- G - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
Gradient
- G_bar - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
Gradient bar
- get(int, int) - Method in class it.uniroma2.sag.kelp.kernel.tree.DeltaMatrix
-
Get a value from the matrix
- get_nr_variable() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
-
- get_QD() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
For each example i, it return the K_ii score
- get_QD() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- get_Qij(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- get_Qij(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- getA() - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- getActiveFeatures() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
-
- getAllClasses() - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.ClassificationOutput
-
Returns all the classes involved in the classification process (both predicted and not)
- getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
-
- getAllClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
-
- getAllNodes() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
Get recursively all Tree Nodes below the target node
- getAllProperties() - Method in interface it.uniroma2.sag.kelp.predictionfunction.regressionfunction.RegressionOutput
-
Returns all the properties on which the regressor has to provide predictions
- getAllProperties() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionOutput
-
- getAlpha() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
Returns the learning rate, i.e.
- getAlphas() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- getB() - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
This method will return the base algorithm.
- getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
This method will return the base algorithm.
- getBaseAlgorithm() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.MetaLearningAlgorithm
-
Returns the base algorithm this meta algorithm is based on
- getBaseAlgorithm() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- getBaseKernel() - Method in class it.uniroma2.sag.kelp.kernel.KernelComposition
-
Returns the kernel this kernel is enriching
- getBias() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
-
- getBinaryClassifiers() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- getBinaryClassifiers() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- getBudget() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
Returns the budget, i.e.
- getC() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- getC() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- getCacheHits() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
-
- getCacheMisses() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
-
- getCharArray() - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
-
- getChildren() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
Get the direct children of the target node
- getClassificationLabels() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns all the classification labels in the dataset.
- getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- getClassName() - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
-
- getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- getCn() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- getCounter() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
-
- getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- getCp() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- getCSvmAlpha(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
-
Get the initial weight for the future Support Vectors
- getDegree() - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- getDist() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- getEpochs() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- getEpsilon() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
Returns epsilon, i.e.
- getExample() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExample
-
- getExample(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
Return the example stored in the exampleIndex position
- getExample() - Method in class it.uniroma2.sag.kelp.data.representation.tree.EnrichedTreeNode
-
- getExample() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- getExamples() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
-
This function returns the set of objects inside the cluster
- getExamples() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns an array containing all the stored examples
- getExamples() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getFather() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
Get the father of the target node
- getFeatureValue(int) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
Returns the feature value of the featureIndex-th element
- getFeatureValue(int) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
Returns the feature value of the featureIndex-th element
- getFeatureValues() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
Returns the feature values in the EJML format
- getGamma() - Method in class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
-
- getHyperplane() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
-
- getId() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Returns a unique identifier of the example.
- getId() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
- getIndex() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeature
-
- getIndex() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
-
- getInstance() - Static method in class it.uniroma2.sag.kelp.data.representation.RepresentationFactory
-
Returns an instance of the class RepresentatioFactory
- getInstance() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
-
- getIterations() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
Returns the number of iterations
- getK() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
Returns the number of examples k that Pegasos exploits in its
mini-batch learning approach
- getK() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
-
- getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
-
- getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- getKernel() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.KernelMethod
-
Returns the kernel exploited by this learner
- getKernel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
-
- getKernel() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
- getKernel() - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
-
- getKernelComputations() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
-
Returns the number of times the kernel function has been invoked
- getKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
-
Retrieves in the cache the kernel operation between two examples
- getLabel() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
-
This function returns the label of the cluster
- getLabel() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
- getLabel() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- getLabel() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- getLabel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- getLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Returns the classification classificationLabels of this example
- getLabels() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
-
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
Returns the labels to be learned applying a one-vs-all strategy
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
Returns the labels to be learned applying a one-vs-one strategy
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- getLabels() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
-
Returns the labels representing the concept to be learned.
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- getLabels() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- getLabels() - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
-
Returns the labels representing the concept to be predicted.
- getLabels() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
-
- getLambda() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
Get the Vertical Decay factor
- getLambda() - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
-
Get the decay factor
- getLambda() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
Returns the regularization coefficient
- getLeftExample() - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
-
Returns the left example in the pair
- getLoss() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- getMargin() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
Returns the desired margin, i.e.
