File feature_based_search.hpp¶
-
class FeatureBasedSearch¶
- #include <feature_based_search.hpp>
Allows to conduct a search for finding the best refinement of an existing rule that can be created from a given feature vector.
Public Functions
-
void searchForNumericalRefinement(const NumericalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, SingleRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
NumericalFeatureVector
.- Parameters:
featureVector – A reference to an object of type
NumericalFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
SingleRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinement
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForNumericalRefinement(const NumericalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, FixedRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
NumericalFeatureVector
.- Parameters:
featureVector – A reference to an object of type
NumericalFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
MultiRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinements
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForNominalRefinement(const NominalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, SingleRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
NominalFeatureVector
.- Parameters:
featureVector – A reference to an object of type
NominalFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
SingleRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinement
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForNominalRefinement(const NominalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, FixedRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
NominalFeatureVector
.- Parameters:
featureVector – A reference to an object of type
NominalFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
MultiRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinements
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForBinaryRefinement(const BinaryFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, SingleRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
BinaryFeatureVector
.- Parameters:
featureVector – A reference to an object of type
BinaryFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
SingleRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinement
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForBinaryRefinement(const BinaryFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, FixedRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
BinaryFeatureVector
.- Parameters:
featureVector – A reference to an object of type
BinaryFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
MultiRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinements
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForOrdinalRefinement(const OrdinalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, SingleRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from an
OrdinalFeatureVector
.- Parameters:
featureVector – A reference to an object of type
OrdinalFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
SingleRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinement
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForOrdinalRefinement(const OrdinalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, FixedRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from an
OrdinalFeatureVector
.- Parameters:
featureVector – A reference to an object of type
OrdinalFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
MultiRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinements
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForBinnedRefinement(const BinnedFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, SingleRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
BinnedFeatureVector
.- Parameters:
featureVector – A reference to an object of type
BinnedFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
SingleRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinement
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForBinnedRefinement(const BinnedFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, FixedRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶
Conducts a search for the best refinement of an existing rule that can be created from a
BinnedFeatureVector
.- Parameters:
featureVector – A reference to an object of type
BinnedFeatureVector
, the refinements should be created frommissingFeatureVector – A reference to an object of type
MissingFeatureVector
that provides access to the indices of training examples with missing feature valuesstatisticsSubset – A reference to an object of type
IWeightedStatisticsSubset
that provides access to weighted statistics about the labels of the training examples, which should serve as the basis for evaluating the quality of potential refinementscomparator – A reference to an object of type
MultiRefinementComparator
that should be used for comparing potential refinementsnumExamplesWithNonZeroWeights – The total number of examples with non-zero weights that may be covered by a refinement
minCoverage – The minimum number of examples that must be covered by the refinements
refinement – A reference to an object of type
Refinement
that should be used for storing the properties of the best refinement that is found
-
void searchForNumericalRefinement(const NumericalFeatureVector &featureVector, const MissingFeatureVector &missingFeatureVector, IWeightedStatisticsSubset &statisticsSubset, SingleRefinementComparator &comparator, uint32 numExamplesWithNonZeroWeights, uint32 minCoverage, Refinement &refinement) const¶