C L2R_L2_SvcFunction
c PassiveAggressive
calculate_r() LibNuSvmSolver
calculate_rho() LibCSvmSolver
calculate_rho() LibNuSvmSolver
calculate_rho() LibSvmSolver
calculateDistance(Example, Cluster) KernelBasedKMeansEngine
Estimate the distance of an example from the centroid
calculateVector(Example) NystromMethod
It derives an array of doubles containing the linearized representation
checkConsistency(int, int) KernelBasedKMeansEngine
checkRowValidity() DatasetReader
checkThresholdCondition(TreeNode) PruneNodeLowerThanThreshold
ClassificationDemo it.uniroma2.sag.kelp.examples.main
ClassificationDemo() ClassificationDemo
ClassificationLearningAlgorithm it.uniroma2.sag.kelp.learningalgorithm.classification
It is a generic Machine Learning algorithm for Classification tasks
ClassificationOutput it.uniroma2.sag.kelp.predictionfunction.classifier
It is a generic output provided by a classifier
classifier BinaryCSvmClassification
The classifier to be returned
classifier PassiveAggressiveClassification
classifier Perceptron
classifier SoftConfidenceWeightedClassification
Classifier it.uniroma2.sag.kelp.predictionfunction.classifier
It is a generic classifier, i.e.
Classify it.uniroma2.sag.kelp.main
Classify() Classify
classStats MulticlassClassificationEvaluator
clear() Cluster
This function clear the set of object inside the cluster
clear() DeltaMatrix
Clear the delta matrix
clear() DynamicDeltaMatrix
Deprecated.
Clear the delta matrix
clear() StaticDeltaMatrix
Clear the delta matrix
clear() LinearKMeansCluster
clear() BinaryClassificationEvaluator
Clear all the counters for a new processing.
clear() Evaluator
This method should reset the state of the evaluator
clear() MulticlassClassificationEvaluator
Clear all the counters for a new processing.
clear() RegressorEvaluator
clone() TreeRepresentation
close() DatasetReader
Closes the reading buffer
close() DatasetWriter
close() SequenceDatasetReader
Closes the reading buffer
close() SequenceDatasetWriter
Close the output file
Cluster 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() Cluster
The cluster is initialized without any label
Cluster(String) Cluster
The cluster is initialized and labeled
cluster(Dataset) ClusteringAlgorithm
It starts the clustering process exploiting the provided
dataset
cluster(Dataset, ExampleSelector) ClusteringAlgorithm
It starts the clustering process exploiting the provided
dataset
cluster(Dataset) KernelBasedKMeansEngine
cluster(Dataset, ExampleSelector) KernelBasedKMeansEngine
cluster(Dataset) LinearKMeansEngine
cluster(Dataset, ExampleSelector) LinearKMeansEngine
ClusterExample it.uniroma2.sag.kelp.data.clustering
ClusterExample(Example, float) ClusterExample
ClusterExample() ClusterExample
ClusterExampleTypeResolver it.uniroma2.sag.kelp.data.clustering
It is a class implementing TypeIdResolver which will be used by
Jackson library during the serialization in JSON and deserialization of
ClusterExamples
ClusterExampleTypeResolver() ClusterExampleTypeResolver
ClusteringAlgorithm it.uniroma2.sag.kelp.learningalgorithm.clustering
It is a generic Clustering algorithm
ClusteringEvaluator it.uniroma2.sag.kelp.utils.evaluation
Implements Evaluation methods for clustering algorithms.
ClusteringEvaluator() ClusteringEvaluator
ClusterList it.uniroma2.sag.kelp.data.clustering
ClusterList() ClusterList
ClusterTypeResolver it.uniroma2.sag.kelp.data.clustering
It is a class implementing TypeIdResolver which will be used by
Jackson library during the serialization in JSON and deserialization of
Clusters
ClusterTypeResolver() ClusterTypeResolver
cn LibSvmSolver
The regularization parameter of negative examples
COMMENT_REPRESENTATION_NAME LibsvmDatasetReader
COMMENT_SEPARATOR LibsvmDatasetReader
compareTo(ClusterExample) ClusterExample
compareTo(SequencePath) SequencePath
compareTo(SequenceEmission) SequenceEmission
compareTo(StringLabel) StringLabel
compareTo(NodeDistance) NodeDistance
compareTo(char[], char[]) TreeKernelUtils
CompositionalNodeSimilarity 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() CompositionalNodeSimilarity
Default constructor, used only for JSON serialization/deserialization
purposes.
CompositionalNodeSimilarity(WordspaceI) CompositionalNodeSimilarity
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarity(WordspaceI, boolean, boolean) CompositionalNodeSimilarity
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityDilation 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() CompositionalNodeSimilarityDilation
Default constructor, used only for JSON serialization/deserialization
purposes.
CompositionalNodeSimilarityDilation(WordspaceI) CompositionalNodeSimilarityDilation
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityDilation(WordspaceI, boolean, boolean) CompositionalNodeSimilarityDilation
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityProduct 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() CompositionalNodeSimilarityProduct
Default constructor, used only for JSON serialization/deserialization
purposes.
CompositionalNodeSimilarityProduct(WordspaceI) CompositionalNodeSimilarityProduct
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilarityProduct(WordspaceI, boolean, boolean) CompositionalNodeSimilarityProduct
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilaritySum 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() CompositionalNodeSimilaritySum
Default constructor, used only for JSON serialization/deserialization
purposes.
CompositionalNodeSimilaritySum(WordspaceI) CompositionalNodeSimilaritySum
Constructor that enable to specify the wordspace path.
CompositionalNodeSimilaritySum(WordspaceI, boolean, boolean) CompositionalNodeSimilaritySum
Constructor that enable to specify the wordspace path.
CompositionalStructureElement it.uniroma2.sag.kelp.data.representation.structure
CompositionalStructureElement represents a compositional node.
CompositionalStructureElement() CompositionalStructureElement
CompositionalStructureElement(String, LexicalStructureElement, LexicalStructureElement) CompositionalStructureElement
compute() BinaryClassificationEvaluator
compute() Evaluator
This method is intented to force the computation of the performance measure.
compute() MulticlassClassificationEvaluator
compute() RegressorEvaluator
computed Evaluator
computeTotalNumberOfNodes(SimpleDataset, String) TreePruningDemo
computeWeight(Example, float, float, float) PassiveAggressive
concatenateVectors(Example, List<String>, List<Float>) 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) 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) StructureElement
Verifies whether this element contains the additional information
identified by infoName
ContentBasedTreeNodeFilter 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) ContentBasedTreeNodeFilter
Constructor for ContentBasedTreeNodeFilter
ContentBasedTreeNodeSimilarity it.uniroma2.sag.kelp.data.representation.tree.node.similarity
Evaluates the similarity between two TreeNodes comparing their StructureElements
using a StructureElementSimilarityI
ContentBasedTreeNodeSimilarity(StructureElementSimilarityI) ContentBasedTreeNodeSimilarity
Constructor
copyVector() Vector
Returns a copy of this vector.
copyVector() DenseVector
copyVector() SparseVector
correct MulticlassClassificationEvaluator
cp LibSvmSolver
The regularization parameter of positive examples
cp PassiveAggressiveClassification
createInputStream(String) FileUtils
Creates an InputStream for reading a file.
createMaxChildrenPrunerObject(int, String) TreePruningDemo
createMaxNumberOfLeavesPrunerObject(int, String) TreePruningDemo
createOutputStream(String) FileUtils
Creates an OutputStream for reading a file.
CsvDatasetReader it.uniroma2.sag.kelp.data.dataset
A utility class to read dataset in the csv format.
CsvDatasetReader(String, String, boolean, CsvDatasetReader.LabelPosition) CsvDatasetReader
Constructor for reading dataset in csv format.
CsvDatasetReader(String, String, boolean, CsvDatasetReader.LabelPosition, StringLabel) CsvDatasetReader
Constructor for reading dataset in csv format for regression tasks (the regression value is assumed to
be in the first column).
CsvDatasetReader.LabelPosition it.uniroma2.sag.kelp.data.dataset
G LibSvmSolver
Gradient
G_bar LibSvmSolver
Gradient bar
generateExampleWithHistory(SequenceExample, SequencePath, int) SequenceExampleGenerator
At labeling time, this method allows to enrich a specific
Example with the labels assigned by the classifier to the
previous Examples
generateExampleWithHistory(SequenceExample, SequencePath, int) SequenceExampleGeneratorKernel
generateExampleWithHistory(SequenceExample, SequencePath, int) SequenceExampleGeneratorLinear
generateSequenceExampleEnrichedWithHistory(SequenceExample) SequenceExampleGenerator
This method allows to enrich each Example from an input
SequenceExample with the labels assigned by the classifier
to the previous Examples
generateSequenceExampleEnrichedWithHistory(SequenceExample) SequenceExampleGeneratorKernel
generateSequenceExampleEnrichedWithHistory(SequenceExample) SequenceExampleGeneratorLinear
get(int, int) DeltaMatrix
Get a value from the matrix
get(int, int) DynamicDeltaMatrix
Deprecated.
Get a value from the matrix
get(int, int) StaticDeltaMatrix
Get a value from the matrix
get_nr_variable() L2R_L2_SvcFunction
get_QD() LibSvmSolver
For each example i, it return the K_ii score
get_QD() EpsilonSvmRegression
get_Qij(int, int) LibSvmSolver
get_Qij(int, int) EpsilonSvmRegression
getA() PolynomialKernel
getAccuracy() BinaryClassificationEvaluator
Return the accuracy
getAccuracy() MulticlassClassificationEvaluator
Returns the accuracy
getActiveFeatures() DenseVector
getActiveFeatures() Vector
Returns a map containing all the non-zero features
getActiveFeatures() SparseVector
getAdditionalInformation(String) StructureElement
Returns the additional information identified by infoName
getAdditionalInfos() SameAdditionalInfoStructureElementSimilarity
Returns the list of additionalInfos two structure elements must
both have or not have in order to have a non zero similarity
getAllClasses() BinaryMarginClassifierOutput
getAllClasses() ClassificationOutput
Returns all the classes involved in the classification process (both predicted and not)
getAllClasses() MultiLabelClassificationOutput
getAllClasses() OneVsAllClassificationOutput
getAllClasses() OneVsOneClassificationOutput
getAllNodes() TreeNode
Get recursively all Tree Nodes below the target node
getAllNodes() TreeRepresentation
getAllowDifferentPOS() LexicalStructureElementSimilarity
Returns whether the similarity between words having different Part-of-Speech is allowed
or if it must be set to 0
getAllProperties() RegressionOutput
Returns all the properties on which the regressor has to provide predictions
getAllProperties() UnivariateRegressionOutput
getAlpha() Perceptron
Returns the learning rate, i.e.
getAlphas() SvmSolution
getAncestor(int) TreeNode
Returns the generation generation ancestor of this node
(for instance 1-generation ancestor is the father, 2-generation ancestor
is the grandfather, etc)
getAssignedLabel(int) SequencePath
getAssignedSequnceLabels() SequencePath
getB() PolynomialKernel
getBaseAlgorithm() RandomizedBudgetPerceptron
getBaseAlgorithm() Stoptron
getBaseAlgorithm() SequenceClassificationKernelBasedLearningAlgorithm
getBaseAlgorithm() SequenceClassificationLinearLearningAlgorithm
getBaseAlgorithm() MultiLabelClassificationLearning
This method will return the base algorithm.
getBaseAlgorithm() OneVsAllLearning
This method will return the base algorithm.
getBaseAlgorithm() OneVsOneLearning
This method will return the base algorithm.
getBaseAlgorithm() MetaLearningAlgorithm
Returns the base algorithm this meta algorithm is based on
getBaseAlgorithm() MultiEpochLearning
getBaseKernel() KernelComposition
Returns the kernel this kernel is enriching
getBaseLearningAlgorithm() SequenceClassificationLearningAlgorithm
getBasePredictionFunction() SequenceModel
getBaseSimilarity() SameAdditionalInfoStructureElementSimilarity
Returns the base similarity applied when two structure elements have
the same additional infos
getBeamSize() SequenceClassificationLearningAlgorithm
getBeamSize() SequencePredictionFunction
getBias() BinaryModel
getBinaryClassifiers() MultiLabelClassifier
getBinaryClassifiers() OneVsAllClassifier
getBinaryClassifiers() OneVsOneClassifier
getBranchingFactor() TreeNode
Returns the branching factor of the tree rooted by this node
getBranchingFactor() TreeRepresentation
getBudget() BudgetedLearningAlgorithm
Returns the budget, i.e.
getC() PassiveAggressiveClassification
getC() PassiveAggressive
getC() LibLinearRegression
getCacheHits() KernelCache
getCacheMisses() KernelCache
getCentroid() LinearKMeansCluster
getCharArray() StringRepresentation
getChildren() TreeNode
Get the direct children of the target node
getClassificationLabels() Dataset
Returns all the classification labels in the dataset.
getClassificationLabels() SequenceDataset
getClassificationLabels() SimpleDataset
getClassificationLabels() Example
getClassificationLabels() SequenceExample
getClassName() StringLabel
getCn() DCDLearningAlgorithm
getCn() LibLinearLearningAlgorithm
getCn() LibSvmSolver
getCn() BudgetedPassiveAggressiveClassification
getCn() PassiveAggressiveClassification
getCn() SoftConfidenceWeightedClassification
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) CompositionalNodeSimilarity
This method takes in input two LexicalStructureElement representing a
head and a modifier.
