTATION - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
enrichmentName - Variable in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
enrichTreeRepresentation(TreeRepresentation, WordspaceI, String) - Static method in class it.uniroma2.sag.kelp.data.manipulator.LexicalStructureElementManipulator
This function enriches each LexicalStructureElement in the tree with a vector from the Word Space
EnsembleLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is an ensemble method that operates combining various learning algorithms
eps - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Tolerance of termination criterion
epsilon - Variable in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
 
EpsilonSvmRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm
It implements the \(\epsilon\)-SVR learning algorithm discussed in [CC Chang & CJ Lin, 2011].
EpsilonSvmRegression(Kernel, Label, float, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
EpsilonSvmRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
EpsilonSVRegressionExample - Class in it.uniroma2.sag.kelp.examples.demo.regression
This class contains an example of the usage of the Regression Example.
EpsilonSVRegressionExample() - Constructor for class it.uniroma2.sag.kelp.examples.demo.regression.EpsilonSVRegressionExample
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.example.Example
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
equals(Object) - Method in interface it.uniroma2.sag.kelp.data.label.Label
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
equals(Object) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
equals(Object) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
equalsIgnoreDistance(Object) - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
equalsIgnoreLabels(SimpleExample) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
Asserts whether this example and the input one have identical representations, ignoring their labels
ESPILON - Static variable in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
establishNodeRelations(TreeRepresentation, TreeRepresentation) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger
Establishes relations between treeA and treeA Let \(N_A=\left \{ n_1^A, n_2^A...
euclideanDistance(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
euclideanDistance(Vector) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Returns the euclidean distance between this vector and vector
euclideanDistance(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
evaluateKernel(Example, Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
evaluateKernelNotNormalize(TreeRepresentation, TreeRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
Evaluate the Partial Tree Kernel
Evaluator - Class in it.uniroma2.sag.kelp.utils.evaluation
 
Evaluator() - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
 
ExactMatchingStructureElementSimilarity - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity
Implements a similarity between StructureElements applying a hard match on their textual representation (i.e., the method sim will return 1 if two structure elements matches, 0 otherwise)
ExactMatchingStructureElementSimilarity() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.ExactMatchingStructureElementSimilarity
 
ExactMatchingStructureElementSimilarity(boolean) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.ExactMatchingStructureElementSimilarity
Constructor for ExactMatchingStructureElementSimilarity
example - Variable in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
Example - Class in it.uniroma2.sag.kelp.data.example
It is the instance of an example in the Machine Learning context.
Example() - Constructor for class it.uniroma2.sag.kelp.data.example.Example
Initializes an empty example (0 classificationLabels and 0 regression values)
ExampleFactory - Class in it.uniroma2.sag.kelp.data.example
It is a factory that provides methods for instantiating an example described in a textual format The expected inputs for examples with N labels and M representations are String of the form: Label_1 Label_2 ...
ExampleFactory() - Constructor for class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
ExamplePair - Class in it.uniroma2.sag.kelp.data.example
It is the instance of an example pair, i.e.
ExamplePair() - Constructor for class it.uniroma2.sag.kelp.data.example.ExamplePair
 
ExamplePair(Example, Example) - Constructor for class it.uniroma2.sag.kelp.data.example.ExamplePair
 
examples - Variable in class it.uniroma2.sag.kelp.data.clustering.Cluster
The set of objects within the cluster
examples - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The input examples
ExampleSelector - Interface in it.uniroma2.sag.kelp.data.dataset.selector
This interface allows defining an Example selectors: given a Dataset a select should select a subset of Examples to be used, e.g.
ExampleTypeResolver - Class in it.uniroma2.sag.kelp.data.example
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of Examples
ExampleTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
ExperimentUtils - Class in it.uniroma2.sag.kelp.utils
Class containing some useful methods for evaluating the performance of a learning algorithm
ExperimentUtils() - Constructor for class it.uniroma2.sag.kelp.utils.ExperimentUtils
 
extractExamplesOfClasses(Dataset, List<Label>) - Static method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
This method extracts examples of given labels from dataset
extractRepresentation(Example) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
Given an Example, extracts the object related to the given representation (determined at construction time of class SelectRepresentationFromExample).

F

f1 - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
fairness - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
featureDict - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
 
featureInverseDict - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
 
FileUtils - Class in it.uniroma2.sag.kelp.utils
This class contains util methods related to file reading and writing
FileUtils() - Constructor for class it.uniroma2.sag.kelp.utils.FileUtils
 
findCommonNodesByProduction(TreeRepresentation, TreeRepresentation, DeltaMatrix, boolean) - Static method in class it.uniroma2.sag.kelp.kernel.tree.TreeKernelUtils
Determine the nodes sharing the same production in the given trees.
FirstExamplesSelector - Class in it.uniroma2.sag.kelp.data.dataset.selector
This class allows to select the first m examples from a Dataset.
FirstExamplesSelector(int) - Constructor for class it.uniroma2.sag.kelp.data.dataset.selector.FirstExamplesSelector
 
FixIndexKernelCache - Class in it.uniroma2.sag.kelp.kernel.cache
Deprecated.
FixIndexKernelCache(int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
Initializes a FixIndexKernelCache that can contain all the possible pairwise kernel computations between up to examplesToStore examples
FixIndexKernelCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
 
FixIndexSquaredNormCache - Class in it.uniroma2.sag.kelp.kernel.cache
 
FixIndexSquaredNormCache(int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
Initializes a cache with a defined dimension for squared norms
FixIndexSquaredNormCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
 
FixSizeKernelCache - Class in it.uniroma2.sag.kelp.kernel.cache
Cache for kernel computations.
FixSizeKernelCache(int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
Initializes a DynamicIndexKernelCache that can contain all the possible pairwise kernel computations between up to examplesToStore examples
FixSizeKernelCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
 
flush() - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
 
flush() - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
 
flush() - Method in interface it.uniroma2.sag.kelp.kernel.cache.SquaredNormCache
Empties the cache
flushCache() - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
 
flushCache() - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
 
flushCache() - Method in class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
 
flushCache() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
Empties the cache
flushCache() - Method in class it.uniroma2.sag.kelp.kernel.cache.SimpleDynamicKernelCache
 
flushCache() - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
fn - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
foldLearn(float, int, SimpleDataset) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorization
 
fp - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
fun(double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
fun(double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvrFunction
 

G

G - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Gradient
G_bar - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Gradient bar
generateExampleWithHistory(SequenceExample, SequencePath, int) - Method in interface it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGenerator
At labeling time, this method allows to enrich a specific Example with the labels assigned by the classifier to the previous Examples
generateExampleWithHistory(SequenceExample, SequencePath, int) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
generateExampleWithHistory(SequenceExample, SequencePath, int) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
generateSequenceExampleEnrichedWithHistory(SequenceExample) - Method in interface it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGenerator
This method allows to enrich each Example from an input SequenceExample with the labels assigned by the classifier to the previous Examples
generateSequenceExampleEnrichedWithHistory(SequenceExample) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
generateSequenceExampleEnrichedWithHistory(SequenceExample) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
get(int, int) - Method in interface it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DeltaMatrix
Get a value from the matrix
get(int, int) - Method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DynamicDeltaMatrix
Deprecated.
Get a value from the matrix
get(int, int) - Method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.StaticDeltaMatrix
Get a value from the matrix
get_nr_variable() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
get_QD() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
For each example i, it return the K_ii score
get_QD() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
get_Qij(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
get_Qij(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
getA() - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
getAccuracy() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the accuracy
getAccuracy() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Returns the accuracy
getActiveFeatures() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
getActiveFeatures() - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Returns a map containing all the non-zero features
getActiveFeatures() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getAdditionalInformation(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
Returns the additional information identified by infoName
getAdditionalInfos() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
Returns the list of additionalInfos two structure elements must both have or not have in order to have a non zero similarity
getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
 
getAllClasses() - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.ClassificationOutput
Returns all the classes involved in the classification process (both predicted and not)
getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassificationOutput
 
getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
 
getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
getAllNodes() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Get recursively all Tree Nodes below the target node
getAllNodes() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getAllowDifferentPOS() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
Returns whether the similarity between words having different Part-of-Speech is allowed or if it must be set to 0
getAllProperties() - Method in interface it.uniroma2.sag.kelp.predictionfunction.regressionfunction.RegressionOutput
Returns all the properties on which the regressor has to provide predictions
getAllProperties() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionOutput
 
getAlpha() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
Returns the learning rate, i.e.
getAlphas() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
getAncestor(int) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Returns the generation generation ancestor of this node (for instance 1-generation ancestor is the father, 2-generation ancestor is the grandfather, etc)
getAssignedLabel(int) - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
getAssignedSequnceLabels() - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
getB() - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm
 
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
This method will return the base algorithm.
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
This method will return the base algorithm.
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
This method will return the base algorithm.
getBaseAlgorithm() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.MetaLearningAlgorithm
Returns the base algorithm this meta algorithm is based on
getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
getBaseKernel() - Method in class it.uniroma2.sag.kelp.kernel.KernelComposition
Returns the kernel this kernel is enriching
getBaseLearningAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getBasePredictionFunction() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
getBaseSimilarity() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
Returns the base similarity applied when two structure elements have the same additional infos
getBeamSize() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getBeamSize() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
getBias() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
 
getBinaryClassifiers() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
getBinaryClassifiers() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
getBinaryClassifiers() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
getBranchingFactor() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Returns the branching factor of the tree rooted by this node
getBranchingFactor() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getBudget() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
Returns the budget, i.e.
getC() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
getC() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
getC() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
getCacheHits() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
 
getCacheMisses() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
 
getCentroid() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster
 
getCharArray() - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
 
getChildren() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Get the direct children of the target node
getClassificationLabels() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns all the classification labels in the dataset.
getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDataset
 
getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
 
getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.example.SequenceExample
 
getClassName() - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
 
getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
This method takes in input two LexicalStructureElement representing a head and a modifier.
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
This method takes in input two LexicalStructureElement representing a head and a modifier.
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
This method takes in input two LexicalStructureElement representing a head and a modifier.
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
This method takes in input two LexicalStructureElement representing a head and a modifier.
getContent() - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
 
getContent() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
Returns the content of this SequenceElement
getContent() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
getCorrespondingVector(StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Returns the vector associated to element.
getCounter() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getCSvmAlpha(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
Get the initial weight for the future Support Vectors
getDcdLoss() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getDegree() - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
getDeletingPolicy() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
getDeltaMatrix() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
getDeltaMatrix() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
getDeltaMatrix() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
Get the DeltaMatrix used to store the evaluated delta functions of this tree kernel
getDeltaMatrix() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
Get the DeltaMatrix used to store the evaluated delta functions of this tree kernel
getDenseVectorByEnsembleAndJuxtaposition(Example) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
Given an example, this method produces a DenseVector that is the concatenation of the vectors obtained by each projection functions used in the Ensemble.
getDenseVectorByEnsembleAndJuxtaposition(Example, List<Float>) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
Given an example, this method produces a DenseVector that is the concatenation of the vectors obtained by each projection functions used in the Ensemble.
getDependencyRelation() - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
getDescendants(int) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Returns all the generation generations descendants of this node (for instance 1-generation descendants are the children, the 2-generations descendants are the grandchildren, etc)
getDictionaryDanilo() - Method in class it.uniroma2.sag.kelp.wordspace.Wordspace
 
getDictionaryDanilo() - Method in interface it.uniroma2.sag.kelp.wordspace.WordspaceI
Returns the complete set of words in the vocabulary (words having an associated vector in this wordspace)
getDist() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
getDistance() - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
getElements() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
Returns the elements of this sequence
getEmbeddingSize() - Method in interface it.uniroma2.sag.kelp.linearization.LinearizationFunction
 
getEmbeddingSize() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getEmbeddingSize() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 
getEmission() - Method in class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
getEnrichmentName() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
getEnrichmentName() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Returns the identifier of the vectors associated to a StructureElement during the manipulation operation performed by a Manipulator (i.e.
getEpochs() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
getEps() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
getEpsilon() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
Returns epsilon, i.e.
getEta() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getExample() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
getExample(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Return the example stored in the exampleIndex position
getExample(int) - Method in class it.uniroma2.sag.kelp.data.example.SequenceExample
 
getExample() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
 
getExample() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansExample
 
getExamples() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
This function returns the set of objects inside the cluster
getExamples() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns an array containing all the stored examples
getExamples() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getExamples() - Method in class it.uniroma2.sag.kelp.data.example.SequenceExample
 
getExamplesToStore() - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
Returns the maximum number of examples whose pairwise kernel computations can be simultaneously stored
getExamplesToStore() - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
Returns the maximum number of norms that can be simultaneously stored
getExamplesToStore() - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
Returns the maximum number of examples whose pairwise kernel computations can be simultaneously stored
getExamplesToStore() - Method in class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
Returns the maximum number of examples whose pairwise kernel computations can be simultaneously stored
getExampleString() - Method in exception it.uniroma2.sag.kelp.data.example.ParsingExampleException
 
getF1() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the f1 of the positive class
getF1For(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the f1 for the specified label
getFather() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Get the father of the target node
getFeatureValue(int) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Returns the feature value of the featureIndex-th element
getFeatureValue(Object) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
getFeatureValue(Object) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Returns the value of the feature identified with featureIdentifier
getFeatureValue(String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
Returns the value associated to a feature
getFeatureValue(Object) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getFeatureValues() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Returns the feature values in the EJML format
getFinalNode() - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
getFirst() - Method in exception it.uniroma2.sag.kelp.data.representation.vector.exception.VectorOperationException
 
getFn() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the false negatives
getFnFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the false negatives for the specified label
getFp() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the false positives
getFpFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the false positives for the specified label
getFromIntToWord() - Static method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getFromWordToInt() - Static method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getGamma() - Method in class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
 
getHead() - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
getHeight() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Returns the height of the tree rooted by this node (i.e.
getHeight() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getHistoryBefore(int, int) - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
This method generate a string representing an artificial feature used in the labeling process.
getHyperplane() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
getId() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns a unique identifier of the example.
getId() - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
Returns the id of this node
getId() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
getIgnorePosInLemmaMatches() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
Returns whether two lexical structure elements must provide a perfect match if their lemmas are the same, regardless their part-of-speeches
getIgnorePosOnLexicals() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.ExactMatchingStructureElementSimilarity
Returns whether the part-of-speech is ignored in comparing two LexicalStructureElements
getIncludeLeaves() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
Returns whether the leaves must be involved in the kernel computation
getIncludeLeaves() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
Returns whether the leaves must be involved in the kernel computation
getIndex() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeature
 
getIndex() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
getInitialNode() - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
getInstance() - Static method in class it.uniroma2.sag.kelp.data.representation.RepresentationFactory
Returns an instance of the class RepresentatioFactory
getInstance() - Static method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElementFactory
Returns an instance of the class StructureElementFactory
getInstance() - Static method in class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.StaticDeltaMatrix
 
getInstance() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
 
getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.pairs.BestPairwiseAlignmentKernel
 
getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.pairs.PairwiseProductKernel
 
getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.pairs.PairwiseSumKernel
 
getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseProductKernel
 
getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseSumKernel
 
getIterationNumber() - Method in class it.uniroma2.sag.kelp.data.manipulator.WLSubtreeMapper
Returns the maximum depth of the visits of the WL kernel
getIterations() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
Returns the number of iterations
getK() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
Returns the number of examples k that Pegasos exploits in its mini-batch learning approach
getK() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
getK() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
getKernel() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Returns the kernel used in comparing two vectors
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
getKernel() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.KernelMethod
Returns the kernel exploited by this learner
getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
getKernel() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getKernel() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
getKernel() - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
 
