Class Hyperspace

Class Documentation

class Hyperspace

Represents a hyperspace composed of all the possible hyperplanes in this space and that manages a hyperbox and its associated discriminative hyperplanes.

This class encapsulates the attributes and methods needed to handle a hyperspace which includes a hyperbox and the possible hyperplanes in the feature space that discriminate between different classes of samples.

Public Functions

Hyperspace()

Default constructor for Hyperspace.

explicit Hyperspace(const std::vector<std::vector<double>> &matHypLocus)

Constructs a Hyperspace object with a specified matrix of possible hyperplanes.

Constructs a Hyperspace object with a specified 2D vector of doubles of possible hyperplanes.

Parameters:

matHypLocus – 2D vector of doubles representing all the possible hyperplanes.

std::shared_ptr<Hyperbox> getHyperbox() const

Gets the hyperbox associated with the hyperspace.

Returns:

Shared pointer to the Hyperbox object.

std::vector<std::vector<double>> getHyperLocus() const

Gets the matrix of possible hyperplanes in the hyperspace.

Gets the 2D vector of doubles of possible hyperplanes in the hyperspace.

Returns:

2D vector of doubles representing all the possible hyperplanes.

Rule ruleExtraction(std::vector<double> &mainSampleData, const int mainSamplePred, double ruleAccuracy, double ruleConfidence, const std::vector<double> &mus = std::vector<double>(), const std::vector<double> &sigmas = std::vector<double>(), const std::vector<int> &normalizationIndices = std::vector<int>())

Extracts a rule from the hyperspace based on the main sample data and its prediction.

Parameters:

  • mainSampleData – Data of the main sample.

  • mainSamplePred – Prediction of the main sample.

  • ruleAccuracy – Accuracy of the rule.

  • ruleConfidence – Confidence of the rule.

  • mus – Means for the denormalization of the rule values (optional).

  • sigmas – Standard deviations for the denormalization of the rule values (optional).

  • normalizationIndices – Indices for the denormalization of the rule values (optional).

Returns:

A Rule object representing the extracted rule.

double computeRuleAccuracy(std::vector<int> &trainPreds, std::vector<int> &trainTrueClass, bool hasTrueClasses, bool mainSampleCorrect = false) const

Computes the accuracy of the rule with respect to the training predictions and true classes of the covered samples.

Parameters:

  • trainPreds – Predictions of the training data.

  • trainTrueClass – True classes of the training data.

  • hasTrueClasses – Boolean indicating if true classes are available.

  • mainSampleCorrect – Boolean indicating if the main sample is correctly classified.

Returns:

The accuracy of the rule.

double computeRuleConfidence(std::vector<std::vector<double>> &trainOutputValuesPredictions, const int mainSamplePred, double mainSamplePredValue = -1.0) const

Computes the confidence of the rule with respect to the main sample prediction and training output values of the covered samples.

Parameters:

  • trainOutputValuesPredictions – Output values of the training data predictions.

  • rulePred – Prediction of the rule.

  • mainSamplePredValueOnRulePred – Output value of the main sample prediction (optional).

Returns:

The confidence of the rule.