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) CompositionalNodeSimilarityDilation
This method takes in input two LexicalStructureElement representing a
head and a modifier.
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) CompositionalNodeSimilarityProduct
This method takes in input two LexicalStructureElement representing a
head and a modifier.
getCompositionalInformationFor(LexicalStructureElement, LexicalStructureElement) CompositionalNodeSimilaritySum
This method takes in input two LexicalStructureElement representing a
head and a modifier.
getContent() GraphNode
getContent() SequenceElement
Returns the content of this SequenceElement
getContent() TreeNode
getCorrespondingVector(StructureElement) VectorBasedStructureElementSimilarity
Returns the vector associated to element.
getCounter() OneVsOneClassificationOutput
getCp() DCDLearningAlgorithm
getCp() LibLinearLearningAlgorithm
getCp() LibSvmSolver
getCp() BudgetedPassiveAggressiveClassification
getCp() PassiveAggressiveClassification
getCp() SoftConfidenceWeightedClassification
getCSvmAlpha(Dataset) LibCSvmSolver
Get the initial weight for the future Support Vectors
getDcdLoss() DCDLearningAlgorithm
getDegree() PolynomialKernel
getDeletingPolicy() BudgetedPassiveAggressiveClassification
getDeltaMatrix() PartialTreeKernel
getDeltaMatrix() SmoothedPartialTreeKernel
getDeltaMatrix() SubSetTreeKernel
Get the DeltaMatrix used to store the evaluated delta functions
of this tree kernel
getDeltaMatrix() SubTreeKernel
Get the DeltaMatrix used to store the evaluated delta functions
of this tree kernel
getDenseVectorByEnsembleAndJuxtaposition(Example) NystromMethodEnsemble
Given an example, this method produces a DenseVector that is
the concatenation of the vectors obtained by each projection functions
used in the Ensemble.
getDenseVectorByEnsembleAndJuxtaposition(Example, List<Float>) NystromMethodEnsemble
Given an example, this method produces a DenseVector that is
the concatenation of the vectors obtained by each projection functions
used in the Ensemble.
getDependencyRelation() CompositionalStructureElement
getDescendants(int) 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() Wordspace
getDictionaryDanilo() WordspaceI
Returns the complete set of words in the vocabulary (words having an associated vector in this wordspace)
getDist() ClusterExample
getDistance() NodeDistance
getElements() SequenceRepresentation
Returns the elements of this sequence
getEmbeddingSize() LinearizationFunction
getEmbeddingSize() NystromMethod
getEmbeddingSize() NystromMethodEnsemble
getEmission() SequenceEmission
getEnrichmentName() CompositionalNodeSimilarity
getEnrichmentName() VectorBasedStructureElementSimilarity
Returns the identifier of the vectors associated to
a StructureElement during the manipulation operation performed
by a Manipulator (i.e.
getEpochs() MultiEpochLearning
getEps() LibSvmSolver
getEpsilon() PassiveAggressiveRegression
Returns epsilon, i.e.
getEta() SoftConfidenceWeightedClassification
getExample() ClusterExample
getExample(int) SimpleDataset
Return the example stored in the exampleIndex position
getExample(int) SequenceExample
getExample() KernelBasedKMeansExample
getExample() LinearKMeansExample
getExamples() Cluster
This function returns the set of objects inside the cluster
getExamples() Dataset
Returns an array containing all the stored examples
getExamples() SimpleDataset
getExamples() SequenceExample
getExamplesToStore() DynamicIndexKernelCache
Deprecated.
Returns the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
getExamplesToStore() DynamicIndexSquaredNormCache
Returns the maximum number of norms that
can be simultaneously stored
getExamplesToStore() FixIndexKernelCache
Deprecated.
Returns the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
getExamplesToStore() FixSizeKernelCache
Returns the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
getExampleString() ParsingExampleException
getF1() BinaryClassificationEvaluator
Return the f1 of the positive class
getF1For(Label) MulticlassClassificationEvaluator
Return the f1 for the specified label
getFather() TreeNode
Get the father of the target node
getFeatureValue(int) DenseVector
Returns the feature value of the featureIndex-th element
getFeatureValue(Object) DenseVector
getFeatureValue(Object) Vector
Returns the value of the feature identified with featureIdentifier
getFeatureValue(String) SparseVector
Returns the value associated to a feature
getFeatureValue(Object) SparseVector
getFeatureValues() DenseVector
Returns the feature values in the EJML format
getFinalNode() NodeDistance
getFirst() VectorOperationException
getFn() BinaryClassificationEvaluator
Return the false negatives
getFnFor(Label) MulticlassClassificationEvaluator
Return the false negatives for the specified label
getFp() BinaryClassificationEvaluator
Return the false positives
getFpFor(Label) MulticlassClassificationEvaluator
Return the false positives for the specified label
getGamma() RbfKernel
getHead() CompositionalStructureElement
getHeight() TreeNode
Returns the height of the tree rooted by this node (i.e.
getHeight() TreeRepresentation
getHistoryBefore(int, int) SequencePath
This method generate a string representing an artificial feature used in
the labeling process.
getHyperplane() BinaryLinearModel
getId() Example
Returns a unique identifier of the example.
getId() GraphNode
Returns the id of this node
getId() TreeNode
getIgnorePosInLemmaMatches() LexicalStructureElementSimilarity
Returns whether two lexical structure elements must provide a perfect match if their lemmas are the same,
regardless their part-of-speeches
getIgnorePosOnLexicals() ExactMatchingStructureElementSimilarity
Returns whether the part-of-speech is ignored in comparing two
LexicalStructureElements
getIncludeLeaves() SubSetTreeKernel
Returns whether the leaves must be involved in the kernel computation
getIncludeLeaves() SubTreeKernel
Returns whether the leaves must be involved in the kernel computation
getIndex() LibLinearFeature
getIndex() LibLinearFeatureNode
getInitialNode() NodeDistance
getInstance() RepresentationFactory
Returns an instance of the class RepresentatioFactory
getInstance() StructureElementFactory
Returns an instance of the class StructureElementFactory
getInstance() StaticDeltaMatrix
getInstance() SupportVector
getIntraPairSimProduct() BestPairwiseAlignmentKernel
getIntraPairSimProduct() PairwiseProductKernel
getIntraPairSimProduct() PairwiseSumKernel
getIntraPairSimProduct() UncrossedPairwiseProductKernel
getIntraPairSimProduct() UncrossedPairwiseSumKernel
getIterationNumber() WLSubtreeMapper
Returns the maximum depth of the visits of the WL kernel
getIterations() PegasosLearningAlgorithm
Returns the number of iterations
getK() PegasosLearningAlgorithm
Returns the number of examples k that Pegasos exploits in its
mini-batch learning approach
getK() KernelBasedKMeansEngine
getK() LinearKMeansEngine
getKernel() VectorBasedStructureElementSimilarity
Returns the kernel used in comparing two vectors
getKernel() RandomizedBudgetPerceptron
getKernel() Stoptron
getKernel() SequenceClassificationKernelBasedLearningAlgorithm
getKernel() LibSvmSolver
getKernel() BudgetedPassiveAggressiveClassification
getKernel() KernelizedPassiveAggressiveClassification
getKernel() KernelizedPerceptron
getKernel() KernelBasedKMeansEngine
getKernel() KernelMethod
Returns the kernel exploited by this learner
getKernel() KernelizedPassiveAggressiveRegression
getKernel() NystromMethod
getKernel() BinaryKernelMachineModel
getKernel() KernelMachineModel
getKernelCache() Kernel
Returns the cache in which storing the kernel operations in the RKHS defined
by this kernel
getKernelComputations() Kernel
Returns the number of times the kernel function has been invoked
getKernelValue(Example, Example) KernelCache
Retrieves in the cache the kernel operation between two examples
getLabel() Cluster
This function returns the label of the cluster
getLabel() SequenceEmission
getLabel() UntypedStructureElement
getLabel() BinaryLearningAlgorithm
getLabel() BudgetedLearningAlgorithm
getLabel() DCDLearningAlgorithm
getLabel() LibLinearLearningAlgorithm
getLabel() LibSvmSolver
getLabel() PegasosLearningAlgorithm
getLabel() Perceptron
getLabel() SoftConfidenceWeightedClassification
getLabel() PassiveAggressive
getLabel() LibLinearRegression
getLabel() BinaryClassifier
getLabelDistances(DirectedGraphRepresentation) GraphUtils
getLabels() Example
Returns the classification classificationLabels of this example
getLabels() BinaryLearningAlgorithm
getLabels() BudgetedLearningAlgorithm
getLabels() DCDLearningAlgorithm
getLabels() SequenceClassificationLearningAlgorithm
getLabels() LibLinearLearningAlgorithm
getLabels() LibSvmSolver
getLabels() MultiLabelClassificationLearning
Returns the labels to be learned
getLabels() OneVsAllLearning
Returns the labels to be learned applying a one-vs-all strategy
getLabels() OneVsOneLearning
Returns the labels to be learned applying a one-vs-one strategy
getLabels() PegasosLearningAlgorithm
getLabels() Perceptron
getLabels() SoftConfidenceWeightedClassification
getLabels() LearningAlgorithm
Returns the labels representing the concept to be learned.
getLabels() MultiEpochLearning
getLabels() PassiveAggressive
getLabels() LibLinearRegression
getLabels() BinaryClassifier
getLabels() MultiLabelClassifier
getLabels() OneVsAllClassifier
getLabels() OneVsOneClassifier
getLabels() MulticlassModel
getLabels() PredictionFunction
Returns the labels representing the concept to be predicted.
getLabels() UnivariateRegressionFunction
getLabels() SequencePredictionFunction
getLambda() SequenceKernel
getLambda() PartialTreeKernel
Get the Vertical Decay factor
getLambda() SmoothedPartialTreeKernel
Get the Vertical Decay factor
getLambda() SubSetTreeKernel
Get the decay factor
getLambda() SubTreeKernel
Get the decay factor
getLambda() PegasosLearningAlgorithm
Returns the regularization coefficient
getLandmarks() NystromMethod
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorization
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationDCD
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationDCDExperimentUtils
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationLibLinear
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationLibLinearExperimentUtils
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationPA
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationPAExperimentUtils
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationPegasos
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationPegasosExperimentUtils
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationSCW
getLearningAlgorithm(float, String, StringLabel) RCV1BinaryTextCategorizationSCWExperimentUtils
getLeaves() TreeNode
getLeaves() TreeRepresentation
Returns all the leaves, i.e.