getKernelCache() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the cache in which storing the kernel operations in the RKHS defined by this kernel
getKernelComputations() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the number of times the kernel function has been invoked
getKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
Retrieves in the cache the kernel operation between two examples
getLabel() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
This function returns the label of the cluster
getLabel() - Method in class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
getLabel() - Method in class it.uniroma2.sag.kelp.data.representation.structure.UntypedStructureElement
 
getLabel() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
getLabel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
getLabelDistances(DirectedGraphRepresentation) - Static method in class it.uniroma2.sag.kelp.data.representation.graph.GraphUtils
 
getLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the classification classificationLabels of this example
getLabels() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
Returns the labels to be learned
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
Returns the labels to be learned applying a one-vs-all strategy
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
Returns the labels to be learned applying a one-vs-one strategy
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getLabels() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
Returns the labels representing the concept to be learned.
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
getLabels() - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
Returns the labels representing the concept to be predicted.
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
getLambda() - Method in class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
getLambda() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
Get the Vertical Decay factor
getLambda() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
Get the Vertical Decay factor
getLambda() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
Get the decay factor
getLambda() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
Get the decay factor
getLambda() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
Returns the regularization coefficient
getLandmarks() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getLearningAlgorithm(float, String, StringLabel) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorization
 
getLearningAlgorithm(float, String, StringLabel) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCD
 
getLearningAlgorithm(float, String, StringLabel) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCDExperimentUtils
 
getLearningAlgorithm(float, String, StringLabel) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinear
 
getLearningAlgorithm(float, String, StringLabel) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinearExperimentUtils
 
getLearningAlgorithm(float, String, StringLabel) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPA
 
getLearningAlgorithm(float, String, StringLabel) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPAExperimentUtils
 
getLearningAlgorithm(float, String, StringLabel) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasos
 
getLearningAlgorithm(float, String, StringLabel) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasosExperimentUtils
 
getLearningAlgorithm(float, String, StringLabel) - Method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCW
 
getLearningAlgorithm(float, String, StringLabel) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCWExperimentUtils
 
getLeaves() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
getLeaves() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
Returns all the leaves, i.e.
getLeftExample() - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
Returns the left example in the pair
getLemma() - Method in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
getLenght() - Method in class it.uniroma2.sag.kelp.data.example.SequenceExample
 
getLinearizedDataset(Dataset, String) - Method in interface it.uniroma2.sag.kelp.linearization.LinearizationFunction
This method linearizes all the examples in the input dataset , generating a corresponding linearized dataset.
getLinearizedDataset(Dataset, String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getLinearizedDataset(Dataset, String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 
getLinearizedExample(Example, String) - Method in interface it.uniroma2.sag.kelp.linearization.LinearizationFunction
This method linearizes an input example, providing a new example containing only a representation with a specific name, provided as input.
getLinearizedExample(Example, String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getLinearizedExample(Example, String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 
getLinearRepresentation(Example) - Method in interface it.uniroma2.sag.kelp.linearization.LinearizationFunction
Given an input Example, this method generates a linear Representation>, i.e.
getLinearRepresentation(Example) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getLinearRepresentation(Example) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 
getLoss() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
getM() - Method in class it.uniroma2.sag.kelp.data.dataset.selector.FirstExamplesSelector
 
getM() - Method in class it.uniroma2.sag.kelp.data.dataset.selector.RandomExampleSelector
 
getMacroF1() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the macro-F1
getMacroPrecision() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the macro-precision
getMacroRecall() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the macro-recall
getMargin() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
Returns the desired margin, i.e.
getMarkingPrefix() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger
Returns the prefix used to mark the related nodes
getMatrixPath() - Method in class it.uniroma2.sag.kelp.wordspace.Wordspace
 
getMaxEmissionCandidates() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getMaxEmissionCandidates() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
getMaxId() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Get the max id within all node under the target node
getMaxId() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
Get the max id within all nodes
getMaxIterations() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getMaxIterations() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
getMaxIterations() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
getMaxMarginForLabel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
getMaxNumberOfRows() - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
getMaxSubseqLeng() - Method in class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
getMaxSubseqLeng() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
getMaxSubseqLeng() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
getMean(float[]) - Static method in class it.uniroma2.sag.kelp.utils.Math
Computes the arithmetic mean \(\bar{x}\) of the input values \(x_1, \ldots x_n\)
getMeanF1() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Deprecated.
getMeanF1For(ArrayList<Label>) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the mean of the F1 scores considering the specified labels only
getMeanSquaredError(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
Returns the mean square error of the Label label.
getMeanSquaredErrors() - Method in class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
Returns the mean error between the different Label{s}
getMechanism() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
getMechanism() - Method in class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 
getMessage() - Method in exception it.uniroma2.sag.kelp.data.example.ParsingExampleException
 
getMI(ClusterList) - Static method in class it.uniroma2.sag.kelp.utils.evaluation.ClusteringEvaluator
 
getMicroF1() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Returns the micro-f1
getMicroPrecision() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Returns the micro-precision
getMicroRecall() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Returns the micro-recall
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
Returns the model
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
getModel() - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
Returns the model
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
getModels() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
getModifier() - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
getMu() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
Get the Horizontal Decay factor
getMu() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
Get the Horizontal Decay factor
getNegativeLabelsForClassifier() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
Return the negative labels associated to each classifier
getNeighbours() - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
 
getNext() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
Returns the next element in the sequence
getNextExample() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns the next n Examples stored in the Dataset or a fewer number if n examples are not available.
getNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getNextExamples(int) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns the next Example stored in the Dataset
getNextExamples(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getNMI(ClusterList) - Static method in class it.uniroma2.sag.kelp.utils.evaluation.ClusteringEvaluator
 
getNodeDistances() - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
Returns the list of NodeDistance Objects
getNodeFromID(Integer) - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
Given a node id, return the corresponding GraphNode object
getNodeLabel() - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
Get the label of a node
getNodeList() - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
Returns a list containing all the nodes in this graph
getNodeList(TreeNode) - Method in interface it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelector
 
getNodeList(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllChildren
 
getNodeList(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllDescendants
 
getNodeList(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllLeaves
 
getNodeSimilarity() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
getNodesWithContentType(Class<? extends StructureElement>) - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
Returns all the nodes whose content has type clazz
getNoOfChildren() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
getNu() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
getNu() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
 
getNumberOfClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the number of classification classificationLabels whose this instance is a positive example
getNumberOfColumns() - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
getNumberOfExamples() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterList
 
getNumberOfExamples() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns the number of Examples in the dataset
getNumberOfExamples() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getNumberOfFeatures() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Returns the number of featuresValues
getNumberOfHits() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the number of times a cache hit happened
getNumberOfMisses() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the number of times a cache miss happened
getNumberOfNegativeExamples(Label) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns the number of negative Examples of a given class
getNumberOfNegativeExamples(Label) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getNumberOfNodes() - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
Returns the number of nodes in the graph.
getNumberOfNodes() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getNumberOfPositiveExamples(Label) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns the number of positive Examples of a given class
getNumberOfPositiveExamples(Label) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getNumberOfRegressionLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the number of regression classificationLabels
getNumberOfRepresentations() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the number of representations in which this example is modeled
getNumberOfRepresentations() - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
getNumberOfSupportVectors() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
getNumberOfSupportVectors() - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
 
getNx() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNodePairs
 
getNz() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNodePairs
 
getObj() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
getOrderedNodeSetByLabel() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getOrderedNodeSetByProduction() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getOrderedNodeSetByProductionIgnoringLeaves() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getOverallF1() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Deprecated.
getOverallPrecision() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Deprecated.
getOverallRecall() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Deprecated.
getP() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
getPaths() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePrediction
 
getPerformanceMeasure(String, Object...) - Method in class it.uniroma2.sag.kelp.utils.evaluation.Evaluator
This method allow to retrieve a performance measure by specifying the name of the method to be used.
getPolicy() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
getPos() - Method in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
getPos() - Method in class it.uniroma2.sag.kelp.data.representation.structure.PosStructureElement
 
getPrecision() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the precision of the positive class
getPrecisionFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the precision for the specified label
getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
 
getPredictedClasses() - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.ClassificationOutput
Returns all the classes that the classifier has predicted
getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassificationOutput
 
getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
 
getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
getPredictionFunction() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.ClassificationLearningAlgorithm
Returns the classifier learned during the training process
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
This method returns the learned PredictionFunction.
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
This method returns the learned PredictionFunction.
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
This method returns the learned PredictionFunction.
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getPredictionFunction() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
Returns the predictionFunction learned during the training process
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
 
getPredictionFunction() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.regression.RegressionLearningAlgorithm
Returns the regressor learned during the training process
getpReg() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
getPreLeafNodes(int) - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
Returns all the nodes that have at least a generationHops-generation descendant being a leaf (for instance using generationHops=1 will produce a list of all the fathers of the leaves, generationHops=2 will produce a list of all the grandfathers of the leaves, etc)
getPrevious() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
Returns the previous element in the sequence
getProduction() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Get the node production in the form of string.
getProductionIgnoringLeaves() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Get the node production in the form of string.
getProjectionMatrix() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getProperty() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
Returns the property
getPurity(ClusterList) - Static method in class it.uniroma2.sag.kelp.utils.evaluation.ClusteringEvaluator
 
getQCKernelFunction(SimpleDataset, SimpleDataset, String) - Static method in class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassification
Get one of the kernel functions used in the Question Classification examples.
getRandExample() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
 
getRandExample() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getRandExamples(int) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
 
getRandExamples(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getRandomSeed() - Method in class it.uniroma2.sag.kelp.data.dataset.selector.RandomExampleSelector
 
getRank() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
getRanks() - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 
getRecall() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the recall of the positive class
getRecallFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the recall for the specified label
getRegressionLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the classificationLabels of this example
getRegressionProperties() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns all the regression properties in the dataset.
getRegressionProperties() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getRegressionValue(Label) - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the numeric value associated to a label
getRegressionValues() - Method in class it.uniroma2.sag.kelp.data.example.Example
 
getRepresentation(String) - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the representation corresponding to representationName
getRepresentation(String) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.kernel.DirectKernel
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getRepresentation() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LinearMethod
Returns the representation this learning algorithm exploits
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
getRepresentation() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
getRepresentationIdentifier(Class<? extends Representation>) - Static method in class it.uniroma2.sag.kelp.data.representation.RepresentationFactory
Returns the identifier of a given class
getRepresentationName() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
getRepresentationName() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
getRepresentationName() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
getRepresentations() - Method in class it.uniroma2.sag.kelp.data.example.Example
Returns the example representations
getRepresentations() - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
getRepresentationToBeEnriched() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
 
getRepresentationToBeEnriched() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
 
getRepresentationToBeEnriched() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
 
getRho() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
getRightExample() - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
Returns the right example in the pair
getRoot() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
getRoot() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getScore() - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
getScore(CompositionalStructureElement, CompositionalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
getScore(CompositionalStructureElement, CompositionalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
 
getScore(CompositionalStructureElement, CompositionalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
 
getScore(CompositionalStructureElement, CompositionalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
 
getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
 
getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassificationOutput
 
getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
 
getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
getScore(Label) - Method in interface it.uniroma2.sag.kelp.predictionfunction.Prediction
Return the prediction score associated to a given label
getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionOutput
 
getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePrediction
 
getScwType() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
getSecond() - Method in exception it.uniroma2.sag.kelp.data.representation.vector.exception.VectorOperationException
 
getSeed() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
getSequenceExampleGenerator() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getSequenceExampleGenerator() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
getSequenceExamples() - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDataset
 
getShuffledDataset() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
 
getShuffledDataset() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getSimilarity(Vector, Vector) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Returns the similarity between vector1 and vector2 computed using the kernel function
getSimilarity(TreeNode, TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.similarity.ContentBasedTreeNodeSimilarity
 
getSimilarity(TreeNode, TreeNode) - Method in interface it.uniroma2.sag.kelp.data.representation.tree.node.similarity.TreeNodeSimilarity
Returns the similarity between nodeA and nodeB
getSimilarityThreshold() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
getSize() - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
Returns the size of the cache, i.e.
getSquaredNorm() - Method in interface it.uniroma2.sag.kelp.data.representation.Normalizable
Returns the squared norm of this vector
getSquaredNorm() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
getSquaredNorm() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
 
getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
 
getSquaredNorm(Example) - Method in interface it.uniroma2.sag.kelp.kernel.cache.SquaredNormCache
Returns a previously stored norm of a given example
getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
getSquaredNorm() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
getSquaredNorm() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
Computes the squared norm of a given example according to the space in which the model is operating
getSquaredNorm() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
Computes the squared norm of the hyperplane this model is based on
getSquaredNormCache() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the cache in which storing the squared norms in the RKHS defined by this kernel
getStandardDeviation(float[]) - Static method in class it.uniroma2.sag.kelp.utils.Math
Estimates the unbiased standard deviation \(\sigma\) of population using some samples \(x_1, \ldots x_n\) whose estimated mean is \(\bar{x}\)
getStatistics(ClusterList) - Static method in class it.uniroma2.sag.kelp.utils.evaluation.ClusteringEvaluator
 
getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
 
getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
 
getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
 
getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
Retrieves in the cache the kernel operation between two examples
getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.SimpleDynamicKernelCache
 
getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
getStructureElementIdentifier(Class<? extends StructureElement>) - Static method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElementFactory
Returns the identifier of a given class
getSupportVector(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
Returns the support vector associated to a given instance, null the instance is not a support vector in this model
getSupportVectorIndex(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
Returns the index of the vector associated to a given instance, null the instance is not a support vector in this model
getSupportVectors() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
Returns all the support vectors
getSyntacticRelation() - Method in class it.uniroma2.sag.kelp.data.representation.structure.SyntacticStructureElement
 
getTerminalFactor() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
Get the Terminal Factor
getTerminalFactor() - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
getTextFromData() - Method in interface it.uniroma2.sag.kelp.data.representation.Representation
Returns a textual representation of the data stored in this representation
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.structure.PosStructureElement
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
Returns a textual representation of the data stored in this structureElement
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.structure.SyntacticStructureElement
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.structure.UntypedStructureElement
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getTextFromDataWithAdditionalInfo() - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
Returns the textual format of the content, concatenated with all the additional information added to this element
getTextualEnrichedFormat() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
getTextualEnrichedTree() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
getTextualLabelPart() - Method in class it.uniroma2.sag.kelp.data.example.Example
 
getTextualRepresentation(Representation) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
getTextualRepresentation(Representation, String) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
getTextualRepresentation(StructureElement) - Static method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElementFactory
 
getTn() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the true negatives
getTnFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the true negatives for the specified label
getToCombine() - Method in class it.uniroma2.sag.kelp.kernel.KernelCombination
Returns a list of the kernels this kernel is combining
getToCombine() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.EnsembleLearningAlgorithm
Returns a list of the learning algorithm this ensemble method is combining
getTp() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
Return the true positives
getTpFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Return the true positives for the specified label
getTransitionRepresentationName() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
getTransitionsOrder() - Method in interface it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGenerator
 
getTransitionsOrder() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
getTransitionsOrder() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
getTransitionsOrder() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
getTransitionWeight() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
getUpper_bound_n() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
getUpper_bound_p() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
getValue() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
Returns the value of the value
getValue() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeature
 
getValue() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
getVariance(float[]) - Static method in class it.uniroma2.sag.kelp.utils.Math
Estimates the unbiased sample variance \(\sigma^2\) of population using some samples \(x_1, \ldots x_n\) whose estimated mean is \(\bar{x}\)
getVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getVector(String) - Method in class it.uniroma2.sag.kelp.wordspace.Wordspace
 
getVector(String) - Method in interface it.uniroma2.sag.kelp.wordspace.WordspaceI
Returns the vector associated to the given word
getW(double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
 
getWeight() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
 
getWeights() - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
 
getWordCounter() - Static method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getWordspace() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Returns the wordspace from which the vectors associated to a word must be retrieved
getZeroVector(String) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns a zero vector compliant with the representation identifier by representationIdentifier containing all zeros
getZeroVector(String) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
getZeroVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
getZeroVector() - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Returns a vector whose values are all 0.
getZeroVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
getZeroVector() - Method in class it.uniroma2.sag.kelp.wordspace.Wordspace
 
getZeroVector() - Method in interface it.uniroma2.sag.kelp.wordspace.WordspaceI
Returns the zero vector in the wordspace, i.e, a zero vector having the worspace dimensionality
grad(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
grad(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvrFunction
 