- getMaxId() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
Get the max id within all node under the target node
- getMaxId() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
Get the max id within all nodes
- getMaxIterations() - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- getMaxMarginForLabel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.data.clustering.ClusterExampleTypeResolver
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.data.example.ExampleTypeResolver
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.kernel.KernelTypeResolver
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
-
- getMechanism() - Method in class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
-
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
-
Returns the model
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
-
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- getModel() - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
-
Returns the model
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
-
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
-
- getModel() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
-
- getModels() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- getMu() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
Get the Horizontal Decay factor
- getNegativeLabelsForClassifier() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
Return the negative labels associated to each classifier
- getNextExample() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the next n Examples stored in the Dataset or a fewer number
if n examples are not available.
- getNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getNextExamples(int) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the next Example stored in the Dataset
- getNextExamples(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getNodeIdsSortedByName() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getNodeIdsSortedByProduction() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getNodeNames() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getNoOfChildren() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
- getNu() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
-
- getNu() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
-
- getNumberOfClassificationLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Returns the number of classification classificationLabels whose this instance is a positive example
- getNumberOfExamples() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the number of Examples in the dataset
- getNumberOfExamples() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getNumberOfFeatures() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
Returns the number of featuresValues
- getNumberOfHits() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
-
Returns the number of times a cache hit happened
- getNumberOfMisses() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
-
Returns the number of times a cache miss happened
- getNumberOfNegativeExamples(Label) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the number of negative Examples of a given class
- getNumberOfNegativeExamples(Label) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getNumberOfPositiveExamples(Label) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns the number of positive Examples of a given class
- getNumberOfPositiveExamples(Label) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getNumberOfRegressionLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Returns the number of regression classificationLabels
- getNumberOfRepresentations() - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
-
Returns the number of representations in which this example is modeled
- getNumberOfSupportVectors() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
- getNumberOfSupportVectors() - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
-
- getNx() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNodePairs
-
- getNz() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNodePairs
-
- getObj() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- getOrderedNodeSetByLabel() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getOrderedNodeSetByProduction() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getPolicy() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
-
- getPredictedClasses() - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.ClassificationOutput
-
Returns all the classes that the classifier has predicted
- getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
-
- getPredictedClasses() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- getPredictionFunction() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.ClassificationLearningAlgorithm
-
Returns the classifier learned during the training process
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
This method returns the learned PredictionFunction.
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
This method returns the learned PredictionFunction.
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- getPredictionFunction() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
-
Returns the predictionFunction learned during the training process
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
-
- getPredictionFunction() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
- getPredictionFunction() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.regression.RegressionLearningAlgorithm
-
Returns the regressor learned during the training process
- getpReg() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- getProduction() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
Get the node production in the form of string.
- getProductionNames() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getProperty() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
-
Returns the property
- getRandExample() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
- getRandExample() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getRandExamples(int) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
- getRandExamples(int) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getRegressionLabels() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Returns the classificationLabels of this example
- getRegressionProperties() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns all the regression properties in the dataset.
- getRegressionProperties() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getRegressionValue(Label) - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Returns the numeric value associated to a label
- getRegressionValues() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- getRepresentation(String) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
-
Returns the representation corresponding to representationName
- getRepresentation() - Method in class it.uniroma2.sag.kelp.kernel.DirectKernel
-
- getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
-
- getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
-
- getRepresentation() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LinearMethod
-
Returns the representation this learning algorithm exploits
- getRepresentation() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
-
- getRepresentation() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
-
- getRepresentationIdentifier(Class<? extends Representation>) - Static method in class it.uniroma2.sag.kelp.data.representation.RepresentationFactory
-
Returns the identifier of a given class
- getRepresentations() - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
-
Returns the example representations
- getRho() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- getRightExample() - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
-
Returns the right example in the pair
- getRoot() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryMarginClassifierOutput
-
- getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassificationOutput
-
- getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassificationOutput
-
- getScore(Label) - Method in interface it.uniroma2.sag.kelp.predictionfunction.Prediction
-
Return the prediction score associated to a given label
- getScore(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionOutput
-
- getShuffledDataset() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
- getShuffledDataset() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getSquaredNorm() - Method in interface it.uniroma2.sag.kelp.data.representation.Normalizable
-
Returns the squared norm of this vector
- getSquaredNorm() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
- getSquaredNorm() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
-
- getSquaredNorm(Example) - Method in interface it.uniroma2.sag.kelp.kernel.cache.SquaredNormCache
-
Returns a previously stored norm of a given example
- getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
- getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
-
- getSquaredNorm(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
-
Computes the squared norm of a given example according to the space in which the model
is operating
- getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
-
- getStoredKernelValue(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
-
Retrieves in the cache the kernel operation between two examples
- getSuffix() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
If given, it return the node suffix, e.g.
