Index (JCP API)

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

A

AbstractPriorMultiProbabilisticMeasure - Class in se.hb.jcp.cp.measures: The prior measure depends only on the prediction made.
AbstractPriorMultiProbabilisticMeasure() - Constructor for class se.hb.jcp.cp.measures.AbstractPriorMultiProbabilisticMeasure
AbstractSignificanceBasedMeasure - Class in se.hb.jcp.cp.measures: Base class for measures that depend on the significance level.
AbstractSignificanceBasedMeasure(String, double) - Constructor for class se.hb.jcp.cp.measures.AbstractSignificanceBasedMeasure: Creates an abstract significance based measure.
add(ConformalClassification) - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasure: Adds the supplied conformal prediction to the aggregated prior measure.
add(ConformalClassification) - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasures: Adds the supplied conformal prediction to the aggregated prior measures.
add(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasure: Adds the supplied conformal prediction to the aggregated measure.
add(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasures: Adds the supplied conformal prediction to the aggregated measures.
AggregatedObservedMeasure - Class in se.hb.jcp.cp.measures: Maintains a running average of an observed measure.
AggregatedObservedMeasure(IObservedMeasure) - Constructor for class se.hb.jcp.cp.measures.AggregatedObservedMeasure: Creates an aggregating observed measure from the single prediction one.
AggregatedObservedMeasures - Class in se.hb.jcp.cp.measures: Maintains running averages for a set of observed measures.
AggregatedObservedMeasures() - Constructor for class se.hb.jcp.cp.measures.AggregatedObservedMeasures: Creates the default set of aggregating observed measures.
AggregatedObservedMeasures(IObservedMeasure[]) - Constructor for class se.hb.jcp.cp.measures.AggregatedObservedMeasures: Creates a set of aggregating observed measures from an array of single prediction ones.
AggregatedPriorMeasure - Class in se.hb.jcp.cp.measures: Maintains a running average of a prior measure.
AggregatedPriorMeasure(IPriorMeasure) - Constructor for class se.hb.jcp.cp.measures.AggregatedPriorMeasure: Creates an aggregating prior measure from the single prediction one.
AggregatedPriorMeasures - Class in se.hb.jcp.cp.measures: Maintains running averages for a set of prior measures.
AggregatedPriorMeasures() - Constructor for class se.hb.jcp.cp.measures.AggregatedPriorMeasures: Creates the default set of aggregating prior measures.
AggregatedPriorMeasures(IPriorMeasure[]) - Constructor for class se.hb.jcp.cp.measures.AggregatedPriorMeasures: Creates a set of aggregating prior measures from an array of single prediction ones.
assign(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Replaces all cell values of the receiver with the values of another matrix.
assign(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Replaces all cell values of the receiver with the values of another matrix.
assign(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Replaces all cell values of the receiver with the values of another matrix.
assign(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Replaces all cell values of the receiver with the values of another matrix.
AverageClassificationNonconformityFunction - Class in se.hb.jcp.nc
AverageClassificationNonconformityFunction(double[]) - Constructor for class se.hb.jcp.nc.AverageClassificationNonconformityFunction

B

BogusClassProbabilityClassifier - Class in se.hb.jcp.ml: Adds a bogus class probability estimate to the underlying machine learning classification algorithm.
BogusClassProbabilityClassifier(IClassifier, double[]) - Constructor for class se.hb.jcp.ml.BogusClassProbabilityClassifier

