- 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
- 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
-
- 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 - 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
-
- 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
-
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.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.example.Example
-
- 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
-
- 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) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- ClusterExample - Class in it.uniroma2.sag.kelp.data.clustering
-
- 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
ClusterExample
s
- 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
- cn - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
The regularization parameter of negative examples
- compareTo(ClusterExample) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
- 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
-
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
-
Return 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)
- 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.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
- 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
- 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
-
- 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
-
- 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.SimpleDataset
-
- getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- getClassName() - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
-
- 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
-
- 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.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.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
-
- getCSvmAlpha(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
-
Get the initial weight for the future Support Vectors
- 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
- 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.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- getElements() - Method in class it.uniroma2.sag.kelp.data.representation.sequence.SequenceRepresentation
-
Returns the elements of this sequence
- 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
-
- getEpsilon() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
Returns epsilon, i.e.
- 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() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- 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
-
- getExamplesToStore() - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
-
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
-
Returns the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
- getF1() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
-
Return the f1 considering all classes together
- getF1For(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the f1 for the specified label
- getF1s() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the F1 map
- 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
- 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.
- 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.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
-
- 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.onPairs.BestPairwiseAlignmentKernel
-
- getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.onPairs.PairwiseProductKernel
-
- getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.onPairs.PairwiseSumKernel
-
- getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.onPairs.UncrossedPairwiseProductKernel
-
- getIntraPairSimProduct() - Method in class it.uniroma2.sag.kelp.kernel.onPairs.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
-
- 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.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.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.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.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.PassiveAggressive
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
-
- getLabel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- 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.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 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
-
- 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
- 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
-
- getLoss() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- 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
-
- 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.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- 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
-
- 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
-
Return the mean of the F1 scores considering all the labels involved
- getMeanF1For(ArrayList<Label>) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the mean of the F1 scores considering the specified labels
- 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.example.ExampleTypeResolver
-
- 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
-
- 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
-
- 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
- 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 Example
s 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
-
- 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 interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the number of Example
s 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 Example
s of a given class
- getNumberOfNegativeExamples(Label) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getNumberOfPositiveExamples(Label) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the number of positive Example
s 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
-
Return the f1 considering all classes together
- getOverallPrecision() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the precision considering all classes together
- getOverallRecall() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the recall considering all classes together
- getP() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.liblinear.LibLinearRegression
-
- 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 considering all classes together
- getPrecisionFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the precision for the specified label
- getPrecisions() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the precision map
- 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.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 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.
- getProperty() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
-
Returns the property
- 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
-
- getRecall() - Method in class it.uniroma2.sag.kelp.utils.evaluation.BinaryClassificationEvaluator
-
Return the recall considering all classes together
- getRecallFor(Label) - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the recall for the specified label
- getRecalls() - Method in class it.uniroma2.sag.kelp.utils.evaluation.MulticlassClassificationEvaluator
-
Return the recall map
- 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.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 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
- 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(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
-
- 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 \(\bar{x}\)
- getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
-
- getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
-
- 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.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 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
-
- 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
- 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
-
- 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
-
- 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
-
- 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
-
- labels - Variable in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- 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 Label
s
- 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.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 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 LearningAlgorithm
s
- 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
StructureElement
s.
- 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
-
- LinearKernel - Class in it.uniroma2.sag.kelp.kernel.vector
-
Linear Kernel for Vector
s
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
-
- 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.
- 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
- 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.onPairs
-
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.onPairs.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.onPairs.PairwiseProductKernel
-
- PairwiseSumKernel - Class in it.uniroma2.sag.kelp.kernel.onPairs
-
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.onPairs.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.onPairs.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
-
- 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
-
- 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.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
- 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
-
- 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 PredictionFunction
s
- PredictionFunctionTypeResolver() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
-
- PreferenceKernel - Class in it.uniroma2.sag.kelp.kernel.onPairs
-
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.onPairs.PreferenceKernel
-
- PreferenceKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.onPairs.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
-
- 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.
- 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
-
- 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.
- 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
-
- 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
-
- 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
-
- 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
-
- 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.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
-
- 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
- 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
-
- 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.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
-
- 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.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
-
- 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
-
- setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- 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
-
- setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.SmoothedPartialTreeKernel
-
- setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubSetTreeKernel
-
Sets the DeltaMatrix used to store the evaluated delta functions
of this tree kernel
- setDeltaMatrix(DeltaMatrix) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
-
Sets the DeltaMatrix used to store the evaluated delta functions
of this tree kernel
- setDependencyRelation(String) - Method in class it.uniroma2.sag.kelp.data.representation.structure.CompositionalStructureElement
-
- setDist(Float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- 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
-
- setEpsilon(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
Sets epsilon, i.e.
- setExample(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- 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
-
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
-
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.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
-
- 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.
- 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.onPairs.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.onPairs.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.onPairs.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.onPairs.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.onPairs.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
-
- 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.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.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
- setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.DynamicIndexKernelCache
-
- setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
-
- 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.StripeKernelCache
-
- setLabel(String) - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
-
- 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.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.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.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 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
-
- 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
- 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.
- setMaxIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- setMaxNumberOfRows(int) - Method in class it.uniroma2.sag.kelp.kernel.cache.StripeKernelCache
-
- setMaxSubseqLeng(int) - Method in class it.uniroma2.sag.kelp.kernel.sequence.SequenceKernel
-
- 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
-
- 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
-
- 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
-
- 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
- setProperty(Label) - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
-
- 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.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 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
-
- 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
-
- 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
- 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
-
- 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
- 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
-
- 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
-
- 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
- SimpleExample - Class in it.uniroma2.sag.kelp.data.example
-
An Example
composed by a set of Representation
s.
- 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
-
- 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
-
- 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.SimpleDataset
-
Returns two datasets created by splitting this dataset accordingly to
percentage
.
- 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 SquaredNormCache
s
- 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 StructuredElement
s
- 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
-