getLeftExample() ExamplePair
Returns the left example in the pair
getLemma() LexicalStructureElement
getLenght() SequenceExample
getLinearizedDataset(Dataset, String) LinearizationFunction
This method linearizes all the examples in the input dataset
, generating a corresponding linearized dataset.
getLinearizedDataset(Dataset, String) NystromMethod
getLinearizedDataset(Dataset, String) NystromMethodEnsemble
getLinearizedExample(Example, String) LinearizationFunction
This method linearizes an input example, providing a new example
containing only a representation with a specific name, provided as input.
getLinearizedExample(Example, String) NystromMethod
getLinearizedExample(Example, String) NystromMethodEnsemble
getLinearRepresentation(Example) LinearizationFunction
Given an input Example, this method generates a linear
Representation>, i.e.
getLinearRepresentation(Example) NystromMethod
getLinearRepresentation(Example) NystromMethodEnsemble
getLoss() PassiveAggressiveClassification
getM() FirstExamplesSelector
getM() RandomExampleSelector
getMacroF1() MulticlassClassificationEvaluator
Return the macro-F1
getMacroPrecision() MulticlassClassificationEvaluator
Return the macro-precision
getMacroRecall() MulticlassClassificationEvaluator
Return the macro-recall
getMargin() Perceptron
Returns the desired margin, i.e.
getMarkingPrefix() TreePairRelTagger
Returns the prefix used to mark the related nodes
getMatrixPath() Wordspace
getMaxEmissionCandidates() SequenceClassificationLearningAlgorithm
getMaxEmissionCandidates() SequencePredictionFunction
getMaxId() TreeNode
Get the max id within all node under the target node
getMaxId() TreeRepresentation
Get the max id within all nodes
getMaxIterations() DCDLearningAlgorithm
getMaxIterations() KernelBasedKMeansEngine
getMaxIterations() LinearKMeansEngine
getMaxMarginForLabel() OneVsOneClassificationOutput
getMaxNumberOfRows() StripeKernelCache
getMaxSubseqLeng() SequenceKernel
getMaxSubseqLeng() PartialTreeKernel
getMaxSubseqLeng() SmoothedPartialTreeKernel
getMean(float[]) Math
Computes the arithmetic mean \(\bar{x}\) of the input values \(x_1, \ldots x_n\)
getMeanF1() MulticlassClassificationEvaluator
Deprecated.
getMeanF1For(ArrayList<Label>) MulticlassClassificationEvaluator
Return the mean of the F1 scores considering the specified labels only
getMeanSquaredError(Label) RegressorEvaluator
Returns the mean square error of the Label label.
getMeanSquaredErrors() RegressorEvaluator
Returns the mean error between the different Label{s}
getMechanism() ClusterExampleTypeResolver
getMechanism() ClusterTypeResolver
getMechanism() ExampleTypeResolver
getMechanism() SequenceExampleGeneratorTypeResolver
getMechanism() LabelTypeResolver
getMechanism() RepresentationTypeResolver
getMechanism() StructureElementSimilarityTypeResolver
getMechanism() KernelCacheTypeResolver
getMechanism() SquaredNormCacheTypeResolver
getMechanism() KernelTypeResolver
getMechanism() LearningAlgorithmTypeResolver
getMechanism() PredictionFunctionTypeResolver
getMechanism() WordspaceTypeResolver
getMessage() ParsingExampleException
getMI(ClusterList) ClusteringEvaluator
getMicroF1() MulticlassClassificationEvaluator
Returns the micro-f1
getMicroPrecision() MulticlassClassificationEvaluator
Returns the micro-precision
getMicroRecall() MulticlassClassificationEvaluator
Returns the micro-recall
getModel() BinaryClassifier
getModel() BinaryKernelMachineClassifier
Returns the model
getModel() BinaryLinearClassifier
getModel() MultiLabelClassifier
getModel() OneVsAllClassifier
getModel() OneVsOneClassifier
getModel() PredictionFunction
Returns the model
getModel() UnivariateKernelMachineRegressionFunction
getModel() UnivariateLinearRegressionFunction
getModel() UnivariateRegressionFunction
getModel() SequencePredictionFunction
getModels() MulticlassModel
getModifier() CompositionalStructureElement
getMu() PartialTreeKernel
Get the Horizontal Decay factor
getMu() SmoothedPartialTreeKernel
Get the Horizontal Decay factor
getNegativeLabelsForClassifier() OneVsOneClassifier
Return the negative labels associated to each classifier
getNeighbours() GraphNode
getNext() SequenceElement
Returns the next element in the sequence
getNextExample() Dataset
Returns the next n Examples stored in the Dataset or a fewer number
if n examples are not available.
getNextExample() SimpleDataset
getNextExamples(int) Dataset
Returns the next Example stored in the Dataset
getNextExamples(int) SimpleDataset
getNMI(ClusterList) ClusteringEvaluator
getNodeDistances() DirectedGraphRepresentation
Returns the list of NodeDistance Objects
getNodeFromID(Integer) DirectedGraphRepresentation
Given a node id, return the corresponding GraphNode object
getNodeLabel() GraphNode
Get the label of a node
getNodeList() DirectedGraphRepresentation
Returns a list containing all the nodes in this graph
getNodeList(TreeNode) TreeNodeSelector
getNodeList(TreeNode) TreeNodeSelectorAllChildren
getNodeList(TreeNode) TreeNodeSelectorAllDescendants
getNodeList(TreeNode) TreeNodeSelectorAllLeaves
getNodeSimilarity() SmoothedPartialTreeKernel
getNodesWithContentType(Class<? extends StructureElement>) TreeRepresentation
Returns all the nodes whose content has type clazz
getNoOfChildren() TreeNode
getNu() BinaryNuSvmClassification
getNu() OneClassSvmClassification
getNumberOfClassificationLabels() Example
Returns the number of classification classificationLabels whose this
instance is a positive example
getNumberOfColumns() StripeKernelCache
getNumberOfExamples() ClusterList
getNumberOfExamples() Dataset
Returns the number of Examples in the dataset
getNumberOfExamples() SimpleDataset
getNumberOfFeatures() DenseVector
Returns the number of featuresValues
getNumberOfHits() Kernel
Returns the number of times a cache hit happened
getNumberOfMisses() Kernel
Returns the number of times a cache miss happened
getNumberOfNegativeExamples(Label) Dataset
Returns the number of negative Examples of a given class
getNumberOfNegativeExamples(Label) SimpleDataset
getNumberOfNodes() DirectedGraphRepresentation
Returns the number of nodes in the graph.
getNumberOfNodes() TreeRepresentation
getNumberOfPositiveExamples(Label) Dataset
Returns the number of positive Examples of a given class
getNumberOfPositiveExamples(Label) SimpleDataset
getNumberOfRegressionLabels() Example
Returns the number of regression classificationLabels
getNumberOfRepresentations() Example
Returns the number of representations in which this example is modeled
getNumberOfRepresentations() SimpleExample
getNumberOfSupportVectors() BinaryKernelMachineModel
getNumberOfSupportVectors() KernelMachineModel
getNx() TreeNodePairs
getNz() TreeNodePairs
getObj() SvmSolution
getOrderedNodeSetByLabel() TreeRepresentation
getOrderedNodeSetByProduction() TreeRepresentation
getOrderedNodeSetByProductionIgnoringLeaves() TreeRepresentation
getOverallF1() MulticlassClassificationEvaluator
Deprecated.
getOverallPrecision() MulticlassClassificationEvaluator
Deprecated.
getOverallRecall() MulticlassClassificationEvaluator
Deprecated.
getP() LibLinearRegression
getPaths() SequencePrediction
getPerformanceMeasure(String, Object...) Evaluator
This method allow to retrieve a performance measure by specifying the name of the method to be used.
getPolicy() PassiveAggressive
getPos() LexicalStructureElement
getPos() PosStructureElement
getPrecision() BinaryClassificationEvaluator
Return the precision of the positive class
getPrecisionFor(Label) MulticlassClassificationEvaluator
Return the precision for the specified label
getPredictedClasses() BinaryMarginClassifierOutput
getPredictedClasses() ClassificationOutput
Returns all the classes that the classifier has predicted
getPredictedClasses() MultiLabelClassificationOutput
getPredictedClasses() OneVsAllClassificationOutput
getPredictedClasses() OneVsOneClassificationOutput
getPredictionFunction() RandomizedBudgetPerceptron
getPredictionFunction() Stoptron
getPredictionFunction() ClassificationLearningAlgorithm
Returns the classifier learned during the training process
getPredictionFunction() DCDLearningAlgorithm
getPredictionFunction() SequenceClassificationLearningAlgorithm
getPredictionFunction() LibLinearLearningAlgorithm
getPredictionFunction() BinaryCSvmClassification
getPredictionFunction() BinaryNuSvmClassification
getPredictionFunction() MultiLabelClassificationLearning
This method returns the learned PredictionFunction.
getPredictionFunction() OneVsAllLearning
This method returns the learned PredictionFunction.
getPredictionFunction() OneVsOneLearning
This method returns the learned PredictionFunction.
getPredictionFunction() BudgetedPassiveAggressiveClassification
getPredictionFunction() KernelizedPassiveAggressiveClassification
getPredictionFunction() LinearPassiveAggressiveClassification
getPredictionFunction() PassiveAggressiveClassification
getPredictionFunction() PegasosLearningAlgorithm
getPredictionFunction() KernelizedPerceptron
getPredictionFunction() LinearPerceptron
getPredictionFunction() Perceptron
getPredictionFunction() SoftConfidenceWeightedClassification
getPredictionFunction() LearningAlgorithm
Returns the predictionFunction learned during the training process
getPredictionFunction() MultiEpochLearning
getPredictionFunction() LibLinearRegression
getPredictionFunction() EpsilonSvmRegression
getPredictionFunction() KernelizedPassiveAggressiveRegression
getPredictionFunction() LinearPassiveAggressiveRegression
getPredictionFunction() PassiveAggressiveRegression
getPredictionFunction() RegressionLearningAlgorithm
Returns the regressor learned during the training process
getpReg() EpsilonSvmRegression
getPreLeafNodes(int) 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() SequenceElement
Returns the previous element in the sequence
getProduction() TreeNode
Get the node production in the form of string.
getProductionIgnoringLeaves() TreeNode
Get the node production in the form of string.
getProjectionMatrix() NystromMethod
getProperty() NumericLabel
Returns the property
getPurity(ClusterList) ClusteringEvaluator
getQCKernelFunction(SimpleDataset, SimpleDataset, String) QuestionClassification
Get one of the kernel functions used in the Question Classification
examples.
getRandExample() Dataset
getRandExample() SimpleDataset
getRandExamples(int) Dataset
getRandExamples(int) SimpleDataset
getRandomSeed() RandomExampleSelector
getRank() NystromMethod
getRanks() NystromMethodEnsemble
getRecall() BinaryClassificationEvaluator
Return the recall of the positive class
getRecallFor(Label) MulticlassClassificationEvaluator
Return the recall for the specified label
getRegressionLabels() Example
Returns the classificationLabels of this example
getRegressionProperties() Dataset
Returns all the regression properties in the dataset.