C

C - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
C_SVC - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
cache_size - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
calc_nc(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction: Deprecated.
calc_nc(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase: Deprecated.
calc_nc(DoubleMatrix2D, double[]) - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction: Deprecated.
calc_nc(DoubleMatrix2D, double[], DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
calc_nc(DoubleMatrix2D, double[], DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
calc_nc(DoubleMatrix2D, double[], DoubleMatrix1D, double) - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction
calculateNonConformityScore(DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
calculateNonConformityScore(DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
calculateNonConformityScore(DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.ClassProbabilityNonconformityFunctionBase
calculateNonConformityScore(DoubleMatrix1D, double) - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction: Computes the non-conformity score for the instance x with the target y.
calculateNonConformityScore(DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.kNearestSameClassNeighbourNonconformityFunction
calculateNonConformityScore(DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.SVMDistanceNonconformityFunction
calculatePValue(double, double[]) - Static method in class se.hb.jcp.cp.Util
calibrate(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier: Calibrates this multi-probabilistic conformal classifier using the supplied data.
calibrate(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Calibrates this conformal classifier using the supplied data.
CCTools - Class in se.hb.jcp.cli: Higher-level tools for Conformal Classification.
CCTools() - Constructor for class se.hb.jcp.cli.CCTools
ClassificationNonconformityFunctionFactory - Class in se.hb.jcp.nc: Singleton factory for JCP classification nonconformity functions.
ClassifierBase - Class in se.hb.jcp.ml: Base class for classifiers that provide implementations of some of the generic IClassifierInformation methods.
ClassifierBase() - Constructor for class se.hb.jcp.ml.ClassifierBase
ClassifierFactory - Class in se.hb.jcp.ml: Singleton factory for JCP classifiers.
ClassifierNonconformityFunctionBase - Class in se.hb.jcp.nc: Base class for nonconformity functions that use a classifier.
ClassifierNonconformityFunctionBase(double[], IClassifier) - Constructor for class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
ClassProbabilityNonconformityFunctionBase - Class in se.hb.jcp.nc: A base class for nonconformity functions based on the predicted class probabilities given by a classifier.
ClassProbabilityNonconformityFunctionBase(double[], IClassProbabilityClassifier) - Constructor for class se.hb.jcp.nc.ClassProbabilityNonconformityFunctionBase
clear() - Method in class se.hb.jcp.util.RealIndexedMatrix2D
clone() - Method in class se.hb.jcp.bindings.libsvm.svm_parameter
coef0 - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
compute() - Method in class se.hb.jcp.util.ParallelizedAction: Inherited from RecursiveAction.
compute(int) - Method in class se.hb.jcp.util.ParallelizedAction: The action to be performed for index i.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.AbstractPriorMultiProbabilisticMeasure
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.ExcessCriterion: Computes the E/Excess criterion measure for this prediction.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.FuzzinessCriterion: Computes the F/Fuzziness criterion measure for this prediction.
compute(ConformalClassification) - Method in interface se.hb.jcp.cp.measures.IPriorMeasure: Compute the measure for the supplied conformal prediction.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.MultipleCriterion: Computes the M/Multiple criterion measure for this prediction.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.NumberCriterion: Computes the N/Number criterion measure for this prediction.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.OneCCriterion: Computes the OneC criterion measure for this prediction.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.SumCriterion: Computes the Sum criterion measure for this prediction.
compute(ConformalClassification) - Method in class se.hb.jcp.cp.measures.UnconfidenceCriterion: Computes the U/Unconfidence criterion measure for this prediction.
compute(ConformalClassification, double) - Method in interface se.hb.jcp.cp.measures.IObservedMeasure: Compute the measure for the supplied conformal prediction and true label.
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedAccuracy: Computes the Observed Accuracy measure for this prediction and true label.
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedExcessCriterion: Computes the OE/Observed Excess criterion measure for this prediction and true label.
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedFuzzinessCriterion: Computes the OF/Observed Fuzziness criterion measure for this prediction and true label.
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedMultipleCriterion: Computes the OM/Observed Multiple criterion measure for this prediction and true label.
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedOneCCriterion: Computes the Observed OneC criterion measure for this prediction and true label.
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedProbabilityLogLoss
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedProbabilitySquareLoss
compute(ConformalClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedUnconfidenceCriterion: Computes the OU/Observed Unconfidence criterion measure for this prediction and true label.
compute(ConformalMultiProbabilisticClassification) - Method in class se.hb.jcp.cp.measures.AbstractPriorMultiProbabilisticMeasure
compute(ConformalMultiProbabilisticClassification) - Method in interface se.hb.jcp.cp.measures.IPriorMultiProbabilisticMeasure: Compute the measure for the supplied conformal prediction.
compute(ConformalMultiProbabilisticClassification) - Method in class se.hb.jcp.cp.measures.MultiProbabilisticLowerBound
compute(ConformalMultiProbabilisticClassification) - Method in class se.hb.jcp.cp.measures.MultiProbabilisticUpperBound
compute(ConformalMultiProbabilisticClassification, double) - Method in interface se.hb.jcp.cp.measures.IObservedProbabilisticMeasure: Compute the measure for the supplied conformal prediction and true label.
compute(ConformalMultiProbabilisticClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedProbabilityLogLoss
compute(ConformalMultiProbabilisticClassification, double) - Method in class se.hb.jcp.cp.measures.ObservedProbabilitySquareLoss
ConformalClassification - Class in se.hb.jcp.cp: Represents a prediction made by a conformal classifier.
ConformalClassification(IConformalClassifier, DoubleMatrix1D) - Constructor for class se.hb.jcp.cp.ConformalClassification
ConformalMultiProbabilisticClassification - Class in se.hb.jcp.cp: Represents a multi-probabilistic prediction made by a conformal classifier with bivariate isotonic regression.
ConformalMultiProbabilisticClassification(IConformalClassifier, DoubleMatrix1D, double, double) - Constructor for class se.hb.jcp.cp.ConformalMultiProbabilisticClassification
ConformalMultiProbabilisticClassifier - Class in se.hb.jcp.cp: Represents a multi-probabilistic conformal classifier with bivariate isotonic regression.
ConformalMultiProbabilisticClassifier(IConformalClassifier) - Constructor for class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier: Creates a multi-probabilistic conformal classifier with bivariate isotonic regression using the supplied information.
contains(double, double) - Method in class se.hb.jcp.util.RealIndexedMatrix2D
Cptr - Variable in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: C-side pointer to an array of svm_node arrays storing the matrix contents.
createClassifier(int) - Method in class se.hb.jcp.ml.ClassifierFactory
createClassifier(int, JSONObject) - Method in class se.hb.jcp.ml.ClassifierFactory
createNonconformityFunction(int, double[], IClassifier) - Method in class se.hb.jcp.nc.ClassificationNonconformityFunctionFactory
createSubtask(int, int) - Method in class se.hb.jcp.util.ParallelizedAction: Creates a ParallelizedAction for the sub-interval.

D

DataSet - Class in se.hb.jcp.cp: A data set.
DataSet() - Constructor for class se.hb.jcp.cp.DataSet
DataSetReader - Class in se.hb.jcp.io: Public abstract base class for data set readers.
DataSetReader() - Constructor for class se.hb.jcp.io.DataSetReader
DataSetTools - Class in se.hb.jcp.cli: Higher-level tools for DataSets.
DataSetTools() - Constructor for class se.hb.jcp.cli.DataSetTools
degree - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
DenseDoubleMatrix1D - Class in se.hb.jcp.bindings.opencv: Class for dense 1-d matrices (aka vectors) holding double elements in the dense format used by OpenCV.
DenseDoubleMatrix1D(double[]) - Constructor for class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
DenseDoubleMatrix1D(int) - Constructor for class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Constructs a matrix with a given number of columns.
DenseDoubleMatrix1D(int, int[], double[]) - Constructor for class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
DenseDoubleMatrix2D - Class in se.hb.jcp.bindings.opencv: Class for dense 2-d matrices holding double elements in double elements in the dense format used by OpenCV.
DenseDoubleMatrix2D(int, int) - Constructor for class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Constructs a matrix with a given number of rows and columns.
distanceFromSeparatingPlane(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.jlibsvm.SVMClassifier: Returns the signed distance between the separating hyperplane and the instance.
distanceFromSeparatingPlane(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.libsvm.SVMClassifier: Returns the signed distance between the separating hyperplane and the instance.
distanceFromSeparatingPlane(DoubleMatrix1D) - Method in interface se.hb.jcp.ml.ISVMClassifier: Returns the signed distance between the separating hyperplane and the instance.

E

eps - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
EPSILON_SVR - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
ExcessCriterion - Class in se.hb.jcp.cp.measures: The E/Excess criterion is a prior efficiency measure based on how much the size of the label set exceeds 1.
ExcessCriterion(double) - Constructor for class se.hb.jcp.cp.measures.ExcessCriterion: Creates an E/Excess criterion measure.
extractClasses(DataSet) - Static method in class se.hb.jcp.cli.DataSetTools
ExtremelyRandomizedTreesClassifier - Class in se.hb.jcp.bindings.opencv
ExtremelyRandomizedTreesClassifier() - Constructor for class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier

