Package | Description |
---|---|
pl.poznan.put.cs.idss.jrs.approximations | |
pl.poznan.put.cs.idss.jrs.apriori | |
pl.poznan.put.cs.idss.jrs.cbr.core | |
pl.poznan.put.cs.idss.jrs.cbr.test | |
pl.poznan.put.cs.idss.jrs.classifiers | |
pl.poznan.put.cs.idss.jrs.core |
Contains base classes and interfaces common for whole data management module.
|
pl.poznan.put.cs.idss.jrs.core.mem |
Implements decision table stored in memory with random and serial access.
|
pl.poznan.put.cs.idss.jrs.dominance | |
pl.poznan.put.cs.idss.jrs.ensembles | |
pl.poznan.put.cs.idss.jrs.indiscernibility | |
pl.poznan.put.cs.idss.jrs.jmaf | |
pl.poznan.put.cs.idss.jrs.jmaf.reducts | |
pl.poznan.put.cs.idss.jrs.lorenz | |
pl.poznan.put.cs.idss.jrs.pct | |
pl.poznan.put.cs.idss.jrs.ranking | |
pl.poznan.put.cs.idss.jrs.reducts | |
pl.poznan.put.cs.idss.jrs.rules | |
pl.poznan.put.cs.idss.jrs.utilities | |
pl.poznan.put.cs.idss.jrs.validators | |
pl.poznan.put.cs.idss.jrs.wrappers |
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
ApproximatedEntity.getMemoryContainer() |
Gets reference to memory container for which this entity is defined
|
MemoryContainer |
ApproximatedEntityContainer.getMemoryContainer() |
Gets reference to memory container for which this container is defined
|
Modifier and Type | Method | Description |
---|---|---|
byte[] |
ConsistencyMeasure.drawAttributes(MemoryContainer container,
int number) |
Constructor | Description |
---|---|
ConfirmatoryMonotonicDecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer) |
|
ConfirmatoryMonotonicDecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer,
boolean oppositeSet) |
|
ConfirmatoryMonotonicDecisionClassContainer(MemoryContainer memoryContainer) |
Constructor for
ConfirmatoryMonotonicDecisionClassContainer class. |
ConfirmatoryMonotonicDecisionClassContainer(MemoryContainer memoryContainer,
int decisionAttributeNumber) |
Constructor for
ConfirmatoryMonotonicDecisionClassContainer class. |
ConfirmatoryMonotonicUnion(int type,
int decisionCriterionNumber,
Field basicClass,
MemoryContainer memoryContainer) |
|
ConfirmatoryMonotonicUnionContainer(MemoryContainer memoryContainer) |
Constructor for
ConfirmatoryMonotonicUnionContainer class. |
ConfirmatoryMonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber) |
Constructor for
ConfirmatoryMonotonicUnionContainer class. |
ConfirmatoryMonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber,
ConfirmatoryMonotonicUnion[] upwardUnions,
ConfirmatoryMonotonicUnion[] downwardUnions) |
Constructor for
ConfirmatoryMonotonicUnionContainer class. |
DecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer) |
Constructs decision class, using decision attribute with given number, given basic decision class and
given reference to memory container for which this decision class is defined.
|
DecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer,
boolean oppositeSet) |
Constructs decision class, using decision attribute with given number, given basic decision class and
given reference to memory container for which this decision class is defined.
|
DecisionClassContainer(MemoryContainer memoryContainer) |
Constructor for
DecisionClassContainer class.Stores given reference to memory container, calculates decision attribute's number, calculates all decision attribute's values and creates all decision classes stored within this container. The algorithm of determining decision attribute's number (index) is as follows: 1) if there is more than one active decision attribute without preference type assigned, then InvalidValueException is thrown,2) if there is exactly one active decision attribute without preference type assigned, then it is chosen, 3) if there is no active decision attribute without preference type assigned, then: 3a) if there is more than one active decision attribute with preference type assigned, then InvalidValueException is thrown,3b) if there is exactly one active decision attribute with preference type assigned, then it is chosen, 3c) if there is no active decision attribute with preference type assigned, then InvalidValueException is thrown.Found active decision attribute becomes the one for which this decision class container is defined. All other exceptions that may be thrown by this method come from protected DecisionClassContainer.validateMemoryContainer()
and validateDecisionAttributeNumber methods, which are called here. |
DecisionClassContainer(MemoryContainer memoryContainer,
int decisionAttributeNumber) |
Constructor for
DecisionClassContainer class.Stores given reference to memory container and decision attribute's number, calculates all decision attribute's values and creates all decision classes stored within this container. All exceptions that may be thrown by this method come from protected DecisionClassContainer.validate() method,
which is called here. |
MonotonicDecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer) |
|
MonotonicDecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer,
boolean oppositeSet) |
|
MonotonicDecisionClassContainer(MemoryContainer memoryContainer) |
Constructor for
MonotonicDecisionClassContainer class. |
MonotonicDecisionClassContainer(MemoryContainer memoryContainer,
int decisionAttributeNumber) |
Constructor for
MonotonicDecisionClassContainer class. |
MonotonicUnion(int type,
int decisionCriterionNumber,
Field basicClass,
MemoryContainer memoryContainer) |
Constructs union of given type, using decision criterion with given number, given basic decision class and
given reference to memory container for which this union is defined.
|
MonotonicUnionContainer(MemoryContainer memoryContainer) |
Constructor for
MonotonicUnionContainer class. |
MonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber) |
Constructor for
MonotonicUnionContainer class. |
MonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber,
MonotonicUnion[] upwardUnions,
MonotonicUnion[] downwardUnions) |
Constructor for
MonotonicUnionContainer class. |
PairDecisionMonotonicUnion(int type,
int decisionCriterionNumber,
Field basicClass,
MemoryContainer memoryContainer) |
Constructs union of given type, using decision criterion with given number, given basic decision class and
given reference to memory container for which this union is defined.