GraphNode - Class in it.uniroma2.sag.kelp.data.representation.graph
This class implements a graph node
GraphNode(int, StructureElement) - Constructor for class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
Initializes a graph node
GraphUtils - Class in it.uniroma2.sag.kelp.data.representation.graph
This class contains some useful methods operating on graphs
GraphUtils() - Constructor for class it.uniroma2.sag.kelp.data.representation.graph.GraphUtils
 

H

hasChildren() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
hashCode() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
hashCode() - Method in interface it.uniroma2.sag.kelp.data.label.Label
 
hashCode() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
 
hashCode() - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
 
hashCode() - Method in class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
hashCode() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
hashCode() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
hasNext() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
Checks whether there is at least another example to read
hasNextExample() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Returns a boolean declaring whether there are other Examples in the dataset
hasNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
HelloKernelLearning - Class in it.uniroma2.sag.kelp.examples.main
This is a classification example based on the kernelized version of the Passive Aggressive algorithm.
HelloKernelLearning() - Constructor for class it.uniroma2.sag.kelp.examples.main.HelloKernelLearning
 
HelloLearning - Class in it.uniroma2.sag.kelp.examples.main
This is a very simple classification example based on a linear version of the Passive Aggressive algorithm.
HelloLearning() - Constructor for class it.uniroma2.sag.kelp.examples.main.HelloLearning
 
Hv(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 

I

I - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
idFromBaseType() - Method in class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
idFromValue(Object) - Method in class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
idFromValueAndType(Object, Class<?>) - Method in class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 
incrementFeature(String, float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
Increments the value associated to a feature
incrementWeight(float) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
Increments the weight of this support vector
INDENTIFIER - Static variable in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
 
index - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
info(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
init(String, int) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
init(JavaType) - Method in class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 
initialize() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
 
initializeExamples(ArrayList<Vector>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
 
initializeWithPredictionFunction(OneVsAllClassifier) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
Deprecated.
initPruner() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodePruner
 
initPruner() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodeToBePrunedCheckerAbstractClass
 
initPruner() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLeafNumber
 
innerProduct(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
innerProduct(Vector) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Returns the dot product between this vector and vector
innerProduct(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
innerProduct(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the kernel similarity between the given examples.
inputBuffer - Variable in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
 
is_free(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Check if 0 < alpha[i] < get_C(i)
is_lower_bound(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Check if alpha[i] <= 0
is_upper_bound(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Check if alpha[i] >= get_C(i)
isClassPredicted(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
 
isClassPredicted(Label) - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.ClassificationOutput
Returns a boolean identifying the predicted membership to a specified class
isClassPredicted(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassificationOutput
 
isClassPredicted(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
 
isClassPredicted(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
isCompatible(Example) - Method in class it.uniroma2.sag.kelp.data.example.Example
Evaluates whether an example is compatible with this one, i.e., they have the same structure in terms of Example type and representations
isCompatible(Example) - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
 
isCompatible(Example) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
isCompatible(Representation) - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
isCompatible(Representation) - Method in interface it.uniroma2.sag.kelp.data.representation.Representation
Evaluates whether a representation is compatible with this one, e.g.., they have same type and pass additional checks that are type-dependent
isCompatible(Representation) - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
 
isCompatible(Representation) - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
 
isCompatible(Representation) - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
isCompatible(Representation) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
isCompatible(Representation) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
isConsistent() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Evaluates whether the examples included in this dataset are compatible with each other.
isElementOfInterest(StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.filter.LexicalStructureElementFilter
 
isElementOfInterest(StructureElement) - Method in interface it.uniroma2.sag.kelp.data.representation.structure.filter.StructureElementFilter
Returns whether element must be selected or not according to a policy specified by this object
isExampleOf(Label) - Method in class it.uniroma2.sag.kelp.data.example.Example
Asserts whether this is a positive example for the input label or not
isFairness() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
isFairness() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
isFairness() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
isFairness() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
isFairness() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
isFairness() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
isInvokedForAllPairElements() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
isNodeOfInterest(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.filter.ContentBasedTreeNodeFilter
 
isNodeOfInterest(TreeNode) - Method in interface it.uniroma2.sag.kelp.data.representation.tree.node.filter.TreeNodeFilter
Returns whether node must be selected or not according to a policy specified by this object
isNodeToBePruned(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodeToBePrunedCheckerAbstractClass
 
isNodeToBePruned(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeIfLeaf
 
isNodeToBePruned(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLeafNumber
 
isNodeToBePruned(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
isNodeToBePruned(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
isPosRestriction() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
isPreterminal() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Returns whether this has only leaf children (i.e., the child is terminal)
isSupportVector(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
isSupportVector(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
Returns whether instance is a support vector in this model
isSyntacticRestriction() - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
isUnbiased() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
Returns whether the bias, i.e.
isUseBias() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
it.uniroma2.sag.kelp.data.clustering - package it.uniroma2.sag.kelp.data.clustering
 
it.uniroma2.sag.kelp.data.dataset - package it.uniroma2.sag.kelp.data.dataset
 
it.uniroma2.sag.kelp.data.dataset.selector - package it.uniroma2.sag.kelp.data.dataset.selector
 
it.uniroma2.sag.kelp.data.example - package it.uniroma2.sag.kelp.data.example
 
it.uniroma2.sag.kelp.data.examplegenerator - package it.uniroma2.sag.kelp.data.examplegenerator
 
it.uniroma2.sag.kelp.data.label - package it.uniroma2.sag.kelp.data.label
 
it.uniroma2.sag.kelp.data.manipulator - package it.uniroma2.sag.kelp.data.manipulator
 
it.uniroma2.sag.kelp.data.representation - package it.uniroma2.sag.kelp.data.representation
 
it.uniroma2.sag.kelp.data.representation.graph - package it.uniroma2.sag.kelp.data.representation.graph
 
it.uniroma2.sag.kelp.data.representation.sequence - package it.uniroma2.sag.kelp.data.representation.sequence
 
it.uniroma2.sag.kelp.data.representation.string - package it.uniroma2.sag.kelp.data.representation.string
 
it.uniroma2.sag.kelp.data.representation.structure - package it.uniroma2.sag.kelp.data.representation.structure
 
it.uniroma2.sag.kelp.data.representation.structure.filter - package it.uniroma2.sag.kelp.data.representation.structure.filter
 
it.uniroma2.sag.kelp.data.representation.structure.similarity - package it.uniroma2.sag.kelp.data.representation.structure.similarity
 
it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional - package it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional
 
it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation - package it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation
 
it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product - package it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product
 
it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum - package it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum
 
it.uniroma2.sag.kelp.data.representation.tree - package it.uniroma2.sag.kelp.data.representation.tree
 
it.uniroma2.sag.kelp.data.representation.tree.node - package it.uniroma2.sag.kelp.data.representation.tree.node
 
it.uniroma2.sag.kelp.data.representation.tree.node.filter - package it.uniroma2.sag.kelp.data.representation.tree.node.filter
 
it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner - package it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
 
it.uniroma2.sag.kelp.data.representation.tree.node.similarity - package it.uniroma2.sag.kelp.data.representation.tree.node.similarity
 
it.uniroma2.sag.kelp.data.representation.tree.utils - package it.uniroma2.sag.kelp.data.representation.tree.utils
 
it.uniroma2.sag.kelp.data.representation.vector - package it.uniroma2.sag.kelp.data.representation.vector
 
it.uniroma2.sag.kelp.data.representation.vector.exception - package it.uniroma2.sag.kelp.data.representation.vector.exception
 
it.uniroma2.sag.kelp.examples.demo.clustering - package it.uniroma2.sag.kelp.examples.demo.clustering
 
it.uniroma2.sag.kelp.examples.demo.mutag - package it.uniroma2.sag.kelp.examples.demo.mutag
 
it.uniroma2.sag.kelp.examples.demo.nystrom - package it.uniroma2.sag.kelp.examples.demo.nystrom
 
it.uniroma2.sag.kelp.examples.demo.pruning - package it.uniroma2.sag.kelp.examples.demo.pruning
 
it.uniroma2.sag.kelp.examples.demo.qc - package it.uniroma2.sag.kelp.examples.demo.qc
 
it.uniroma2.sag.kelp.examples.demo.rcv1 - package it.uniroma2.sag.kelp.examples.demo.rcv1
 
it.uniroma2.sag.kelp.examples.demo.regression - package it.uniroma2.sag.kelp.examples.demo.regression
 
it.uniroma2.sag.kelp.examples.demo.seqlearn - package it.uniroma2.sag.kelp.examples.demo.seqlearn
 
it.uniroma2.sag.kelp.examples.demo.tweetsent2013 - package it.uniroma2.sag.kelp.examples.demo.tweetsent2013
 
it.uniroma2.sag.kelp.examples.main - package it.uniroma2.sag.kelp.examples.main
 
it.uniroma2.sag.kelp.kernel - package it.uniroma2.sag.kelp.kernel
 
it.uniroma2.sag.kelp.kernel.cache - package it.uniroma2.sag.kelp.kernel.cache
 
it.uniroma2.sag.kelp.kernel.graph - package it.uniroma2.sag.kelp.kernel.graph
 
it.uniroma2.sag.kelp.kernel.pairs - package it.uniroma2.sag.kelp.kernel.pairs
 
it.uniroma2.sag.kelp.kernel.sequence - package it.uniroma2.sag.kelp.kernel.sequence
 
it.uniroma2.sag.kelp.kernel.standard - package it.uniroma2.sag.kelp.kernel.standard
 
it.uniroma2.sag.kelp.kernel.tree - package it.uniroma2.sag.kelp.kernel.tree
 
it.uniroma2.sag.kelp.kernel.tree.deltamatrix - package it.uniroma2.sag.kelp.kernel.tree.deltamatrix
 
it.uniroma2.sag.kelp.kernel.vector - package it.uniroma2.sag.kelp.kernel.vector
 
it.uniroma2.sag.kelp.learningalgorithm - package it.uniroma2.sag.kelp.learningalgorithm
 
it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm - package it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
 
it.uniroma2.sag.kelp.learningalgorithm.classification - package it.uniroma2.sag.kelp.learningalgorithm.classification
 
it.uniroma2.sag.kelp.learningalgorithm.classification.dcd - package it.uniroma2.sag.kelp.learningalgorithm.classification.dcd
 
it.uniroma2.sag.kelp.learningalgorithm.classification.hmm - package it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
 
it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear - package it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear
 
it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver - package it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
 
it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm - package it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm
 
it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver - package it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
 
it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification - package it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification
 
it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive - package it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
 
it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos - package it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos
 
it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron - package it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
 
it.uniroma2.sag.kelp.learningalgorithm.classification.scw - package it.uniroma2.sag.kelp.learningalgorithm.classification.scw
 
it.uniroma2.sag.kelp.learningalgorithm.clustering - package it.uniroma2.sag.kelp.learningalgorithm.clustering
 
it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans - package it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans
 
it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans - package it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
 
it.uniroma2.sag.kelp.learningalgorithm.regression - package it.uniroma2.sag.kelp.learningalgorithm.regression
 
it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear - package it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear
 
it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm - package it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm
 
it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive - package it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
 
it.uniroma2.sag.kelp.linearization - package it.uniroma2.sag.kelp.linearization
 
it.uniroma2.sag.kelp.linearization.nystrom - package it.uniroma2.sag.kelp.linearization.nystrom
 
it.uniroma2.sag.kelp.main - package it.uniroma2.sag.kelp.main
 
it.uniroma2.sag.kelp.predictionfunction - package it.uniroma2.sag.kelp.predictionfunction
 
it.uniroma2.sag.kelp.predictionfunction.classifier - package it.uniroma2.sag.kelp.predictionfunction.classifier
 
it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass - package it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
 
it.uniroma2.sag.kelp.predictionfunction.model - package it.uniroma2.sag.kelp.predictionfunction.model
 
it.uniroma2.sag.kelp.predictionfunction.regressionfunction - package it.uniroma2.sag.kelp.predictionfunction.regressionfunction
 
it.uniroma2.sag.kelp.utils - package it.uniroma2.sag.kelp.utils
 
it.uniroma2.sag.kelp.utils.evaluation - package it.uniroma2.sag.kelp.utils.evaluation
 
it.uniroma2.sag.kelp.utils.exception - package it.uniroma2.sag.kelp.utils.exception
 
it.uniroma2.sag.kelp.wordspace - package it.uniroma2.sag.kelp.wordspace
 

J

JacksonSerializerWrapper - Class in it.uniroma2.sag.kelp.utils
It is a serializer, i.e., an object that is able to convert objects into a String representation, preserving all their properties.
JacksonSerializerWrapper() - Constructor for class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 

K

Kernel - Class in it.uniroma2.sag.kelp.kernel
Abstract class for a generic kernel function
Kernel() - Constructor for class it.uniroma2.sag.kelp.kernel.Kernel
 
kernel - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The Kernel function between examples, i.e.
kernel(Example, Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
This function embeds the call to the kernel function
KernelBasedClusteringExample - Class in it.uniroma2.sag.kelp.examples.demo.clustering
This class contains an example of the usage of the Kernel-based clustering.
KernelBasedClusteringExample() - Constructor for class it.uniroma2.sag.kelp.examples.demo.clustering.KernelBasedClusteringExample
 
KernelBasedKMeansEngine - Class in it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans
Implements the Kernel Based K-means described in: Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney.
KernelBasedKMeansEngine() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
KernelBasedKMeansEngine(Kernel, int, int) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
KernelBasedKMeansExample - Class in it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans
 
KernelBasedKMeansExample() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
 
KernelBasedKMeansExample(Example, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
 
KernelCache - Class in it.uniroma2.sag.kelp.kernel.cache
Generic Cache for kernel computations
KernelCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.KernelCache
 
KernelCacheExample - Class in it.uniroma2.sag.kelp.examples.main
Caching is an important feature of KeLP.
KernelCacheExample() - Constructor for class it.uniroma2.sag.kelp.examples.main.KernelCacheExample
 
KernelCacheTypeResolver - Class in it.uniroma2.sag.kelp.kernel.cache
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of KernelCaches
KernelCacheTypeResolver() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
KernelCombination - Class in it.uniroma2.sag.kelp.kernel
It is a kernel that operates combining other kernels
KernelCombination() - Constructor for class it.uniroma2.sag.kelp.kernel.KernelCombination
 
KernelComposition - Class in it.uniroma2.sag.kelp.kernel
It is a kernel that operates enriching the computation performed by another kernel
KernelComposition() - Constructor for class it.uniroma2.sag.kelp.kernel.KernelComposition
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.DirectKernel
 
kernelComputation(T, T) - Method in class it.uniroma2.sag.kelp.kernel.DirectKernel
Computes the kernel similarity between two specific representations
kernelComputation(DirectedGraphRepresentation, DirectedGraphRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.graph.ShortestPathKernel
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the kernel similarity between the given examples.
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.KernelOnPairs
 
kernelComputation(SequenceRepresentation, SequenceRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.KernelMultiplication
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.NormalizationKernel
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
kernelComputation(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
 
kernelComputation(TreeRepresentation, TreeRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
kernelComputation(TreeRepresentation, TreeRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
kernelComputation(TreeRepresentation, TreeRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
 
kernelComputation(TreeRepresentation, TreeRepresentation) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
 
kernelComputation(Vector, Vector) - Method in class it.uniroma2.sag.kelp.kernel.vector.LinearKernel
 