F

FIFOParallelExecutor<E> - Class in se.hb.jcp.util: A FIFOParallelExecutor executes Callables in parallel and returns the results in the order the Callables were issued.
FIFOParallelExecutor(int, ExecutorService) - Constructor for class se.hb.jcp.util.FIFOParallelExecutor: Creates a FIFOParallelExecutor using the provided level of parallelism and the ExecutorService to execute the Callables.
FIFOParallelExecutor(BlockingQueue<Future<E>>, ExecutorService) - Constructor for class se.hb.jcp.util.FIFOParallelExecutor: Creates a FIFOParallelExecutor using the provided BlockingQueue and the ExecutorService to execute the Callables.
FIFOParallelExecutor(ExecutorService) - Constructor for class se.hb.jcp.util.FIFOParallelExecutor: Creates a FIFOParallelExecutor using the provided ExecutorService to execute the Callables.
finalize() - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D
finalize() - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D
finalize() - Method in class se.hb.jcp.bindings.libsvm.svm_model
finalize(int, int) - Method in class se.hb.jcp.util.ParallelizedAction: Performs any needed finalization for the sub-interval once all sequential compute(i) calls for it has been completed.
fit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Trains this conformal classifier using the supplied data.
fit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.ml.ClassifierBase
fit(DoubleMatrix2D, double[]) - Method in interface se.hb.jcp.ml.IClassifier: Trains this classifier using the supplied data.
fit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
fit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
fit(DoubleMatrix2D, double[]) - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction: Initializes this non-conformity function with the supplied data.
fit(DoubleMatrix2D, double[], DoubleMatrix2D, double[]) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Trains and calibrates this conformal classifier using the supplied data.
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.jliblinear.LinearClassifier
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.libsvm.SVMClassifier
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.opencv.RandomForestClassifier
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.opencv.SVMClassifier
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.ml.BogusClassProbabilityClassifier: Trains and returns a copy of this classifier using the supplied data.
fitNew(DoubleMatrix2D, double[]) - Method in interface se.hb.jcp.ml.IClassifier: Trains and returns a copy of this classifier using the supplied data.
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.HingeLossNonconformityFunction
fitNew(DoubleMatrix2D, double[]) - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction: Returns a new non-conformity function based on the same parameters as the current one initialized with the supplied data.
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.kNearestSameClassNeighbourNonconformityFunction
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.MarginNonconformityFunction
fitNew(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.nc.SVMDistanceNonconformityFunction
fitNew(DoubleMatrix2D, double[], DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
fitNew(DoubleMatrix2D, double[], DoubleMatrix1D, double) - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
fitNew(DoubleMatrix2D, double[], DoubleMatrix1D, double) - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction: Returns a new non-conformity function based on the same parameters as the current one initialized with the supplied data.
FuzzinessCriterion - Class in se.hb.jcp.cp.measures: The F/Fuzziness criterion is a prior efficiency measure based on the difference between the sum of all p-values and the largest p-value.
FuzzinessCriterion() - Constructor for class se.hb.jcp.cp.measures.FuzzinessCriterion

G

gamma - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
get(double, double) - Method in class se.hb.jcp.util.RealIndexedMatrix2D
getAttributeCount() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
getAttributeCount() - Method in class se.hb.jcp.cp.InductiveConformalClassifier
getAttributeCount() - Method in class se.hb.jcp.cp.TransductiveConformalClassifier
getAttributeCount() - Method in class se.hb.jcp.ml.ClassifierBase
getAttributeCount() - Method in interface se.hb.jcp.ml.IClassifierInformation: Returns the number of attributes the classifier has been trained on.
getAttributeCount() - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
getAttributeCount() - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
getClassifier() - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
getClassifier() - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
getClassifier() - Method in interface se.hb.jcp.nc.IClassificationNonconformityFunction: Returns the classifier used by this non-conformity function.
getClassifierTypes() - Method in class se.hb.jcp.ml.ClassifierFactory
getClassPointPrediction() - Method in class se.hb.jcp.cp.ConformalClassification: Returns the maximum credibility point prediction class number.
getClassSet(double) - Method in class se.hb.jcp.cp.ConformalClassification: Region prediction at a selected significance level.
getColumnIndices(double) - Method in class se.hb.jcp.util.RealIndexedMatrix2D
getConformalClassifier() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier: Returns the underlying conformal classifier.
getInstance() - Static method in class se.hb.jcp.ml.ClassifierFactory
getInstance() - Static method in class se.hb.jcp.nc.ClassificationNonconformityFunctionFactory
getLabelPointPrediction() - Method in class se.hb.jcp.cp.ConformalClassification: Returns the maximum credibility point prediction label.
getLabels() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
getLabels() - Method in class se.hb.jcp.cp.InductiveConformalClassifier
getLabels() - Method in class se.hb.jcp.cp.TransductiveConformalClassifier
getLabels() - Method in class se.hb.jcp.ml.ClassifierBase
getLabels() - Method in interface se.hb.jcp.ml.IClassifierInformation: Returns the set of class labels the classifier has been trained on.
getLabels() - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
getLabels() - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
getLabelSet(double) - Method in class se.hb.jcp.cp.ConformalClassification: Region prediction at a selected significance level.
getLower(double, double) - Method in class se.hb.jcp.util.RealIndexedMatrix2D
getMean() - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasure: Gets the current mean of the aggregated observed measure.
getMean() - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasure: Gets the current mean of the aggregated prior measure.
getMeasure(int) - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasures: Gets one of the aggregated observed measures in this set.
getMeasure(int) - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasures: Gets one of the aggregated prior measures in this set.
getName() - Method in class se.hb.jcp.cp.measures.AbstractSignificanceBasedMeasure: Get the name of this measure.
getName() - Method in class se.hb.jcp.cp.measures.FuzzinessCriterion: Get the name of this measure.
getName() - Method in interface se.hb.jcp.cp.measures.IMeasure: Get the name of this measure.
getName() - Method in class se.hb.jcp.cp.measures.MultiProbabilisticLowerBound
getName() - Method in class se.hb.jcp.cp.measures.MultiProbabilisticUpperBound
getName() - Method in class se.hb.jcp.cp.measures.ObservedFuzzinessCriterion: Get the name of this measure.
getName() - Method in class se.hb.jcp.cp.measures.ObservedProbabilityLogLoss
getName() - Method in class se.hb.jcp.cp.measures.ObservedProbabilitySquareLoss
getName() - Method in class se.hb.jcp.cp.measures.ObservedUnconfidenceCriterion: Get the name of this measure.
getName() - Method in class se.hb.jcp.cp.measures.SumCriterion: Get the name of this measure.
getName() - Method in class se.hb.jcp.cp.measures.UnconfidenceCriterion: Get the name of this measure.
getNewInstance() - Method in class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier
getNewInstance() - Method in class se.hb.jcp.bindings.opencv.RandomForestClassifier
getNewInstance() - Method in class se.hb.jcp.bindings.opencv.SVMClassifier
getNonconformityFunction() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
getNonconformityFunction() - Method in interface se.hb.jcp.cp.IConformalClassifier: Returns the associated nonconformity function.
getNonconformityFunction() - Method in class se.hb.jcp.cp.InductiveConformalClassifier
getNonconformityFunction() - Method in class se.hb.jcp.cp.TransductiveConformalClassifier
getNonconformityFunctions() - Method in class se.hb.jcp.nc.ClassificationNonconformityFunctionFactory
getNumberOfObservations() - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasure: Gets the current number of observations of the measure.
getNumberOfObservations() - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasure: Gets the current number of observations of the measure.
getOrDefault(double, double, V) - Method in class se.hb.jcp.util.RealIndexedMatrix2D
getPointPredictionConfidence() - Method in class se.hb.jcp.cp.ConformalClassification: Returns the confidence of the class/label point prediction, i.e.
getPointPredictionCredibility() - Method in class se.hb.jcp.cp.ConformalClassification: Returns the credibility of the class/label point prediction, i.e.
getPointPredictionLowerBoundProbability() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassification: Returns the lower end of the probabilistic interval for the class/label point prediction.
getPointPredictionUpperBoundProbability() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassification: Returns the upper end of the probabilistic interval for the class/label point prediction.
getPValues() - Method in class se.hb.jcp.cp.ConformalClassification: Returns the predicted p-values.
getQuick(int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Returns the matrix cell value at coordinate column.
getQuick(int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Returns the matrix cell value at coordinate column.
getQuick(int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Returns the matrix cell value at coordinate column.
getQuick(int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Returns the matrix cell value at coordinate column.
getQuick(int, int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Returns the matrix cell value at coordinate [row,column].
getQuick(int, int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Returns the matrix cell value at coordinate [row,column].
getQuick(int, int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Returns the matrix cell value at coordinate [row,column].
getQuick(int, int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Returns the matrix cell value at coordinate [row,column].
getRow(int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Returns one row of the matrix as a SparseDoubleMatrix1D.
getRowIndices() - Method in class se.hb.jcp.util.RealIndexedMatrix2D
getSource() - Method in class se.hb.jcp.cp.ConformalClassification: Returns the conformal classifier that made this prediction.
getUpper(double, double) - Method in class se.hb.jcp.util.RealIndexedMatrix2D