getRegressionProperties() SimpleDataset
getRegressionValue(Label) Example
Returns the numeric value associated to a label
getRegressionValues() Example
getRepresentation(String) Example
Returns the representation corresponding to
representationName
getRepresentation(String) SimpleExample
getRepresentation() DirectKernel
getRepresentation() DCDLearningAlgorithm
getRepresentation() SequenceClassificationLinearLearningAlgorithm
getRepresentation() LibLinearLearningAlgorithm
getRepresentation() LinearPassiveAggressiveClassification
getRepresentation() PegasosLearningAlgorithm
getRepresentation() LinearPerceptron
getRepresentation() SoftConfidenceWeightedClassification
getRepresentation() LinearMethod
Returns the representation this learning algorithm exploits
getRepresentation() LibLinearRegression
getRepresentation() LinearPassiveAggressiveRegression
getRepresentation() BinaryLinearModel
getRepresentationIdentifier(Class<? extends Representation>) RepresentationFactory
Returns the identifier of a given class
getRepresentationName() SequenceExampleGeneratorLinear
getRepresentationName() SelectRepresentationFromExample
getRepresentationName() LinearKMeansEngine
getRepresentations() Example
Returns the example representations
getRepresentations() SimpleExample
getRepresentationToBeEnriched() CompositionalNodeSimilarityDilation
getRepresentationToBeEnriched() CompositionalNodeSimilarityProduct
getRepresentationToBeEnriched() CompositionalNodeSimilaritySum
getRho() SvmSolution
getRightExample() ExamplePair
Returns the right example in the pair
getRoot() TreeNode
getRoot() TreeRepresentation
getScore() SequencePath
getScore(CompositionalStructureElement, CompositionalStructureElement) CompositionalNodeSimilarity
getScore(CompositionalStructureElement, CompositionalStructureElement) CompositionalNodeSimilarityDilation
getScore(CompositionalStructureElement, CompositionalStructureElement) CompositionalNodeSimilarityProduct
getScore(CompositionalStructureElement, CompositionalStructureElement) CompositionalNodeSimilaritySum
getScore(Label) BinaryMarginClassifierOutput
getScore(Label) MultiLabelClassificationOutput
getScore(Label) OneVsAllClassificationOutput
getScore(Label) OneVsOneClassificationOutput
getScore(Label) Prediction
Return the prediction score associated to a given label
getScore(Label) UnivariateRegressionOutput
getScore(Label) SequencePrediction
getScwType() SoftConfidenceWeightedClassification
getSecond() VectorOperationException
getSeed() DCDLearningAlgorithm
getSequenceExampleGenerator() SequenceClassificationLearningAlgorithm
getSequenceExampleGenerator() SequenceModel
getSequenceExamples() SequenceDataset
getShuffledDataset() Dataset
getShuffledDataset() SimpleDataset
getSimilarity(Vector, Vector) VectorBasedStructureElementSimilarity
Returns the similarity between vector1 and vector2
computed using the kernel function
getSimilarity(TreeNode, TreeNode) ContentBasedTreeNodeSimilarity
getSimilarity(TreeNode, TreeNode) TreeNodeSimilarity
Returns the similarity between nodeA and nodeB
getSimilarityThreshold() SmoothedPartialTreeKernel
getSize() FixIndexSquaredNormCache
Returns the size of the cache, i.e.
getSquaredNorm() Normalizable
Returns the squared norm of this vector
getSquaredNorm() DenseVector
getSquaredNorm() SparseVector
getSquaredNorm(Example) DynamicIndexSquaredNormCache
getSquaredNorm(Example) FixIndexSquaredNormCache
getSquaredNorm(Example) SquaredNormCache
Returns a previously stored norm of a given example
getSquaredNorm(Example) BinaryKernelMachineModel
getSquaredNorm() BinaryKernelMachineModel
getSquaredNorm(Example) BinaryLinearModel
getSquaredNorm() BinaryLinearModel
getSquaredNorm(Example) BinaryModel
Computes the squared norm of a given example according to the space in which the model
is operating
getSquaredNorm() BinaryModel
Computes the squared norm of the hyperplane this model is based on
getSquaredNormCache() Kernel
Returns the cache in which storing the squared norms in the RKHS defined
by this kernel
getStandardDeviation(float[]) Math
Estimates the unbiased standard deviation \(\sigma\) of population using some samples
\(x_1, \ldots x_n\) whose estimated mean is \(\bar{x}\)
getStatistics(ClusterList) ClusteringEvaluator
getStoredKernelValue(Example, Example) DynamicIndexKernelCache
Deprecated.
getStoredKernelValue(Example, Example) FixIndexKernelCache
Deprecated.
getStoredKernelValue(Example, Example) FixSizeKernelCache
getStoredKernelValue(Example, Example) KernelCache
Retrieves in the cache the kernel operation between two examples
getStoredKernelValue(Example, Example) SimpleDynamicKernelCache
getStoredKernelValue(Example, Example) StripeKernelCache
getStructureElementIdentifier(Class<? extends StructureElement>) StructureElementFactory
Returns the identifier of a given class
getSupportVector(Example) BinaryKernelMachineModel
Returns the support vector associated to a given instance, null the instance
is not a support vector in this model
getSupportVectorIndex(Example) BinaryKernelMachineModel
Returns the index of the vector associated to a given instance, null the instance
is not a support vector in this model
getSupportVectors() BinaryKernelMachineModel
Returns all the support vectors
getSyntacticRelation() SyntacticStructureElement
getTerminalFactor() PartialTreeKernel
Get the Terminal Factor
getTerminalFactor() SmoothedPartialTreeKernel
getTextFromData() DirectedGraphRepresentation
getTextFromData() Representation
Returns a textual representation of the data stored in this
representation
getTextFromData() SequenceRepresentation
getTextFromData() StringRepresentation
getTextFromData() CompositionalStructureElement
getTextFromData() LexicalStructureElement
getTextFromData() PosStructureElement
getTextFromData() StructureElement
Returns a textual representation of the data stored in this
structureElement
getTextFromData() SyntacticStructureElement
getTextFromData() UntypedStructureElement
getTextFromData() TreeRepresentation
getTextFromData() DenseVector
getTextFromData() SparseVector
getTextFromDataWithAdditionalInfo() StructureElement
Returns the textual format of the content, concatenated with all the
additional information added to this element
getTextualEnrichedFormat() TreeNode
getTextualEnrichedTree() TreeRepresentation
getTextualLabelPart() Example
getTextualRepresentation(Representation) ExampleFactory
getTextualRepresentation(Representation, String) ExampleFactory
getTextualRepresentation(StructureElement) StructureElementFactory
getTn() BinaryClassificationEvaluator
Return the true negatives
getTnFor(Label) MulticlassClassificationEvaluator
Return the true negatives for the specified label
getToCombine() KernelCombination
Returns a list of the kernels this kernel is combining
getToCombine() EnsembleLearningAlgorithm
Returns a list of the learning algorithm this ensemble method is combining
getTp() BinaryClassificationEvaluator
Return the true positives
getTpFor(Label) MulticlassClassificationEvaluator
Return the true positives for the specified label
getTransitionRepresentationName() SequenceExampleGeneratorKernel
getTransitionsOrder() SequenceExampleGenerator
getTransitionsOrder() SequenceExampleGeneratorKernel
getTransitionsOrder() SequenceExampleGeneratorLinear
getTransitionsOrder() SequenceClassificationLearningAlgorithm
getTransitionWeight() SequenceExampleGeneratorLinear
getUpper_bound_n() SvmSolution
getUpper_bound_p() SvmSolution
getValue() NumericLabel
Returns the value of the value
getValue() LibLinearFeature
getValue() LibLinearFeatureNode
getVariance(float[]) 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() SparseVector
getVector(String) Wordspace
getVector(String) WordspaceI
Returns the vector associated to the given word
getW(double[]) Problem
getWeight() SupportVector
getWeights() LinearKernelCombination
getWordspace() VectorBasedStructureElementSimilarity
Returns the wordspace from which the vectors associated to a word must be retrieved
getZeroVector(String) Dataset
Returns a zero vector compliant with the representation identifier by representationIdentifier containing all zeros
getZeroVector(String) SimpleDataset
getZeroVector() DenseVector
getZeroVector() Vector
Returns a vector whose values are all 0.
getZeroVector() SparseVector
getZeroVector() Wordspace
getZeroVector() WordspaceI
Returns the zero vector in the wordspace, i.e, a zero vector having the worspace dimensionality
grad(double[], double[]) L2R_L2_SvcFunction
grad(double[], double[]) L2R_L2_SvrFunction
GraphNode it.uniroma2.sag.kelp.data.representation.graph
This class implements a graph node
GraphNode(int, StructureElement) GraphNode
Initializes a graph node
GraphUtils it.uniroma2.sag.kelp.data.representation.graph
This class contains some useful methods operating on graphs
GraphUtils() GraphUtils
l Problem
the number of training data
l LibSvmSolver
Total number of Support Vectors
L2R_L2_SvcFunction 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[]) L2R_L2_SvcFunction
L2R_L2_SvrFunction 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) L2R_L2_SvrFunction
Label it.uniroma2.sag.kelp.data.label
A generic Label for supervised learning.
label BudgetedLearningAlgorithm
label LibSvmSolver
The label to be learned by the classifier
label Perceptron
label PassiveAggressive
LABEL_SEPARATOR ExampleFactory
LabelFactory it.uniroma2.sag.kelp.data.label
It is a factory that provides methods for instantiating labels described in a
textual format
LabelFactory() LabelFactory
labelPrefix PruneNodeThresholdAndDistanceFromSpecificLabelledNode
labels MulticlassModel
labels MulticlassClassificationEvaluator
LabelTypeResolver it.uniroma2.sag.kelp.data.label
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of Labels
LabelTypeResolver() LabelTypeResolver
learn(Dataset) BudgetedLearningAlgorithm
learn(Example) BudgetedLearningAlgorithm
learn(Dataset) DCDLearningAlgorithm
learn(Dataset) SequenceClassificationLearningAlgorithm
learn(Dataset) LibLinearLearningAlgorithm
learn(Dataset) BinaryCSvmClassification
learn(Dataset) BinaryNuSvmClassification
learn(Dataset) OneClassSvmClassification
learn(Dataset) 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) 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) 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) BudgetedPassiveAggressiveClassification
learn(Example) BudgetedPassiveAggressiveClassification
learn(Example) PassiveAggressiveClassification
learn(Dataset) PassiveAggressiveClassification
learn(Dataset) PegasosLearningAlgorithm
learn(Dataset) Perceptron
learn(Example) Perceptron
learn(Dataset) SoftConfidenceWeightedClassification
learn(Example) SoftConfidenceWeightedClassification
learn(Dataset) LearningAlgorithm
It starts the training process exploiting the provided dataset
learn(Dataset) MultiEpochLearning
learn(Example) OnlineLearningAlgorithm
Applies the learning process on a single example, updating its current model
learn(Dataset) PassiveAggressive
learn(Dataset) LibLinearRegression
learn(Dataset) EpsilonSvmRegression
learn(Dataset) PassiveAggressiveRegression
learn(Example) PassiveAggressiveRegression
Learn it.uniroma2.sag.kelp.main
Learn() Learn
LearningAlgorithm it.uniroma2.sag.kelp.learningalgorithm
It is a generic Machine Learning algorithm
LearningAlgorithmTypeResolver it.uniroma2.sag.kelp.learningalgorithm
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of LearningAlgorithms
LearningAlgorithmTypeResolver() LearningAlgorithmTypeResolver
LexicalStructureElement it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement that contains lexical information, i.e.
LexicalStructureElement() LexicalStructureElement
LexicalStructureElement(String, String) LexicalStructureElement
LexicalStructureElementFilter 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>) LexicalStructureElementFilter
Constructor for LexicalStructureElementFilter
LexicalStructureElementManipulator 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) LexicalStructureElementManipulator
LexicalStructureElementManipulator(WordspaceI, String) LexicalStructureElementManipulator
LexicalStructureElementSimilarity it.uniroma2.sag.kelp.data.representation.structure.similarity
This class implements a similarity function between
StructureElements.