|
PairDecisionMonotonicUnionContainer(MemoryContainer memoryContainer) |
Constructor for
StandardUnionContainer class. |
PairDecisionMonotonicUnionContainer(MemoryContainer memoryContainer,
boolean limitNumberOfUnions) |
Constructor for
PairDecisionMonotonicUnionContainer class. |
PairDecisionMonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber) |
Constructor for
PairDecisionMonotonicUnionContainer class. |
PairDecisionMonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber,
boolean limitNumberOfUnions) |
Constructor for
PairDecisionMonotonicUnionContainer class. |
PairDecisionMonotonicUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber,
PairDecisionMonotonicUnion[] upwardUnions,
PairDecisionMonotonicUnion[] downwardUnions) |
Constructor for
MonotonicUnionContainer class. |
StandardDecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer) |
|
StandardDecisionClass(int decisionAttributeNumber,
Field basicClass,
MemoryContainer memoryContainer,
boolean oppositeSet) |
|
StandardDecisionClassContainer(MemoryContainer memoryContainer) |
Constructor for
StandardDecisionClassContainer class. |
StandardDecisionClassContainer(MemoryContainer memoryContainer,
int decisionAttributeNumber) |
Constructor for
StandardDecisionClassContainer class. |
StandardJmafUnion(int type,
int decisionCriterionNumber,
Field basicClass,
MemoryContainer memoryContainer) |
|
StandardJmafUnionContainer(MemoryContainer memoryContainer) |
|
StandardJmafUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber) |
|
StandardUnion(int type,
int decisionCriterionNumber,
Field basicClass,
MemoryContainer memoryContainer) |
Constructor for
StandardUnion class. |
StandardUnionContainer(MemoryContainer memoryContainer) |
Constructor for
StandardUnionContainer class. |
StandardUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber) |
Constructor for
StandardUnionContainer class. |
StandardUnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber,
StandardUnion[] upwardUnions,
StandardUnion[] downwardUnions) |
Constructor for
StandardUnionContainer class. |
UnionContainer(MemoryContainer memoryContainer) |
Constructor for
UnionContainer class.Stores given reference to memory container, calculates decision criterion number and all basic classes and creates all upward and downward unions. |
UnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber) |
Constructor for
UnionContainer class.Stores given reference to memory container and decision criterion number, calculates all basic classes and creates all upward and downward unions. All exceptions that may be thrown by this method come from protected UnionContainer.validate() method,
which is called here. |
UnionContainer(MemoryContainer memoryContainer,
int decisionCriterionNumber,
Union[] upwardUnions,
Union[] downwardUnions) |
Constructor for
UnionContainer class.Stores given reference to memory container, decision criterion number, upward unions, downward unions and calculates all basic classes. This method may throw described exceptions as well as exceptions which come from protected UnionContainer.validate() method,
which is called here. |
Modifier and Type | Method | Description |
---|---|---|
int |
Group.calculateCoverWith(int complexNumber,
int[] examplesNumbers,
MemoryContainer container) |
Calculates cover for complex with given number
|
void |
Group.circlesCrossesOuter(int[] samples1,
int[] samples2,
MemoryContainer container) |
Performs Circles and crosses minimality test in groups.
|
RulesContainer |
DomAprioriAlg.generateRules(MemoryContainer container,
int ruletype,
double strengthPercent,
double consistencyLevel,
int maxLegnth) |
Generates rules according to DomApriori algorithm
|
boolean |
Group.isCovered(int n_ComplexNumber,
Complex givenCommonPart,
ElementaryCondition EC,
MemoryContainer container) |
|
Rule |
Complex.toForemkaRule(Union union,
int typeOfRule,
MemoryContainer learningMemoryContainer,
double consistencyLevel,
ApproximatedEntityDecisionsPredictor unionsDecisionsPredictor) |
Creates and returns
ForemkaRule object that represents this complex |
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
InformationTable2SimilarityTableConverter.convert(MemoryContainer informationTable,
PairSimilarity[] similarity,
int referenceObjectNumber) |
Constructs similarity table from given information table,
using given array with similarity functions and given reference object number
|
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
InformationTable2SimilarityTableConverter.convert(MemoryContainer informationTable,
PairSimilarity[] similarity,
int referenceObjectNumber) |
Constructs similarity table from given information table,
using given array with similarity functions and given reference object number
|
static int |
SimilarityTableDetector.getIndexOfDecisionAttribute(MemoryContainer similarityTable) |
Checks if given similarity table has a criterion storing decision value of limit object and if so - returns its index.
|
static int |
SimilarityTableDetector.getReferenceObjectNumber(MemoryContainer similarityTable) |
Gets index of reference object for which given similarity table has been created
|
static boolean |
SimilarityTableDetector.isSimilarityTable(MemoryContainer memoryContainer) |
Tests if given memory container is a similarity table.
|
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
Cadabra.classify2(MemoryContainer newData,
RulesContainer rulesContainer,
PairSimilarity[] similarities) |
|
static MemoryContainer |
Cadabra.classify3(MemoryContainer newData,
RulesContainer rulesContainer,
PairSimilarity[] similarities) |
Modifier and Type | Method | Description |
---|---|---|
static java.lang.String[][] |
Cadabra.classify(MemoryContainer newData,
RulesContainer rulesContainer,
PairSimilarity[] similarities) |
|
static MemoryContainer |
Cadabra.classify2(MemoryContainer newData,
RulesContainer rulesContainer,
PairSimilarity[] similarities) |
|
static MemoryContainer |
Cadabra.classify3(MemoryContainer newData,
RulesContainer rulesContainer,
PairSimilarity[] similarities) |
|
static UnionContainer |
Cadabra.createTableBFullWithApproximationsETC(MemoryContainer informationTable,
PairSimilarity[] similarity,
int[] refObject) |
|
static java.util.Vector<ConeContainer> |
Cadabra.getAllCones(MemoryContainer informationTable,
PairSimilarity[] similarity,
int[] refObject) |
Constructor | Description |
---|---|
Cadabra(MemoryContainer informationTable,
PairSimilarity[] similarity,
int[] refObject) |
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
ClassificationResultsContainer.getTestContainer() |
Gets test dataset
|
Modifier and Type | Method | Description |
---|---|---|
void |
ClassificationResultsFoldValidationContainer.addFold(Classifier classifier,
MemoryContainer testContainer) |
Adds results of classification of another fold to the results stored in
the container.