- getSupportVector(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
Returns the support vector associated to a given instance, null the instance
is not a support vector in this model
- getSupportVectors() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
Returns all the support vectors
- getSupporVectors() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- getTerminalFactor() - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
Get the Terminal Factor
- getTextFromData() - Method in interface it.uniroma2.sag.kelp.data.representation.Representation
-
Returns a textual representation of the data stored in this
representation
- getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
-
- getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
- getTextFromData() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- getTextualLabelPart() - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- getTextualRepresentation(Representation) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- getTextualRepresentation(Representation, String) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- getToCombine() - Method in class it.uniroma2.sag.kelp.kernel.KernelCombination
-
Returns a list of the kernels this kernel is combining
- getToCombine() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.EnsembleLearningAlgorithm
-
Returns a list of the learning algorithm this ensemble method is combining
- getUpper_bound_n() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- getUpper_bound_p() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- getValue() - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
-
Returns the value of the value
- getValue() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeature
-
- getValue() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
-
- getVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- getW(double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
-
- getWeight() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
-
- getWeights() - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
-
- getZeroVector(String) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Returns a zero vector compliant with the representation identifier by representationIdentifier containings all zero
- getZeroVector(String) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- getZeroVector(String) - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- getZeroVector(String) - Method in class it.uniroma2.sag.kelp.data.example.ExamplePair
-
- getZeroVector(String) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
-
- getZeroVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
- getZeroVector() - Method in interface it.uniroma2.sag.kelp.data.representation.Vector
-
Returns a vector whose values are all 0.
- getZeroVector() - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- grad(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
-
- l - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
-
the number of training data
- l - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
Total number of Support Vectors
- L2R_L2_SvcFunction - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
-
NOTE: This code has been adapted from the Java port of the original LIBLINEAR
C++ sources.
- L2R_L2_SvcFunction(Problem, double[]) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
-
- Label - Interface in it.uniroma2.sag.kelp.data.label
-
A generic Label for supervised learning.
- label - Variable in class it.uniroma2.sag.kelp.data.representation.tree.TreeNode
-
The node label
- label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
The label to be learned by the classifier
- label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- label - Variable in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- LABEL_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- LabelFactory - Class in it.uniroma2.sag.kelp.data.label
-
It is a factory that provides methods for instantiating labels described in a
textual format
- LabelFactory() - Constructor for class it.uniroma2.sag.kelp.data.label.LabelFactory
-
- labels - Variable in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- LabelTypeResolver - Class in it.uniroma2.sag.kelp.data.label
-
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of Labels
- LabelTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.label.LabelTypeResolver
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
This method will cause the meta-learning algorithm to learn
N classifiers, where N is the number of classes in the dataset.
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
This method will cause the meta-learning algorithm to learn
N*(N-1)/2 classifiers, where N is the number of classes in the dataset.
- learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- learn(Dataset) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
-
It starts the training process exploiting the provided dataset
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- learn(Example) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.OnlineLearningAlgorithm
-
Applies the learning process on a single example, updating its current model
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- learn(Dataset) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
- learn(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
- LearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm
-
It is a generic Machine Learning algorithm
- LearningAlgorithmTypeResolver - Class in it.uniroma2.sag.kelp.learningalgorithm
-
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of LearningAlgorithms
- LearningAlgorithmTypeResolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithmTypeResolver
-
- LibCSvmSolver - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
-
This class implements the solver of the C-SVM quadratic problem described in
[CC Chang & CJ Lin, 2011].
- LibCSvmSolver(Kernel, float, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
-
- LibCSvmSolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
-
- LibLinearFeature - Interface in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
-
NOTE: This code has been adapted from the Java port of the original LIBLINEAR
C++ sources.
- LibLinearFeatureNode - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
-
NOTE: This code has been adapted from the Java port of the original LIBLINEAR
C++ sources.
- LibLinearFeatureNode(int, double) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
-
- LibLinearLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear
-
This class implements linear SVMs models trained using a coordinate descent
algorithm [Fan et al, 2008].
- LibLinearLearningAlgorithm(Label, double, double, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- LibLinearLearningAlgorithm(Label, double, double, boolean, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- LibLinearLearningAlgorithm(double, double, String) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- LibLinearLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- LibNuSvmSolver - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
-
It is the instance of a solution provided the \(\nu\)-SVM solver of the
optimization problem.