LexicalStructureElementSimilarity() LexicalStructureElementSimilarity
LexicalStructureElementSimilarity(WordspaceI) LexicalStructureElementSimilarity
LibCSvmSolver 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) LibCSvmSolver
LibCSvmSolver() LibCSvmSolver
LibLinearFeature 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 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) LibLinearFeatureNode
LibLinearLearningAlgorithm 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) LibLinearLearningAlgorithm
LibLinearLearningAlgorithm(Label, double, double, boolean, String) LibLinearLearningAlgorithm
LibLinearLearningAlgorithm(double, double, String) LibLinearLearningAlgorithm
LibLinearLearningAlgorithm() LibLinearLearningAlgorithm
LibLinearRegression 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) LibLinearRegression
LibLinearRegression(double, double, String) LibLinearRegression
LibLinearRegression() LibLinearRegression
LibNuSvmSolver 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) LibNuSvmSolver
LibNuSvmSolver() LibNuSvmSolver
LibsvmDatasetReader it.uniroma2.sag.kelp.data.dataset
A utility class to read dataset in the libsvm/liblinear/svmLight format.
LibsvmDatasetReader(String, String) LibsvmDatasetReader
Constructor for reading dataset in libsvm/liblinear/svmLight format for classification tasks.
LibsvmDatasetReader(String, String, StringLabel) LibsvmDatasetReader
Constructor for reading dataset in libsvm/liblinear/svmLight format for regression tasks.
LibSvmSolver 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() LibSvmSolver
LibSvmSolver(Kernel, float, float) LibSvmSolver
LibSvmSolver.Pair it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
The pair of indices i and j that are selected as working set
LibSvmSolver.Pair() LibSvmSolver.Pair
LINEAR_REP_NAME NystromExampleMain
LinearizationFunction it.uniroma2.sag.kelp.linearization
This interface allows implementing function to linearized examples through
linear representations, i.e.
linearizeByEnsembleAndJuxtaposition(Example, String) NystromMethodEnsemble
Given an example, this method produces a new Example
containing a single representation, i.e.
LinearKernel it.uniroma2.sag.kelp.kernel.vector
Linear Kernel for Vectors Vector representations
LinearKernel(String) LinearKernel
LinearKernel() LinearKernel
LinearKernelCombination it.uniroma2.sag.kelp.kernel.standard
Weighted Linear Combination of Kernels
LinearKernelCombination() LinearKernelCombination
LinearKMeansCluster it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
It is the instance of a Cluster for Linear Algorithms, intended as a set of
objects, instantiated as Examples, grouped together according to a measure of
similarity.
LinearKMeansCluster() LinearKMeansCluster
LinearKMeansCluster(String) LinearKMeansCluster
LinearKMeansClusteringExample it.uniroma2.sag.kelp.examples.demo.clustering
This class contains an example of the usage of the Linear K-means clustering.
LinearKMeansClusteringExample() LinearKMeansClusteringExample
LinearKMeansEngine it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
Implements the K-means Clustering Algorithm, that works on an Explicit
feature Space.
LinearKMeansEngine() LinearKMeansEngine
LinearKMeansEngine(String, int, int) LinearKMeansEngine
LinearKMeansExample it.uniroma2.sag.kelp.learningalgorithm.clustering.kmeans
LinearKMeansExample() LinearKMeansExample
LinearKMeansExample(Example, float) LinearKMeansExample
LinearMethod it.uniroma2.sag.kelp.learningalgorithm
It is a linear algorithm operating directly on an explicit vector space
LinearPassiveAggressiveClassification it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
Online Passive-Aggressive Learning Algorithm for classification tasks (linear version) .
LinearPassiveAggressiveClassification() LinearPassiveAggressiveClassification
LinearPassiveAggressiveClassification(float, float, PassiveAggressiveClassification.Loss, PassiveAggressive.Policy, String, Label) LinearPassiveAggressiveClassification
LinearPassiveAggressiveRegression it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
Online Passive-Aggressive Learning Algorithm for regression tasks (linear version).
LinearPassiveAggressiveRegression() LinearPassiveAggressiveRegression
LinearPassiveAggressiveRegression(float, float, PassiveAggressive.Policy, String, Label) LinearPassiveAggressiveRegression
LinearPerceptron it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
The perceptron learning algorithm algorithm for classification tasks (linear version).
LinearPerceptron() LinearPerceptron
LinearPerceptron(float, float, boolean, String, Label) LinearPerceptron
load(String) Kernel
Load a kernel function from a file path.
load(String) NystromMethod
Load a Nystrom-based projection function from a file
load(String) NystromMethodEnsemble
Load an Ensemble of Nystrom projectors saved on file.
logger SimpleExample
logIteration LibSvmSolver
The number of iteration to be accomplished to print info in the standard
output
loss PassiveAggressiveClassification
LRB TreeIO
The left parenthesis character within the tree
p LibSvmSolver
PAIR_SEPARATOR ExampleFactory
PairSimilarityExtractor 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...) PairSimilarityExtractor
PairwiseProductKernel it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
PairwiseProductKernel(Kernel, boolean) PairwiseProductKernel
Defines a Kernel operating on pairs that applies the following formula:
PairwiseProductKernel() PairwiseProductKernel
PairwiseSumKernel it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
PairwiseSumKernel(Kernel, boolean) PairwiseSumKernel
Defines a Kernel operating on pairs that applies the following formula:
PairwiseSumKernel() PairwiseSumKernel
parseCharniakSentence(String) 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) ExampleFactory
parseLabel(String) LabelFactory
Initializes and returns the label described in
labelDescription
parseNode(int, String, TreeNode) TreeIO
parseRepresentation(String, String) RepresentationFactory
Initializes and returns the representation described in
representationBody
parseSingleRepresentation(String) ExampleFactory
Parse a single Representation from its string representation
parseStructureElement(String, String) StructureElementFactory
Initializes and returns the structureElement described in
structureElementBody
parseStructureElement(String) StructureElementFactory
ParsingExampleException it.uniroma2.sag.kelp.data.example
An Exception to model problems when parsing examples
ParsingExampleException(String) ParsingExampleException
ParsingExampleException(Exception, String) ParsingExampleException
PartialTreeKernel it.uniroma2.sag.kelp.kernel.tree
Partial Tree Kernel implementation.
PartialTreeKernel() PartialTreeKernel
Default constructor.
PartialTreeKernel(float, float, float, String) PartialTreeKernel
A Constructor for the Partial Tree Kernel in which parameters can be set
manually.
PartialTreeKernel(String) PartialTreeKernel
This constructor by default uses lambda=0.4, mu=0.4, terminalFactor=1
PassiveAggressive it.uniroma2.sag.kelp.learningalgorithm
It is an online learning algorithms that implements the Passive Aggressive algorithms described in
[Crammer, JMLR2006] K.
PassiveAggressive() PassiveAggressive
PassiveAggressive.Policy it.uniroma2.sag.kelp.learningalgorithm
It is the updating policy applied by the Passive Aggressive Algorithm when a miss-prediction occurs
PassiveAggressiveClassification it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
Online Passive-Aggressive Learning Algorithm for classification tasks.
PassiveAggressiveClassification() PassiveAggressiveClassification
PassiveAggressiveClassification.Loss it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
PassiveAggressiveRegression it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
Online Passive-Aggressive Learning Algorithm for regression tasks.
PassiveAggressiveRegression() PassiveAggressiveRegression
PegasosLearningAlgorithm it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos
It implements the Primal Estimated sub-GrAdient SOlver (PEGASOS) for SVM.
PegasosLearningAlgorithm() PegasosLearningAlgorithm
PegasosLearningAlgorithm(int, float, int, String, Label) PegasosLearningAlgorithm
Perceptron it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
The perceptron learning algorithm algorithm for classification tasks.
Perceptron() Perceptron
phi SoftConfidenceWeightedClassification
Phi is the standard score computed from the confidence.
pointWiseProduct(Vector) DenseVector
pointWiseProduct(Vector) Vector
Compute the point-wise product of this vector with the one in
vector.
pointWiseProduct(Vector) SparseVector
policy PassiveAggressive
PolynomialKernel it.uniroma2.sag.kelp.kernel.standard
PolynomialKernel(float, float, float, Kernel) PolynomialKernel
PolynomialKernel(float, Kernel) PolynomialKernel
PolynomialKernel() PolynomialKernel
populate(String) SequenceDataset
populate(String) SimpleDataset
Populate the dataset by reading it from a KeLP
compliant file.
populate(DatasetReader) SimpleDataset
Populate the dataset using the provided reader
POS_LEMMA_SEPARATOR LexicalStructureElement
positiveClass BinaryClassifier
PosStructureElement it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement that contains Part-of-Speech , i.e.
PosStructureElement() PosStructureElement
PosStructureElement(String) PosStructureElement
pow(float, int) Math
It evaluates the power of a number
precision MulticlassClassificationEvaluator.ClassStats
predict(Example) BinaryClassifier
predict(Example) BinaryKernelMachineClassifier
Classifies an example applying the following formula:
y(x) = \sum_{i \in SV}\alpha_i k(x_i, x) + b
predict(Example) BinaryLinearClassifier
predict(Example) Classifier
predict(Example) MultiLabelClassifier
predict(Example) OneVsAllClassifier
predict(Example) OneVsOneClassifier
predict(Example) PredictionFunction
predict(Example) RegressionFunction
predict(Example) UnivariateKernelMachineRegressionFunction
predict(Example) UnivariateLinearRegressionFunction
predict(Example) UnivariateRegressionFunction
predict(Example) SequencePredictionFunction
predictAndLearnWithAvailableBudget(Example) BudgetedLearningAlgorithm
predictAndLearnWithAvailableBudget(Example) RandomizedBudgetPerceptron
predictAndLearnWithAvailableBudget(Example) Stoptron
predictAndLearnWithAvailableBudget(Example) BudgetedPassiveAggressiveClassification
predictAndLearnWithFullBudget(Example) BudgetedLearningAlgorithm
Learns from a single example applying a specific policy that must be adopted when the budget is reached
predictAndLearnWithFullBudget(Example) RandomizedBudgetPerceptron
predictAndLearnWithFullBudget(Example) Stoptron
predictAndLearnWithFullBudget(Example) BudgetedPassiveAggressiveClassification
Prediction it.uniroma2.sag.kelp.predictionfunction
It is a generic output provided by a machine learning systems on a test data
PredictionFunction it.uniroma2.sag.kelp.predictionfunction
It is a generic prediction function that can be learned with a machine learning algorithm
PredictionFunctionTypeResolver it.uniroma2.sag.kelp.predictionfunction
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of PredictionFunctions
PredictionFunctionTypeResolver() PredictionFunctionTypeResolver
PreferenceKernel it.uniroma2.sag.kelp.kernel.pairs
It is a kernel operating on ExamplePairs applying the following formula:
PreferenceKernel(Kernel) PreferenceKernel
PreferenceKernel() PreferenceKernel
printCounters(Label) MulticlassClassificationEvaluator
Print the counters of the specified Label l.
printExample(String...) SimpleExample
prob L2R_L2_SvcFunction
Problem it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
Describes the problem
Problem(Dataset, String, Label, Problem.LibLinearSolverType) Problem
Problem.LibLinearSolverType it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
productionBasedDeltaFunction(TreeNode, TreeNode, int, float, DeltaMatrix) TreeKernelUtils
Delta Function for tree kernels operation at production level, like
SubTreeKernel and SubSetTreeKernel.
property UnivariateRegressionFunction
PruneNodeIfLeaf it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
Prune a node if it is a leaf.
PruneNodeIfLeaf() PruneNodeIfLeaf
PruneNodeLeafNumber it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
This strategy keeps the first numberOfLeavesToKeep leaves and
discard all subsequent ones.
PruneNodeLeafNumber(int) PruneNodeLeafNumber
PruneNodeLowerThanThreshold it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
Prune a node if the absolute value of the field weightfield
(default value "weight") is lower than threshold.