H

HingeLossNonconformityFunction - Class in se.hb.jcp.nc: A hinge loss nonconformity function based on the predicted class probabilities given by a classifier.
HingeLossNonconformityFunction(double[]) - Constructor for class se.hb.jcp.nc.HingeLossNonconformityFunction
HingeLossNonconformityFunction(double[], IClassProbabilityClassifier) - Constructor for class se.hb.jcp.nc.HingeLossNonconformityFunction

I

IClassificationNonconformityFunction - Interface in se.hb.jcp.nc: Represents an instance of a specific non-conformity function for conformal classification.
IClassifier - Interface in se.hb.jcp.ml: Represents an instance of a specific machine learning classification algorithm.
IClassifierInformation - Interface in se.hb.jcp.ml: Specifies a set of information that every classifier should be able to provide.
IClassProbabilityClassifier - Interface in se.hb.jcp.ml: Represents an instance of a specific machine learning classification algorithm.
IConformalClassifier - Interface in se.hb.jcp.cp: Represents an instance of a specific conformal classification algorithm.
IMeasure - Interface in se.hb.jcp.cp.measures: A measure of prediction "quality".
index - Variable in class se.hb.jcp.bindings.libsvm.svm_node
InductiveConformalClassifier - Class in se.hb.jcp.cp: Represents an instance of a specific inductive conformal classification algorithm.
InductiveConformalClassifier(IClassificationNonconformityFunction, double[]) - Constructor for class se.hb.jcp.cp.InductiveConformalClassifier: Creates an inductive conformal classifier using the supplied information.
InductiveConformalClassifier(IClassificationNonconformityFunction, double[], boolean) - Constructor for class se.hb.jcp.cp.InductiveConformalClassifier: Creates an inductive conformal classifier using the supplied information.
InductiveConformalRegressor - Class in se.hb.jcp.cp
InductiveConformalRegressor() - Constructor for class se.hb.jcp.cp.InductiveConformalRegressor
initialize(int, int) - Method in class se.hb.jcp.util.ParallelizedAction: Performs any needed intialization for the sub-interval once the split threshold has been reached.
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.jliblinear.LinearClassifier
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.libsvm.SVMClassifier
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.opencv.RandomForestClassifier
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.bindings.opencv.SVMClassifier
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.ml.BogusClassProbabilityClassifier: Trains this classifier using the supplied data.
internalFit(DoubleMatrix2D, double[]) - Method in class se.hb.jcp.ml.ClassifierBase
IObservedMeasure - Interface in se.hb.jcp.cp.measures: An observed measure depends on the prediction made and the true label.
IObservedProbabilisticMeasure - Interface in se.hb.jcp.cp.measures: An observed measure depends on the prediction made and the true label.
IOTools - Class in se.hb.jcp.cli: Utility functions for reading/writing data to/from JSON.
IOTools() - Constructor for class se.hb.jcp.cli.IOTools
IPriorMeasure - Interface in se.hb.jcp.cp.measures: A prior measure depends only on the prediction made.
IPriorMultiProbabilisticMeasure - Interface in se.hb.jcp.cp.measures: A prior measure depends only on the prediction made.
IRegressionNonconformityFunction - Interface in se.hb.jcp.nc
isEmpty() - Method in class se.hb.jcp.util.RealIndexedMatrix2D
isTrained() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier: Returns whether this classifier has been trained and calibrated.
isTrained() - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Returns whether this classifier has been trained and calibrated.
isTrained() - Method in class se.hb.jcp.cp.TransductiveConformalClassifier
isTrained() - Method in class se.hb.jcp.ml.ClassifierBase
isTrained() - Method in interface se.hb.jcp.ml.IClassifierInformation: Returns whether this classifier has been trained.
isTrained() - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
isTrained() - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
ISVMClassifier - Interface in se.hb.jcp.ml: Specifies an interface for SVM classifiers giving access to internal SVM specific information.

J

jcp_cat - Class in se.hb.jcp.cli: Command line tool for converting a data set file to JSON format written on stdout.
jcp_cat() - Constructor for class se.hb.jcp.cli.jcp_cat
jcp_predict - Class in se.hb.jcp.cli: Command line prediction tool for JCP.
jcp_predict() - Constructor for class se.hb.jcp.cli.jcp_predict
jcp_predict_filter - Class in se.hb.jcp.cli: Command line filter for making predictions for JSON formatted instances read from stdin and write the JSON formatted predictions to stdout.
jcp_predict_filter() - Constructor for class se.hb.jcp.cli.jcp_predict_filter
jcp_train - Class in se.hb.jcp.cli: Command line training tool for JCP.
jcp_train() - Constructor for class se.hb.jcp.cli.jcp_train

K

kernel_type - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
kNearestSameClassNeighbourNonconformityFunction - Class in se.hb.jcp.nc: This class implements a nonconformity function based on the k-nearest same class neighbour function.
kNearestSameClassNeighbourNonconformityFunction(double[], int) - Constructor for class se.hb.jcp.nc.kNearestSameClassNeighbourNonconformityFunction
kNearestSameClassNeighbourNonconformityFunction(double[], int, IClassifier) - Constructor for class se.hb.jcp.nc.kNearestSameClassNeighbourNonconformityFunction