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.BestPairwiseAlignmentKernel
 
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.KernelOnPairs
Returns the kernel computation
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.PairwiseProductKernel
 
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.PairwiseSumKernel
 
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.PreferenceKernel
 
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseProductKernel
 
kernelComputationOverPairs(Example, Example, Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseSumKernel
 
KernelizedPassiveAggressiveClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
Online Passive-Aggressive Learning Algorithm for classification tasks (Kernel Machine version) .
KernelizedPassiveAggressiveClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
KernelizedPassiveAggressiveClassification(float, float, PassiveAggressiveClassification.Loss, PassiveAggressive.Policy, Kernel, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
KernelizedPassiveAggressiveRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
Online Passive-Aggressive Learning Algorithm for regression tasks (kernel machine version).
KernelizedPassiveAggressiveRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
KernelizedPassiveAggressiveRegression(float, float, PassiveAggressive.Policy, Kernel, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
KernelizedPerceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
The perceptron learning algorithm algorithm for classification tasks (Kernel machine version).
KernelizedPerceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
KernelizedPerceptron(float, float, boolean, Kernel, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
KernelMachineModel - Interface in it.uniroma2.sag.kelp.predictionfunction.model
It is the model for a Kernel Machine Method
KernelMethod - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is a kernel-based algorithm
KernelMultiplication - Class in it.uniroma2.sag.kelp.kernel.standard
Multiplication of Kernels
Given the kernels \(K_1 \ldots K_n\), the combination formula is:
\(\prod_{i}K_i\)
KernelMultiplication() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.KernelMultiplication
 
KernelOnPairs - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying a simpler kernel to the pair elements
KernelOnPairs() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.KernelOnPairs
 
KernelTypeResolver - Class in it.uniroma2.sag.kelp.kernel
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of Kernels
KernelTypeResolver() - Constructor for class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 

L

l - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
the number of training data
l - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Total number of Support Vectors
L2R_L2_SvcFunction - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
NOTE: This code has been adapted from the Java port of the original LIBLINEAR C++ sources.
L2R_L2_SvcFunction(Problem, double[]) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
L2R_L2_SvrFunction - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
NOTE: This code has been adapted from the Java port of the original LIBLINEAR C++ sources.
L2R_L2_SvrFunction(Problem, double[], double) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvrFunction
 
Label - Interface in it.uniroma2.sag.kelp.data.label
A generic Label for supervised learning.
label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The label to be learned by the classifier
label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
LABEL_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
LabelFactory - Class in it.uniroma2.sag.kelp.data.label
It is a factory that provides methods for instantiating labels described in a textual format
LabelFactory() - Constructor for class it.uniroma2.sag.kelp.data.label.LabelFactory
 
labelPrefix - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
labels - Variable in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
labels - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
LabelTypeResolver - Class in it.uniroma2.sag.kelp.data.label
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of Labels
LabelTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
This method will cause the meta-learning algorithm to learn N classifiers, where N is the number of classes in the dataset.
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
This method will cause the meta-learning algorithm to learn N classifiers, where N is the number of classes in the dataset.
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
This method will cause the meta-learning algorithm to learn N*(N-1)/2 classifiers, where N is the number of classes in the dataset.
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
learn(Dataset) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
It starts the training process exploiting the provided dataset
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
learn(Example) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.OnlineLearningAlgorithm
Applies the learning process on a single example, updating its current model
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
 
learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
 
Learn - Class in it.uniroma2.sag.kelp.main
 
Learn() - Constructor for class it.uniroma2.sag.kelp.main.Learn
 
LearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is a generic Machine Learning algorithm
LearningAlgorithmTypeResolver - Class in it.uniroma2.sag.kelp.learningalgorithm
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of LearningAlgorithms
LearningAlgorithmTypeResolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
LexicalStructureElement - Class in it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement that contains lexical information, i.e.
LexicalStructureElement() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
LexicalStructureElement(String, String) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
LexicalStructureElementFilter - Class in it.uniroma2.sag.kelp.data.representation.structure.filter
This implementation of StructureElementFilter selects only LexicalStructureElements whose lemma is not a stopword and whose pos is among the set of posOfInterest
LexicalStructureElementFilter(Set<String>, Set<String>) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.filter.LexicalStructureElementFilter
Constructor for LexicalStructureElementFilter
LexicalStructureElementManipulator - Class in it.uniroma2.sag.kelp.data.manipulator
This class implements functions to enrich LexicalStructureElement s with a vector from the Word Space.
LexicalStructureElementManipulator(WordspaceI, String, String) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.LexicalStructureElementManipulator
 
LexicalStructureElementManipulator(WordspaceI, String) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.LexicalStructureElementManipulator
 
LexicalStructureElementSimilarity - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity
This class implements a similarity function between StructureElements.
LexicalStructureElementSimilarity() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
 
LexicalStructureElementSimilarity(WordspaceI) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
 
LibCSvmSolver - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
This class implements the solver of the C-SVM quadratic problem described in [CC Chang & CJ Lin, 2011].
LibCSvmSolver(Kernel, float, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
 
LibCSvmSolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
 
LibLinearFeature - Interface in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
NOTE: This code has been adapted from the Java port of the original LIBLINEAR C++ sources.
LibLinearFeatureNode - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
NOTE: This code has been adapted from the Java port of the original LIBLINEAR C++ sources.
LibLinearFeatureNode(int, double) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
LibLinearLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear
This class implements linear SVMs models trained using a coordinate descent algorithm [Fan et al, 2008].
LibLinearLearningAlgorithm(Label, double, double, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
LibLinearLearningAlgorithm(Label, double, double, boolean, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
LibLinearLearningAlgorithm(double, double, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
LibLinearLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
LibLinearRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear
This class implements linear SVM regression trained using a coordinate descent algorithm [Fan et al, 2008].
LibLinearRegression(Label, double, double, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
LibLinearRegression(double, double, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
LibLinearRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
LibNuSvmSolver - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
It is the instance of a solution provided the \(\nu\)-SVM solver of the optimization problem.
LibNuSvmSolver(Kernel, int, int) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
LibNuSvmSolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
LibsvmDatasetReader - Class in it.uniroma2.sag.kelp.data.dataset
A utility class to read dataset in the libsvm/liblinear/svmLight format.
LibsvmDatasetReader(String, String) - Constructor for class it.uniroma2.sag.kelp.data.dataset.LibsvmDatasetReader
Constructor for reading dataset in libsvm/liblinear/svmLight format for classification tasks.
LibsvmDatasetReader(String, String, StringLabel) - Constructor for class it.uniroma2.sag.kelp.data.dataset.LibsvmDatasetReader
Constructor for reading dataset in libsvm/liblinear/svmLight format for regression tasks.
LibSvmSolver - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
This class implements the solver of the SVM quadratic problem described in [CC Chang & CJ Lin, 2011].
LibSvmSolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
LibSvmSolver(Kernel, float, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
LibSvmSolver.Pair - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
The pair of indices i and j that are selected as working set
LibSvmSolver.Pair() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver.Pair
 
LINEAR_REP_NAME - Static variable in class it.uniroma2.sag.kelp.examples.demo.nystrom.NystromExampleMain
 
LinearizationFunction - Interface in it.uniroma2.sag.kelp.linearization
This interface allows implementing function to linearized examples through linear representations, i.e.
linearizeByEnsembleAndJuxtaposition(Example, String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
Given an example, this method produces a new Example containing a single representation, i.e.
LinearKernel - Class in it.uniroma2.sag.kelp.kernel.vector
Linear Kernel for Vectors
It executes the dot product between two Vector representations
LinearKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.vector.LinearKernel
 
LinearKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.vector.LinearKernel
 
LinearKernelCombination - Class in it.uniroma2.sag.kelp.kernel.standard
Weighted Linear Combination of Kernels
Given the kernels \(K_1 \ldots K_n\), with weights \(c_1 \ldots c_n\), the combination formula is:
\(\sum_{i}c_iK_i\)
LinearKernelCombination() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
 
LinearKMeansCluster - Class in it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
It is the instance of a Cluster for Linear Algorithms, intended as a set of objects, instantiated as Examples, grouped together according to a measure of similarity.
LinearKMeansCluster() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster
 
LinearKMeansCluster(String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster
 
LinearKMeansClusteringExample - Class in it.uniroma2.sag.kelp.examples.demo.clustering
This class contains an example of the usage of the Linear K-means clustering.
LinearKMeansClusteringExample() - Constructor for class it.uniroma2.sag.kelp.examples.demo.clustering.LinearKMeansClusteringExample
 
LinearKMeansEngine - Class in it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
Implements the K-means Clustering Algorithm, that works on an Explicit feature Space.
LinearKMeansEngine() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
LinearKMeansEngine(String, int, int) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
LinearKMeansExample - Class in it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
 
LinearKMeansExample() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansExample
 
LinearKMeansExample(Example, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansExample
 
LinearMethod - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is a linear algorithm operating directly on an explicit vector space
LinearPassiveAggressiveClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
Online Passive-Aggressive Learning Algorithm for classification tasks (linear version) .
LinearPassiveAggressiveClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
LinearPassiveAggressiveClassification(float, float, PassiveAggressiveClassification.Loss, PassiveAggressive.Policy, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
LinearPassiveAggressiveRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
Online Passive-Aggressive Learning Algorithm for regression tasks (linear version).
LinearPassiveAggressiveRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
LinearPassiveAggressiveRegression(float, float, PassiveAggressive.Policy, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
LinearPerceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
The perceptron learning algorithm algorithm for classification tasks (linear version).
LinearPerceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
LinearPerceptron(float, float, boolean, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
load(String) - Static method in class it.uniroma2.sag.kelp.kernel.Kernel
Load a kernel function from a file path.
load(String) - Static method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
Load a Nystrom-based projection function from a file
load(String) - Static method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
Load an Ensemble of Nystrom projectors saved on file.
logger - Variable in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
logIteration - Static variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The number of iteration to be accomplished to print info in the standard output
loss - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
LRB - Static variable in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
The left parenthesis character within the tree

M

main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.clustering.KernelBasedClusteringExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.clustering.LinearKMeansClusteringExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.mutag.MutagClassification
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.nystrom.NystromExampleMain
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.pruning.TreePruningDemo
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassification
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassificationIncrementalLearning
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassificationLearningFromJson
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCD
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCDExperimentUtils
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinear
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinearExperimentUtils
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPA
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPAExperimentUtils
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasos
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasosExperimentUtils
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCW
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCWExperimentUtils
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.regression.EpsilonSVRegressionExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.seqlearn.SequenceLearningKernelMain
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.seqlearn.SequenceLearningLinearMain
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.demo.tweetsent2013.TweetSentimentAnalysisSemeval2013
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.ClassificationDemo
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.HelloKernelLearning
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.HelloLearning
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.KernelCacheExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.MultipleRepresentationExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.OneVsAllPassiveAggressiveExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.OneVsAllSVMExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.SequenceKernelExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.examples.main.SerializationExample
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.main.Classify
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.main.Learn
 
main(String[]) - Static method in class it.uniroma2.sag.kelp.utils.evaluation.ClusteringEvaluator
 
manipulate(Manipulator...) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Manipulates all the examples in the dataset accordingly to the strategies defined by the given manipulators.
manipulate(Manipulator...) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
manipulate(Manipulator) - Method in class it.uniroma2.sag.kelp.data.example.Example
Manipulate this example accordingly to the provided manipulator
manipulate(Manipulator) - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
Manipulates this example according to the provided manipulator.
manipulate(Manipulator) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.LexicalStructureElementManipulator
 
manipulate(Example) - Method in interface it.uniroma2.sag.kelp.data.manipulator.Manipulator
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.NormalizationManipolator
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.PairSimilarityExtractor
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.StandardizationManipulator
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.VectorConcatenationManipulator
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.manipulator.WLSubtreeMapper
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
 
manipulate(Example) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
 
Manipulator - Interface in it.uniroma2.sag.kelp.data.manipulator
It is an example manipulator, i.e.
margin - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
matchPosWith(CompositionalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
This method verifies if this object and csz have the same Part of speech on the heads and modifiers
matchSyntacticRelationWith(CompositionalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
This method verifies if the dependency relation between this object and csz matches.
Math - Class in it.uniroma2.sag.kelp.utils
Implements static utility methods for mathematical operations and statistics
Math() - Constructor for class it.uniroma2.sag.kelp.utils.Math
 
maxNumberOfChildren - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeNumberOfChildren
 
merge(Vector, float, String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
Merge this vector with vector (it is like a vector concatenation) If V1 is the space where this vector lies and V2 is the space where vector lies, then the resulting vector lies in V1xV2
MetaLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is a meta algorithm that operates modifying exploiting another learning algorithm
Model - Interface in it.uniroma2.sag.kelp.predictionfunction.model
It is a generic model that can be learned by a learning algorithm
MulticlassClassificationEvaluator - Class in it.uniroma2.sag.kelp.utils.evaluation
This is an instance of an Evaluator.
MulticlassClassificationEvaluator(List<Label>) - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Initialize a new F1Evaluator that will work on the specified classes
MulticlassClassificationEvaluator.ClassStats - Class in it.uniroma2.sag.kelp.utils.evaluation
 
MulticlassClassificationEvaluator.ClassStats() - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
MulticlassModel - Class in it.uniroma2.sag.kelp.predictionfunction.model
It is a model which aggregates BinaryModels.
MulticlassModel() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
MulticlassSequenceClassificationEvaluator - Class in it.uniroma2.sag.kelp.utils.evaluation
This is an instance of an Evaluator.
MulticlassSequenceClassificationEvaluator(List<Label>) - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.MulticlassSequenceClassificationEvaluator
Initialize a new F1Evaluator that will work on the specified classes
MultiEpochLearning - Class in it.uniroma2.sag.kelp.learningalgorithm
It is a meta learning algorithms for online learning methods.
MultiEpochLearning() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
MultiEpochLearning(int, LearningAlgorithm, List<Label>) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
MultiEpochLearning(int, LearningAlgorithm) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
MultiLabelClassificationLearning - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification
It is a meta algorithm that operates applying a multi label learning strategy over the base learning algorithm which is intended to be a binary learner.
MultiLabelClassificationLearning() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
 
MultiLabelClassificationOutput - Class in it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
It is the output provided by a multi label classifier.
MultiLabelClassificationOutput() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassificationOutput
 
MultiLabelClassifier - Class in it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
It is a multi label classifier.
MultiLabelClassifier() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
MultipleRepresentationExample - Class in it.uniroma2.sag.kelp.examples.main
KeLP supports natively a multiple representation formalism.
MultipleRepresentationExample() - Constructor for class it.uniroma2.sag.kelp.examples.main.MultipleRepresentationExample
 
MutagClassification - Class in it.uniroma2.sag.kelp.examples.demo.mutag
This code performs a 10-fold cross validation on the MUTAG dataset [1] training a C-SVM using a linear kernel combination the ShortestPathKernel (operating on DirectedGraphRepresentations) and the Weisfeiler-Lehman Subtree Kernel for Graphs.
MutagClassification() - Constructor for class it.uniroma2.sag.kelp.examples.demo.mutag.MutagClassification
 