|
void |
RulesHybridClassificationMethod.setMemoryContainer(MemoryContainer memoryContainer) |
|
void |
RulesVCDRSAClassificationMethod.setMemoryContainer(MemoryContainer memoryContainer) |
|
void |
VCDRSAMethod.setMemoryContainer(MemoryContainer memoryContainer) |
|
void |
ClassificationResultsContainer.setTestContainer(MemoryContainer testContainer) |
Sets test dataset.
|
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
InstancesConverter.convertInstancesToMemoryContainer(weka.core.Instances instances) |
Method converting given weka's Instances object to JRS MemoryContainer.
|
static MemoryContainer |
Transfer.loadIsf(java.io.InputStream stream) |
Loads data in ISF format from a stream into a memory container.
|
static MemoryContainer |
Transfer.loadIsf(java.io.InputStream stream,
ParseLog log) |
Loads data in ISF format from a stream into a memory container.
|
static MemoryContainer |
Transfer.loadIsf(java.lang.String filename) |
Loads data from an ISF file into a memory container.
|
static MemoryContainer |
Transfer.loadIsf(java.lang.String filename,
ParseLog log) |
Loads data from an ISF file into a memory container.
|
static MemoryContainer |
Transfer.loadXml(java.io.InputStream stream) |
Loads data in XML format from a stream into a memory container.
|
static MemoryContainer |
Transfer.loadXml(java.io.InputStream stream,
ParseLog log) |
Loads data in XML format from a stream into a memory container.
|
static MemoryContainer |
Transfer.loadXml(java.lang.String filename) |
Loads data from an XML file into a memory container.
|
static MemoryContainer |
Transfer.loadXml(java.lang.String filename,
ParseLog log) |
Loads data from an XML file into a memory container.
|
Modifier and Type | Method | Description |
---|---|---|
static void |
Transfer.saveIsf(java.io.OutputStream stream,
MemoryContainer container) |
Saves data contained in a memory container to a stream in ISF format.
|
static void |
Transfer.saveIsf(java.lang.String filename,
MemoryContainer container) |
Saves data contained in a memory container to an ISF file.
|
static void |
Transfer.saveSimpleIsf(java.io.OutputStream stream,
MemoryContainer container) |
Saves data contained in a memory container to a stream in ISF format.
|
static void |
Transfer.saveSimpleIsf(java.lang.String filename,
MemoryContainer container) |
Saves data contained in a memory container to an ISF file.
|
static void |
Transfer.saveXml(java.io.OutputStream stream,
MemoryContainer container) |
Saves data contained in a memory container to a stream in XML format.
|
static void |
Transfer.saveXml(java.lang.String filename,
MemoryContainer container) |
Saves data contained in a memory container to an XML file.
|
Modifier and Type | Class | Description |
---|---|---|
class |
AllDirectionsMemoryContainer |
Extention of
MemoryContainer that can produce memory container with all possible orders of preferences for all attributes. |
class |
RandomizableMemoryContainer |
Memory container that can be randomized.
|
class |
WekaTransferableMemoryContainer |
Extention of
MemoryContainer that can produce Weka's Instances object. |
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
AllDirectionsMemoryContainer.addAllDirections() |
Converts memory container to all possible orders of preference.
|
MemoryContainer |
RandomizableMemoryContainer.getSubContainer(int startIndex,
int endIndex) |
Provides a sub container that is composed from all examples between
parameters
startIndex and endIndex . |
static MemoryContainer |
TestMemoryContainerDeliverer.getTestMemoryContainer() |
Gets reference to stored test memory container
|
Modifier and Type | Method | Description |
---|---|---|
static boolean |
MemoryContainersDescriptions.containsDescription(MemoryContainer memoryContainer) |
Checks if description of given memory container is stored.
|
static void |
MemoryContainersDescriptions.createAndStoreBasicDescription(MemoryContainer memoryContainer,
int memoryContainerType,
int evaluationSpaceUsed) |
Automatically creates and stores description (object of type
MemoryContainerDescription )
for given memory container.Assumes that: - information about how to convert ordinal values to cardinal values is not set (equal to null ),- information about how to convert differences of evaluations on conditional cardinal criteria to the degrees of the intensity of preference is not set (equal to null ).Additionally assumes that: a) in case of information table: - if Lorenz evaluation space is used, attribute is active, conditional, cardinal and has gain preference type assigned then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.SUM_OF_MIN_VALUES ,- if attribute is active, conditional or decision and of type SimpleField ,
then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.VALUE
(whatever evaluation space is used),- if attribute is active, conditional and of type PairField ,
then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.PAIR_OF_VALUES
(whatever evaluation space is used),- if Pareto evaluation space is used and attribute is active, conditional and of type SimilarityField ,
then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.SIMILARITY ,- if none of two above conditions is fulfilled, then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.OTHER .b) in case of PCT: - if attribute is active conditional cardinal criterion then: if Lorenz evaluation space is used and criterion is of type GAIN, then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.SUM_OF_MIN_PREFERENCE_INTENSITY_DEGREES ;
otherwise (for Pareto evaluation space) description of the meaning of such attribute is resolved to
AttributesMeaningsDescriptions.PREFERENCE_INTENSITY_DEGREE or AttributesMeaningsDescriptions.EVALUATIONS_DIFFERENCE ,
depending on criterion's preference type, type of criterion's initial value and, eventually, criterion's name,- if attribute is active, conditional and of type PairField ,
then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.PAIR_OF_VALUES
(whatever evaluation space is used),- if Pareto evaluation space is used and attribute is active, conditional and of type SimilarityField ,
then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.SIMILARITY ,- if attribute is active decision gain criterion of type FloatField ,
then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.VALUE ,- if none of two above conditions is fulfilled, then description of the meaning of such attribute is set to AttributesMeaningsDescriptions.OTHER . |
static int |
MemoryContainerAttrManager.getAttrCount(MemoryContainer memoryContainer) |
Gets quantity of attributes in memory container.