L

l - Variable in class se.hb.jcp.bindings.libsvm.svm_problem
libsvmReader - Class in se.hb.jcp.io: Data set reader for the libsvm sparse data format.
libsvmReader() - Constructor for class se.hb.jcp.io.libsvmReader
like(int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified size.
like(int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified size.
like(int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified size.
like(int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified size.
like(int, int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified number of rows and columns.
like(int, int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified number of rows and columns.
like(int, int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified number of rows and columns.
like(int, int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Construct and returns a new empty matrix of the same dynamic type as the receiver, having the specified number of rows and columns.
like1D(int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like1D(int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like1D(int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like1D(int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like1D(int, int, int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, sharing the same cells.
like1D(int, int, int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, sharing the same cells.
like1D(int, int, int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, sharing the same cells.
like1D(int, int, int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Construct and returns a new 1-d matrix of the corresponding dynamic type, sharing the same cells.
like2D(int, int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Construct and returns a new 2-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like2D(int, int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Construct and returns a new 2-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like2D(int, int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Construct and returns a new 2-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
like2D(int, int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Construct and returns a new 2-d matrix of the corresponding dynamic type, entirelly independent of the receiver.
LINEAR - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
LinearClassifier - Class in se.hb.jcp.bindings.jliblinear
LinearClassifier() - Constructor for class se.hb.jcp.bindings.jliblinear.LinearClassifier
LinearClassifier(JSONObject) - Constructor for class se.hb.jcp.bindings.jliblinear.LinearClassifier
loadDataSet(String) - Static method in class se.hb.jcp.cli.DataSetTools
loadDataSet(String, DoubleMatrix1D) - Static method in class se.hb.jcp.cli.DataSetTools
loadDataSet(String, IConformalClassifier) - Static method in class se.hb.jcp.cli.DataSetTools
loadModel(String) - Static method in class se.hb.jcp.cli.CCTools

M

main(String[]) - Static method in class se.hb.jcp.cli.jcp_cat
main(String[]) - Static method in class se.hb.jcp.cli.jcp_predict_filter
main(String[]) - Static method in class se.hb.jcp.cli.jcp_predict
main(String[]) - Static method in class se.hb.jcp.cli.jcp_train
MarginNonconformityFunction - Class in se.hb.jcp.nc: A margin nonconformity function based on the predicted class probabilities given by a classifier.
MarginNonconformityFunction(double[]) - Constructor for class se.hb.jcp.nc.MarginNonconformityFunction
MarginNonconformityFunction(double[], IClassProbabilityClassifier) - Constructor for class se.hb.jcp.nc.MarginNonconformityFunction
MultipleCriterion - Class in se.hb.jcp.cp.measures: The M/Multiple criterion is a prior efficiency measure based on the size of the label set.
MultipleCriterion(double) - Constructor for class se.hb.jcp.cp.measures.MultipleCriterion: Creates a M/Multiple criterion measure.
MultiProbabilisticLowerBound - Class in se.hb.jcp.cp.measures: This prior measure returns the predicted lower bound probability.
MultiProbabilisticLowerBound() - Constructor for class se.hb.jcp.cp.measures.MultiProbabilisticLowerBound
MultiProbabilisticUpperBound - Class in se.hb.jcp.cp.measures: This prior measure returns the predicted upper bound probability.
MultiProbabilisticUpperBound() - Constructor for class se.hb.jcp.cp.measures.MultiProbabilisticUpperBound

N

nativeStorageTemplate() - Method in class se.hb.jcp.bindings.jliblinear.LinearClassifier
nativeStorageTemplate() - Method in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
nativeStorageTemplate() - Method in class se.hb.jcp.bindings.libsvm.SVMClassifier
nativeStorageTemplate() - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
nativeStorageTemplate() - Method in class se.hb.jcp.cp.InductiveConformalClassifier
nativeStorageTemplate() - Method in class se.hb.jcp.cp.TransductiveConformalClassifier
nativeStorageTemplate() - Method in class se.hb.jcp.ml.BogusClassProbabilityClassifier: Returns a value of the DoubleMatrix1D derived class that is the native storage format for the classifier.
nativeStorageTemplate() - Method in interface se.hb.jcp.ml.IClassifierInformation: Returns a value of the DoubleMatrix1D derived class that is the native storage format for the classifier.
nativeStorageTemplate() - Method in class se.hb.jcp.nc.AverageClassificationNonconformityFunction
nativeStorageTemplate() - Method in class se.hb.jcp.nc.ClassifierNonconformityFunctionBase
nr_weight - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
nu - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
NU_SVC - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
NU_SVR - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
NumberCriterion - Class in se.hb.jcp.cp.measures: The N/Number/AvgC criterion is a prior efficiency measure based on the size of the label set.
NumberCriterion(double) - Constructor for class se.hb.jcp.cp.measures.NumberCriterion: Creates a N/Number criterion measure.

O

ObservedAccuracy - Class in se.hb.jcp.cp.measures: The Observed Accuracy is the fraction of predictions that include the true label in their label set.
ObservedAccuracy(double) - Constructor for class se.hb.jcp.cp.measures.ObservedAccuracy: Creates an Observed Accuracy measure.
ObservedExcessCriterion - Class in se.hb.jcp.cp.measures: The OE/Observed Excess criterion is an observed efficiency measure based on the number of false labels in the label set.
ObservedExcessCriterion(double) - Constructor for class se.hb.jcp.cp.measures.ObservedExcessCriterion: Creates an OE/Observed Excess criterion measure.
ObservedFuzzinessCriterion - Class in se.hb.jcp.cp.measures: The OF/Observed Fuzziness criterion is a prior efficiency measure based on the sum of all p-values for false labels.
ObservedFuzzinessCriterion() - Constructor for class se.hb.jcp.cp.measures.ObservedFuzzinessCriterion
ObservedMultipleCriterion - Class in se.hb.jcp.cp.measures: The OM/Observed Multiple criterion is an observed efficiency measure based on the number of false labels in the label set.
ObservedMultipleCriterion(double) - Constructor for class se.hb.jcp.cp.measures.ObservedMultipleCriterion: Creates an OM/Observed Multiple criterion measure.
ObservedOneCCriterion - Class in se.hb.jcp.cp.measures: The Observed OneC criterion is the fraction of predictions that only have the true label in their label set.
ObservedOneCCriterion(double) - Constructor for class se.hb.jcp.cp.measures.ObservedOneCCriterion: Creates an Observed OneC measure.
ObservedProbabilityLogLoss - Class in se.hb.jcp.cp.measures: The observed log loss measure depends on the prediction made and the true label.
ObservedProbabilityLogLoss() - Constructor for class se.hb.jcp.cp.measures.ObservedProbabilityLogLoss
ObservedProbabilitySquareLoss - Class in se.hb.jcp.cp.measures: The observed square loss measure depends on the prediction made and the true label.
ObservedProbabilitySquareLoss() - Constructor for class se.hb.jcp.cp.measures.ObservedProbabilitySquareLoss
ObservedUnconfidenceCriterion - Class in se.hb.jcp.cp.measures: The UO/Observed Unconfidence criterion is a prior efficiency measure based on the (un)confidence of the point prediction.
ObservedUnconfidenceCriterion() - Constructor for class se.hb.jcp.cp.measures.ObservedUnconfidenceCriterion
ONE_CLASS - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
OneCCriterion - Class in se.hb.jcp.cp.measures: The OneC is the fraction of predictions that only have one label in their label set.
OneCCriterion(double) - Constructor for class se.hb.jcp.cp.measures.OneCCriterion: Creates an OneC measure.