PruneNodeLowerThanThreshold(double, String, boolean, double) PruneNodeLowerThanThreshold
PruneNodeLowerThanThreshold(double) PruneNodeLowerThanThreshold
PruneNodeLowerThanThreshold.nodeComparisonOperator it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
PruneNodeNumberOfChildren it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
This pruning strategy makes sure that a node does not have more than
maxNumberOfChildren children.
PruneNodeNumberOfChildren(int) PruneNodeNumberOfChildren
pruneNodes(TreeNode) NodePruner
given a node, prunes any of the descendant nodes
pruneNodes(TreeNode) PruneNodeNumberOfChildren
PruneNodeThresholdAndDistanceFromSpecificLabelledNode it.uniroma2.sag.kelp.data.representation.tree.node.nodePruner
Prune a node if the following conditions are met:
1) there is no node at distance not greater than distance whose
label starts with the prefix labelPrefix
2) the conditions specified in the parent class
PruneNodeThresholdAndDistanceFromSpecificLabelledNode(double, String, boolean, double, String, int) PruneNodeThresholdAndDistanceFromSpecificLabelledNode
PruneNodeThresholdAndDistanceFromSpecificLabelledNode(double, String) PruneNodeThresholdAndDistanceFromSpecificLabelledNode
pruneTree(TreeRepresentation) TreeNodePruner
SameAdditionalInfoStructureElementSimilarity 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() SameAdditionalInfoStructureElementSimilarity
SameAdditionalInfoStructureElementSimilarity(List<String>, StructureElementSimilarityI) SameAdditionalInfoStructureElementSimilarity
save(String) SimpleDataset
Save the dataset in a file.
save(Kernel, String) Kernel
Save the input kernel in a file.
save(String) NystromMethod
Save a Nystrom-based projection function in a file.
save(String) NystromMethodEnsemble
Save an Ensemble of Nystrom projectors on file.
scale(float) Normalizable
Multiplies each element of this representation by coeff
scale(float) DenseVector
scale(float) SparseVector
SCWType it.uniroma2.sag.kelp.learningalgorithm.classification.scw
The two types of Soft Confidence-Weighted implemented variants
select(Dataset) ExampleSelector
This function allows to select a subset of Examples from the
input Dataset
select(Dataset) FirstExamplesSelector
select(DatasetReader) FirstExamplesSelector
This function allows to select a subset of Examples from the
input DatasetReader
select(Dataset) RandomExampleSelector
select_working_set(LibSvmSolver.Pair) LibCSvmSolver
select_working_set(LibSvmSolver.Pair) LibNuSvmSolver
select_working_set(LibSvmSolver.Pair) LibSvmSolver
Select the working set in each iteration.
SelectRepresentationFromExample it.uniroma2.sag.kelp.data.representation.tree.utils
SelectRepresentationFromExample(String, SelectRepresentationFromExample.representationSelectorInExample) SelectRepresentationFromExample
SelectRepresentationFromExample(String) SelectRepresentationFromExample
SelectRepresentationFromExample.representationSelectorInExample it.uniroma2.sag.kelp.data.representation.tree.utils
SEPARATOR DenseVector
SEQDELIM SequenceExample
This delimiter is used in the construction of artificial features
representing transitions for the
SequenceClassificationLearningAlgorithm
SEQDELIM SequencePath
This delimiter is used in the construction of artificial features
representing transitions for the
SequenceClassificationLearningAlgorithm
SequenceClassificationKernelBasedLearningAlgorithm it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
/** This class implements a sequential labeling paradigm.
SequenceClassificationKernelBasedLearningAlgorithm() SequenceClassificationKernelBasedLearningAlgorithm
SequenceClassificationKernelBasedLearningAlgorithm(BinaryLearningAlgorithm, int, float) SequenceClassificationKernelBasedLearningAlgorithm
SequenceClassificationLearningAlgorithm it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
This class implements a sequential labeling paradigm.
SequenceClassificationLearningAlgorithm() SequenceClassificationLearningAlgorithm
SequenceClassificationLinearLearningAlgorithm it.uniroma2.sag.kelp.learningalgorithm.classification.hmm
This class implements a sequential labeling paradigm.
SequenceClassificationLinearLearningAlgorithm(BinaryLearningAlgorithm, int, float) SequenceClassificationLinearLearningAlgorithm
SequenceDataset it.uniroma2.sag.kelp.data.dataset
A dataset made of SequenceExamples
SequenceDataset() SequenceDataset
SequenceDatasetReader it.uniroma2.sag.kelp.data.dataset
The methods of this class allows to read SequenceExamples from a
file
SequenceDatasetReader(String) SequenceDatasetReader
SequenceDatasetWriter it.uniroma2.sag.kelp.data.dataset
* The methods of this class allows to write SequenceExamples
into a file
SequenceDatasetWriter(String) SequenceDatasetWriter
SequenceElement it.uniroma2.sag.kelp.data.representation.sequence
A SequenceElement is a generic element in a SequenceRepresentation
SequenceElement(StructureElement) SequenceElement
SequenceElement(StructureElement, SequenceElement, SequenceElement) SequenceElement
SequenceEmission it.uniroma2.sag.kelp.data.label
It represents the pair (class_label,emission_score) assigned to each element
in a sequence.
SequenceEmission(Label, float) SequenceEmission
SequenceExample it.uniroma2.sag.kelp.data.example
A SequenceExample represents a sequence of Examples
s, each containing a set of Representations and a set of
Labels.
SequenceExample() SequenceExample
SequenceExampleGenerator it.uniroma2.sag.kelp.data.examplegenerator
A SequenceExampleGenerator generates a copy of an input
Example (reflecting an item in a SequenceExample)
enriched with information derived from the s assigned to the previous
n examples.
SequenceExampleGeneratorKernel it.uniroma2.sag.kelp.data.examplegenerator
A SequenceExampleGeneratorKernelBasedAlg allows to
implicitly enrich a targeted Example (reflecting an item
in a SequenceExample) with information derived from the s
assigned to the previous n examples.
SequenceExampleGeneratorKernel() SequenceExampleGeneratorKernel
SequenceExampleGeneratorKernel(int, String) SequenceExampleGeneratorKernel
SequenceExampleGeneratorLinear it.uniroma2.sag.kelp.data.examplegenerator
A SequenceExampleGeneratorLinearAlg allows to explicitly
enrich a targeted Example (reflecting an item in a
SequenceExample) with information derived from the s
assigned to the previous n examples.
SequenceExampleGeneratorLinear() SequenceExampleGeneratorLinear
SequenceExampleGeneratorLinear(int, String, float) SequenceExampleGeneratorLinear
SequenceExampleGeneratorTypeResolver it.uniroma2.sag.kelp.data.examplegenerator
It is a class implementing TypeIdResolver which will be used by
Jackson library during the serialization in JSON and deserialization of
SequenceExamplesGenerators
SequenceExampleGeneratorTypeResolver() SequenceExampleGeneratorTypeResolver
SequenceKernel it.uniroma2.sag.kelp.kernel.sequence
Sequence Kernel implementation.
SequenceKernel() SequenceKernel
SequenceKernel(String, int, float) SequenceKernel
SequenceKernelExample it.uniroma2.sag.kelp.examples.main
This example illustrates how to use the sequence kernel on a
Sentiment Analysis task.
SequenceKernelExample() SequenceKernelExample
SequenceLearningKernelMain it.uniroma2.sag.kelp.examples.demo.seqlearn
This class shows how to use a
SequenceClassificationLearningAlgorithm.
SequenceLearningKernelMain() SequenceLearningKernelMain
SequenceLearningLinearMain it.uniroma2.sag.kelp.examples.demo.seqlearn
This class shows how to use a
SequenceClassificationLearningAlgorithm.
SequenceLearningLinearMain() SequenceLearningLinearMain
SequenceModel it.uniroma2.sag.kelp.predictionfunction.model
This class implements a model produced by a
SequenceClassificationLearningAlgorithm
SequenceModel() SequenceModel
SequenceModel(PredictionFunction, SequenceExampleGenerator) SequenceModel
SequencePath it.uniroma2.sag.kelp.data.example
This class defines the output of a sequence labeling process.
SequencePath() SequencePath
SequencePrediction it.uniroma2.sag.kelp.predictionfunction
It is a output provided by a machine learning systems on a sequence.
SequencePrediction() SequencePrediction
SequencePredictionFunction it.uniroma2.sag.kelp.predictionfunction
This class implements a classifier in a sequence labeling process.
SequencePredictionFunction() SequencePredictionFunction
SequencePredictionFunction(SequenceModel) SequencePredictionFunction
SequenceRepresentation it.uniroma2.sag.kelp.data.representation.sequence
Sequence Representation used for example to represent a sentence (i.e.
SequenceRepresentation() SequenceRepresentation
SerializationExample it.uniroma2.sag.kelp.examples.main
This example illustrates how to serialize and deserialize learning algorithms, as well as
classification functions.
SerializationExample() SerializationExample
setA(float) PolynomialKernel
setAdditionalInfos(List<String>) 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) LexicalStructureElementSimilarity
Sets whether the similarity between words having different Part-of-Speech is allowed
or if it must be set to 0
setAlpha(float) Perceptron
Sets the learning rate, i.e.
setAlphas(float[]) SvmSolution
setAssignedSequenceLabels(List<SequenceEmission>) SequencePath
setB(float) PolynomialKernel
setBaseAlgorithm(LearningAlgorithm) RandomizedBudgetPerceptron
setBaseAlgorithm(LearningAlgorithm) Stoptron
setBaseAlgorithm(LearningAlgorithm) SequenceClassificationKernelBasedLearningAlgorithm
setBaseAlgorithm(LearningAlgorithm) SequenceClassificationLinearLearningAlgorithm
setBaseAlgorithm(LearningAlgorithm) MultiLabelClassificationLearning
This method will set the type of the base algorithms to be learned.
setBaseAlgorithm(LearningAlgorithm) OneVsAllLearning
This method will set the type of the base algorithms to be learned.
setBaseAlgorithm(LearningAlgorithm) OneVsOneLearning
This method will set the type of the base algorithms to be learned.
setBaseAlgorithm(LearningAlgorithm) MetaLearningAlgorithm
setBaseAlgorithm(LearningAlgorithm) MultiEpochLearning
setBaseKernel(Kernel) KernelComposition
setBaseLearningAlgorithm(LearningAlgorithm) SequenceClassificationLearningAlgorithm
setBasePredictionFunction(PredictionFunction) SequenceModel
setBaseSimilarity(StructureElementSimilarityI) SameAdditionalInfoStructureElementSimilarity
Sets the base similarity applied when two structure elements have
the same additional infos
setBeamSize(int) SequenceClassificationLearningAlgorithm
setBeamSize(int) SequencePredictionFunction
setBias(float) BinaryModel
setBinaryClassifiers(Classifier[]) MultiLabelClassifier
setBinaryClassifiers(Classifier[]) OneVsAllClassifier
setBinaryClassifiers(Classifier[]) OneVsOneClassifier
setBudget(int) BudgetedLearningAlgorithm
Sets the budget, i.e.