N

n - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
the number of features (including the bias feature if bias >= 0)
NAME_VALUE_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.label.LabelFactory
 
nextRow - Variable in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
 
nFoldCrossValidation(int, LearningAlgorithm, SimpleDataset, T) - Static method in class it.uniroma2.sag.kelp.utils.ExperimentUtils
Performs a n-fold cross validation
nFolding(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Returns n datasets.
nFoldingClassDistributionInvariant(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Returns n datasets.
NO_DISTANCE_CONSTRAINT - Static variable in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
NO_RESPONSE - Static variable in interface it.uniroma2.sag.kelp.kernel.tree.deltamatrix.DeltaMatrix
 
noCloseNodeWithSpecificLabelPrefix(TreeNode, String, int, TreeNode) - Static method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
Check if there is a node at a distance no greater than distance (the distance between a node and its neighbors is 1) whose label prefix starts with labelPrefix.
nodeComparisonType - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
NodeDistance - Class in it.uniroma2.sag.kelp.data.representation.graph
This class corresponds to a triple \(\) where \(n_a\) is the initial node, \(n_b\) is the final node and \(d\) is the distance of the shortest path from \(n_a\) to \(n_b)
NodeDistance(GraphNode, GraphNode, float) - Constructor for class it.uniroma2.sag.kelp.data.representation.graph.NodeDistance
 
NodePruner - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
 
NodePruner() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodePruner
 
nodes - Variable in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
nodesDistances - Variable in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
nodesIdToGraphObjs - Variable in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
NodeToBePrunedCheckerAbstractClass - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
 
NodeToBePrunedCheckerAbstractClass() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodeToBePrunedCheckerAbstractClass
 
Normalizable - Interface in it.uniroma2.sag.kelp.data.representation
It is a representation that has a norm
NormalizationKernel - Class in it.uniroma2.sag.kelp.kernel.standard
Normalization of a generic kernel K
Normalization formula: \(K(x,y) = \frac{K(x,y)}{\sqrt{(K(x,x) \cdot K(y,y))}}\)
NormalizationKernel(Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.NormalizationKernel
 
NormalizationKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.NormalizationKernel
 
NormalizationManipolator - Class in it.uniroma2.sag.kelp.data.manipulator
This manipulator scales every selected representation to be a unit vector in its explicit feature space
NormalizationManipolator(String...) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.NormalizationManipolator
 
normalize() - Method in interface it.uniroma2.sag.kelp.data.representation.Normalizable
Scales the representation in order to have a unit norm in the explicit feature space
normalize() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
normalize() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
normalizeWeights() - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
Scales the weights in order to make their sum being equal to 1
NoSuchPerformanceMeasureException - Exception in it.uniroma2.sag.kelp.utils.exception
 
NoSuchPerformanceMeasureException(String) - Constructor for exception it.uniroma2.sag.kelp.utils.exception.NoSuchPerformanceMeasureException
 
numberOfLeavesToKeep - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLeafNumber
 
NumericLabel - Class in it.uniroma2.sag.kelp.data.label
It value consisting of a real value.
NumericLabel(Label, float) - Constructor for class it.uniroma2.sag.kelp.data.label.NumericLabel
Initializes a NumericLabel whose value is labelValue and whose name is name
NumericLabel() - Constructor for class it.uniroma2.sag.kelp.data.label.NumericLabel
 
NystromExampleMain - Class in it.uniroma2.sag.kelp.examples.demo.nystrom
This class provides an example to apply the Nystorm Method with Convolutional Tree Kernels.
NystromExampleMain() - Constructor for class it.uniroma2.sag.kelp.examples.demo.nystrom.NystromExampleMain
 
NystromMethod - Class in it.uniroma2.sag.kelp.linearization.nystrom
This class implements the Nystrom Method to approximate the implicit space underlying a Kernel Function, thus producing a low-dimensional dense representation.
NystromMethod() - Constructor for class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
NystromMethod(List<Example>, Kernel) - Constructor for class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
Constructor of NystromMethod.
NystromMethod(List<Example>, Kernel, int) - Constructor for class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
NystromMethodEnsemble - Class in it.uniroma2.sag.kelp.linearization.nystrom
This class implements the Ensemble Nystrom Method to approximate the implicit space underlying a Kernel Function, thus producing a low-dimensional dense representation.
NystromMethodEnsemble(List<List<Example>>, Kernel) - Constructor for class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 
NystromMethodEnsemble() - Constructor for class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
 

O

ObjectSerializer - Interface in it.uniroma2.sag.kelp.utils
It is a serializer, i.e., an object that is able to convert objects into a String representation, preserving all their properties.
OneClassSvmClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm
It implements the One class SVM learning algorithm discussed in [CC Chang & CJ Lin, 2011].
OneClassSvmClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
 
OneClassSvmClassification(Kernel, Label, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
 
OneVsAllClassificationOutput - Class in it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
It is the output provided by a classifier operating in a one-vs-all schema.
OneVsAllClassificationOutput() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
 
OneVsAllClassifier - Class in it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
It is a multiclass classifier operating in a one-vs-all schema.
OneVsAllClassifier() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
OneVsAllLearning - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification
It is a meta algorithm that operates applying a One-Vs-All strategy over the base learning algorithm which is intended to be a binary learner.
OneVsAllLearning() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
 
OneVsAllPassiveAggressiveExample - Class in it.uniroma2.sag.kelp.examples.main
This example illustrates how to perform multiclass classification, with a One-Vs-All strategy with the Passive Aggressive Algorithm.
OneVsAllPassiveAggressiveExample() - Constructor for class it.uniroma2.sag.kelp.examples.main.OneVsAllPassiveAggressiveExample
 
OneVsAllSVMExample - Class in it.uniroma2.sag.kelp.examples.main
This example illustrates how to perform multiclass classification, with a One-Vs-All strategy with SVM.
OneVsAllSVMExample() - Constructor for class it.uniroma2.sag.kelp.examples.main.OneVsAllSVMExample
 
OneVsOneClassificationOutput - Class in it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
 
OneVsOneClassificationOutput() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
 
OneVsOneClassifier - Class in it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass
 
OneVsOneClassifier() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
OneVsOneLearning - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification
It is a meta algorithm that operates by applying a One-Vs-One strategy over the base learning algorithm which is intended to be a binary learner.
OneVsOneLearning() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
 
OnlineLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm
It is a Machine Learning algorithm which allows an incremental learning strategy, exploiting a single Example at a time
openBufferedReader(String) - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
 
orderedNodeSetByLabel - Variable in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
The complete set of tree nodes ordered alphabetically by label.
orderedNodeSetByProduction - Variable in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
The complete set of tree nodes ordered alphabetically by production string.
orderedNodeSetByProductionIgnoringLeaves - Variable in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
The of non terminal tree nodes ordered alphabetically by production string.

P

p - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
PAIR_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
PairSimilarityExtractor - Class in it.uniroma2.sag.kelp.data.manipulator
This manipulator manipulates ExamplePair object extracting some similarity scores between the left and the right examples of the pair.
PairSimilarityExtractor(String, Kernel...) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.PairSimilarityExtractor
 
PairwiseProductKernel - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) \cdot BK(x_2, y_2) + BK(x_1, y_2) \cdot BK(x_2, y_1)\)
where BK is another kernel the kernel on pairs relies on.
PairwiseProductKernel(Kernel, boolean) - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.PairwiseProductKernel
Defines a Kernel operating on pairs that applies the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) \cdot BK(x_2, y_2) + BK(x_1, y_2) \cdot BK(x_2, y_1)\)
PairwiseProductKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.PairwiseProductKernel
 
PairwiseSumKernel - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) + BK(x_2, y_2) + BK(x_1, y_2) + BK(x_2, y_1)\)
where BK is another kernel the kernel on pairs relies on.
PairwiseSumKernel(Kernel, boolean) - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.PairwiseSumKernel
Defines a Kernel operating on pairs that applies the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) + BK(x_2, y_2) + BK(x_1, y_2) + BK(x_2, y_1)\)
PairwiseSumKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.PairwiseSumKernel
 
parseCharniakSentence(String) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
This method allows to read a tree in the form (S(NP)(VP)) and returns the TreeNode corresponding to the root of the tree
parseExample(String) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
parseLabel(String) - Static method in class it.uniroma2.sag.kelp.data.label.LabelFactory
Initializes and returns the label described in labelDescription
parseNode(int, String, TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
 
parseRepresentation(String, String) - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationFactory
Initializes and returns the representation described in representationBody
parseSingleRepresentation(String) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
Parse a single Representation from its string representation
parseStructureElement(String, String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElementFactory
Initializes and returns the structureElement described in structureElementBody
parseStructureElement(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElementFactory
 
ParsingExampleException - Exception in it.uniroma2.sag.kelp.data.example
An Exception to model problems when parsing examples
ParsingExampleException(String) - Constructor for exception it.uniroma2.sag.kelp.data.example.ParsingExampleException
 
ParsingExampleException(Exception, String) - Constructor for exception it.uniroma2.sag.kelp.data.example.ParsingExampleException
 
PartialTreeKernel - Class in it.uniroma2.sag.kelp.kernel.tree
Partial Tree Kernel implementation.
PartialTreeKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
Default constructor.
PartialTreeKernel(float, float, float, String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
A Constructor for the Partial Tree Kernel in which parameters can be set manually.
PartialTreeKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
This constructor by default uses lambda=0.4, mu=0.4, terminalFactor=1
PassiveAggressive - Class in it.uniroma2.sag.kelp.learningalgorithm
It is an online learning algorithms that implements the Passive Aggressive algorithms described in [Crammer, JMLR2006] K.
PassiveAggressive() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
PassiveAggressive.Policy - Enum in it.uniroma2.sag.kelp.learningalgorithm
It is the updating policy applied by the Passive Aggressive Algorithm when a miss-prediction occurs
PassiveAggressiveClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
Online Passive-Aggressive Learning Algorithm for classification tasks.
PassiveAggressiveClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
PassiveAggressiveClassification.Loss - Enum in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
 
PassiveAggressiveRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
Online Passive-Aggressive Learning Algorithm for regression tasks.
PassiveAggressiveRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
 
PegasosLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos
It implements the Primal Estimated sub-GrAdient SOlver (PEGASOS) for SVM.
PegasosLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
PegasosLearningAlgorithm(int, float, int, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
Perceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
The perceptron learning algorithm algorithm for classification tasks.
Perceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
phi - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
Phi is the standard score computed from the confidence.
pointWiseProduct(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
pointWiseProduct(Vector) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Compute the point-wise product of this vector with the one in vector.
pointWiseProduct(Vector) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
policy - Variable in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
PolynomialKernel - Class in it.uniroma2.sag.kelp.kernel.standard
 
PolynomialKernel(float, float, float, Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
PolynomialKernel(float, Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
PolynomialKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
populate(String) - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDataset
 
populate(String) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Populate the dataset by reading it from a KeLP compliant file.
populate(DatasetReader) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Populate the dataset using the provided reader
POS_LEMMA_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
positiveClass - Variable in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
PosStructureElement - Class in it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement that contains Part-of-Speech , i.e.
PosStructureElement() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.PosStructureElement
 
PosStructureElement(String) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.PosStructureElement
 
pow(float, int) - Static method in class it.uniroma2.sag.kelp.utils.Math
It evaluates the power of a number
precision - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
Classifies an example applying the following formula: y(x) = \sum_{i \in SV}\alpha_i k(x_i, x) + b
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
 
predict(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.Classifier
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
predict(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
 
predict(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.regressionfunction.RegressionFunction
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
predictAndLearnWithAvailableBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
predictAndLearnWithAvailableBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
predictAndLearnWithAvailableBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
predictAndLearnWithAvailableBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
Learns from a single example applying a specific policy that must be adopted when the budget is reached
predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
Prediction - Interface in it.uniroma2.sag.kelp.predictionfunction
It is a generic output provided by a machine learning systems on a test data
PredictionFunction - Interface in it.uniroma2.sag.kelp.predictionfunction
It is a generic prediction function that can be learned with a machine learning algorithm
PredictionFunctionTypeResolver - Class in it.uniroma2.sag.kelp.predictionfunction
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of PredictionFunctions
PredictionFunctionTypeResolver() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
PreferenceKernel - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) + BK(x_2, y_2) - BK(x_1, y_2) - BK(x_2, y_1)\)
where BK is another kernel the preference kernel relies on.
PreferenceKernel(Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.PreferenceKernel
 
PreferenceKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.PreferenceKernel
 
printCounters(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
Print the counters of the specified Label l.
printExample(String...) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
prob - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
Problem - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
Describes the problem
Problem(Dataset, String, Label, Problem.LibLinearSolverType) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
 
Problem.LibLinearSolverType - Enum in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
 
productionBasedDeltaFunction(TreeNode, TreeNode, int, float, DeltaMatrix) - Static method in class it.uniroma2.sag.kelp.kernel.tree.TreeKernelUtils
Delta Function for tree kernels operation at production level, like SubTreeKernel and SubSetTreeKernel.
property - Variable in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
PruneNodeIfLeaf - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
Prune a node if it is a leaf.
PruneNodeIfLeaf() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeIfLeaf
 
PruneNodeLeafNumber - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
This strategy keeps the first numberOfLeavesToKeep leaves and discard all subsequent ones.
PruneNodeLeafNumber(int) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLeafNumber
 
PruneNodeLowerThanThreshold - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
Prune a node if the absolute value of the field weightfield (default value "weight") is lower than threshold.
PruneNodeLowerThanThreshold(double, String, boolean, double) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
PruneNodeLowerThanThreshold(double) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
PruneNodeLowerThanThreshold.nodeComparisonOperator - Enum in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
 
PruneNodeNumberOfChildren - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
This pruning strategy makes sure that a node does not have more than maxNumberOfChildren children.
PruneNodeNumberOfChildren(int) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeNumberOfChildren
 
pruneNodes(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.NodePruner
given a node, prunes any of the descendant nodes
pruneNodes(TreeNode) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeNumberOfChildren
 
PruneNodeThresholdAndDistanceFromSpecificLabelledNode - Class in it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
Prune a node if the following conditions are met: 1) there is no node at distance not greater than distance whose label starts with the prefix labelPrefix 2) the conditions specified in the parent class
PruneNodeThresholdAndDistanceFromSpecificLabelledNode(double, String, boolean, double, String, int) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
PruneNodeThresholdAndDistanceFromSpecificLabelledNode(double, String) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeThresholdAndDistanceFromSpecificLabelledNode
 
pruneTree(TreeRepresentation) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 

Q

QD - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Q-MATRIX is derived from kernel matrix: Q_{ij}=y_{i}*y_{j}*K_{ij}
QuestionClassification - Class in it.uniroma2.sag.kelp.examples.demo.qc
This class shows how to use Kelp to build a Question classifier.
QuestionClassification() - Constructor for class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassification
 
QuestionClassificationIncrementalLearning - Class in it.uniroma2.sag.kelp.examples.demo.qc
This class shows how to use Kelp to build a Question classifier.
QuestionClassificationIncrementalLearning() - Constructor for class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassificationIncrementalLearning
 
QuestionClassificationLearningFromJson - Class in it.uniroma2.sag.kelp.examples.demo.qc
 
QuestionClassificationLearningFromJson() - Constructor for class it.uniroma2.sag.kelp.examples.demo.qc.QuestionClassificationLearningFromJson
 

R

RandomExampleSelector - Class in it.uniroma2.sag.kelp.data.dataset.selector
This class allows selecting a subset of m examples from a Dataset according to a random selection policy.
RandomExampleSelector(int) - Constructor for class it.uniroma2.sag.kelp.data.dataset.selector.RandomExampleSelector
 
RandomExampleSelector(int, int) - Constructor for class it.uniroma2.sag.kelp.data.dataset.selector.RandomExampleSelector
 
RandomizedBudgetPerceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
It is a variation of the Randomized Budget Perceptron proposed in
RandomizedBudgetPerceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
RandomizedBudgetPerceptron(int, OnlineLearningAlgorithm, long, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
RbfKernel - Class in it.uniroma2.sag.kelp.kernel.standard
Radial Basis Function Kernel.
RbfKernel(float, Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
 
RbfKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
 
RCV1BinaryTextCategorization - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorization() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorization
 