|
static Attribute[] |
MemoryContainerAttrManager.getAttributes(MemoryContainer memoryContainer) |
Gets attrbiutes from mememory container.
|
static java.lang.String |
MemoryContainerDecisionsManager.getDecisionAttributeName(MemoryContainer memoryContainer) |
Gets name of the only active decision attribute from given memory container.
|
static Field[] |
MemoryContainerDecisionsManager.getDecisionAttributeValues(MemoryContainer memoryContainer) |
Gets ordered list of all values of decision attribute present in given memory container.
|
static Field[] |
MemoryContainerDecisionsManager.getDecisionAttributeValues(MemoryContainer memoryContainer,
int decisionAttributeNumber) |
Gets ordered list of all unique values of decision attribute present in given memory container.
|
static java.lang.String |
MemoryContainerDecisionsManager.getDecisionAttributeValuesAsText(MemoryContainer memoryContainer) |
Gets ordered list of all values of decision attribute, present in given memory container,
in textual form.
|
static int |
MemoryContainerDecisionsManager.getFirstDecisionAttributeIndex(MemoryContainer memoryContainer) |
Gets index of the first active decision attribute from given memory container.
|
static int |
MemoryContainerAttrManager.getNumberOfTheOnlyActiveDecisionAttribute(MemoryContainer memoryContainer) |
Gets number of the only active decision attribute present in given memory container
or -1 if there is no exactly one active decision attribute
|
static int |
MemoryContainerAttrManager.getQuantityOfActiveDecisionAttributes(MemoryContainer memoryContainer) |
Gets quantity of active decision attributes in given memory container
|
static MemoryContainerDescription |
MemoryContainersDescriptions.retrieveDescription(MemoryContainer memoryContainer) |
Retrieves description for given memory container.
|
static java.lang.String |
MemoryContainersUniqueIdentifiers.retrieveId(MemoryContainer memoryContainer) |
Deprecated.
Retrieves id of given memory container
|
static void |
TestMemoryContainerDeliverer.setTestMemoryContainer(MemoryContainer testMemoryContainer) |
Stores given reference to test memory container.
|
static void |
MemoryContainersDescriptions.storeDescription(MemoryContainer memoryContainer,
MemoryContainerDescription memoryContainerDescription) |
Stores or replaces description for given memory container.
|
static void |
MemoryContainersUniqueIdentifiers.storeMemoryContainerAndIdPair(MemoryContainer memoryContainer,
java.lang.String id) |
Deprecated.
Stores (unique-reference-to-memory-container, unique-id) pair (1:1 projection).
|
Constructor | Description |
---|---|
AllDirectionsMemoryContainer(MemoryContainer memoryContainer) |
|
MemoryInput(MemoryContainer container) |
Assigns container to be read.
|
MemoryOutput(MemoryContainer container) |
Links input to the decision table stored in memory.
|
RandomizableMemoryContainer(MemoryContainer memoryContainer) |
|
WekaTransferableMemoryContainer(MemoryContainer memoryContainer) |
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
DominanceConesContainer.getMemoryContainer() |
Gets memory container for which this dominance cones container has been created
|
MemoryContainer |
DominanceConesForExample.getMemoryContainer() |
Gets memory container for which this object is created
|
Modifier and Type | Method | Description |
---|---|---|
java.util.HashSet<PairOfIndices> |
DominanceRelationCalculator.calculateDominanceRelation(MemoryContainer memoryContainer) |
Gets dominance relation, i.e., set of pairs of examples (a,b), where a, b belong to given data set, such that a D b.
|
java.util.HashSet<PairOfIndices> |
DominanceRelationCalculator.calculateDominanceRelation(MemoryContainer memoryContainer,
byte[] attributesMask) |
|
static int |
ParetoDominanceCalculator.checkDominance(int exampleNumber1,
int exampleNumber2,
MemoryContainer memoryContainer) |
Establishes presence or absence of Pareto dominance between two examples given by numbers.
|
static int |
ParetoDominanceCalculator.checkDominance(int exampleNumber1,
int exampleNumber2,
MemoryContainer memoryContainer,
byte[] attributesMask) |
Establishes presence or absence of Pareto dominance between two examples given by numbers.
|
static int |
ParetoDominanceCalculator.checkDominance(int exampleNumber1,
int exampleNumber2,
MemoryContainer memoryContainer,
int attributeNumber) |
Establishes presence or absence of Pareto dominance between two examples given by numbers, for one attribute only.
|
static boolean |
ParetoDominanceCalculator.dominates(int x,
int y,
MemoryContainer memoryContainer) |
Checks if the first object Pareto-dominates the second one w.r.t. given memory container.
|
static boolean |
ParetoDominanceCalculator.dominates(int x,
int y,
MemoryContainer memoryContainer,
byte[] attributesMask) |
Checks if the first object Pareto-dominates the second one w.r.t. given memory container.