setC(double) LibLinearLearningAlgorithm
setC(float) LibSvmSolver
setC(float) BudgetedPassiveAggressiveClassification
setC(float) PassiveAggressiveClassification
setC(float) PassiveAggressive
setC(double) LibLinearRegression
setCentroid(Vector) LinearKMeansCluster
setChildren(ArrayList<TreeNode>) TreeNode
Set the direct children of the target node
setClassificationLabels(HashSet<Label>) Example
setClassName(String) StringLabel
setCn(double) DCDLearningAlgorithm
setCn(double) LibLinearLearningAlgorithm
setCn(float) LibSvmSolver
setCn(float) BudgetedPassiveAggressiveClassification
setCn(float) PassiveAggressiveClassification
setCn(float) SoftConfidenceWeightedClassification
setComparisonTypeLowerThanAbsoluteValue() PruneNodeLowerThanThreshold
setComparisonTypeLowerThanValue() PruneNodeLowerThanThreshold
setContent(StructureElement) GraphNode
Sets the content of this node
setContent(StructureElement) SequenceElement
Sets the content of this SequenceElement
setContent(StructureElement) TreeNode
setCp(double) DCDLearningAlgorithm
setCp(double) LibLinearLearningAlgorithm
setCp(float) LibSvmSolver
setCp(float) BudgetedPassiveAggressiveClassification
setCp(float) PassiveAggressiveClassification
setCp(float) SoftConfidenceWeightedClassification
setDataFromText(String) DirectedGraphRepresentation
setDataFromText(String) Representation
Initializes a Representation using its textual description provided in
representationDescription
setDataFromText(String) SequenceRepresentation
setDataFromText(String) StringRepresentation
setDataFromText(String) CompositionalStructureElement
setDataFromText(String) LexicalStructureElement
setDataFromText(String) PosStructureElement
setDataFromText(String) StructureElement
Initializes a StructureElement using its textual description provided in
structureElementDescription
setDataFromText(String) SyntacticStructureElement
setDataFromText(String) UntypedStructureElement
setDataFromText(String) TreeRepresentation
setDataFromText(String) DenseVector
Set the values of the vectors according to the input text, which is expected to be
a sequence of numbers separated by a white space, or a comma, or a semicolon
setDataFromText(String) SparseVector
setDcdLoss(DCDLoss) DCDLearningAlgorithm
setDefaultWeightValue(double) PruneNodeLowerThanThreshold
setDegree(float) PolynomialKernel
setDeletingPolicy(BudgetedPassiveAggressiveClassification.DeletingPolicy) BudgetedPassiveAggressiveClassification
setDeltaMatrix(DeltaMatrix) PartialTreeKernel
Deprecated.
setDeltaMatrix(DeltaMatrix) SmoothedPartialTreeKernel
Deprecated.
setDeltaMatrix(DeltaMatrix) SubSetTreeKernel
Deprecated.
setDeltaMatrix(DeltaMatrix) SubTreeKernel
Deprecated.
setDependencyRelation(String) CompositionalStructureElement
setDist(Float) ClusterExample
setEmission(float) SequenceEmission
setEnrichmentName(String) CompositionalNodeSimilarity
setEnrichmentName(String) VectorBasedStructureElementSimilarity
Sets the identifier of the vectors associated to
a StructureElement during the manipulation operation performed
by a Manipulator (i.e.
setEpochs(int) MultiEpochLearning
setEps(float) LibSvmSolver
setEpsilon(float) PassiveAggressiveRegression
Sets epsilon, i.e.
setEta(float) SoftConfidenceWeightedClassification
setExample(Example) ClusterExample
setExamples(Vector<ClusterExample>) Cluster
This function initialize the set of objects inside the cluster
setExamplesToStore(int) DynamicIndexKernelCache
Deprecated.
Sets the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
setExamplesToStore(int) DynamicIndexSquaredNormCache
Sets the maximum number of norms that
can be simultaneously stored
setExamplesToStore(int) FixIndexKernelCache
Deprecated.
Sets the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
setExamplesToStore(int) FixSizeKernelCache
Sets the maximum number of examples whose pairwise kernel computations
can be simultaneously stored
setFairness(boolean) DCDLearningAlgorithm
setFairness(boolean) LibLinearLearningAlgorithm
setFairness(boolean) BinaryCSvmClassification
setFairness(boolean) BudgetedPassiveAggressiveClassification
setFairness(boolean) PassiveAggressiveClassification
setFairness(boolean) SoftConfidenceWeightedClassification
setFeatureValue(Object, float) DenseVector
setFeatureValue(Object, float) Vector
Assigns value to the feature identified by featureIdentifier
setFeatureValue(String, float) SparseVector
Sets the value of a feature
setFeatureValue(Object, float) SparseVector
setFeatureValues(double[]) DenseVector
Sets the feature values.
setFeatureValues(DenseMatrix64F) DenseVector
Sets the feature values.
setFirst(Vector) VectorOperationException
setGamma(float) RbfKernel
setHead(LexicalStructureElement) CompositionalStructureElement
setHyperplane(Vector) BinaryLinearModel
setIgnorePosInLemmaMatches(boolean) 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) ExactMatchingStructureElementSimilarity
Sets whether the part-of-speech must be ignored in comparing two
LexicalStructureElements
setIncludeLeaves(boolean) SubSetTreeKernel
Sets whether the leaves must be involved in the kernel computation.
setIncludeLeaves(boolean) SubTreeKernel
Sets whether the leaves must be involved in the kernel computation.
setInstance(Example) SupportVector
setIntraPairSimProduct(boolean) 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) 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) 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) 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) 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) WLSubtreeMapper
Sets the maximum depth of the visits of the WL kernel
setIterations(int) PegasosLearningAlgorithm
Sets the number of iterations
setK(int) PegasosLearningAlgorithm
Sets the number of examples k that Pegasos exploits in its
mini-batch learning approach
setK(int) KernelBasedKMeansEngine
setK(int) LinearKMeansEngine
setKernel(Kernel) VectorBasedStructureElementSimilarity
Sets the kernel to be used in comparing two vectors
setKernel(Kernel) RandomizedBudgetPerceptron
setKernel(Kernel) Stoptron
setKernel(Kernel) SequenceClassificationKernelBasedLearningAlgorithm
setKernel(Kernel) BinaryCSvmClassification
setKernel(Kernel) BinaryNuSvmClassification
setKernel(Kernel) BudgetedPassiveAggressiveClassification
setKernel(Kernel) KernelizedPassiveAggressiveClassification
setKernel(Kernel) KernelizedPerceptron
setKernel(Kernel) KernelBasedKMeansEngine
setKernel(Kernel) KernelMethod
Sets the kernel this
setKernel(Kernel) EpsilonSvmRegression
setKernel(Kernel) KernelizedPassiveAggressiveRegression
setKernel(Kernel) NystromMethod
setKernel(Kernel) BinaryKernelMachineModel
setKernel(Kernel) KernelMachineModel
setKernelCache(KernelCache) Kernel
Sets the cache in which storing the kernel operations in the RKHS defined
by this kernel
setKernelCache(KernelCache) SequenceClassificationKernelBasedLearningAlgorithm
setKernelValue(Example, Example, float) DynamicIndexKernelCache
Deprecated.
setKernelValue(Example, Example, float) FixIndexKernelCache
Deprecated.
setKernelValue(Example, Example, float) FixSizeKernelCache
setKernelValue(Example, Example, float) KernelCache
Stores a kernel computation in cache
setKernelValue(Example, Example, float) SimpleDynamicKernelCache
setKernelValue(Example, Example, float) StripeKernelCache
setLabel(String) Cluster
setLabel(Label) SequenceEmission
setLabel(String) UntypedStructureElement
setLabel(Label) BinaryLearningAlgorithm
setLabel(Label) BudgetedLearningAlgorithm
setLabel(Label) DCDLearningAlgorithm
setLabel(Label) LibLinearLearningAlgorithm
setLabel(Label) LibSvmSolver
setLabel(Label) PegasosLearningAlgorithm
setLabel(Label) Perceptron
setLabel(Label) SoftConfidenceWeightedClassification
setLabel(Label) PassiveAggressive
setLabel(Label) LibLinearRegression
setLabel(Label) BinaryClassifier
setLabels(Label[]) Example
Sets the example classificationLabels
setLabels(List<Label>) BinaryLearningAlgorithm
setLabels(List<Label>) BudgetedLearningAlgorithm
setLabels(List<Label>) DCDLearningAlgorithm
setLabels(List<Label>) SequenceClassificationLearningAlgorithm
setLabels(List<Label>) LibLinearLearningAlgorithm
setLabels(List<Label>) LibSvmSolver
setLabels(List<Label>) MultiLabelClassificationLearning
Set the labels associated to this multi-classifier.
setLabels(List<Label>) OneVsAllLearning
Set the labels associated to this multi-classifier.
setLabels(List<Label>) OneVsOneLearning
Set the labels associated to this multi-classifier.
setLabels(List<Label>) PegasosLearningAlgorithm
setLabels(List<Label>) Perceptron
setLabels(List<Label>) SoftConfidenceWeightedClassification
setLabels(List<Label>) LearningAlgorithm
Sets the labels representing the concept to be learned.
setLabels(List<Label>) MultiEpochLearning
setLabels(List<Label>) PassiveAggressive
setLabels(List<Label>) LibLinearRegression
setLabels(Label...) EpsilonSvmRegression
setLabels(List<Label>) BinaryClassifier
setLabels(List<Label>) MultiLabelClassifier
setLabels(List<Label>) OneVsAllClassifier
setLabels(List<Label>) OneVsOneClassifier
setLabels(List<Label>) MulticlassModel
setLabels(List<Label>) PredictionFunction
Sets the labels representing the concept to be predicted.
setLabels(List<Label>) UnivariateRegressionFunction
setLabels(List<Label>) SequencePredictionFunction
setLambda(float) SequenceKernel
setLambda(float) PartialTreeKernel
setLambda(float) SmoothedPartialTreeKernel
setLambda(float) SubSetTreeKernel
Set the decay factor
setLambda(float) SubTreeKernel
Set the decay factor
setLambda(float) PegasosLearningAlgorithm
Sets the regularization coefficient
setLandmarks(List<Example>) NystromMethod
setLemma(String) LexicalStructureElement
setLoss(PassiveAggressiveClassification.Loss) PassiveAggressiveClassification
setMargin(float) Perceptron
Sets the desired margin, i.e.
setMarkingPrefix(String) TreePairRelTagger
Sets the prefix used to mark the related nodes
setMatrixPath(String) Wordspace
Sets the path of the file where the word vectors are stored and loads
them.
