File rule_evaluation_decomposable_single.hpp

namespace seco
class DecomposableSingleOutputRuleEvaluationFactory : public seco::IDecomposableRuleEvaluationFactory
#include <rule_evaluation_decomposable_single.hpp>

Allows to create instances of the class IDecomposableRuleEvaluationFactory that allow to calculate the predictions of single-output rules, which predict for a single output.

Public Functions

DecomposableSingleOutputRuleEvaluationFactory(std::unique_ptr<IHeuristicFactory> heuristicFactoryPtr)
Parameters:

heuristicFactoryPtr – An unique pointer to an object of type IHeuristicFactory, that allows to create implementations of the heuristic to be optimized

virtual std::unique_ptr<IRuleEvaluation<DenseConfusionMatrixVector<uint32>>> create(const DenseConfusionMatrixVector<uint32> &statisticVector, const CompleteIndexVector &indexVector) const override

Creates and returns a new object of type IRuleEvaluation that allows to calculate the predictions of rules that predict for all available labels.

Parameters:

  • statisticVector – A reference to an object of type DenseConfusionMatrixVector<uint32>. This vector is only used to identify the function that is able to deal with this particular type of vector via function overloading

  • indexVector – A reference to an object of type CompleteIndexVector that provides access to the indices of the labels for which the rules may predict

Returns:

An unique pointer to an object of type IRuleEvaluation that has been created

virtual std::unique_ptr<IRuleEvaluation<DenseConfusionMatrixVector<uint32>>> create(const DenseConfusionMatrixVector<uint32> &statisticVector, const PartialIndexVector &indexVector) const override

Creates and returns a new object of type IRuleEvaluation that allows to calculate the predictions of rules that predict for a subset of the available labels.

Parameters:

  • statisticVector – A reference to an object of type DenseConfusionMatrixVector<uint32>. This vector is only used to identify the function that is able to deal with this particular type of vector via function overloading

  • indexVector – A reference to an object of type PartialIndexVector that provides access to the indices of the labels for which the rules may predict

Returns:

An unique pointer to an object of type IRuleEvaluation that has been created

virtual std::unique_ptr<IRuleEvaluation<DenseConfusionMatrixVector<float32>>> create(const DenseConfusionMatrixVector<float32> &statisticVector, const CompleteIndexVector &indexVector) const override

Creates and returns a new object of type IRuleEvaluation that allows to calculate the predictions of rules that predict for all available labels.

Parameters:

  • statisticVector – A reference to an object of type DenseConfusionMatrixVector<float32>. This vector is only used to identify the function that is able to deal with this particular type of vector via function overloading

  • indexVector – A reference to an object of type CompleteIndexVector that provides access to the indices of the labels for which the rules may predict

Returns:

An unique pointer to an object of type IRuleEvaluation that has been created

virtual std::unique_ptr<IRuleEvaluation<DenseConfusionMatrixVector<float32>>> create(const DenseConfusionMatrixVector<float32> &statisticVector, const PartialIndexVector &indexVector) const override

Creates and returns a new object of type IRuleEvaluation that allows to calculate the predictions of rules that predict for a subset of the available labels.

Parameters:

  • statisticVector – A reference to an object of type DenseConfusionMatrixVector<float32>. This vector is only used to identify the function that is able to deal with this particular type of vector via function overloading

  • indexVector – A reference to an object of type PartialIndexVector that provides access to the indices of the labels for which the rules may predict

Returns:

An unique pointer to an object of type IRuleEvaluation that has been created

Private Members

const std::unique_ptr<IHeuristicFactory> heuristicFactoryPtr_