- LibNuSvmSolver(Kernel, int, int) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
-
- LibNuSvmSolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
-
- LibSvmSolver - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
-
This class implements the solver of the SVM quadratic problem described in
[CC Chang & CJ Lin, 2011].
- LibSvmSolver() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- LibSvmSolver(Kernel, float, float) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- LibSvmSolver.Pair - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
-
The pair of indices i and j that are selected as working set
- LibSvmSolver.Pair() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver.Pair
-
- LinearKernel - Class in it.uniroma2.sag.kelp.kernel.vector
-
Linear Kernel for Vectors
It executes the dot product between two Vector representations
- LinearKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.vector.LinearKernel
-
- LinearKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.vector.LinearKernel
-
- LinearKernelCombination - Class in it.uniroma2.sag.kelp.kernel.standard
-
Weighted Linear Combination of Kernels
Given a kernel some kernel K1...Km, with weights c1,...cn, the combination formula is:
SUM(Ki*ci)
- LinearKernelCombination() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
-
- LinearMethod - Interface in it.uniroma2.sag.kelp.learningalgorithm
-
It is a linear algorithm operating directly on an explicit vector space
- LinearPassiveAggressiveClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
-
Online Passive-Aggressive Learning Algorithm for classification tasks (linear version) .
- LinearPassiveAggressiveClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
-
- LinearPassiveAggressiveClassification(float, float, PassiveAggressiveClassification.Loss, PassiveAggressive.Policy, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
-
- LinearPassiveAggressiveRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
-
Online Passive-Aggressive Learning Algorithm for regression tasks (linear version).
- LinearPassiveAggressiveRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
-
- LinearPassiveAggressiveRegression(float, float, PassiveAggressive.Policy, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
-
- LinearPerceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
-
The perceptron learning algorithm algorithm for classification tasks (linear version).
- LinearPerceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
-
- LinearPerceptron(float, float, boolean, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
-
- logIteration - Static variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
The number of iteration to be accomplished to print info in the standard
output
- loss - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- LRB - Static variable in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
-
The left parenthesis character within the tree
- p - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- PAIR_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- parseCharniakSentence(String) - Static method in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
-
This method allows to read a tree in the form (S(NP)(VP)) and returns the
corresponding Tree Representation
- parseExample(String) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- parseLabel(String) - Static method in class it.uniroma2.sag.kelp.data.label.LabelFactory
-
Initializes and returns the label described in
labelDescription
- parseRepresentation(String, String) - Method in class it.uniroma2.sag.kelp.data.representation.RepresentationFactory
-
Initializes and returns the representation described in
representationBody
- parseSingleRepresentation(String) - Static method in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
Parse a single Representation from its string representation
- PartialTreeKernel - Class in it.uniroma2.sag.kelp.kernel.tree
-
Partial Tree Kernel implementation.
- PartialTreeKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
Default constructor.
- PartialTreeKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
This constructor by default uses lambda=0.4, mu=0.4, terminalFactor=1
- PartialTreeKernel(float, float, float, String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
A Constructor for the Partial Tree Kernel in which parameters can be set manually.
- PassiveAggressive - Class in it.uniroma2.sag.kelp.learningalgorithm
-
It is an online learning algorithms that implements the Passive Aggressive algorithms described in
[Crammer, JMLR2006] K.
- PassiveAggressive() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- PassiveAggressive.Policy - Enum in it.uniroma2.sag.kelp.learningalgorithm
-
- PassiveAggressiveClassification - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
-
Online Passive-Aggressive Learning Algorithm for classification tasks.
- PassiveAggressiveClassification() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- PassiveAggressiveClassification.Loss - Enum in it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive
-
- PassiveAggressiveRegression - Class in it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive
-
Online Passive-Aggressive Learning Algorithm for regression tasks.
- PassiveAggressiveRegression() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
- PegasosLearningAlgorithm - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos
-
It implements the Primal Estimated sub-GrAdient SOlver (PEGASOS) for SVM.
- PegasosLearningAlgorithm() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- PegasosLearningAlgorithm(int, float, int, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- Perceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron
-
The perceptron learning algorithm algorithm for classification tasks.
- Perceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- policy - Variable in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- PolynomialKernel - Class in it.uniroma2.sag.kelp.kernel.standard
-
- PolynomialKernel(float, float, float, Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- PolynomialKernel(float, Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- PolynomialKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- populate(String) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
Populate the dataset by reading it from a platform
compliant file.