This method wraps checkDominance(int, int, MemoryContainer) method - sets temporary mask
for attributes, gets result of checkDominance(int, int, MemoryContainer) method
(which uses temporary mask), deletes the mask and then returns processed result. |
static int[] |
DominanceConeCalculator.findNumbersOfDominanceConeExamples(int exampleNumber,
MemoryContainer memoryContainer,
int coneType) |
Finds numbers of all examples from dominance cone of type
DOMINATING or DOMINATED ,
starting at given example, using given reference to memory container. |
static int[] |
DominanceConeCalculator.findNumbersOfDominanceConeExamples(int exampleNumber,
MemoryContainer memoryContainer,
int coneType,
byte[] attributesMask) |
Finds numbers of all examples from dominance cone of type
DOMINATING or DOMINATED ,
starting at given example, using given reference to memory container. |
static int[] |
JmafDominanceConeCalculator.findNumbersOfDominanceConeExamples(int exampleNumber,
MemoryContainer memoryContainer,
int coneType) |
Finds numbers of all examples from dominance cone of type
DOMINATING or DOMINATED ,
starting at given example, using given reference to memory container. |
static int[] |
JmafDominanceConeCalculator.findNumbersOfDominanceConeExamples(int exampleNumber,
MemoryContainer memoryContainer,
int coneType,
byte[] attributesMask) |
Finds numbers of all examples from dominance cone of type
DOMINATING or DOMINATED ,
starting at given example, using given reference to memory container. |
static java.util.List<Example> |
Dominance.getPDominated(Example x,
MemoryContainer exampleSet,
Attribute[] attributes) |
|
static java.util.List<Example> |
Dominance.getPDominating(Example x,
MemoryContainer exampleSet,
Attribute[] attributes) |
Constructor | Description |
---|---|
DominanceConesContainer(MemoryContainer memoryContainer) |
Constructor setting memory container
|
DominanceConesForExample(int exampleNumber,
MemoryContainer memoryContainer,
int[] positiveDominanceCone,
int[] negativeDominanceCone) |
Constructor storing example's number, example's memory container and both dominance cones calculated for
example with given number.
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
Bagging.noReplacementSampleWithWeights(MemoryContainer data,
java.util.Random generator,
double[] weights,
boolean[] sampled) |
Creates a new dataset of the same size using random sampling
without replacement according to the given weight vector.
|
MemoryContainer |
Bagging.replacementSampleWithWeights(MemoryContainer data,
java.util.Random generator,
double[] weights,
boolean[] sampled) |
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
MemoryContainer |
Bagging.resampleWithWeights(MemoryContainer data,
java.util.Random generator,
boolean replacement,
double[] weights,
boolean[] sampled) |
Creates a new dataset of the same size using random sampling
according to replacement option value and to the given weight vector.
|
Modifier and Type | Method | Description |
---|---|---|
double[] |
Bagging.getEqualWeights(MemoryContainer learningContainer) |
|
MemoryContainer |
Bagging.noReplacementSampleWithWeights(MemoryContainer data,
java.util.Random generator,
double[] weights,
boolean[] sampled) |
Creates a new dataset of the same size using random sampling
without replacement according to the given weight vector.
|
MemoryContainer |
Bagging.replacementSampleWithWeights(MemoryContainer data,
java.util.Random generator,
double[] weights,
boolean[] sampled) |
Creates a new dataset of the same size using random sampling
with replacement according to the given weight vector.
|
MemoryContainer |
Bagging.resampleWithWeights(MemoryContainer data,
java.util.Random generator,
boolean replacement,
double[] weights,
boolean[] sampled) |
Creates a new dataset of the same size using random sampling
according to replacement option value and to the given weight vector.
|
Modifier and Type | Method | Description |
---|---|---|
static int |
IndiscernibilityCalculator.checkIndiscernibility(int exampleNumber1,
int exampleNumber2,
MemoryContainer memoryContainer) |
Establishes presence or absence of indiscernibility relation between two examples given by numbers.
|
static int |
IndiscernibilityCalculator.checkIndiscernibility(int exampleNumber1,
int exampleNumber2,
MemoryContainer memoryContainer,
byte[] attributesMask) |
Establishes presence or absence of indiscernibility relation between two examples given by numbers.
|
static int[] |
IndiscernibilityGranuleCalculator.findNumbersOfIndiscernibilityGranuleExamples(int exampleNumber,
MemoryContainer memoryContainer) |
Finds numbers of all examples from indiscernibility granule,
starting at given example, using given reference to memory container.
|
static int[] |
IndiscernibilityGranuleCalculator.findNumbersOfIndiscernibilityGranuleExamples(int exampleNumber,
MemoryContainer memoryContainer,
byte[] attributesMask) |
Finds numbers of all examples from indiscernibility granule,
starting at given example, using given reference to memory container.
|
Constructor | Description |
---|---|
MemoryContainerValidator(MemoryContainer memoryContainer) |
Constructor checks if memoryContainer is valid
|
Modifier and Type | Method | Description |
---|---|---|
static Reducts.ReductsCalculationResult |
Reducts.calculateAllReducts(MemoryContainer container,
jmaf.core.IProgressNotifier monitor,
java.io.File isfFile) |
Calculates list of all reducts using DMT (Discernibility Matrix
Transformation) adapted for domination relation.
|
static Reducts.ReductsCalculationResult |
Reducts.calculateAllReductsWithConsistencyLevel(MemoryContainer container,
jmaf.core.IProgressNotifier monitor,
double consistencyLevel,
java.io.File isfFile) |
Calculates list of all reducts in container by browsing all reducts ->
very inefficient method
|
static byte[] |
Reducts.calculateCore(MemoryContainer container,
jmaf.core.IProgressNotifier monitor,
double consistencyLevel) |
Calculates memory container's core.
|
static byte[] |
Reducts.calculateOneReduct(byte[] core,
MemoryContainer container,
jmaf.core.IProgressNotifier monitor,
double consistencyLevel) |
Calculates one reduct on a base of given core.
|
Constructor | Description |
---|---|
ReductsExamplePair(ReductsExample firstExample,
ReductsExample secondExample,
MemoryContainer container) |
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
LorenzVectorCalculator.applyTo(MemoryContainer oldMemoryContainer) |
Transforms entire memory container by transforming every example.
|
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
LorenzVectorCalculator.applyTo(MemoryContainer oldMemoryContainer) |
Transforms entire memory container by transforming every example.
|
static Example |
LorenzVectorCalculator.transform(int paretoExampleNumber,
MemoryContainer memoryContainer) |
Transforms example's evaluations from Pareto to Lorenz evaluation space.