RCV1BinaryTextCategorizationDCD - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationDCD() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCD
 
RCV1BinaryTextCategorizationDCDExperimentUtils - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationDCDExperimentUtils() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationDCDExperimentUtils
 
RCV1BinaryTextCategorizationLibLinear - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationLibLinear() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinear
 
RCV1BinaryTextCategorizationLibLinearExperimentUtils - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationLibLinearExperimentUtils() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationLibLinearExperimentUtils
 
RCV1BinaryTextCategorizationPA - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationPA() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPA
 
RCV1BinaryTextCategorizationPAExperimentUtils - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationPAExperimentUtils() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPAExperimentUtils
 
RCV1BinaryTextCategorizationPegasos - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationPegasos() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasos
 
RCV1BinaryTextCategorizationPegasosExperimentUtils - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationPegasosExperimentUtils() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationPegasosExperimentUtils
 
RCV1BinaryTextCategorizationSCW - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationSCW() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCW
 
RCV1BinaryTextCategorizationSCWExperimentUtils - Class in it.uniroma2.sag.kelp.examples.demo.rcv1
 
RCV1BinaryTextCategorizationSCWExperimentUtils() - Constructor for class it.uniroma2.sag.kelp.examples.demo.rcv1.RCV1BinaryTextCategorizationSCWExperimentUtils
 
readNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.CsvDatasetReader
 
readNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
Returns the next example
readNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.LibsvmDatasetReader
 
readNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetReader
Returns the next example
readValue(String, Class<T>) - Method in class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 
readValue(File, Class<T>) - Method in class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 
readValue(InputStream, Class<T>) - Method in class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 
readValue(String, Class<T>) - Method in interface it.uniroma2.sag.kelp.utils.ObjectSerializer
Deserializes an object that has been previously converted into a textual format
readValue(File, Class<T>) - Method in interface it.uniroma2.sag.kelp.utils.ObjectSerializer
Deserializes an object that has been previously converted into a textual format
readValue(InputStream, Class<T>) - Method in interface it.uniroma2.sag.kelp.utils.ObjectSerializer
Deserializes an object that has been previously converted into a textual format
recall - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
reconstruct_gradient() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Reconstruct inactive elements of G from G_bar and free variables
RegressionFunction - Interface in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
It is a generic regression prediction function, i.e.
RegressionLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm.regression
It is a learning algorithm that learn how to solve a generic regression task
RegressionOutput - Interface in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
It is the output of a generic Regressor
regressor - Variable in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
The regression function to be returned
regressor - Variable in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
 
RegressorEvaluator - Class in it.uniroma2.sag.kelp.utils.evaluation
This is an instance of an Evaluator.
RegressorEvaluator(List<Label>) - Constructor for class it.uniroma2.sag.kelp.utils.evaluation.RegressorEvaluator
 
removeAdditionalInformation(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
 
Representation - Interface in it.uniroma2.sag.kelp.data.representation
It is a generic way to represent an object that is intended to be exploited through Machine Learning techniques.
representation - Variable in class it.uniroma2.sag.kelp.kernel.DirectKernel
 
REPRESENTATION_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
REPRESENTATION_TYPE_NAME_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
 
RepresentationFactory - Class in it.uniroma2.sag.kelp.data.representation
It is a factory that provides methods for instantiating a representation described in a textual format The factory is able to automatically support all the implementations of the class Representation that have an empty constructor and that have been included in the project (as local class or imported via Maven)
RepresentationTypeResolver - Class in it.uniroma2.sag.kelp.data.representation
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of Representations
RepresentationTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
reset() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Reset the reading pointer
reset() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
reset() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Resets the kernel statistics (number of kernel computations, cache hits and misses)
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
This method will cause the reset of all the base algorithms
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
This method will cause the reset of all the base algorithms
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
This method will cause the reset of all the base algorithms
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
reset() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
Resets all the learning process, returning to the default state.
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
reset() - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.Model
Resets the model parameters to the default state.
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
reset() - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
Resets all the predictor parameters to the default state.
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
resetCacheStats() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
Sets cache hits and misses to 0
restartReading() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
Resets the reading such that the next example will be the first one
restartReading() - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetReader
Resets the reading such that the next example will be the first one
root - Variable in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
The root node of the tree
RRB - Static variable in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
The right parenthesis character within the tree

S

SameAdditionalInfoStructureElementSimilarity - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity
Implements a similarity between StructureElements that first verifies whether two structure elements contains the same additional informations in a list of specified additional informations.
SameAdditionalInfoStructureElementSimilarity() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
 
SameAdditionalInfoStructureElementSimilarity(List<String>, StructureElementSimilarityI) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
 
save(String) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Save the dataset in a file.
save(Kernel, String) - Static method in class it.uniroma2.sag.kelp.kernel.Kernel
Save the input kernel in a file.
save(String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
Save a Nystrom-based projection function in a file.
save(String) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethodEnsemble
Save an Ensemble of Nystrom projectors on file.
scale(float) - Method in interface it.uniroma2.sag.kelp.data.representation.Normalizable
Multiplies each element of this representation by coeff
scale(float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
scale(float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
SCWType - Enum in it.uniroma2.sag.kelp.learningalgorithm.classification.scw
The two types of Soft Confidence-Weighted implemented variants
select(Dataset) - Method in interface it.uniroma2.sag.kelp.data.dataset.selector.ExampleSelector
This function allows to select a subset of Examples from the input Dataset
select(Dataset) - Method in class it.uniroma2.sag.kelp.data.dataset.selector.FirstExamplesSelector
 
select(DatasetReader) - Method in class it.uniroma2.sag.kelp.data.dataset.selector.FirstExamplesSelector
This function allows to select a subset of Examples from the input DatasetReader
select(Dataset) - Method in class it.uniroma2.sag.kelp.data.dataset.selector.RandomExampleSelector
 
select_working_set(LibSvmSolver.Pair) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
 
select_working_set(LibSvmSolver.Pair) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
 
select_working_set(LibSvmSolver.Pair) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Select the working set in each iteration.
SelectRepresentationFromExample - Class in it.uniroma2.sag.kelp.data.representation.tree.utils
 
SelectRepresentationFromExample(String, SelectRepresentationFromExample.representationSelectorInExample) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
SelectRepresentationFromExample(String) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
SelectRepresentationFromExample.representationSelectorInExample - Enum in it.uniroma2.sag.kelp.data.representation.tree.utils
 
SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
SEQDELIM - Static variable in class it.uniroma2.sag.kelp.data.example.SequenceExample
This delimiter is used in the construction of artificial features representing transitions for the SequenceClassificationLearningAlgorithm
SEQDELIM - Static variable in class it.uniroma2.sag.kelp.data.example.SequencePath
This delimiter is used in the construction of artificial features representing transitions for the SequenceClassificationLearningAlgorithm
SequenceClassificationKernelBasedLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
/** This class implements a sequential labeling paradigm.
SequenceClassificationKernelBasedLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
SequenceClassificationKernelBasedLearningAlgorithm(BinaryLearningAlgorithm, int, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
SequenceClassificationLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
This class implements a sequential labeling paradigm.
SequenceClassificationLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
SequenceClassificationLinearLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
This class implements a sequential labeling paradigm.
SequenceClassificationLinearLearningAlgorithm(BinaryLearningAlgorithm, int, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm
 
SequenceDataset - Class in it.uniroma2.sag.kelp.data.dataset
A dataset made of SequenceExamples
SequenceDataset() - Constructor for class it.uniroma2.sag.kelp.data.dataset.SequenceDataset
 
SequenceDatasetReader - Class in it.uniroma2.sag.kelp.data.dataset
The methods of this class allows to read SequenceExamples from a file
SequenceDatasetReader(String) - Constructor for class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetReader
 
SequenceDatasetWriter - Class in it.uniroma2.sag.kelp.data.dataset
* The methods of this class allows to write SequenceExamples into a file
SequenceDatasetWriter(String) - Constructor for class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetWriter
 
SequenceElement - Class in it.uniroma2.sag.kelp.data.representation.sequence
A SequenceElement is a generic element in a SequenceRepresentation
SequenceElement(StructureElement) - Constructor for class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
 
SequenceElement(StructureElement, SequenceElement, SequenceElement) - Constructor for class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
 
SequenceEmission - Class in it.uniroma2.sag.kelp.data.label
It represents the pair (class_label,emission_score) assigned to each element in a sequence.
SequenceEmission(Label, float) - Constructor for class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
SequenceExample - Class in it.uniroma2.sag.kelp.data.example
A SequenceExample represents a sequence of Examples s, each containing a set of Representations and a set of Labels.
SequenceExample() - Constructor for class it.uniroma2.sag.kelp.data.example.SequenceExample
 
SequenceExampleGenerator - Interface in it.uniroma2.sag.kelp.data.examplegenerator
A SequenceExampleGenerator generates a copy of an input Example (reflecting an item in a SequenceExample) enriched with information derived from the
SequenceExampleGeneratorKernel - Class in it.uniroma2.sag.kelp.data.examplegenerator
A SequenceExampleGeneratorKernelBasedAlg allows to implicitly enrich a targeted Example (reflecting an item in a SequenceExample) with information derived from the
SequenceExampleGeneratorKernel() - Constructor for class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
SequenceExampleGeneratorKernel(int, String) - Constructor for class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
SequenceExampleGeneratorLinear - Class in it.uniroma2.sag.kelp.data.examplegenerator
A SequenceExampleGeneratorLinearAlg allows to explicitly enrich a targeted Example (reflecting an item in a SequenceExample) with information derived from the
SequenceExampleGeneratorLinear() - Constructor for class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
SequenceExampleGeneratorLinear(int, String, float) - Constructor for class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
SequenceExampleGeneratorTypeResolver - Class in it.uniroma2.sag.kelp.data.examplegenerator
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of SequenceExamplesGenerators
SequenceExampleGeneratorTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
SequenceKernel - Class in it.uniroma2.sag.kelp.kernel.sequence
Sequence Kernel implementation.
SequenceKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
SequenceKernel(String, int, float) - Constructor for class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
SequenceKernelExample - Class in it.uniroma2.sag.kelp.examples.main
This example illustrates how to use the sequence kernel on a Sentiment Analysis task.
SequenceKernelExample() - Constructor for class it.uniroma2.sag.kelp.examples.main.SequenceKernelExample
 
SequenceLearningKernelMain - Class in it.uniroma2.sag.kelp.examples.demo.seqlearn
This class shows how to use a SequenceClassificationLearningAlgorithm.
SequenceLearningKernelMain() - Constructor for class it.uniroma2.sag.kelp.examples.demo.seqlearn.SequenceLearningKernelMain
 
SequenceLearningLinearMain - Class in it.uniroma2.sag.kelp.examples.demo.seqlearn
This class shows how to use a SequenceClassificationLearningAlgorithm.
SequenceLearningLinearMain() - Constructor for class it.uniroma2.sag.kelp.examples.demo.seqlearn.SequenceLearningLinearMain
 
SequenceModel - Class in it.uniroma2.sag.kelp.predictionfunction.model
This class implements a model produced by a SequenceClassificationLearningAlgorithm
SequenceModel() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
SequenceModel(PredictionFunction, SequenceExampleGenerator) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
SequencePath - Class in it.uniroma2.sag.kelp.data.example
This class defines the output of a sequence labeling process.
SequencePath() - Constructor for class it.uniroma2.sag.kelp.data.example.SequencePath
 
SequencePrediction - Class in it.uniroma2.sag.kelp.predictionfunction
It is a output provided by a machine learning systems on a sequence.
SequencePrediction() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.SequencePrediction
 
SequencePredictionFunction - Class in it.uniroma2.sag.kelp.predictionfunction
This class implements a classifier in a sequence labeling process.
SequencePredictionFunction() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
SequencePredictionFunction(SequenceModel) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
SequenceRepresentation - Class in it.uniroma2.sag.kelp.data.representation.sequence
Sequence Representation used for example to represent a sentence (i.e.
SequenceRepresentation() - Constructor for class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
 
SerializationExample - Class in it.uniroma2.sag.kelp.examples.main
This example illustrates how to serialize and deserialize learning algorithms, as well as classification functions.
SerializationExample() - Constructor for class it.uniroma2.sag.kelp.examples.main.SerializationExample
 
setA(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
setAdditionalInfos(List<String>) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
Sets the list of additionalInfos two structure elements must both have or not have in order to have a non zero similarity
setAllowDifferentPOS(boolean) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
Sets whether the similarity between words having different Part-of-Speech is allowed or if it must be set to 0
setAlpha(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
Sets the learning rate, i.e.
setAlphas(float[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
setAssignedSequenceLabels(List<SequenceEmission>) - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
setB(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm
 
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
This method will set the type of the base algorithms to be learned.
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
This method will set the type of the base algorithms to be learned.
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
This method will set the type of the base algorithms to be learned.
setBaseAlgorithm(LearningAlgorithm) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.MetaLearningAlgorithm
 
setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
setBaseKernel(Kernel) - Method in class it.uniroma2.sag.kelp.kernel.KernelComposition
 
setBaseLearningAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
setBasePredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
setBaseSimilarity(StructureElementSimilarityI) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
Sets the base similarity applied when two structure elements have the same additional infos
setBeamSize(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
setBeamSize(int) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
setBias(float) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
 
setBinaryClassifiers(Classifier[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
setBinaryClassifiers(Classifier[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
setBinaryClassifiers(Classifier[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
setBudget(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
Sets the budget, i.e.
setC(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setC(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
setC(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setC(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
setC(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
setC(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
setCentroid(Vector) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster
 
setChildren(ArrayList<TreeNode>) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Set the direct children of the target node
setClassificationLabels(HashSet<Label>) - Method in class it.uniroma2.sag.kelp.data.example.Example
 
setClassName(String) - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
 
setCn(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setCn(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setCn(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
setCn(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setCn(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
setCn(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setComparisonTypeLowerThanAbsoluteValue() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
setComparisonTypeLowerThanValue() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
setContent(StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
Sets the content of this node
setContent(StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
Sets the content of this SequenceElement
setContent(StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
setCp(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setCp(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setCp(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
setCp(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setCp(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
setCp(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.graph.DirectedGraphRepresentation
 
setDataFromText(String) - Method in interface it.uniroma2.sag.kelp.data.representation.Representation
Initializes a Representation using its textual description provided in representationDescription
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.PosStructureElement
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
Initializes a StructureElement using its textual description provided in structureElementDescription
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.SyntacticStructureElement
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.UntypedStructureElement
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Set the values of the vectors according to the input text, which is expected to be a sequence of numbers separated by a white space, or a comma, or a semicolon
setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
setDcdLoss(DCDLoss) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setDefaultWeightValue(double) - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
setDegree(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
 
setDeletingPolicy(BudgetedPassiveAggressiveClassification.DeletingPolicy) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
Deprecated.
setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
Deprecated.
setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
Deprecated.
setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
Deprecated.
setDependencyRelation(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
setDist(Float) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
setEmission(float) - Method in class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
setEnrichmentName(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
setEnrichmentName(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Sets the identifier of the vectors associated to a StructureElement during the manipulation operation performed by a Manipulator (i.e.
setEpochs(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
setEps(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
setEpsilon(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
Sets epsilon, i.e.
setEta(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setExample(Example) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
setExamples(Vector<ClusterExample>) - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
This function initialize the set of objects inside the cluster
setExamplesToStore(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
Sets the maximum number of examples whose pairwise kernel computations can be simultaneously stored
setExamplesToStore(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
Sets the maximum number of norms that can be simultaneously stored
setExamplesToStore(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
Sets the maximum number of examples whose pairwise kernel computations can be simultaneously stored
setExamplesToStore(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
Sets the maximum number of examples whose pairwise kernel computations can be simultaneously stored
setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setFeatureValue(Object, float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
setFeatureValue(Object, float) - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
Assigns value to the feature identified by featureIdentifier
setFeatureValue(String, float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
Sets the value of a feature
setFeatureValue(Object, float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
setFeatureValues(double[]) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Sets the feature values.
setFeatureValues(DenseMatrix64F) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
Sets the feature values.
setFirst(Vector) - Method in exception it.uniroma2.sag.kelp.data.representation.vector.exception.VectorOperationException
 