- positiveClass - Variable in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- pow(float, int) - Static method in class it.uniroma2.sag.kelp.utils.Math
-
It evaluates the power of a number
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
-
Classifies an example applying the following formula:
y(x) = \sum_{i \in SV}\alpha_i k(x_i, x) + b
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
-
- predict(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.classifier.Classifier
-
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- predict(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
-
- predict(Example) - Method in interface it.uniroma2.sag.kelp.predictionfunction.regressionfunction.RegressionFunction
-
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
-
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
-
- predict(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
-
- predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
Learns from a single example applying a specific policy that must be adopted when the budget is reached
- predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
-
- predictAndLearnWithFullBudget(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
-
- Prediction - Interface in it.uniroma2.sag.kelp.predictionfunction
-
It is a generic output provided by a machine learning systems on a test data
- PredictionFunction - Interface in it.uniroma2.sag.kelp.predictionfunction
-
It is a generic prediction function that can be learned with a machine learning algorithm
- PredictionFunctionTypeResolver - Class in it.uniroma2.sag.kelp.predictionfunction
-
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of PredictionFunctions
- PredictionFunctionTypeResolver() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.PredictionFunctionTypeResolver
-
- PreferenceKernel - Class in it.uniroma2.sag.kelp.kernel.examplepair
-
It is a kernel operating of ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = K(x_1, y_1) + K(x_2, y_2) - K(x_1, y_2) - K(x_2, y_1)\)
where K is another kernel the preference kernel relies on.
- PreferenceKernel(Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.examplepair.PreferenceKernel
-
- PreferenceKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.examplepair.PreferenceKernel
-
- printExample(String...) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
-
- prob - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
-
- Problem - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver
-
Describes the problem
- Problem(Dataset, String, Label) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.Problem
-
- property - Variable in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
-
- RandomizedBudgetPerceptron - Class in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
-
It is a variation of the Randomized Budget Perceptron proposed in
- RandomizedBudgetPerceptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
-
- RandomizedBudgetPerceptron(int, OnlineLearningAlgorithm, long, List<Label>) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
-
- RbfKernel - Class in it.uniroma2.sag.kelp.kernel.standard
-
Radial Basis Function Kernel.
- RbfKernel(float, Kernel) - Constructor for class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
-
- RbfKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
-
- readNextExample() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
-
Returns the next example
- reconstruct_gradient() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
Reconstruct inactive elements of G from G_bar and free variables
- RegressionFunction - Interface in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
-
It is a generic regression prediction function, i.e.
- RegressionLearningAlgorithm - Interface in it.uniroma2.sag.kelp.learningalgorithm.regression
-
It is a learning algorithm that learn how to solve a generic regression task
- RegressionOutput - Interface in it.uniroma2.sag.kelp.predictionfunction.regressionfunction
-
It is the output of a generic Regressor
- regressor - Variable in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
The regression function to be returned
- regressor - Variable in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
- removeExample() - Method in class it.uniroma2.sag.kelp.data.representation.tree.EnrichedTreeNode
-
- Representation - Interface in it.uniroma2.sag.kelp.data.representation
-
It is a generic way to represent an object that is intended to be exploited
through Machine Learning techniques.
- representation - Variable in class it.uniroma2.sag.kelp.kernel.DirectKernel
-
- REPRESENTATION_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- REPRESENTATION_TYPE_NAME_SEPARATOR - Static variable in class it.uniroma2.sag.kelp.data.example.ExampleFactory
-
- RepresentationFactory - Class in it.uniroma2.sag.kelp.data.representation
-
It is a factory that provides methods for instantiating a representation
described in a textual format The factory is able to automatically support
all the implementations of the class Representation that have an
empty constructor and that have been included in the project (as local class
or imported via Maven)
- RepresentationTypeResolver - Class in it.uniroma2.sag.kelp.data.representation
-
It is a class implementing TypeIdResolver which will be used by Jackson library during
the serialization in JSON and deserialization of Representations
- RepresentationTypeResolver() - Constructor for class it.uniroma2.sag.kelp.data.representation.RepresentationTypeResolver
-
- reset() - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
-
Reset the reading pointer
- reset() - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
-
- reset() - Method in class it.uniroma2.sag.kelp.kernel.Kernel
-
Resets the kernel statistics (number of kernel computations,
cache hits and misses)
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
This method will cause the reset of all the base algorithms
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
This method will cause the reset of all the base algorithms
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- reset() - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
-
Resets all the learning process, returning to the default state.
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- reset() - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
-
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
-
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
-
- reset() - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.Model
-
Resets the model parameters to the default state.
