File statistics_subset_resettable.hpp

class IResettableStatisticsSubset : public virtual IStatisticsSubset

#include <statistics_subset_resettable.hpp>

Defines an interface for all classes that provide access to a subset of the weighted statistics and allows to calculate the scores to be predicted by rules that cover such a subset. In addition, the state of the subset can be reset multiple times and the scores to be predicted by rules that cover the previous subsets can be calculated as well.

Public Functions

inline virtual ~IResettableStatisticsSubset() override

virtual void resetSubset() = 0

Resets the subset by removing all statistics that have been added via preceding calls to the function addToSubset.

This function is supposed to reset the internal state of the subset to the state when the subset was created via the function IWeightedStatistics::createSubset. When calling this function, the current state must not be purged entirely, but it must be cached and made available for use by the functions evaluateAccumulated and evaluateUncoveredAccumulated.

This function may be invoked multiple times with one or several calls to addToSubset in between, which is supposed to update the previously cached state by accumulating the current one, i.e., the accumulated cached state should be the same as if resetSubset would not have been called at all.

virtual std::unique_ptr<IStatisticsUpdateCandidate> calculateScoresAccumulated() = 0

Calculates and returns the scores to be predicted by a rule that covers all statistics that have been added to the subset via the function addToSubset, as well as a numerical score that assesses the quality of the predicted scores. All statistics that have been added since the subset was created via the function IWeightedStatistics::createSubset are taken into account even if the function resetSubset was called since then.

Returns:

An unique pointer to an object of type IStatisticsUpdateCandidate that stores the scores to be predicted by the rule for each considered output, as well as a numerical score that assesses their overall quality

virtual std::unique_ptr<IStatisticsUpdateCandidate> calculateScoresUncovered() = 0

Calculates and returns the scores to be predicted by a rule that covers all statistics that correspond to the difference between the statistics that have been added to the subset via the function addToSubset and those that have been marked as covered via the function IWeightedStatistics::addCoveredStatistic or IWeightedStatistics::removeCoveredStatistic, as well as a numerical score that assesses the quality of the predicted scores.

Returns:

An unique pointer to an object of type IStatisticsUpdateCandidate that stores the scores to be predicted by the rule for each considered output, as well as a numerical score that assesses their overall quality

virtual std::unique_ptr<IStatisticsUpdateCandidate> calculateScoresUncoveredAccumulated() = 0

Calculates and returns the scores to be predicted by a rule that covers all statistics that correspond to the difference between the statistics that have been added to the subset via the function addToSubset and those that have been marked as covered via the function IWeightedStatistics::addCoveredStatistic or IWeightedStatistics::removeCoveredStatistic, as well as a numerical score that assesses the quality of the predicted scores. All statistics that have been added since the subset was created via the function IWeightedStatistics::createSubset are taken into account even if the function resetSubset was called since then.

Returns:

An unique pointer to an object of type IStatisticsUpdateCandidate that stores the scores to be predicted by the rule for each considered output, as well as a numerical score that assesses their overall quality