setGamma(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
 
setHead(LexicalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
setHyperplane(Vector) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
setIgnorePosInLemmaMatches(boolean) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
Sets whether two lexical structure elements must provide a perfect match if their lemmas are the same, regardless their part-of-speeches
setIgnorePosOnLexicals(boolean) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.ExactMatchingStructureElementSimilarity
Sets whether the part-of-speech must be ignored in comparing two LexicalStructureElements
setIncludeLeaves(boolean) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
Sets whether the leaves must be involved in the kernel computation.
setIncludeLeaves(boolean) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
Sets whether the leaves must be involved in the kernel computation.
setInstance(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
 
setIntraPairSimProduct(boolean) - Method in class it.uniroma2.sag.kelp.kernel.pairs.BestPairwiseAlignmentKernel
Sets whether adding or not to the kernel combination an extra term equivalent to the multiplication of the intra-pair similarities, i.e.: \(BK(x_1,x_2) \cdot BK(y_1,y_2)\)
setIntraPairSimProduct(boolean) - Method in class it.uniroma2.sag.kelp.kernel.pairs.PairwiseProductKernel
Sets whether adding or not to the kernel combination an extra term equivalent to the multiplication of the intra-pair similarities, i.e.: \(BK(x_1,x_2) \cdot BK(y_1,y_2)\)
setIntraPairSimProduct(boolean) - Method in class it.uniroma2.sag.kelp.kernel.pairs.PairwiseSumKernel
Sets whether adding or not to the kernel combination an extra term equivalent to the multiplication of the intra-pair similarities, i.e.: \(BK(x_1,x_2) \cdot BK(y_1,y_2)\)
setIntraPairSimProduct(boolean) - Method in class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseProductKernel
Sets whether adding or not to the kernel combination an extra term equivalent to the multiplication of the intra-pair similarities, i.e.: \(BK(x_1,x_2) \cdot BK(y_1,y_2)\)
setIntraPairSimProduct(boolean) - Method in class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseSumKernel
Sets whether adding or not to the kernel combination an extra term equivalent to the multiplication of the intra-pair similarities, i.e.: \(BK(x_1,x_2) \cdot BK(y_1,y_2)\)
setIterationNumber(int) - Method in class it.uniroma2.sag.kelp.data.manipulator.WLSubtreeMapper
Sets the maximum depth of the visits of the WL kernel
setIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
Sets the number of iterations
setK(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
Sets the number of examples k that Pegasos exploits in its mini-batch learning approach
setK(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
setK(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Sets the kernel to be used in comparing two vectors
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
setKernel(Kernel) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.KernelMethod
Sets the kernel this
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
setKernel(Kernel) - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
 
setKernelCache(KernelCache) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Sets the cache in which storing the kernel operations in the RKHS defined by this kernel
setKernelCache(KernelCache) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationKernelBasedLearningAlgorithm
 
setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
Deprecated.
 
setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
Deprecated.
 
setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixSizeKernelCache
 
setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
Stores a kernel computation in cache
setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.SimpleDynamicKernelCache
 
setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
setLabel(String) - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
setLabel(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.UntypedStructureElement
 
setLabel(Label) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
setLabel(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
setLabels(Label[]) - Method in class it.uniroma2.sag.kelp.data.example.Example
Sets the example classificationLabels
setLabels(List<Label>) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
Set the labels associated to this multi-classifier.
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
Set the labels associated to this multi-classifier.
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
Set the labels associated to this multi-classifier.
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setLabels(List<Label>) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
Sets the labels representing the concept to be learned.
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
setLabels(Label...) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
setLabels(List<Label>) - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
Sets the labels representing the concept to be predicted.
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
Set the decay factor
setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
Set the decay factor
setLambda(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
Sets the regularization coefficient
setLandmarks(List<Example>) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
setLemma(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
setLoss(PassiveAggressiveClassification.Loss) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
 
setMargin(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
Sets the desired margin, i.e.
setMarkingPrefix(String) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger
Sets the prefix used to mark the related nodes
setMatrixPath(String) - Method in class it.uniroma2.sag.kelp.wordspace.Wordspace
Sets the path of the file where the word vectors are stored and loads them.
setMaxEmissionCandidates(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
setMaxEmissionCandidates(int) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
setMaximumDepthOfVisits(int) - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
setMaxIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setMaxIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
 
setMaxIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
setMaxNumberOfRows(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
setMaxSubseqLeng(int) - Method in class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
 
setMaxSubseqLeng(int) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
setMaxSubseqLeng(int) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.MultiLabelClassifier
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
 
setModel(Model) - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
Sets the model
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
 
setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction
 
setModels(List<BinaryModel>) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
 
setModifier(LexicalStructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
 
setMu(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
setMu(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
setNegativeLabelsForClassifier(Label[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
Set the negative label classifier array
setNext(SequenceElement) - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
Sets the previous element in the sequence
setNodeSimilarity(StructureElementSimilarityI) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
setNu(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
setNu(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
 
setNumberOfColumns(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
setObj(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
setP(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
setPaths(List<SequencePath>) - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePrediction
 
setPolicy(PassiveAggressive.Policy) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
 
setPos(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.LexicalStructureElement
 
setPos(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.PosStructureElement
 
setPosRestriction(boolean) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setPredictionFunction(PredictionFunction) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
Sets the predictionFunction learned during the training process.
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
 
setPredictionFunction(PredictionFunction) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
setpReg(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
setPrevious(SequenceElement) - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceElement
Sets the previous element in the sequence
setProjectionMatrix(List<Double>) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
setProperty(Label) - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
 
setRank(int) - Method in class it.uniroma2.sag.kelp.linearization.nystrom.NystromMethod
 
setRegressionValues(ArrayList<NumericLabel>) - Method in class it.uniroma2.sag.kelp.data.example.Example
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.kernel.DirectKernel
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setRepresentation(String) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LinearMethod
Sets the representation this learning algorithm will exploit
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
 
setRepresentation(String) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
 
setRepresentationName(String) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorLinear
 
setRepresentationName(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansEngine
 
setRepresentations(HashMap<String, Representation>) - Method in class it.uniroma2.sag.kelp.data.example.Example
Sets the example representations
setRepresentations(HashMap<String, Representation>) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
setRepresentationToBeEnriched(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.dilation.CompositionalNodeSimilarityDilation
 
setRepresentationToBeEnriched(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.product.CompositionalNodeSimilarityProduct
 
setRepresentationToBeEnriched(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.sum.CompositionalNodeSimilaritySum
 
setRho(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
setScore(Double) - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
setScwType(SCWType) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
setSecond(Vector) - Method in exception it.uniroma2.sag.kelp.data.representation.vector.exception.VectorOperationException
 
setSeed(long) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
Sets the seed of the random generator used to shuffling examples and getting random examples
setSeed(long) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
 
setSeed(long) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
Sets the seed for the random generator adopted to select the support vector to delete
setSeed(long) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setSelectionAllExamples() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
setSelectionToLeftExampleInPairOnly() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
setSelectionToRightExampleInPairOnly() - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample
 
setSequenceExampleGenerator(SequenceExampleGenerator) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm
 
setSequenceExampleGenerator(SequenceExampleGenerator) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SequenceModel
 
setSimilarityThreshold(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
setSize(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
Sets the size of the cache, i.e.
setSquaredNormCache(SquaredNormCache) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Sets the cache in which storing the squared norms in the RKHS defined by this kernel
setSquaredNormValue(Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexSquaredNormCache
 
setSquaredNormValue(Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
 
setSquaredNormValue(Example, float) - Method in interface it.uniroma2.sag.kelp.kernel.cache.SquaredNormCache
Stores a squared norm in the cache
setSupportVector(SupportVector, int) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
Substitutes the support vector in position position with sv
setSupportVectors(List<SupportVector>) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
setSyntacticRelation(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.SyntacticStructureElement
 
setSyntacticRestriction(boolean) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
 
setTerminalFactor(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
 
setTerminalFactor(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
setToCombine(List<Kernel>) - Method in class it.uniroma2.sag.kelp.kernel.KernelCombination
 
setToCombine(List<LearningAlgorithm>) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.EnsembleLearningAlgorithm
 
setTransitionRepresentationName(String) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
setTransitionsOrder(int) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorKernel
 
setUnbiased(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
Sets whether the bias, i.e.
setUpper_bound_n(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
Set the \(C_n\) value
setUpper_bound_p(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
Set the \(C_p\) value
setUseBias(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLearningAlgorithm
 
setValue(float) - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
 
setValue(double) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeature
 
setValue(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
setVector(TIntFloatMap) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
setVisitTypePostOrder() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
setVisitTypePreOrder() - Method in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
setWeight(float) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
 
setWeights(List<Float>) - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
 
setWordCounter(int) - Static method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
setWordspace(WordspaceI) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
Sets the wordspace from which the vectors associated to a word must be retrieved
ShortestPathKernel - Class in it.uniroma2.sag.kelp.kernel.graph
Implementation of the Shortest Path Kernel for Graphs Reference paper: [1] K.
ShortestPathKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.graph.ShortestPathKernel
 
ShortestPathKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.graph.ShortestPathKernel
 
shrinkingIteration - Static variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Number of iterations to be accomplished before shrinking
shuffleExamples(Random) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Shuffles the examples in the dataset
sim(StructureElement, StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.compositional.CompositionalNodeSimilarity
This method computes the similarity between two compositional nodes by applying the "sum" operator.
sim(StructureElement, StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.ExactMatchingStructureElementSimilarity
 
sim(StructureElement, StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.LexicalStructureElementSimilarity
 
sim(StructureElement, StructureElement) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.SameAdditionalInfoStructureElementSimilarity
 
sim(StructureElement, StructureElement) - Method in interface it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityI
This function measure the similarity between structure elements
SimpleDataset - Class in it.uniroma2.sag.kelp.data.dataset
A SimpleDataset that represent a whole dataset in memory.
SimpleDataset() - Constructor for class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Initializes an empty dataset
SimpleDynamicKernelCache - Class in it.uniroma2.sag.kelp.kernel.cache
Cache for kernel computations.
SimpleDynamicKernelCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.SimpleDynamicKernelCache
 
SimpleExample - Class in it.uniroma2.sag.kelp.data.example
An Example composed by a set of Representations.
SimpleExample() - Constructor for class it.uniroma2.sag.kelp.data.example.SimpleExample
Initializes an empty example (0 labels and 0 representations)
SimpleExample(Label[], HashMap<String, Representation>) - Constructor for class it.uniroma2.sag.kelp.data.example.SimpleExample
Initializes a SimpleExample with the input representations and labels
size() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
This functions returns the number of objects inside the cluster
sizeI - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
skipExample() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
Skip an example: this function is useful when an example cannot be read
SmoothedPartialTreeKernel - Class in it.uniroma2.sag.kelp.kernel.tree
Partial Tree Kernel implementation.
SmoothedPartialTreeKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
SmoothedPartialTreeKernel(float, float, float, float, StructureElementSimilarityI, String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
 
SoftConfidenceWeightedClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.scw
Implements Exact Soft Confidence-Weighted (SCW) algorithms, an on-line learning algorithm for binary classification.
SoftConfidenceWeightedClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
SoftConfidenceWeightedClassification(SCWType, float, float, float, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
SoftConfidenceWeightedClassification(Label, SCWType, float, float, float, boolean, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SoftConfidenceWeightedClassification
 
softmax(float, float) - Static method in class it.uniroma2.sag.kelp.utils.Math
Approximates the max of two values with the following formula: \(softmax(a,b) = \frac{log(e^{Fa} + e^{Fb})}{F}\)
solve(int, Dataset, float[], int[], float[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
It solves the SMO algorithm in [CC Chang & CJ Lin, 2011] min 0.5(\alpha^T Q \alpha) + p^T \alpha y^T \alpha = \delta
y_i = +1 or -1
0 <= alpha_i <= Cp for y_i = 1
0 <= alpha_i <= Cn for y_i = -1
Given: Q, p, y, Cp, Cn, and an initial feasible point \alpha l is the size of vectors and matrices eps is the stopping tolerance solution will be put in \alpha, objective value will be put in obj
sortAscendingOrder() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
 
sortDescendingOrder() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
 
SparseVector - Class in it.uniroma2.sag.kelp.data.representation.vector
Sparse Feature Vector.
SparseVector() - Constructor for class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
split(float) - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDataset
 
split(float) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Returns two datasets created by splitting this dataset accordingly to percentage.
splitClassDistributionInvariant(float) - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDataset
 
splitClassDistributionInvariant(float) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
Returns two datasets created by splitting this dataset accordingly to percentage.
squaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the squared norm of the given example in the RKHS defined by this kernel
squaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.NormalizationKernel
 
SquaredNormCache - Interface in it.uniroma2.sag.kelp.kernel.cache
Cache for store squared norms
SquaredNormCacheTypeResolver - Class in it.uniroma2.sag.kelp.kernel.cache
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of SquaredNormCaches
SquaredNormCacheTypeResolver() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
squaredNormOfTheDifference(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
Returns the squared norm of the difference between the given examples in the RKHS.
StandardizationManipulator - Class in it.uniroma2.sag.kelp.data.manipulator
It standardizes the feature values of a vectorial representation.
StandardizationManipulator(String, List<Example>) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.StandardizationManipulator
Constructor
standardize(Vector) - Method in class it.uniroma2.sag.kelp.data.manipulator.StandardizationManipulator
It standardizes the feature values of vector.
StaticDeltaMatrix - Class in it.uniroma2.sag.kelp.kernel.tree.deltamatrix
 
StaticDeltaMatrix() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.StaticDeltaMatrix
 
StaticDeltaMatrix(int) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.deltamatrix.StaticDeltaMatrix
 
Stoptron - Class in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
It is a variation of the Stoptron proposed in
Stoptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
Stoptron(int, OnlineLearningAlgorithm, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
 
StringLabel - Class in it.uniroma2.sag.kelp.data.label
It is a label consisting of an String value.
StringLabel(String) - Constructor for class it.uniroma2.sag.kelp.data.label.StringLabel
Initializes a label to a specific String value
StringLabel() - Constructor for class it.uniroma2.sag.kelp.data.label.StringLabel
 
StringRepresentation - Class in it.uniroma2.sag.kelp.data.representation.string
String representation.
StringRepresentation() - Constructor for class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
Empty constructor necessary for making RepresentationFactory support this implementation.
StringRepresentation(String) - Constructor for class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
Initializing constructor.
StripeKernelCache - Class in it.uniroma2.sag.kelp.kernel.cache
Given a dataset, this cache stores kernel computations in "Stripes", i.e.
StripeKernelCache(Dataset) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
StripeKernelCache(Dataset, int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
StripeKernelCache(int, int) - Constructor for class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
StripeKernelCache() - Constructor for class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
 
StructureElement - Class in it.uniroma2.sag.kelp.data.representation.structure
This class represent the atomic element of a discrete structure.
StructureElement() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.StructureElement
 