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- reset() - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
-
Resets all the predictor parameters to the default state.
- reset() - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
-
- resetCacheStats() - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
-
Sets cache hits and misses to 0
- restartReading() - Method in class it.uniroma2.sag.kelp.data.dataset.DatasetReader
-
Resets the reading such that the next example will be the first one
- RRB - Static variable in class it.uniroma2.sag.kelp.data.representation.tree.utils.TreeIO
-
The right parenthesis character within the tree
- scale(float) - Method in interface it.uniroma2.sag.kelp.data.representation.Normalizable
-
Multiplies each element of this representation by coeff
- scale(float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
- scale(float) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- select_working_set(LibSvmSolver.Pair) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver
-
- select_working_set(LibSvmSolver.Pair) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibNuSvmSolver
-
- select_working_set(LibSvmSolver.Pair) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
Select the working set in each iteration.
- setA(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- setAlpha(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
Sets the learning rate, i.e.
- setAlphas(float[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- setB(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
This method will set the type of the base algorithms to be learned.
- setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
This method will set the type of the base algorithms to be learned.
- setBaseAlgorithm(LearningAlgorithm) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.MetaLearningAlgorithm
-
- setBaseAlgorithm(LearningAlgorithm) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- setBaseKernel(Kernel) - Method in class it.uniroma2.sag.kelp.kernel.KernelComposition
-
- setBias(float) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryModel
-
- setBinaryClassifiers(Classifier[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- setBinaryClassifiers(Classifier[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- setBudget(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
Sets the budget, i.e.
- setC(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- setC(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- setClassificationLabels(HashSet<Label>) - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- setClassName(String) - Method in class it.uniroma2.sag.kelp.data.label.StringLabel
-
- setCn(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- setCn(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- setCp(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- setCp(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- setDataFromText(String) - Method in interface it.uniroma2.sag.kelp.data.representation.Representation
-
Initializes a Representation using its textual description provided in
representationDescription
- setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
-
- setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.tree.TreeRepresentation
-
- setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
- setDataFromText(String) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
-
- setDegree(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.PolynomialKernel
-
- setDist(Float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- setEpochs(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- setEpsilon(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression
-
Sets epsilon, i.e.
- setExample(Example, float) - Method in class it.uniroma2.sag.kelp.data.representation.tree.EnrichedTreeNode
-
- setExample(Example) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansExample
-
- setExamples(Vector<ClusterExample>) - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
-
This function initialize the set of objects inside the cluster
- setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
-
- setFairness(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- setFeatureValues(float[]) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
Sets the feature values.
- setFeatureValues(DenseMatrix64F) - Method in class it.uniroma2.sag.kelp.data.representation.vector.DenseVector
-
Sets the feature values.
- setGamma(float) - Method in class it.uniroma2.sag.kelp.kernel.standard.RbfKernel
-
- setHyperplane(Vector) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
-
- setInstance(Example) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
-
- setIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
Sets the number of iterations
- setK(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
Sets the number of examples k that Pegasos exploits in its
mini-batch learning approach
- setK(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryCSvmClassification
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.KernelizedPassiveAggressiveClassification
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.KernelizedPerceptron
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- setKernel(Kernel) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.KernelMethod
-
Sets the kernel this
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.KernelizedPassiveAggressiveRegression
-
- setKernel(Kernel) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
-
- setKernel(Kernel) - Method in interface it.uniroma2.sag.kelp.predictionfunction.model.KernelMachineModel
-
- setKernelCache(KernelCache) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
-
Sets the cache in which storing the kernel operations in the RKHS defined
by this kernel
- setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexKernelCache
-
- setKernelValue(Example, Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.KernelCache
-
Stores a kernel computation in cache
- setLabel(String) - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
-
- setLabel(String) - Method in class it.uniroma2.sag.kelp.data.representation.tree.EnrichedTreeNode
-
- setLabel(Label) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- setLabel(Label) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- setLabels(Label[]) - Method in class it.uniroma2.sag.kelp.data.example.Example
-
Sets the example classificationLabels
- setLabels(List<Label>) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.BinaryLearningAlgorithm
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsAllLearning
-
Set the labels associated to this multi-classifier.
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.OneVsOneLearning
-
Set the labels associated to this multi-classifier.
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
- setLabels(List<Label>) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm
-
Sets the labels representing the concept to be learned.
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.MultiEpochLearning
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- setLabels(Label...) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryClassifier
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- setLabels(List<Label>) - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
-
Sets the labels representing the concept to be predicted.
