Index (Core API)

A B C D E F G H I J L M N P R S T V W

A

Accuracy - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the accuracy of predictions based on a ConfusionMatrix.
Accuracy() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Accuracy
add(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds the elements of another confusion matrix to this confusion matrix.
add(double, double, double, double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds specific values to the elements of the confusion matrix.
add(Condition) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
add(Rule) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
addAll(Collection<? extends Condition>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
addAll(Collection<? extends Rule>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
addElement(ConfusionMatrix.Element) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a new ConfusionMatrix.Element with weight 1 to the confusion matrix.
addElement(ConfusionMatrix.Element, double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a new ConfusionMatrix.Element with a certain weight to the confusion matrix.
addFalseNegative() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a false negative with weight 1.
addFalseNegative(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a false negative with a certain weight.
addFalsePositive() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a false positive with weight 1.
addFalsePositive(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a false positive with a certain weight.
addTrueNegative() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a true negative with weight 1.
addTrueNegative(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a true negative with a certain weight.
addTruePositive() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a true positive with weight 1.
addTruePositive(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Adds a true positive with a certain weight.
aggregate(ConfusionMatrix, ConfusionMatrix) - Static method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Creates and returns a confusion matrix that results from element-wise aggregating two existing confusion matrices.
Attribute - Interface in de.tud.ke.mrapp.rulelearning.core.model: Defines the interface, a class that represents an attribute, i.e.
AttributeHashMap - Class in de.tud.ke.mrapp.rulelearning.core.model: An AttributeMap that uses a hash map for storing attribute-value mappings.
AttributeHashMap() - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap: Creates an empty attribute map.
AttributeHashMap(int) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap: Creates an empty attribute map with a specific size.
AttributeMap - Interface in de.tud.ke.mrapp.rulelearning.core.model: Defines the interface, a class that maps Attributes to certain values, must implement.

B

Body - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: The body (adjacent) of a Rule.
Body() - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Body: Creates an empty body, whose conditions are concatenated using conjunctions.
Body(boolean) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Body: Creates an empty body, whose conditions are concatenated using either conjunctions or disjunctions.
Body(Condition...) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Body: Creates a new body that consists of several conditions that are concatenated using conjunctions.
Body(boolean, Condition...) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Body: Creates a new body that consists of several conditions that are concatenated using either conjunctions or disjunctions.
Body(Iterable<? extends Condition>) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Body: Creates a new body that consists of several conditions that are concatenated using conjunctions.
Body(boolean, Iterable<? extends Condition>) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Body: Creates a new body that consists of several conditions that are concatenated using either conjunctions or disjunctions.

C

clear() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Clears the confusion matrix by setting all elements to zero.
clear() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
clear() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
Comparator(Heuristic) - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic.Comparator: Creates a new comparator that allows to compare Measurables.
Comparator(Heuristic, TieBreaker<T>) - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic.Comparator: Creates a new comparator that allows to compare Measurables and uses a specific TieBreaker.
compare(T, T) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic.Comparator
Condition - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: An abstract base class for all conditions that allow to check whether a value that is associated with an Attribute satisfies a certain condition.
Condition(Attribute, double) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition: creates a new condition.
ConditionSet - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: An abstract base class for all classes that consist of several (unordered) Conditions.
ConditionSet() - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet: Creates an empty set of conditions.
ConditionSet(Condition...) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet: Creates a new set of conditions that consists of several conditions.
ConditionSet(Iterable<? extends Condition>) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet: Creates a new set of conditions that consists of several conditions.
ConfusionMatrix - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A 2-dimensional confusion matrix that stores how many true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) have been predicted by a classifier.
ConfusionMatrix() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Creates an empty confusion matrix, where all elements are set to zero.
ConfusionMatrix(ConfusionMatrix) - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Creates a new confusion matrix by copying an existing one.
ConfusionMatrix(double, double, double, double) - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Creates a new confusion matrix, where the individual elements are set to certain values.
ConfusionMatrix.Element - Enum in de.tud.ke.mrapp.rulelearning.core.heuristics: Contains all types of elements of a ConfusionMatrix.
contains(Attribute) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.AttributeMap: Returns, whether a value is associated with a specific Attribute, or not.
contains(int) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.AttributeMap: Returns, whether a value is associated with the Attribute with a specific index, or not.
contains(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
contains(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
containsAll(Collection<?>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
containsAll(Collection<?>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet

D

de.tud.ke.mrapp.rulelearning.core.heuristics - package de.tud.ke.mrapp.rulelearning.core.heuristics
de.tud.ke.mrapp.rulelearning.core.model - package de.tud.ke.mrapp.rulelearning.core.model
de.tud.ke.mrapp.rulelearning.core.model.rules - package de.tud.ke.mrapp.rulelearning.core.model.rules

E

equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Body
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleCollection
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
equals(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Accuracy
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.HammingLoss
evaluate(Measurable) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic: Calculates the heuristic value of a Measurable.
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic: Calculates a heuristic values based on a ConfusionMatrix.
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.JaccardIndex
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Laplace
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Precision
evaluate(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Recall

F

FMeasure - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the F-Measure of predictions based on a ConfusionMatrix.
FMeasure() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure: Creates the default f-measure with the beta-parameter set to 1 (also known as F1-measure).
FMeasure(double) - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure: Creates a new f-measure with a specific beta-parameter.
forPrediction(double, double) - Static method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Creates and returns an element of a ConfusionMatrix for a specific prediction and the corresponding ground truth.
forSingleClass(Attribute, double) - Static method in interface de.tud.ke.mrapp.rulelearning.core.model.GroundTruth: Creates and returns a GroundTruth for a single class.
forSingleClass(int, double) - Static method in interface de.tud.ke.mrapp.rulelearning.core.model.GroundTruth: Creates and returns a GroundTruth for a single class.

G

get(Attribute) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet: Returns the condition that corresponds to a specific Attribute, if such condition is contained by the set.
get(int) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet: Returns the condition that corresponds to the Attribute with a specific index, if such condition is contained by the set.
getAllMatches(AttributeMap) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.rules.Proposition: Returns a set that contains all predicates of the proposition that match a specific example (given as an AttributeMap).
getAttribute() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition: Returns the attribute, the condition corresponds to.
getBeta() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure: Returns the value of the beta-parameter.
getBody() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Returns the body of the rule.
getComparator() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition: Returns the comparator that is used by the condition.
getConfusionMatrix() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix
getConfusionMatrix() - Method in interface de.tud.ke.mrapp.rulelearning.core.heuristics.Measurable: Returns the confusion matrix that can be used to calculate the heuristic value of the Measurable.
getConfusionMatrix(Attribute) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Head
getConfusionMatrix(int) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Head
getConfusionMatrix() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Returns the confusion matrix that specifies how many true positives, false positives, true negatives, and false negatives are predicted by the rule.
getCorrectPredictions() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of correct predictions.
getDefaultPrediction() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleCollection: Returns the default prediction that is made, if no rule covers an example.
getElement(ConfusionMatrix.Element) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of a specific ConfusionMatrix.Element.
getFalseNegatives() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of false negatives.
getFalsePositives() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of false positives.
getFirstMatch(AttributeMap) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.rules.Proposition: Returns the first predicate of the proposition that matches a specific example (given as an AttributeMap), if such a predicate is part of the proposition.
getGroundTruth() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Instance: Returns the ground truth of the instance.
getGroundTruth() - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
getHead() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Returns the head of the rule.
getIndex() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Attribute: Returns the (unique) index of the attribute.
getM() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate: Returns the value of the m-parameter.
getName() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Attribute: Returns the (unique) name of the attribute.
getNegativeExamples() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of negative examples.
getNegativePredictions() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of negative predictions.
getPositiveExamples() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of positive examples.
getPositivePredictions() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of positive predictions.
getTotalPredictions() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the total number of predictions.
getTrueNegatives() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of true negatives.
getTruePositives() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of true positives.
getValue(int) - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap
getValue(Attribute) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.AttributeMap: Returns the value that is associated with a specific Attribute, if available.
getValue(int) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.AttributeMap: Returns the value that is associated with the Attribute with a specific index.
getValue() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition: Returns the value of the condition.
getValue(int) - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
getWeight() - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
getWeight() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Weighted: Returns the weight of the object.
getWrongPredictions() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns the number of wrong predictions.
GroundTruth - Interface in de.tud.ke.mrapp.rulelearning.core.model: Defines the interface, all classes that represent the ground truth of an Instance, must implement.

H

HammingLoss - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the heuristic value of predictions according to the Hamming Loss based on a ConfusionMatrix.
HammingLoss() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.HammingLoss
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Body
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleCollection
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
hashCode() - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
Head - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: The head (consequence) of a Rule.
Head() - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Head: Creates an empty head.
Head(Condition...) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Head: Creates a new head that consists of several conditions.
Head(Iterable<? extends Condition>) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Head: Creates a new head that consists of several conditions.
Heuristic - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: Defines the interface, a heuristic that calculates a heuristic value based on a ConfusionMatrix, must implement.
Heuristic() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic
Heuristic.Comparator<T extends Measurable> - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A comparator that allows to compare Measurables based on their heuristic values according to a certain Heuristic.