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
OrdinalValue2CardinalValue.applyTo(MemoryContainer oldMemoryContainer) |
Converts entire memory container by converting every ordinal value of each conditional ordinal criterion
which number is specified in this class.
|
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable) |
Returns
convert(informationTable, null) (null for parameter of type EvaluationDifference2PreferenceIntensityDegree ). |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
int[] rankingOfExamples) |
Returns
convert(informationTable, rankingOfExamples, null) . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
int[] rankingOfExamples,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree) |
Constructs PCT from given information table, for pairs of examples resulting from given ranking (examples' numbers starts from zero)
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
Takes into account InformationTable2PCTConverter.superviseConversionToPCT , InformationTable2PCTConverter.throwExceptionOnUnexpectedAssignmentToNonOutrankingRelation ,
and Settings.getTypeOfFamilyOfCriteria() .For example, if superviseConversionToPCT == false , for ranking of examples equal to 1, 0, 3 (these are examples' numbers) resulting PCT will consist of nine rows,
created for pairs (in that order): (1,1), (1,0), (1,3); (0,1), (0,0), (0,3); (3,1), (3,0), (3,3).Grade of comprehensive preference of first example from pair over second example will be automatically set to comprehensiveWeakPreferenceGrade for pairs, for which preference occurs
and to comprehensiveNotWeakPreferenceGrade for pairs, for which not preference occurs. |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree) |
Constructs PCT from given information table, for all possible pairs of examples
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
|
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
PairOfIndices[] pairsOfExamplesNumbers,
java.lang.Double[] preferenceRelations) |
Converts an information table to a PCT given sample evaluations of pairs of examples from
informationTable . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
SimpleRanking simpleRanking,
boolean considerIndifferentObjects) |
Returns
convert(informationTable, simpleRanking, null) . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
SimpleRanking simpleRanking,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
boolean considerIndifferentObjects) |
Constructs PCT from given information table, for pairs of examples resulting from given simple ranking
(examples' numbers start from zero)
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
|
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
PairField[] pairsOfExamplesNumbers,
boolean createDecisionCriterion) |
Returns
convert(informationTable, pairsOfExamplesNumbers, null, createDecisionCriterion, EVALUATIONS_DIFFERENCE) . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
PairField[] pairsOfExamplesNumbers,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
boolean createDecisionCriterion) |
If
evaluationDifference2PreferenceIntensityDegree is equal to null ,
returns convert(informationTable, pairsOfExamplesNumbers, evaluationDifference2PreferenceIntensityDegree, createDecisionCriterion, EVALUATIONS_DIFFERENCE) ,
else returns convert(informationTable, pairsOfExamplesNumbers, evaluationDifference2PreferenceIntensityDegree, createDecisionCriterion, PREFERENCE_INTENSITY_DEGREE) . |
static MemoryContainer |
InformationTable2PCTConverter.convertWithPairDecision(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree) |
Constructs PCT from given information table, for all possible pairs of examples
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
OrdinalValue2CardinalValue.applyTo(MemoryContainer oldMemoryContainer) |
Converts entire memory container by converting every ordinal value of each conditional ordinal criterion
which number is specified in this class.
|
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable) |
Returns
convert(informationTable, null) (null for parameter of type EvaluationDifference2PreferenceIntensityDegree ). |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
int[] rankingOfExamples) |
Returns
convert(informationTable, rankingOfExamples, null) . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
int[] rankingOfExamples,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree) |
Constructs PCT from given information table, for pairs of examples resulting from given ranking (examples' numbers starts from zero)
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
Takes into account InformationTable2PCTConverter.superviseConversionToPCT , InformationTable2PCTConverter.throwExceptionOnUnexpectedAssignmentToNonOutrankingRelation ,
and Settings.getTypeOfFamilyOfCriteria() .For example, if superviseConversionToPCT == false , for ranking of examples equal to 1, 0, 3 (these are examples' numbers) resulting PCT will consist of nine rows,
created for pairs (in that order): (1,1), (1,0), (1,3); (0,1), (0,0), (0,3); (3,1), (3,0), (3,3).Grade of comprehensive preference of first example from pair over second example will be automatically set to comprehensiveWeakPreferenceGrade for pairs, for which preference occurs
and to comprehensiveNotWeakPreferenceGrade for pairs, for which not preference occurs. |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree) |
Constructs PCT from given information table, for all possible pairs of examples
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
|
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
PairOfIndices[] pairsOfExamplesNumbers,
java.lang.Double[] preferenceRelations) |
Converts an information table to a PCT given sample evaluations of pairs of examples from
informationTable . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
SimpleRanking simpleRanking,
boolean considerIndifferentObjects) |
Returns
convert(informationTable, simpleRanking, null) . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
SimpleRanking simpleRanking,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
boolean considerIndifferentObjects) |
Constructs PCT from given information table, for pairs of examples resulting from given simple ranking
(examples' numbers start from zero)
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
|
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
PairField[] pairsOfExamplesNumbers,
boolean createDecisionCriterion) |
Returns
convert(informationTable, pairsOfExamplesNumbers, null, createDecisionCriterion, EVALUATIONS_DIFFERENCE) . |
static MemoryContainer |
InformationTable2PCTConverter.convert(MemoryContainer informationTable,
PairField[] pairsOfExamplesNumbers,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
boolean createDecisionCriterion) |
If
evaluationDifference2PreferenceIntensityDegree is equal to null ,
returns convert(informationTable, pairsOfExamplesNumbers, evaluationDifference2PreferenceIntensityDegree, createDecisionCriterion, EVALUATIONS_DIFFERENCE) ,
else returns convert(informationTable, pairsOfExamplesNumbers, evaluationDifference2PreferenceIntensityDegree, createDecisionCriterion, PREFERENCE_INTENSITY_DEGREE) . |
static MemoryContainer |
InformationTable2PCTConverter.convertWithPairDecision(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree) |
Constructs PCT from given information table, for all possible pairs of examples
and for given information (if present) about how to convert differences of evaluations on condition cardinal criteria
from given information table to the degrees of the intensity of preference, which will be stored in PCT.