- setLabels(List<Label>) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateRegressionFunction
-
- setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
- setLambda(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
-
Set the decay factor
- setLambda(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
-
Sets the regularization coefficient
- setLoss(PassiveAggressiveClassification.Loss) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.PassiveAggressiveClassification
-
- setMargin(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
-
Sets the desired margin, i.e.
- setMaxIterations(int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.clustering.kernelbasedkmeans.KernelBasedKMeansEngine
-
- setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryKernelMachineClassifier
-
- setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.BinaryLinearClassifier
-
- setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsAllClassifier
-
- setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
- setModel(Model) - Method in interface it.uniroma2.sag.kelp.predictionfunction.PredictionFunction
-
Sets the model
- setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateKernelMachineRegressionFunction
-
- setModel(Model) - Method in class it.uniroma2.sag.kelp.predictionfunction.regressionfunction.UnivariateLinearRegressionFunction
-
- setModels(List<BinaryModel>) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.MulticlassModel
-
- setMu(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
-
- setNegativeLabelsForClassifier(Label[]) - Method in class it.uniroma2.sag.kelp.predictionfunction.classifier.multiclass.OneVsOneClassifier
-
Set the negative label classifier array
- setNormCache(SquaredNormCache) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
-
Sets the cache in which storing the quadratic norms in the RKHS defined
by this kernel
- setNu(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification
-
- setNu(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.OneClassSvmClassification
-
- setObj(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
-
- setPolicy(PassiveAggressive.Policy) - Method in class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive
-
- setpReg(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
-
- setProperty(Label) - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
-
- setRegressionValues(ArrayList<NumericLabel>) - Method in class it.uniroma2.sag.kelp.data.example.Example
-
- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.kernel.DirectKernel
-
- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm
-
- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.passiveaggressive.LinearPassiveAggressiveClassification
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- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm
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- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.LinearPerceptron
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- setRepresentation(String) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.LinearMethod
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Sets the representation this learning algorithm will exploit
- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.LinearPassiveAggressiveRegression
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- setRepresentation(String) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryLinearModel
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- setRepresentations(HashMap<String, Representation>) - Method in class it.uniroma2.sag.kelp.data.example.SimpleExample
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Sets the example representations
- setRho(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
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- setSeed(long) - Method in interface it.uniroma2.sag.kelp.data.dataset.Dataset
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Sets the seed of the random generator used to shuffling examples and getting random examples
- setSeed(long) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
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- setSeed(long) - Method in class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron
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Sets the seed for the random generator adopted to select the support vector to delete
- setSquaredNormVaue(Example, float) - Method in class it.uniroma2.sag.kelp.kernel.cache.FixIndexSquaredNormCache
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- setSquaredNormVaue(Example, float) - Method in interface it.uniroma2.sag.kelp.kernel.cache.SquaredNormCache
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Stores a squared norm in the cache
- setSupportVector(SupportVector, int) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
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Substitutes the support vector in position position with
sv
- setSupportVectors(List<SupportVector>) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.BinaryKernelMachineModel
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- setSupporVectors(Example[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
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- setTerminalFactor(float) - Method in class it.uniroma2.sag.kelp.kernel.tree.PartialTreeKernel
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- setToCombine(List<Kernel>) - Method in class it.uniroma2.sag.kelp.kernel.KernelCombination
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- setToCombine(List<LearningAlgorithm>) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.EnsembleLearningAlgorithm
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- setUnbiased(boolean) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.perceptron.Perceptron
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Sets whether the bias, i.e.
- setUpper_bound_n(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
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Set the \(C_n\) value
- setUpper_bound_p(float) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
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Set the \(C_p\) value
- setValue(float) - Method in class it.uniroma2.sag.kelp.data.label.NumericLabel
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- setValue(double) - Method in interface it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeature
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- setValue(double) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.LibLinearFeatureNode
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- setVector(TIntFloatMap) - Method in class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
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- setWeight(float) - Method in class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
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- setWeights(List<Float>) - Method in class it.uniroma2.sag.kelp.kernel.standard.LinearKernelCombination
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- shrinkingIteration - Static variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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Number of iterations to be accomplished before shrinking
- shuffleExamples(Random) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
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Shuffles the examples in the dataset
- SimpleDataset - Class in it.uniroma2.sag.kelp.data.dataset
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A SimpleDataset that represent a whole dataset in memory.
