All Classes (JCP API)

All Classes Interface Summary Class Summary
Class	Description
AbstractPriorMultiProbabilisticMeasure	The prior measure depends only on the prediction made.
AbstractSignificanceBasedMeasure	Base class for measures that depend on the significance level.
AggregatedObservedMeasure	Maintains a running average of an observed measure.
AggregatedObservedMeasures	Maintains running averages for a set of observed measures.
AggregatedPriorMeasure	Maintains a running average of a prior measure.
AggregatedPriorMeasures	Maintains running averages for a set of prior measures.
AverageClassificationNonconformityFunction
BogusClassProbabilityClassifier	Adds a bogus class probability estimate to the underlying machine learning classification algorithm.
CCTools	Higher-level tools for Conformal Classification.
ClassificationNonconformityFunctionFactory	Singleton factory for JCP classification nonconformity functions.
ClassifierBase	Base class for classifiers that provide implementations of some of the generic IClassifierInformation methods.
ClassifierFactory	Singleton factory for JCP classifiers.
ClassifierNonconformityFunctionBase	Base class for nonconformity functions that use a classifier.
ClassProbabilityNonconformityFunctionBase	A base class for nonconformity functions based on the predicted class probabilities given by a classifier.
ConformalClassification	Represents a prediction made by a conformal classifier.
ConformalMultiProbabilisticClassification	Represents a multi-probabilistic prediction made by a conformal classifier with bivariate isotonic regression.
ConformalMultiProbabilisticClassifier	Represents a multi-probabilistic conformal classifier with bivariate isotonic regression.
DataSet	A data set.
DataSetReader	Public abstract base class for data set readers.
DataSetTools	Higher-level tools for DataSets.
DenseDoubleMatrix1D	Class for dense 1-d matrices (aka vectors) holding `double` elements in the dense format used by OpenCV.
DenseDoubleMatrix2D	Class for dense 2-d matrices holding `double` elements in `double` elements in the dense format used by OpenCV.
ExcessCriterion	The E/Excess criterion is a prior efficiency measure based on how much the size of the label set exceeds 1.
ExtremelyRandomizedTreesClassifier
FIFOParallelExecutor<E>	A `FIFOParallelExecutor` executes `Callable`s in parallel and returns the results in the order the `Callable`s were issued.
FuzzinessCriterion	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.
HingeLossNonconformityFunction	A hinge loss nonconformity function based on the predicted class probabilities given by a classifier.
IClassificationNonconformityFunction	Represents an instance of a specific non-conformity function for conformal classification.
IClassifier	Represents an instance of a specific machine learning classification algorithm.
IClassifierInformation	Specifies a set of information that every classifier should be able to provide.
IClassProbabilityClassifier	Represents an instance of a specific machine learning classification algorithm.
IConformalClassifier	Represents an instance of a specific conformal classification algorithm.
IMeasure	A measure of prediction "quality".
InductiveConformalClassifier	Represents an instance of a specific inductive conformal classification algorithm.
InductiveConformalRegressor
IObservedMeasure	An observed measure depends on the prediction made and the true label.
IObservedProbabilisticMeasure	An observed measure depends on the prediction made and the true label.
IOTools	Utility functions for reading/writing data to/from JSON.
IPriorMeasure	A prior measure depends only on the prediction made.
IPriorMultiProbabilisticMeasure	A prior measure depends only on the prediction made.
IRegressionNonconformityFunction
ISVMClassifier	Specifies an interface for SVM classifiers giving access to internal SVM specific information.
jcp_cat	Command line tool for converting a data set file to JSON format written on stdout.
jcp_predict	Command line prediction tool for JCP.
jcp_predict_filter	Command line filter for making predictions for JSON formatted instances read from stdin and write the JSON formatted predictions to stdout.
jcp_train	Command line training tool for JCP.
kNearestSameClassNeighbourNonconformityFunction	This class implements a nonconformity function based on the k-nearest same class neighbour function.
libsvmReader	Data set reader for the libsvm sparse data format.
LinearClassifier
MarginNonconformityFunction	A margin nonconformity function based on the predicted class probabilities given by a classifier.
MultipleCriterion	The M/Multiple criterion is a prior efficiency measure based on the size of the label set.
MultiProbabilisticLowerBound	This prior measure returns the predicted lower bound probability.
MultiProbabilisticUpperBound	This prior measure returns the predicted upper bound probability.
NumberCriterion	The N/Number/AvgC criterion is a prior efficiency measure based on the size of the label set.
ObservedAccuracy	The Observed Accuracy is the fraction of predictions that include the true label in their label set.
ObservedExcessCriterion	The OE/Observed Excess criterion is an observed efficiency measure based on the number of false labels in the label set.
ObservedFuzzinessCriterion	The OF/Observed Fuzziness criterion is a prior efficiency measure based on the sum of all p-values for false labels.
ObservedMultipleCriterion	The OM/Observed Multiple criterion is an observed efficiency measure based on the number of false labels in the label set.
ObservedOneCCriterion	The Observed OneC criterion is the fraction of predictions that only have the true label in their label set.
ObservedProbabilityLogLoss	The observed log loss measure depends on the prediction made and the true label.
ObservedProbabilitySquareLoss	The observed square loss measure depends on the prediction made and the true label.
ObservedUnconfidenceCriterion	The UO/Observed Unconfidence criterion is a prior efficiency measure based on the (un)confidence of the point prediction.
OneCCriterion	The OneC is the fraction of predictions that only have one label in their label set.
ParallelizedAction	Base class for parallel actions over contiguous int intervals.
RandomForestClassifier
RealIndexedMatrix2D<V>	Real value indexed 2D matrix.
SparseDoubleMatrix1D	Class for sparse 1-d matrices (aka vectors) holding `double` elements in the sparse format expected by the Java library liblinear.
SparseDoubleMatrix1D	Class for sparse 1-d matrices (aka vectors) holding `double` elements in the sparse format expected by the Java version of libsvm.
SparseDoubleMatrix1D	Class for sparse 1-d matrices (aka vectors) holding `double` elements in the sparse format expected by the C library libsvm.
SparseDoubleMatrix2D	Class for sparse 2-d matrices holding `double` elements in the sparse format expected by the Java library liblinear.
SparseDoubleMatrix2D	Class for sparse 2-d matrices holding `double` elements in the sparse format expected by the Java version of libsvm.
SparseDoubleMatrix2D	Class for sparse 2-d matrices holding `double` elements in the sparse format expected by the C library libsvm.
SumCriterion	The S/Sum criterion is a prior efficiency measure based on the sum of the p-values.
svm
svm_model
svm_node
svm_parameter
svm_print_interface
svm_problem
SVMClassifier
SVMClassifier
SVMClassifier
SVMDistanceNonconformityFunction	This class implements a nonconformity function based on the signed distance from the separating hyperplane of a two-class SVM classifier.
TransductiveConformalClassifier
TransductiveConformalRegressor
UnconfidenceCriterion	The U/Unconfidence criterion is a prior efficiency measure based on the (un)confidence of the point prediction.
Util