P

p - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
ParallelizedAction - Class in se.hb.jcp.util: Base class for parallel actions over contiguous int intervals.
ParallelizedAction(int, int) - Constructor for class se.hb.jcp.util.ParallelizedAction: Constructs a set of actions for the interval [first, last).
parent - Variable in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: To protect the parent's data from reclamation for SparseDoubleMatrix2D row views.
POLY - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
PRECOMPUTED - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
predict(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.jliblinear.LinearClassifier
predict(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
predict(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.libsvm.SVMClassifier
predict(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier
predict(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.opencv.RandomForestClassifier
predict(DoubleMatrix1D) - Method in class se.hb.jcp.bindings.opencv.SVMClassifier
predict(DoubleMatrix1D) - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier: Makes a prediction for the instance x.
predict(DoubleMatrix1D) - Method in interface se.hb.jcp.cp.IConformalClassifier: Makes a prediction for the instance x.
predict(DoubleMatrix1D) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Makes a prediction for the instance x.
predict(DoubleMatrix1D) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Makes a prediction for the instance x.
predict(DoubleMatrix1D) - Method in class se.hb.jcp.ml.BogusClassProbabilityClassifier: Predicts the target for the supplied instance.
predict(DoubleMatrix1D) - Method in interface se.hb.jcp.ml.IClassifier: Predicts the target for the supplied instance.
predict(DoubleMatrix1D, double[]) - Method in class se.hb.jcp.bindings.jliblinear.LinearClassifier
predict(DoubleMatrix1D, double[]) - Method in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
predict(DoubleMatrix1D, double[]) - Method in class se.hb.jcp.bindings.libsvm.SVMClassifier
predict(DoubleMatrix1D, double[]) - Method in class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier
predict(DoubleMatrix1D, double[]) - Method in class se.hb.jcp.ml.BogusClassProbabilityClassifier: Predicts the target probabilities for the supplied instance.
predict(DoubleMatrix1D, double[]) - Method in interface se.hb.jcp.ml.IClassProbabilityClassifier: Predicts the target probabilities for the supplied instance.
predict(DoubleMatrix2D) - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier: Makes a prediction for each instance in x.
predict(DoubleMatrix2D) - Method in interface se.hb.jcp.cp.IConformalClassifier: Makes a prediction for each instance in x.
predict(DoubleMatrix2D) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Makes a prediction for each instance in x.
predict(DoubleMatrix2D) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Makes a prediction for each instance in x.
predict(AbstractMatrix2D, double) - Method in interface se.hb.jcp.nc.IRegressionNonconformityFunction
predictPValues(DoubleMatrix1D) - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
predictPValues(DoubleMatrix1D) - Method in interface se.hb.jcp.cp.IConformalClassifier: Computes the predicted p-values for the instance x.
predictPValues(DoubleMatrix1D) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Computes the predicted p-values for the instance x.
predictPValues(DoubleMatrix1D) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Computes the predicted p-values for the instance x.
predictPValues(DoubleMatrix1D, DoubleMatrix1D) - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
predictPValues(DoubleMatrix1D, DoubleMatrix1D) - Method in interface se.hb.jcp.cp.IConformalClassifier: Computes the predicted p-values for the instance x.
predictPValues(DoubleMatrix1D, DoubleMatrix1D) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Computes the predicted p-values for the instance x.
predictPValues(DoubleMatrix1D, DoubleMatrix1D) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Computes the predicted p-values for the instance x.
predictPValues(DoubleMatrix2D) - Method in class se.hb.jcp.cp.ConformalMultiProbabilisticClassifier
predictPValues(DoubleMatrix2D) - Method in interface se.hb.jcp.cp.IConformalClassifier: Computes the predicted p-values for each target and instance in x.
predictPValues(DoubleMatrix2D) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Computes the predicted p-values for each target and instance in x.
predictPValues(DoubleMatrix2D) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Computes the predicted p-values for each target and instance in x.
print(String) - Method in interface se.hb.jcp.bindings.libsvm.svm_print_interface
probability - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
put(double, double, V) - Method in class se.hb.jcp.util.RealIndexedMatrix2D

R

random3Partition(DataSet, DataSet, DataSet, double, double) - Method in class se.hb.jcp.cp.DataSet
RandomForestClassifier - Class in se.hb.jcp.bindings.opencv
RandomForestClassifier() - Constructor for class se.hb.jcp.bindings.opencv.RandomForestClassifier
RandomForestClassifier(JSONObject) - Constructor for class se.hb.jcp.bindings.opencv.RandomForestClassifier
RBF - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
read(InputStream) - Method in class se.hb.jcp.io.DataSetReader
read(InputStream, DoubleMatrix1D) - Method in class se.hb.jcp.io.DataSetReader
read(InputStream, DoubleMatrix1D) - Method in class se.hb.jcp.io.libsvmReader
readInstanceFromJSON(JSONTokener, DoubleMatrix1D) - Static method in class se.hb.jcp.cli.IOTools: Read an instance from a JSON stream.
RealIndexedMatrix2D<V> - Class in se.hb.jcp.util: Real value indexed 2D matrix.
RealIndexedMatrix2D() - Constructor for class se.hb.jcp.util.RealIndexedMatrix2D
rowViews - Variable in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: SparseDoubleMatrix1D views of rows in this matrix.
run(String[]) - Method in class se.hb.jcp.cli.jcp_cat
run(String[]) - Method in class se.hb.jcp.cli.jcp_predict_filter
run(String[]) - Method in class se.hb.jcp.cli.jcp_predict
run(String[]) - Method in class se.hb.jcp.cli.jcp_train
runTest(String, String, String, String, String, double, boolean) - Static method in class se.hb.jcp.cli.CCTools
runTest(IConformalClassifier, DataSet, String, String, String, double, boolean) - Static method in class se.hb.jcp.cli.CCTools