|
static int |
PCTDetector.getIndexOfComprehensivePreferenceGrade(MemoryContainer pct) |
Checks if given PCT has a criterion storing comprehensive preference grades and if so - returns its index.
|
static boolean |
PCTDetector.hasActiveConditionCriterionStoringPairsOfValues(MemoryContainer pct) |
Checks if given PCT has at least one active condition criterion of type
PairField . |
static boolean |
PCTDetector.hasAttributeStoringPairsOfValues(MemoryContainer pct) |
Checks if given PCT has at least one attribute of type
PairField ,
different than the attribute with the index equal to PCTDetector.getIndexOfPairOfExamplesNumbers() . |
static boolean |
PCTDetector.isPCT(MemoryContainer memoryContainer) |
Tests if given memory container is a PCT (pairwise comparison table).
|
Modifier and Type | Field | Description |
---|---|---|
MemoryContainer |
RankerParameters.learningInformationTable |
Learning information table
|
MemoryContainer |
RankerResults.learningPct |
PCT created using the preference information
|
MemoryContainer |
RankerParameters.testInformationTable |
Test information table
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
PreferenceGraphGenerator.getFullPct() |
Gets test "full" PCT stored in this class
|
MemoryContainer |
PreferenceStructure.getFullPct() |
Deprecated.
Gets test "full" PCT stored in this class
|
MemoryContainer |
PreferenceGraphGenerator.getInformationTable() |
Gets test information table stored in this class
|
MemoryContainer |
PreferenceStructure.getInformationTable() |
Deprecated.
Gets test information table stored in this class
|
Constructor | Description |
---|---|
PreferenceGraphGenerator(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
RulesContainer rulesContainer,
int typeOfRules,
int consideredRelations,
boolean calculateFuzzySatisfactionDegrees,
int ruleConsistencyMeasure) |
Constructor for
PreferenceGraphGenerator class. |
PreferenceGraphGeneratorVirtualAllRules(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
UnionContainer unionContainer,
int consideredRelations,
boolean calculateFuzzySatisfactionDegrees,
int objectConsistencyMeasure,
java.util.HashMap<Field,java.lang.Double> objectConsistencyMeasureUpwardThresholds,
java.util.HashMap<Field,java.lang.Double> objectConsistencyMeasureDownwardThresholds,
int negativeExamplesTreatment) |
Constructor for
PreferenceGraphGeneratorVirtualAllRules class,
which sets this generator to work in VC-DRSA mode with certain rules only,
using one of monotonic object consistency measures $\epsilon$, $\epsilon*$ or $\epsilon'$. |
PreferenceGraphGeneratorVirtualAllRules(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
UnionContainer unionContainer,
int typeOfRules,
int consideredRelations,
boolean calculateFuzzySatisfactionDegrees,
int ruleConsistencyMeasure) |
Constructor for
PreferenceGraphGeneratorVirtualAllRules class,
which sets this generator to work in DRSA mode. |
PreferenceStructure(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
RulesContainer rulesContainer) |
Deprecated.
Constructor for
PreferenceStructure class. |
PreferenceStructureVirtualAllRulesDRSA(MemoryContainer informationTable,
EvaluationDifference2PreferenceIntensityDegree evaluationDifference2PreferenceIntensityDegree,
UnionContainer unionContainer) |
Deprecated.
Constructor for
PreferenceStructureVirtualAllRulesDRSA class. |
SimpleRanking(MemoryContainer memoryContainer) |
Converts given memory container to a linear ranking (weak order) of objects for which the decision class is known.
|
Constructor | Description |
---|---|
ReductsGenerator(MemoryContainer memoryContainer) |
Constructor
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
EntireGranulesRuleGenerator.getLearningMemoryContainer() |
Gets learning memory container for which all rules are generated, used by
EntireGranulesRuleGenerator.generateRules() method. |
MemoryContainer |
Rule.getLearningMemoryContainer() |
Gets reference to learning memory container for which this rule was generated.
|
MemoryContainer |
VCDomLem.getLearningMemoryContainer() |
Gets learning memory container for which all rules are generated, used by
VCDomLem.generateRules() method. |
MemoryContainer |
Rule.getTestMemoryContainer() |
Gets reference to test memory container used by this rule, which is memory container stored in
TestMemoryContainerDeliverer class. |
Modifier and Type | Method | Description |
---|---|---|
boolean |
Rule.covers(int exampleNumber,
MemoryContainer memoryContainer) |
Checks if example with given number from given memory container is covered by this rule.
|
static java.util.ArrayList<Rule> |
EntireDominanceConesRulesForBordersOfApproximations.generateRules(ApproximatedEntity[] approximatedEntities,
double consistencyLevel,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer) |
TODO
|
java.util.ArrayList<Rule> |
EntireGranulesRuleGenerator.generateRules(ApproximatedEntity[] approximatedEntities,
double[] variableConsistencyParameterValueThresholds,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer) |
Generates decision rules of given type (certain, possible or approximate),
for given set (array) of approximated entities (unions of decision classes or single decision classes),
for given variable consistency parameter value thresholds (support for variable consistency approach),
for given characteristic decision class usage tip (which affects type of created conditions and decisions)
and for given learning memory container which may be either decision table, or PCT, or similarity table.