I

Instance - Interface in de.tud.ke.mrapp.rulelearning.core.model: Defines the interface, all classes that represent instances (also referred to as "examples") must implement.
isConjunctive() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Body
isConjunctive() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.rules.Proposition: Returns, whether the proposition is conjunctive, or not.
isConjunctive() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule
isConjunctive() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
isCorrectPrediction() - Method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns, whether the element represents a correct prediction, or not.
isDisjunctive() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.rules.Proposition: Returns, whether the proposition is disjunctive, or not.
isEmpty() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Returns, whether the confusion matrix is empty, i.e.
isEmpty() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.AttributeMap: Returns, whether the map is empty, or not.
isEmpty() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
isEmpty() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Accuracy
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.HammingLoss
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic: Returns, whether the heuristic is a gain metric, i.e.
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.JaccardIndex
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Laplace
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Precision
isGainMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Recall
isLossMetric() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic: Returns, whether the heuristic is a loss metric, i.e.
isNegativeExample() - Method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns, whether the element corresponds to a negative example, or not.
isNegativePrediction() - Method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns, whether the element represents a negative prediction, or not.
isNominal() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Attribute: Returns, whether the attribute is nominal, or not.
isNumeric() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Attribute: Returns, whether the attribute is numeric, or not.
isPositiveExample() - Method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns, whether the element corresponds to a positive example, or not.
isPositivePrediction() - Method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns, whether the element represents a positive prediction, or not.
isWrongPrediction() - Method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns, whether the element represents a wrong prediction, or not.
iterator() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
iterator() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule
iterator() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet

J

JaccardIndex - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the heuristic value of predictions according to the Jaccard index based on a ConfusionMatrix.
JaccardIndex() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.JaccardIndex

L

Laplace - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the heuristic value of predictions according to the Laplace metric based on a ConfusionMatrix.
Laplace() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Laplace

M

maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Accuracy
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.HammingLoss
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic: Returns the maximum heuristic value that may be calculated by the heuristic.
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.JaccardIndex
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Laplace
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Precision
maxValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Recall
Measurable - Interface in de.tud.ke.mrapp.rulelearning.core.heuristics: Defines the interface, a class for which heuristic values can be calculated according to different Heuristics, must implement.
MEstimate - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the m-estimate of predictions based on a ConfusionMatrix.
MEstimate() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate: Creates the default m-estimate with the m-parameter set to 22.466.
MEstimate(double) - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate: Creates a new m-estimate with a specific m-parameter.
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Accuracy
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.HammingLoss
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Heuristic: Returns the minimum heuristic value that may be calculated by the heuristic.
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.JaccardIndex
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Laplace
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Precision
minValue() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Recall

N

NominalCondition - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: A nominal condition that allows to check whether the value that is associated with a nominal Attribute is equal to a certain value.
NominalCondition(Attribute, double) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.NominalCondition: Creates a new nominal condition.
NumericCondition - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: A numeric condition that allows to check whether the value that is associated with a numeric Attribute is less, greater or equal to a certain value.
NumericCondition(Attribute, NumericCondition.Comparator, double) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition: Creates a new numeric condition.
NumericCondition.Comparator - Enum in de.tud.ke.mrapp.rulelearning.core.model.rules: All possible comparators that can be used by a NumericCondition to compare two values.

P

Precision - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the precision of predictions based on a ConfusionMatrix.
Precision() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Precision
Predicate - Interface in de.tud.ke.mrapp.rulelearning.core.model.rules: Defines the interface, a predicate, i.e.
Proposition<T extends Predicate> - Interface in de.tud.ke.mrapp.rulelearning.core.model.rules: Defines the interface, a logical proposition consisting of concatenations of Predicates, e.g.
putValue(Attribute, double) - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap: Adds a new attribute-value mapping to the attribute map.
putValue(int, double) - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap: Adds a new attribute-value mapping to the attribute map.