S

saveModel(IConformalClassifier, String) - Static method in class se.hb.jcp.cli.CCTools
se.hb.jcp.bindings.jliblinear - package se.hb.jcp.bindings.jliblinear
se.hb.jcp.bindings.jlibsvm - package se.hb.jcp.bindings.jlibsvm
se.hb.jcp.bindings.libsvm - package se.hb.jcp.bindings.libsvm
se.hb.jcp.bindings.opencv - package se.hb.jcp.bindings.opencv
se.hb.jcp.cli - package se.hb.jcp.cli
se.hb.jcp.cp - package se.hb.jcp.cp
se.hb.jcp.cp.measures - package se.hb.jcp.cp.measures
se.hb.jcp.io - package se.hb.jcp.io
se.hb.jcp.ml - package se.hb.jcp.ml
se.hb.jcp.nc - package se.hb.jcp.nc
se.hb.jcp.util - package se.hb.jcp.util
setNonconformityFunction(IClassificationNonconformityFunction) - Method in class se.hb.jcp.cp.InductiveConformalClassifier: Sets a new non-conformity function in this inductive conformal classifier.
setNonconformityFunction(IClassificationNonconformityFunction) - Method in class se.hb.jcp.cp.TransductiveConformalClassifier: Sets a new non-conformity function in this transductive conformal classifier.
setQuick(int, double) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Sets the matrix cell at coordinate index to the specified value.
setQuick(int, double) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Sets the matrix cell at coordinate index to the specified value.
setQuick(int, double) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Sets the matrix cell at coordinate index to the specified value.
setQuick(int, double) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Sets the matrix cell at coordinate index to the specified value.
setQuick(int, int, double) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Sets the matrix cell at coordinate [row,column] to the specified value.
setQuick(int, int, double) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Sets the matrix cell at coordinate [row,column] to the specified value.
setQuick(int, int, double) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Sets the matrix cell at coordinate [row,column] to the specified value.
setQuick(int, int, double) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Sets the matrix cell at coordinate [row,column] to the specified value.
setRow(int, int[], double[]) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Replaces one row of the matrix.
setRow(int, int[], double[]) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Replaces one row of the matrix.
setRow(int, int[], double[]) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Replaces one row of the matrix.
setUp(int, int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Sets up a matrix with a given number of rows and columns.
setUp(int, int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Sets up a matrix with a given number of rows and columns.
setUp(int, int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Sets up a matrix with a given number of rows and columns.
setUp(int, int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Sets up a matrix with a given number of rows and columns.
shrinking - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
SIGMOID - Static variable in class se.hb.jcp.bindings.libsvm.svm_parameter
size() - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasures: Returns the number of measures in this set.
size() - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasures: Returns the number of measures in this set.
size() - Method in class se.hb.jcp.util.RealIndexedMatrix2D
SparseDoubleMatrix1D - Class in se.hb.jcp.bindings.jliblinear: Class for sparse 1-d matrices (aka vectors) holding double elements in the sparse format expected by the Java library liblinear.
SparseDoubleMatrix1D - Class in se.hb.jcp.bindings.jlibsvm: Class for sparse 1-d matrices (aka vectors) holding double elements in the sparse format expected by the Java version of libsvm.
SparseDoubleMatrix1D - Class in se.hb.jcp.bindings.libsvm: Class for sparse 1-d matrices (aka vectors) holding double elements in the sparse format expected by the C library libsvm.
SparseDoubleMatrix1D(double[]) - Constructor for class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
SparseDoubleMatrix1D(double[]) - Constructor for class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
SparseDoubleMatrix1D(double[]) - Constructor for class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
SparseDoubleMatrix1D(int) - Constructor for class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Constructs a matrix with a given number of columns.
SparseDoubleMatrix1D(int) - Constructor for class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Constructs a matrix with a given number of columns.
SparseDoubleMatrix1D(int) - Constructor for class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Constructs a matrix with a given number of columns.
SparseDoubleMatrix1D(int, int[], double[]) - Constructor for class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
SparseDoubleMatrix1D(int, int[], double[]) - Constructor for class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
SparseDoubleMatrix1D(int, int[], double[]) - Constructor for class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Constructs a matrix with a copy of the given values.
SparseDoubleMatrix2D - Class in se.hb.jcp.bindings.jliblinear: Class for sparse 2-d matrices holding double elements in the sparse format expected by the Java library liblinear.
SparseDoubleMatrix2D - Class in se.hb.jcp.bindings.jlibsvm: Class for sparse 2-d matrices holding double elements in the sparse format expected by the Java version of libsvm.
SparseDoubleMatrix2D - Class in se.hb.jcp.bindings.libsvm: Class for sparse 2-d matrices holding double elements in the sparse format expected by the C library libsvm.
SparseDoubleMatrix2D(int, int) - Constructor for class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Constructs a matrix with a given number of rows and columns.
SparseDoubleMatrix2D(int, int) - Constructor for class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Constructs a matrix with a given number of rows and columns.
SparseDoubleMatrix2D(int, int) - Constructor for class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Constructs a matrix with a given number of rows and columns.
start() - Method in class se.hb.jcp.util.ParallelizedAction: Starts this set of actions.
submit(Callable<E>) - Method in class se.hb.jcp.util.FIFOParallelExecutor: Submits the Callable for execution.
SumCriterion - Class in se.hb.jcp.cp.measures: The S/Sum criterion is a prior efficiency measure based on the sum of the p-values.
SumCriterion() - Constructor for class se.hb.jcp.cp.measures.SumCriterion
svm - Class in se.hb.jcp.bindings.libsvm
svm() - Constructor for class se.hb.jcp.bindings.libsvm.svm
svm_check_parameter(svm_parameter, SparseDoubleMatrix2D, double[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_check_parameter(svm_problem, svm_parameter) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_check_probability_model(svm_model) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_cross_validation(svm_problem, svm_parameter, int, double[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_distance_from_separating_plane(svm_model, SparseDoubleMatrix1D) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_get_labels(svm_model, int[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_get_nr_class(svm_model) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_get_nr_sv(svm_model) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_get_sv_indices(svm_model, int[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_get_svm_type(svm_model) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_get_svr_probability(svm_model) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_load_model(BufferedReader) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_load_model(String) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_model - Class in se.hb.jcp.bindings.libsvm
svm_node - Class in se.hb.jcp.bindings.libsvm
svm_node() - Constructor for class se.hb.jcp.bindings.libsvm.svm_node
svm_parameter - Class in se.hb.jcp.bindings.libsvm
svm_parameter() - Constructor for class se.hb.jcp.bindings.libsvm.svm_parameter
svm_predict(svm_model, SparseDoubleMatrix1D) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_predict(svm_model, svm_node[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_predict_probability(svm_model, SparseDoubleMatrix1D, double[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_predict_probability(svm_model, svm_node[], double[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_predict_values(svm_model, svm_node[], double[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_print_interface - Interface in se.hb.jcp.bindings.libsvm
svm_problem - Class in se.hb.jcp.bindings.libsvm
svm_problem() - Constructor for class se.hb.jcp.bindings.libsvm.svm_problem
svm_save_model(String, svm_model) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_set_print_string_function(svm_print_interface) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_train(svm_parameter, SparseDoubleMatrix2D, double[]) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_train(svm_problem, svm_parameter) - Static method in class se.hb.jcp.bindings.libsvm.svm
svm_type - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
SVMClassifier - Class in se.hb.jcp.bindings.jlibsvm
SVMClassifier - Class in se.hb.jcp.bindings.libsvm
SVMClassifier - Class in se.hb.jcp.bindings.opencv
SVMClassifier() - Constructor for class se.hb.jcp.bindings.jlibsvm.SVMClassifier
SVMClassifier() - Constructor for class se.hb.jcp.bindings.libsvm.SVMClassifier
SVMClassifier() - Constructor for class se.hb.jcp.bindings.opencv.SVMClassifier
SVMClassifier(svm_parameter) - Constructor for class se.hb.jcp.bindings.jlibsvm.SVMClassifier
SVMClassifier(JSONObject) - Constructor for class se.hb.jcp.bindings.jlibsvm.SVMClassifier
SVMClassifier(JSONObject) - Constructor for class se.hb.jcp.bindings.libsvm.SVMClassifier
SVMClassifier(JSONObject) - Constructor for class se.hb.jcp.bindings.opencv.SVMClassifier
SVMClassifier(svm_parameter) - Constructor for class se.hb.jcp.bindings.libsvm.SVMClassifier
SVMDistanceNonconformityFunction - Class in se.hb.jcp.nc: This class implements a nonconformity function based on the signed distance from the separating hyperplane of a two-class SVM classifier.
SVMDistanceNonconformityFunction(double[]) - Constructor for class se.hb.jcp.nc.SVMDistanceNonconformityFunction
SVMDistanceNonconformityFunction(double[], ISVMClassifier) - Constructor for class se.hb.jcp.nc.SVMDistanceNonconformityFunction