StructureElementFactory - Class in it.uniroma2.sag.kelp.data.representation.structure
This class implement a Factory Design pattern to instantiate StructureElement given a string representing it.
StructureElementFilter - Interface in it.uniroma2.sag.kelp.data.representation.structure.filter
This interface provides methods for selecting structureElements accordingly to a policy specified by the classes implementing this interface
StructureElementSimilarityI - Interface in it.uniroma2.sag.kelp.data.representation.structure.similarity
This interface is used to implement similarity functions between Structured Element
StructureElementSimilarityTypeResolver - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of classes implementing StructureElementSimilarityI, that are used to estimate a similarity function between StructuredElements
StructureElementSimilarityTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
SubSetTreeKernel - Class in it.uniroma2.sag.kelp.kernel.tree
A SubSetTree Kernel is a convolution kernel that evaluates the tree fragments shared between two trees.
SubSetTreeKernel(float, String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
SubTree Kernel
SubSetTreeKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
SubTree Kernel constructor.
SubSetTreeKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
SubTree Kernel: default constructor.
substituteSupportVector(int, Example, float) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
 
SubTreeKernel - Class in it.uniroma2.sag.kelp.kernel.tree
SubTree Kernel implementation.
SubTreeKernel(float, String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
SubTree Kernel
SubTreeKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
SubTree Kernel constructor.
SubTreeKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
SubTree Kernel: default constructor.
subXTv(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 
SupportVector - Class in it.uniroma2.sag.kelp.predictionfunction.model
It is a support vector for kernel methods consisting of an example and the associated weight
SupportVector(float, Example) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
 
SupportVector() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
 
SvmSolution - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
It is the instance of a solution provided the LIBSVM solver of the SMO optimization problem.
SvmSolution() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
 
swap(LibSvmSolver.AlphaStatus[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
swap(Example[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
swap(float[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
swap(int[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
swap_index(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
Swap the info of two examples
swap_index(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
 
SyntacticStructureElement - Class in it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement that contains the syntactic informations, i.e.
SyntacticStructureElement() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.SyntacticStructureElement
 
SyntacticStructureElement(String) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.SyntacticStructureElement
 

T

TAU - Static variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
A small positive constant to
test(PredictionFunction, Evaluator, Dataset) - Static method in class it.uniroma2.sag.kelp.utils.ExperimentUtils
Evaluates a prediction function over a testset
threshold - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
threshold - Static variable in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
tn - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
toCombine - Variable in class it.uniroma2.sag.kelp.kernel.KernelCombination
 
toString() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
 
toString() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
 
toString() - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
 
toString() - Method in class it.uniroma2.sag.kelp.data.example.SequenceExample
 
toString() - Method in class it.uniroma2.sag.kelp.data.example.SequencePath
 
toString() - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
 
toString() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
 
toString() - Method in class it.uniroma2.sag.kelp.data.label.SequenceEmission
 
toString() - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.graph.GraphNode
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNodePairs
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
 
toString() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
 
toString() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
toString() - Method in class it.uniroma2.sag.kelp.predictionfunction.SequencePrediction
 
toString() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
total - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
totalFn - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
totalFp - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
totalTn - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
totalTp - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
 
tp - Variable in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator.ClassStats
 
TreeAddAdditionalInfoFromArray - Class in it.uniroma2.sag.kelp.data.manipulator
The class adds to an additional field to a selected number of nodes.
TreeAddAdditionalInfoFromArray(TreeNodeSelector, List<List<Double>>, String, SelectRepresentationFromExample) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
Create a manipulator which adds an additional field to selected tree nodes whose values are determined by the parameter info.
TreeAddAdditionalInfoFromArray(TreeNodeSelector, List<List<Double>>, String, String) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
Create a manipulator which adds an additional field to selected tree nodes whose values are determined by the parameter info.
TreeAddAdditionalInfoFromArray(TreeNodeSelector, List<List<Double>>, String, String, boolean) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
Create a manipulator which adds an additional field to selected tree nodes whose values are given by the List> info passed as parameter to the constructor.
TreeAddAdditionalInfoFromArray(TreeNodeSelector, List<List<Double>>, String, boolean) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeAddAdditionalInfoFromArray
Create a manipulator which adds an additional field to selected tree nodes whose values are given by the List> info passed as parameter to the constructor.
TreeIO - Class in it.uniroma2.sag.kelp.data.representation.tree.utils
Parse a tree in a string format.
TreeIO() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
 
TreeIOException - Exception in it.uniroma2.sag.kelp.data.representation.tree.utils
This exception is trown if any problem in the tree IO phase is experimented
TreeIOException(String) - Constructor for exception it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIOException
 
TreeKernelUtils - Class in it.uniroma2.sag.kelp.kernel.tree
Class providing some static methods useful for various tree kernels
TreeKernelUtils() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.TreeKernelUtils
 
TreeNode - Class in it.uniroma2.sag.kelp.data.representation.tree.node
A TreeNode represents a generic node in a TreeRepresentation
TreeNode(int, StructureElement, TreeNode) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
 
TreeNodeFilter - Interface in it.uniroma2.sag.kelp.data.representation.tree.node.filter
This interface provides methods for selecting tree nodes accordingly to a policy specified by the classes implementing this interface
TreeNodePairs - Class in it.uniroma2.sag.kelp.data.representation.tree.node
This class represents a node pairs, used in the various tree kernel formulation to compare two subtrees rooted in the tree node pairs.
TreeNodePairs(TreeNode, TreeNode) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNodePairs
 
TreeNodePruner - Class in it.uniroma2.sag.kelp.data.manipulator
A manipulator for performing pruning on a tree.
TreeNodePruner(NodeToBePrunedCheckerAbstractClass, SelectRepresentationFromExample, NodeToBePrunedCheckerAbstractClass, TreeNodeSelector, int) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
Create a tree pruner which performs a post-order traversal of the tree.
TreeNodePruner(NodePruner, SelectRepresentationFromExample, TreeNodeSelector, int) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
Create a tree pruner which performs a pre-order traversal of the tree.
TreeNodePruner(NodeToBePrunedCheckerAbstractClass, String, NodeToBePrunedCheckerAbstractClass, TreeNodeSelector, int) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
TreeNodePruner(NodeToBePrunedCheckerAbstractClass, String) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
TreeNodePruner(NodeToBePrunedCheckerAbstractClass, String, boolean) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
TreeNodePruner(NodePruner, String, TreeNodeSelector, int) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
Create a tree pruner which performs a pre-order traversal of the tree.
TreeNodePruner.visitType - Enum in it.uniroma2.sag.kelp.data.manipulator
 
TreeNodeSelector - Interface in it.uniroma2.sag.kelp.data.representation.tree.utils
An interface for selecting a list of nodes of a tree.
TreeNodeSelectorAllChildren - Class in it.uniroma2.sag.kelp.data.representation.tree.utils
 
TreeNodeSelectorAllChildren() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllChildren
 
TreeNodeSelectorAllDescendants - Class in it.uniroma2.sag.kelp.data.representation.tree.utils
 
TreeNodeSelectorAllDescendants() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllDescendants
 
TreeNodeSelectorAllLeaves - Class in it.uniroma2.sag.kelp.data.representation.tree.utils
 
TreeNodeSelectorAllLeaves() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeNodeSelectorAllLeaves
 
TreeNodeSimilarity - Interface in it.uniroma2.sag.kelp.data.representation.tree.node.similarity
Interface to be implemented by class defining a similarity metric between TreeNodes
TreePairRelTagger - Class in it.uniroma2.sag.kelp.data.manipulator
This manipulator establishes relations between two tree representations.
TreePairRelTagger(int, int, String, TreeNodeFilter, TreePairRelTagger.MARKING_POLICY, TreeNodeSimilarity, float) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger
Constructor for TreePairRelTagger
TreePairRelTagger.MARKING_POLICY - Enum in it.uniroma2.sag.kelp.data.manipulator
The marking policy applied by TreePairRelTagger
TreePruningDemo - Class in it.uniroma2.sag.kelp.examples.demo.pruning
This class shows how to use Kelp to prune a tree dataset.
TreePruningDemo() - Constructor for class it.uniroma2.sag.kelp.examples.demo.pruning.TreePruningDemo
 
TreeRepresentation - Class in it.uniroma2.sag.kelp.data.representation.tree
Tree Representation used for example to represent the syntactic tree of a sentence.
TreeRepresentation() - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
 
TreeRepresentation(TreeNode) - Constructor for class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
Build a tree representation from a TreeNode
Tron - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
Trust Region Newton Method optimization
NOTE: This code has been adapted from the Java port of the original LIBLINEAR C++ sources.
Tron(TronFunction) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Tron
 
Tron(TronFunction, double) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Tron
 
Tron(TronFunction, double, int) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Tron
 
tron(double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Tron
 
TweetSentimentAnalysisSemeval2013 - Class in it.uniroma2.sag.kelp.examples.demo.tweetsent2013
 
TweetSentimentAnalysisSemeval2013() - Constructor for class it.uniroma2.sag.kelp.examples.demo.tweetsent2013.TweetSentimentAnalysisSemeval2013
 
TYPE_CONTENT_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.representation.structure.StructureElementFactory
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.examplegenerator.SequenceExampleGeneratorTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.similarity.StructureElementSimilarityTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCacheTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.kernel.cache.SquaredNormCacheTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
 
typeFromId(String) - Method in class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 

U

unbiased - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
 
UncrossedPairwiseProductKernel - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) \cdot BK(x_2, y_2)\)
where BK is another kernel the kernel on pairs relies on.
UncrossedPairwiseProductKernel(Kernel, boolean) - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseProductKernel
Defines a Kernel operating on pairs that applies the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) \cdot BK(x_2, y_2)\)
UncrossedPairwiseProductKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseProductKernel
 
UncrossedPairwiseSumKernel - Class in it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) + BK(x_2, y_2)\)
where BK is another kernel the kernel on pairs relies on.
UncrossedPairwiseSumKernel(Kernel, boolean) - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseSumKernel
Defines a Kernel operating on pairs that applies the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = BK(x_1, y_1) + BK(x_2, y_2)\)
UncrossedPairwiseSumKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.pairs.UncrossedPairwiseSumKernel
 
UnivariateKernelMachineRegressionFunction - Class in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
It is a univariate regression prediction function consisting of an implicit hyperplane in a Reproducing Kernel Hilbert Space.
UnivariateKernelMachineRegressionFunction() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
 
UnivariateLinearRegressionFunction - Class in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
It is a univariate regression prediction function consisting of an explicit hyperplane.
UnivariateLinearRegressionFunction() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
 
UnivariateRegressionFunction - Class in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
It is a univariate regression prediction function.
UnivariateRegressionFunction() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
 
UnivariateRegressionOutput - Class in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
It is the output of a univariate regression prediction function.
UnivariateRegressionOutput(NumericLabel) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionOutput
 
UnivariateRegressionOutput(Label, float) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionOutput
 
UNLIMITED_RECURSION - Static variable in class it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner
 
unshrink - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
 
UntypedStructureElement - Class in it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement without type.
UntypedStructureElement() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.UntypedStructureElement
 
UntypedStructureElement(String) - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.UntypedStructureElement
 
updateCentroid(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans.LinearKMeansCluster
The centroid is calculated as the mean of all Vector representations stored in the clusters.
updateProduction() - Method in class it.uniroma2.sag.kelp.data.representation.tree.node.TreeNode
Notifies a modification in the label of the node, that must reflect in an update of the production of this node and in the one of its father (if it has)
updateTree() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
Updates the tree.

V

value - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
 
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.data.dataset.CsvDatasetReader.LabelPosition
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner.visitType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger.MARKING_POLICY
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold.nodeComparisonOperator
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample.representationSelectorInExample
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLoss
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem.LibLinearSolverType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification.DeletingPolicy
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification.Loss
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SCWType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive.Policy
Returns the enum constant of this type with the specified name.
values() - Static method in enum it.uniroma2.sag.kelp.data.dataset.CsvDatasetReader.LabelPosition
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.data.manipulator.TreeNodePruner.visitType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.data.manipulator.TreePairRelTagger.MARKING_POLICY
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold.nodeComparisonOperator
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.data.representation.tree.utils.SelectRepresentationFromExample.representationSelectorInExample
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.dcd.DCDLoss
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem.LibLinearSolverType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.BudgetedPassiveAggressiveClassification.DeletingPolicy
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification.Loss
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.classification.scw.SCWType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive.Policy
Returns an array containing the constants of this enum type, in the order they are declared.
Vector - Interface in it.uniroma2.sag.kelp.data.representation
It is a Vectorial representation whose dimensions are identified by Objects
VECTOR_NAME - Static variable in class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
 
VectorBasedStructureElementSimilarity - Class in it.uniroma2.sag.kelp.data.representation.structure.similarity
This class provides a useful abstraction of a StructureElement similarity based on a wordspace.
VectorBasedStructureElementSimilarity() - Constructor for class it.uniroma2.sag.kelp.data.representation.structure.similarity.VectorBasedStructureElementSimilarity
 
VectorConcatenationManipulator - Class in it.uniroma2.sag.kelp.data.manipulator
VectorConcatenationManipulator is an implementations of Manipulator that allows to concatenate vectors into a new SparseVector representation.
VectorConcatenationManipulator(String, List<String>, List<Float>) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.VectorConcatenationManipulator
 
VectorOperationException - Exception in it.uniroma2.sag.kelp.data.representation.vector.exception
 
VectorOperationException(String, Vector, Vector) - Constructor for exception it.uniroma2.sag.kelp.data.representation.vector.exception.VectorOperationException
 

W

weightField - Variable in class it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner.PruneNodeLowerThanThreshold
 
WLSubtreeMapper - Class in it.uniroma2.sag.kelp.data.manipulator
Explicit feature extraction for the Weisfeiler-Lehman Subtree Kernel for Graphs.
WLSubtreeMapper(String, String, int) - Constructor for class it.uniroma2.sag.kelp.data.manipulator.WLSubtreeMapper
 
Wordspace - Class in it.uniroma2.sag.kelp.wordspace
This is an implementation of a wordspace used for associating words to vectors.
Wordspace() - Constructor for class it.uniroma2.sag.kelp.wordspace.Wordspace
 
Wordspace(String) - Constructor for class it.uniroma2.sag.kelp.wordspace.Wordspace
 
WordspaceI - Interface in it.uniroma2.sag.kelp.wordspace
This interface provides methods for retrieving vectors associated to words
WordspaceTypeResolver - Class in it.uniroma2.sag.kelp.wordspace
It is a class implementing TypeIdResolver which will be used by Jackson library during the serialization in JSON and deserialization of classes implementing WordspaceI, that are used to estimate a associate vectors to words
WordspaceTypeResolver() - Constructor for class it.uniroma2.sag.kelp.wordspace.WordspaceTypeResolver
 
writeNextExample(Example) - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetWriter
 
writeNextExample(Example) - Method in class it.uniroma2.sag.kelp.data.dataset.SequenceDatasetWriter
Write the next example into the file
writeValueAsString(Object) - Method in class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 
writeValueAsString(Object) - Method in interface it.uniroma2.sag.kelp.utils.ObjectSerializer
Converts an object into a textual representation, preserving all the object properties.
writeValueOnFile(Object, String) - Method in class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 
writeValueOnFile(Object, String) - Method in interface it.uniroma2.sag.kelp.utils.ObjectSerializer
Converts an object into a textual representation, preserving all the object properties, and write this String into a file.
writeValueOnGzipFile(Object, String) - Method in class it.uniroma2.sag.kelp.utils.JacksonSerializerWrapper
 
writeValueOnGzipFile(Object, String) - Method in interface it.uniroma2.sag.kelp.utils.ObjectSerializer
Converts an object into a textual representation, preserving all the object properties, and write this String into a GZip file.

X

x - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
array of sparse feature nodes
Xv(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 

Y

y - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
an array containing the target values
y - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
The integer label \(\pm 1\) of the training example

Z

z - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
 

_

_parseCharniakSentence(String, TreeNode, Integer) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
This recursive function allows to read the tree encoded in a string in the parentheses form, such as (S(NP)(VP))
_preprocess(String) - Method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
This function cleans the input string, such as from spaces, before the parsing phase.
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