R

Recall - Class in de.tud.ke.mrapp.rulelearning.core.heuristics: A heuristic that calculates the recall (also known as true positive rate) of predictions based on a ConfusionMatrix.
Recall() - Constructor for class de.tud.ke.mrapp.rulelearning.core.heuristics.Recall
remove(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
remove(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Head
remove(Object) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
removeAll(Collection<?>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
removeAll(Collection<?>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
removeElement(ConfusionMatrix.Element) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a specific ConfusionMatrix.Element with weight 1 from the confusion matrix.
removeElement(ConfusionMatrix.Element, double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a specific ConfusionMatrix.Element with a certain weight from the confusion matrix.
removeFalseNegative() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a false negative with weight 1.
removeFalseNegative(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a false negative with a certain weight.
removeFalsePositive() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a false positive with weight 1.
removeFalsePositive(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a false positive with a certain weight.
removeTrueNegative() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a true negative with weight 1.
removeTrueNegative(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a true negative with a certain weight.
removeTruePositive() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a true positive with weight 1.
removeTruePositive(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Removes a true positive with a certain weight.
retainAll(Collection<?>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
retainAll(Collection<?>) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
Rule - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: A rule that consists of a Body and a Head.
Rule() - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Creates a new conjunctive rule with an empty body and an empty head.
Rule(Body) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Creates a new rule with a specific body and an empty head.
Rule(Head) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Creates a new conjunctive rule with an empty body and a specific head.
Rule(Body, Head) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule: Creates a new rule with a specific body and head.
RuleCollection - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: An abstract class for all classes that consist of several (ordered or unordered) Rules.
RuleCollection(Head) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleCollection: Creates an empty collection of rules.
RuleSet - Class in de.tud.ke.mrapp.rulelearning.core.model.rules: A rule set that consists of several (unordered) Rules.
RuleSet(Head) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet: Creates an empty rule set.
RuleSet(Head, Rule...) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet: Creates a rule set that contains several rules.
RuleSet(Head, Iterable<? extends Rule>) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet: Creates a new rule set that contains several rules.

S

set(ConfusionMatrix) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Sets the elements of the confusion matrix to those of another confusion matrix.
set(double, double, double, double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Sets the elements of the confusion matrix to specific values.
setFalseNegatives(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Sets the number of false negatives.
setFalsePositives(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Sets the number of false positives.
setTrueNegatives(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Sets the number of true negatives.
setTruePositives(double) - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix: Sets the number of true positives.
setWeight(double) - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
setWeight(double) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.Weighted: Sets the weight of the object.
size() - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap
size() - Method in interface de.tud.ke.mrapp.rulelearning.core.model.AttributeMap: Returns the number of attribute-value mappings that are contained by the map.
size() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
size() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
size() - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance

T

test(AttributeMap) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition
test(double, double) - Method in enum de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition.Comparator: Tests if two values satisfy the comparator.
test(AttributeMap) - Method in interface de.tud.ke.mrapp.rulelearning.core.model.rules.Proposition
testValue(double) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Condition: The method that must be overridden by implementing subclasses in order to check whether a specific value satisfies the condition or not.
testValue(double) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NominalCondition
testValue(double) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition
TieBreaker<T extends Measurable> - Interface in de.tud.ke.mrapp.rulelearning.core.heuristics: Defines the interface, a tie breaker, that allows to decide which one of two Measurables that evaluate to the same heuristic value is considered to be better, must implement.
toArray() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
toArray(T[]) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.ConditionSet
toArray() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
toArray(T[]) - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Accuracy
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.FMeasure
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.HammingLoss
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.JaccardIndex
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Laplace
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.MEstimate
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Precision
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.heuristics.Recall
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.AttributeHashMap
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Body
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Head
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NominalCondition
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.Rule
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.rules.RuleSet
toString() - Method in class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance
TrainingInstance - Class in de.tud.ke.mrapp.rulelearning.core.model: Represents a training instance (also referred to as as a training example).
TrainingInstance(AttributeMap, GroundTruth) - Constructor for class de.tud.ke.mrapp.rulelearning.core.model.TrainingInstance: Creates a new training instance with weight 1.

V

valueOf(String) - Static method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition.Comparator: Returns the enum constant of this type with the specified name.
values() - Static method in enum de.tud.ke.mrapp.rulelearning.core.heuristics.ConfusionMatrix.Element: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum de.tud.ke.mrapp.rulelearning.core.model.rules.NumericCondition.Comparator: Returns an array containing the constants of this enum type, in the order they are declared.

W

Weighted - Interface in de.tud.ke.mrapp.rulelearning.core.model: Defines the interface, all classes that allows to assign a weight to their instantiations, must implement.

A B C D E F G H I J L M N P R S T V W