- SimpleDataset() - Constructor for class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
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Initializes an empty dataset
- SimpleExample - Class in it.uniroma2.sag.kelp.data.example
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An Example composed by a set of Representations.
- SimpleExample() - Constructor for class it.uniroma2.sag.kelp.data.example.SimpleExample
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Initializes an empty example (0 labels and 0 representations)
- SimpleExample(Label[], HashMap<String, Representation>) - Constructor for class it.uniroma2.sag.kelp.data.example.SimpleExample
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Initializes a SimpleExample with the input representations and labels
- size() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
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This functions returns the number of objects inside the cluster
- sizeI - Variable in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
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- solve(int, Dataset, float[], int[], float[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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It solves the SMO algorithm in [CC Chang & CJ Lin, 2011]
min 0.5(\alpha^T Q \alpha) + p^T \alpha
y^T \alpha = \delta
y_i = +1 or -1
0 <= alpha_i <= Cp for y_i = 1
0 <= alpha_i <= Cn for y_i = -1
Given:
Q, p, y, Cp, Cn, and an initial feasible point \alpha l is the size of
vectors and matrices eps is the stopping tolerance
solution will be put in \alpha, objective value will be put in obj
- sortAscendingOrder() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
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- sortDescendingOrder() - Method in class it.uniroma2.sag.kelp.data.clustering.Cluster
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- SparseVector - Class in it.uniroma2.sag.kelp.data.representation.vector
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Sparse Feature Vector.
- SparseVector() - Constructor for class it.uniroma2.sag.kelp.data.representation.vector.SparseVector
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- split(float) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
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Returns two datasets created by splitting this dataset accordingly to
percentage.
- splitClassDistributionInvariant(float) - Method in class it.uniroma2.sag.kelp.data.dataset.SimpleDataset
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Returns two datasets created by splitting this dataset accordingly to
percentage.
- squaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
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Returns the squared norm of the given example in the RKHS defined by this kernel
- squaredNorm(Example) - Method in class it.uniroma2.sag.kelp.kernel.standard.NormalizationKernel
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- SquaredNormCache - Interface in it.uniroma2.sag.kelp.kernel.cache
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Cache for store squared norms
- squaredNormOfTheDifference(Example, Example) - Method in class it.uniroma2.sag.kelp.kernel.Kernel
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Returns the squared norm of the difference between the given examples in the RKHS.
- Stoptron - Class in it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm
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It is a variation of the Stoptron proposed in
- Stoptron() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
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- Stoptron(int, OnlineLearningAlgorithm, List<Label>) - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.Stoptron
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- StringLabel - Class in it.uniroma2.sag.kelp.data.label
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It is a label consisting of an String value.
- StringLabel(String) - Constructor for class it.uniroma2.sag.kelp.data.label.StringLabel
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Initializes a label to a specific String value
- StringLabel() - Constructor for class it.uniroma2.sag.kelp.data.label.StringLabel
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- StringRepresentation - Class in it.uniroma2.sag.kelp.data.representation.string
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String representation.
- StringRepresentation() - Constructor for class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
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Empty constructor necessary for making RepresentationFactory
support this implementation.
- StringRepresentation(String) - Constructor for class it.uniroma2.sag.kelp.data.representation.string.StringRepresentation
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Initializing constructor.
- SubTreeKernel - Class in it.uniroma2.sag.kelp.kernel.tree
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SubTree Kernel implementation.
- SubTreeKernel(float, String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
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SubTree Kernel
- SubTreeKernel(String) - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
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SubTree Kernel constructor.
- SubTreeKernel() - Constructor for class it.uniroma2.sag.kelp.kernel.tree.SubTreeKernel
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SubTree Kernel: default constructor.
- subXTv(double[], double[]) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.solver.L2R_L2_SvcFunction
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- SupportVector - Class in it.uniroma2.sag.kelp.predictionfunction.model
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It is a support vector for kernel methods consisting of an example and the associated weight
- SupportVector(float, Example) - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
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- SupportVector() - Constructor for class it.uniroma2.sag.kelp.predictionfunction.model.SupportVector
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- SvmSolution - Class in it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver
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It is the instance of a solution provided the LIBSVM solver of the SMO
optimization problem.
- SvmSolution() - Constructor for class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.SvmSolution
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- swap(LibSvmSolver.AlphaStatus[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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- swap(Example[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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- swap(float[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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- swap(int[], int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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- swap_index(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver
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Swap the info of two examples
- swap_index(int, int) - Method in class it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression
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