setMaxEmissionCandidates(int) SequenceClassificationLearningAlgorithm
setMaxEmissionCandidates(int) SequencePredictionFunction
setMaximumDepthOfVisits(int) TreeNodePruner
setMaxIterations(int) DCDLearningAlgorithm
setMaxIterations(int) KernelBasedKMeansEngine
setMaxIterations(int) LinearKMeansEngine
setMaxNumberOfRows(int) StripeKernelCache
setMaxSubseqLeng(int) SequenceKernel
setMaxSubseqLeng(int) PartialTreeKernel
setMaxSubseqLeng(int) SmoothedPartialTreeKernel
setModel(Model) BinaryKernelMachineClassifier
setModel(Model) BinaryLinearClassifier
setModel(Model) MultiLabelClassifier
setModel(Model) OneVsAllClassifier
setModel(Model) OneVsOneClassifier
setModel(Model) PredictionFunction
Sets the model
setModel(Model) UnivariateKernelMachineRegressionFunction
setModel(Model) UnivariateLinearRegressionFunction
setModel(Model) SequencePredictionFunction
setModels(List<BinaryModel>) MulticlassModel
setModifier(LexicalStructureElement) CompositionalStructureElement
setMu(float) PartialTreeKernel
setMu(float) SmoothedPartialTreeKernel
setNegativeLabelsForClassifier(Label[]) OneVsOneClassifier
Set the negative label classifier array
setNext(SequenceElement) SequenceElement
Sets the previous element in the sequence
setNodeSimilarity(StructureElementSimilarityI) SmoothedPartialTreeKernel
setNu(float) BinaryNuSvmClassification
setNu(float) OneClassSvmClassification
setNumberOfColumns(int) StripeKernelCache
setObj(float) SvmSolution
setP(double) LibLinearRegression
setPaths(List<SequencePath>) SequencePrediction
setPolicy(PassiveAggressive.Policy) PassiveAggressive
setPos(String) LexicalStructureElement
setPos(String) PosStructureElement
setPosRestriction(boolean) CompositionalNodeSimilarity
setpReg(float) EpsilonSvmRegression
setPrevious(SequenceElement) SequenceElement
Sets the previous element in the sequence
setProjectionMatrix(List<Double>) NystromMethod
setProperty(Label) NumericLabel
setRank(int) NystromMethod
setRegressionValues(ArrayList<NumericLabel>) Example
setRepresentation(String) DirectKernel
setRepresentation(String) DCDLearningAlgorithm
setRepresentation(String) SequenceClassificationLinearLearningAlgorithm
setRepresentation(String) LibLinearLearningAlgorithm
setRepresentation(String) LinearPassiveAggressiveClassification
setRepresentation(String) PegasosLearningAlgorithm
setRepresentation(String) LinearPerceptron
setRepresentation(String) SoftConfidenceWeightedClassification
setRepresentation(String) LinearMethod
Sets the representation this learning algorithm will exploit
setRepresentation(String) LibLinearRegression
setRepresentation(String) LinearPassiveAggressiveRegression
setRepresentation(String) BinaryLinearModel
setRepresentationName(String) SequenceExampleGeneratorLinear
setRepresentationName(String) LinearKMeansEngine
setRepresentations(HashMap<String, Representation>) Example
Sets the example representations
setRepresentations(HashMap<String, Representation>) SimpleExample
setRepresentationToBeEnriched(String) CompositionalNodeSimilarityDilation
setRepresentationToBeEnriched(String) CompositionalNodeSimilarityProduct
setRepresentationToBeEnriched(String) CompositionalNodeSimilaritySum
setRho(float) SvmSolution
setScore(Double) SequencePath
setScwType(SCWType) SoftConfidenceWeightedClassification
setSecond(Vector) VectorOperationException
setSeed(long) Dataset
Sets the seed of the random generator used to shuffling examples and getting random examples
setSeed(long) SimpleDataset
setSeed(long) RandomizedBudgetPerceptron
Sets the seed for the random generator adopted to select the support vector to delete
setSeed(long) DCDLearningAlgorithm
setSelectionAllExamples() SelectRepresentationFromExample
setSelectionToLeftExampleInPairOnly() SelectRepresentationFromExample
setSelectionToRightExampleInPairOnly() SelectRepresentationFromExample
setSequenceExampleGenerator(SequenceExampleGenerator) SequenceClassificationLearningAlgorithm
setSequenceExampleGenerator(SequenceExampleGenerator) SequenceModel
setSimilarityThreshold(float) SmoothedPartialTreeKernel
setSize(int) FixIndexSquaredNormCache
Sets the size of the cache, i.e.
setSquaredNormCache(SquaredNormCache) Kernel
Sets the cache in which storing the squared norms in the RKHS defined
by this kernel
setSquaredNormValue(Example, float) DynamicIndexSquaredNormCache
setSquaredNormValue(Example, float) FixIndexSquaredNormCache
setSquaredNormValue(Example, float) SquaredNormCache
Stores a squared norm in the cache
setSupportVector(SupportVector, int) BinaryKernelMachineModel
Substitutes the support vector in position position with
sv
setSupportVectors(List<SupportVector>) BinaryKernelMachineModel
setSyntacticRelation(String) SyntacticStructureElement
setSyntacticRestriction(boolean) CompositionalNodeSimilarity
setTerminalFactor(float) PartialTreeKernel
setTerminalFactor(float) SmoothedPartialTreeKernel
setToCombine(List<Kernel>) KernelCombination
setToCombine(List<LearningAlgorithm>) EnsembleLearningAlgorithm
setTransitionRepresentationName(String) SequenceExampleGeneratorKernel
setTransitionsOrder(int) SequenceExampleGeneratorKernel
setUnbiased(boolean) Perceptron
Sets whether the bias, i.e.
setUpper_bound_n(float) SvmSolution
Set the \(C_n\) value
setUpper_bound_p(float) SvmSolution
Set the \(C_p\) value
setUseBias(boolean) DCDLearningAlgorithm
setValue(float) NumericLabel
setValue(double) LibLinearFeature
setValue(double) LibLinearFeatureNode
setVector(TIntFloatMap) SparseVector
setVisitTypePostOrder() TreeNodePruner
setVisitTypePreOrder() TreeNodePruner
setWeight(float) SupportVector
setWeights(List<Float>) LinearKernelCombination
setWordspace(WordspaceI) VectorBasedStructureElementSimilarity
Sets the wordspace from which the vectors associated to a word must be retrieved
ShortestPathKernel it.uniroma2.sag.kelp.kernel.graph
Implementation of the Shortest Path Kernel for Graphs
Reference paper:
[1] K.
ShortestPathKernel() ShortestPathKernel
ShortestPathKernel(String) ShortestPathKernel
shrinkingIteration LibSvmSolver
Number of iterations to be accomplished before shrinking
shuffleExamples(Random) SimpleDataset
Shuffles the examples in the dataset
sim(StructureElement, StructureElement) CompositionalNodeSimilarity
This method computes the similarity between two compositional nodes by
applying the "sum" operator.
sim(StructureElement, StructureElement) ExactMatchingStructureElementSimilarity
sim(StructureElement, StructureElement) LexicalStructureElementSimilarity
sim(StructureElement, StructureElement) SameAdditionalInfoStructureElementSimilarity
sim(StructureElement, StructureElement) StructureElementSimilarityI
This function measure the similarity between structure elements
SimpleDataset it.uniroma2.sag.kelp.data.dataset
A SimpleDataset that represent a whole dataset in memory.
SimpleDataset() SimpleDataset
Initializes an empty dataset
SimpleDynamicKernelCache it.uniroma2.sag.kelp.kernel.cache
Cache for kernel computations.
SimpleDynamicKernelCache() SimpleDynamicKernelCache
SimpleExample it.uniroma2.sag.kelp.data.example
An Example composed by a set of Representations.
SimpleExample() SimpleExample
Initializes an empty example (0 labels and 0 representations)
SimpleExample(Label[], HashMap<String, Representation>) SimpleExample
Initializes a SimpleExample with the input representations and labels
size() Cluster
This functions returns the number of objects inside the cluster
sizeI L2R_L2_SvcFunction
SmoothedPartialTreeKernel it.uniroma2.sag.kelp.kernel.tree
Partial Tree Kernel implementation.
SmoothedPartialTreeKernel() SmoothedPartialTreeKernel
SmoothedPartialTreeKernel(float, float, float, float, StructureElementSimilarityI, String) SmoothedPartialTreeKernel
SoftConfidenceWeightedClassification it.uniroma2.sag.kelp.learningalgorithm.classification.scw
Implements Exact Soft Confidence-Weighted (SCW) algorithms, an on-line
learning algorithm for binary classification.
SoftConfidenceWeightedClassification() SoftConfidenceWeightedClassification
SoftConfidenceWeightedClassification(SCWType, float, float, float, String) SoftConfidenceWeightedClassification
SoftConfidenceWeightedClassification(Label, SCWType, float, float, float, boolean, String) SoftConfidenceWeightedClassification
softmax(float, float) 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[]) 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
sortAscendingOrder() Cluster
sortDescendingOrder() Cluster
SparseVector it.uniroma2.sag.kelp.data.representation.vector
Sparse Feature Vector.
SparseVector() SparseVector
split(float) SequenceDataset
split(float) SimpleDataset
Returns two datasets created by splitting this dataset accordingly to
percentage.
splitClassDistributionInvariant(float) SequenceDataset
splitClassDistributionInvariant(float) SimpleDataset
Returns two datasets created by splitting this dataset accordingly to
percentage.
squaredNorm(Example) Kernel
Returns the squared norm of the given example in the RKHS defined by this kernel
squaredNorm(Example) NormalizationKernel
SquaredNormCache it.uniroma2.sag.kelp.kernel.cache
Cache for store squared norms
SquaredNormCacheTypeResolver it.uniroma2.sag.kelp.kernel.cache
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of SquaredNormCaches
SquaredNormCacheTypeResolver() SquaredNormCacheTypeResolver
squaredNormOfTheDifference(Example, Example) Kernel
Returns the squared norm of the difference between the given examples in the RKHS.
StandardizationManipulator it.uniroma2.sag.kelp.data.manipulator
It standardizes the feature values of a vectorial representation.
StandardizationManipulator(String, List<Example>) StandardizationManipulator
Constructor
standardize(Vector) StandardizationManipulator
It standardizes the feature values of vector.
StaticDeltaMatrix it.uniroma2.sag.kelp.kernel.tree.deltamatrix
StaticDeltaMatrix() StaticDeltaMatrix
StaticDeltaMatrix(int) StaticDeltaMatrix
Stoptron it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
It is a variation of the Stoptron proposed in
Stoptron() Stoptron
Stoptron(int, OnlineLearningAlgorithm, Label) Stoptron
StringLabel it.uniroma2.sag.kelp.data.label
It is a label consisting of an String value.
StringLabel(String) StringLabel
Initializes a label to a specific String value
StringLabel() StringLabel
StringRepresentation it.uniroma2.sag.kelp.data.representation.string
String representation.
StringRepresentation() StringRepresentation
Empty constructor necessary for making RepresentationFactory
support this implementation.
StringRepresentation(String) StringRepresentation
Initializing constructor.
StripeKernelCache it.uniroma2.sag.kelp.kernel.cache
Given a dataset, this cache stores kernel computations in "Stripes", i.e.
StripeKernelCache(Dataset) StripeKernelCache
StripeKernelCache(Dataset, int) StripeKernelCache
StripeKernelCache(int, int) StripeKernelCache
StripeKernelCache() StripeKernelCache
StructureElement it.uniroma2.sag.kelp.data.representation.structure
This class represent the atomic element of a discrete structure.
StructureElement() StructureElement
StructureElementFactory it.uniroma2.sag.kelp.data.representation.structure
This class implement a Factory Design pattern to instantiate
StructureElement given a string representing it.
StructureElementFilter 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 it.uniroma2.sag.kelp.data.representation.structure.similarity
This interface is used to implement similarity functions between Structured
Element
StructureElementSimilarityTypeResolver it.uniroma2.sag.kelp.data.representation.structure.similarity
It is a class implementing TypeIdResolver which will be used by
Jackson library during the serialization in JSON and deserialization of
classes implementing StructureElementSimilarityI, that are used
to estimate a similarity function between StructuredElements
StructureElementSimilarityTypeResolver() StructureElementSimilarityTypeResolver
SubSetTreeKernel 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) SubSetTreeKernel
SubTree Kernel
SubSetTreeKernel(String) SubSetTreeKernel
SubTree Kernel constructor.
SubSetTreeKernel() SubSetTreeKernel
SubTree Kernel: default constructor.
substituteSupportVector(int, Example, float) BinaryKernelMachineModel
SubTreeKernel it.uniroma2.sag.kelp.kernel.tree
SubTree Kernel implementation.
SubTreeKernel(float, String) SubTreeKernel
SubTree Kernel
SubTreeKernel(String) SubTreeKernel
SubTree Kernel constructor.
SubTreeKernel() SubTreeKernel
SubTree Kernel: default constructor.
subXTv(double[], double[]) L2R_L2_SvcFunction
SupportVector 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) SupportVector
SupportVector() SupportVector
SvmSolution 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() SvmSolution
swap(LibSvmSolver.AlphaStatus[], int, int) LibSvmSolver
swap(Example[], int, int) LibSvmSolver
swap(float[], int, int) LibSvmSolver
swap(int[], int, int) LibSvmSolver
swap_index(int, int) LibSvmSolver
Swap the info of two examples
swap_index(int, int) EpsilonSvmRegression
SyntacticStructureElement it.uniroma2.sag.kelp.data.representation.structure
It represent a StuctureElement that contains the syntactic informations, i.e.
SyntacticStructureElement() SyntacticStructureElement
SyntacticStructureElement(String) SyntacticStructureElement