T

take() - Method in class se.hb.jcp.util.FIFOParallelExecutor: Returns the next result, blocking to await its arrival or completion if needed.
toString() - Method in class se.hb.jcp.cp.measures.AggregatedObservedMeasure
toString() - Method in class se.hb.jcp.cp.measures.AggregatedPriorMeasure
toString() - Method in class se.hb.jcp.util.RealIndexedMatrix2D
TransductiveConformalClassifier - Class in se.hb.jcp.cp
TransductiveConformalClassifier(IClassificationNonconformityFunction, double[]) - Constructor for class se.hb.jcp.cp.TransductiveConformalClassifier: Creates a transductive conformal classifier using the supplied information.
TransductiveConformalClassifier(IClassificationNonconformityFunction, double[], boolean) - Constructor for class se.hb.jcp.cp.TransductiveConformalClassifier: Creates a transductive conformal classifier using the supplied information.
TransductiveConformalRegressor - Class in se.hb.jcp.cp
TransductiveConformalRegressor() - Constructor for class se.hb.jcp.cp.TransductiveConformalRegressor

U

UnconfidenceCriterion - Class in se.hb.jcp.cp.measures: The U/Unconfidence criterion is a prior efficiency measure based on the (un)confidence of the point prediction.
UnconfidenceCriterion() - Constructor for class se.hb.jcp.cp.measures.UnconfidenceCriterion
Util - Class in se.hb.jcp.cp
Util() - Constructor for class se.hb.jcp.cp.Util

V

value - Variable in class se.hb.jcp.bindings.libsvm.svm_node
viewRow(int) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Constructs and returns a new slice view representing the columns of the given row.
viewRow(int) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Constructs and returns a new slice view representing the columns of the given row.
viewRow(int) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Constructs and returns a new slice view representing the columns of the given row.
viewRow(int) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Constructs and returns a new slice view representing the columns of the given row.
viewSelectionLike(int[]) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix1D: Construct and returns a new selection view.
viewSelectionLike(int[]) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix1D: Construct and returns a new selection view.
viewSelectionLike(int[]) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix1D: Construct and returns a new selection view.
viewSelectionLike(int[]) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D: Construct and returns a new selection view.
viewSelectionLike(int[], int[]) - Method in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: Construct and returns a new selection view.
viewSelectionLike(int[], int[]) - Method in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: Construct and returns a new selection view.
viewSelectionLike(int[], int[]) - Method in class se.hb.jcp.bindings.libsvm.SparseDoubleMatrix2D: Construct and returns a new selection view.
viewSelectionLike(int[], int[]) - Method in class se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D: Construct and returns a new selection view.

W

weight - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
weight_label - Variable in class se.hb.jcp.bindings.libsvm.svm_parameter
writeAsJSON(DoubleMatrix1D, double, JSONWriter) - Static method in class se.hb.jcp.cli.IOTools: Write an instance to a JSON writer.
writeAsJSON(DoubleMatrix1D, JSONWriter) - Static method in class se.hb.jcp.cli.IOTools: Write an instance to a JSON writer.
writeAsJSON(ConformalClassification, DoubleMatrix1D, double, JSONWriter) - Static method in class se.hb.jcp.cli.IOTools: Write a ConformalClassification including internal state as JSON to a JSON writer.
writeAsJSON(ConformalClassification, JSONWriter) - Static method in class se.hb.jcp.cli.IOTools: Write a ConformalClassification as JSON to a JSON writer.

X

x - Variable in class se.hb.jcp.bindings.libsvm.svm_problem
x - Variable in class se.hb.jcp.cp.DataSet

Y

y - Variable in class se.hb.jcp.bindings.libsvm.svm_problem
y - Variable in class se.hb.jcp.cp.DataSet

_

_jsonParameters - Variable in class se.hb.jcp.bindings.jliblinear.LinearClassifier
_model - Variable in class se.hb.jcp.bindings.jliblinear.LinearClassifier
_model - Variable in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
_model - Variable in class se.hb.jcp.bindings.libsvm.SVMClassifier
_model - Variable in class se.hb.jcp.bindings.opencv.ExtremelyRandomizedTreesClassifier
_parameters - Variable in class se.hb.jcp.bindings.jlibsvm.SVMClassifier
_parameters - Variable in class se.hb.jcp.bindings.libsvm.SVMClassifier
_rowViews - Variable in class se.hb.jcp.bindings.jliblinear.SparseDoubleMatrix2D: SparseDoubleMatrix1D views of rows in this matrix.
_rowViews - Variable in class se.hb.jcp.bindings.jlibsvm.SparseDoubleMatrix2D: SparseDoubleMatrix1D views of rows in this matrix.

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