This method can be used to generate all non-minimal robust (object-based) rules of given type, for all possible decision classes or for all possible upward / downward unions of decision classes. This method considers consecutive approximated entities (from the first, with zero index, to the last). |
java.util.ArrayList<Rule> |
EntireGranulesRuleGenerator.generateRules(ApproximatedEntity[] approximatedEntities,
double variableConsistencyParameterValueThreshold,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer) |
|
java.util.ArrayList<Rule> |
VCDomLem.generateRules(ApproximatedEntity[] approximatedEntities,
double[] variableConsistencyParameterValueThresholds,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
Generates decision rules of given type (certain, possible or approximate),
for given set (array) of approximated entities (unions of decision classes or single decision classes),
for given variable consistency parameter value thresholds (support for variable consistency approach),
for given characteristic decision class usage tip (which affects type of created conditions and decisions)
and for given learning memory container which may be either decision table, or PCT, or similarity table.
This method can be used to generate all minimal cover rules of given type, for all possible decision classes or for all possible upward / downward unions of decision classes. This method assumes that if given set of approximated entities contains unions of decision classes, these unions are sorted from most specific to less specific. |
java.util.ArrayList<Rule> |
VCDomLem.generateRules(ApproximatedEntity[] approximatedEntities,
double variableConsistencyParameterValueThreshold,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
Wrapper for
generateRules(ApproximatedEntity[], double[], ApproximatedEntityDecisionsPredictor, int, int, ConditionValidator, MemoryContainer, int, int) method. |
static java.util.ArrayList<Rule> |
EntireDominanceConesRulesForBordersOfApproximations.generateRulesForPositiveExamples(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples,
double requiredMinimalConfidence) |
TODO
|
java.util.ArrayList<Rule> |
EntireGranulesRuleGenerator.generateRulesForPositiveExamples(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Generates decision rules of given type (certain, possible or approximate),
for given characteristic decision class (basic class of some union of decision classes or single decision class),
for given characteristic decision class usage tip (which tells if given characteristic decision class is
basic class of some upward (
Rule.AT_LEAST ) or downward (Rule.AT_MOST ) union of decision classes
or just single decision class (Rule.EQUAL );
value of this parameter affects also type of created conditions),
for given decisions (which will be set on the right (decision) side of all created and returned rules),
for given learning memory container which may be either decision table, or PCT, or similarity table,
and for given array with numbers of positive examples from given learning memory container
(which may come from lower / upper approximation or boundary of some decision class
or from lower / upper approximation of some upward / downward union of decision classes). |
java.util.ArrayList<Rule> |
VCDomLem.generateRulesForPositiveExamples(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples,
int[] numbersOfApproximatedEntityExamples,
double variableConsistencyMeasureValueThreshold,
int conditionsSelectionMethod,
java.util.HashSet<java.lang.Integer> numbersOfPermittedNegativeCoveredExamples,
java.util.HashSet<java.lang.Integer> numbersOfNeutralExamples) |
Generates decision rules of given type (certain, possible or approximate),
for given characteristic decision class (basic class of some union of decision classes or single decision class),
for given characteristic decision class usage tip (which tells if given characteristic decision class is
basic class of some upward (
Rule.AT_LEAST ) or downward (Rule.AT_MOST ) union of decision classes
or just single decision class (Rule.EQUAL );
value of this parameter affects also type of created conditions),
for given decisions (which will be set on the right (decision) side of all created and returned rules),
for given learning memory container which may be either decision table, or PCT, or similarity table,
for given array with numbers of positive examples from given learning memory container
(which may come from lower / upper approximation or boundary of some decision class
or from lower / upper approximation of some upward / downward union of decision classes),
for given variable consistency measure value threshold (support for variable consistency approach),
for given method of selection of conditions
and for given hash set with numbers of permitted negative examples (used only for VC-IRSA / VC-DRSA).SIC! |
static double |
RuleStatistics.getGeneratedRuleMaximumEpsilonPrimMeasureValue(MemoryContainer learningMemoryContainer,
int approximatedEntitySize,
int numberOfNeutralExamples) |
Gets maximum value of $\epsilon'$ rule consistency measure that can be obtained by a generated rule (i.e., rule not in generation mode), given that this rule is induced for a given learning memory container,
for an approximated entity with give size, and in the circumstances when there is a given number of examples being neutral w.r.t. rule's approximated entity.
|
boolean |
Rule.memoryContainerIsCompatible(MemoryContainer memoryContainer) |
Checks if given memory container is compatible with this rule.
|
void |
EntireGranulesRuleGenerator.setLearningMemoryContainer(MemoryContainer learningMemoryContainer) |
Sets learning memory container for which all rules are generated, used by
EntireGranulesRuleGenerator.generateRules() method. |
void |
Rule.setLearningMemoryContainer(MemoryContainer learningMemoryContainer) |
Sets reference to learning memory container.
|
void |
VCDomLem.setLearningMemoryContainer(MemoryContainer learningMemoryContainer) |
Sets learning memory container for which all rules are generated, used by
VCDomLem.generateRules() method. |
Constructor | Description |
---|---|
AttributeInfoManager(MemoryContainer memoryContainer) |
Creates attributes' manager for given memory container.
|
EntireGranulesRuleGenerator(ApproximatedEntity[] approximatedEntities,
double[] variableConsistencyParameterValueThresholds,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer) |
Constructor initializing all fields used by
EntireGranulesRuleGenerator.generateRules() method. |
EntireGranulesRuleGenerator(ApproximatedEntity[] approximatedEntities,
double variableConsistencyParameterValueThreshold,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer) |
Constructor initializing all fields used by
EntireGranulesRuleGenerator.generateRules() method. |
MonotonicVCDomLem(ApproximatedEntity[] approximatedEntities,
double[] variableConsistencyParameterValueThresholds,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
Constructor initializing all fields used by
VCDomLem.generateRules() method. |
MonotonicVCDomLem(ApproximatedEntity[] approximatedEntities,
double variableConsistencyParameterValueThreshold,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
Constructor initializing all fields used by
VCDomLem.generateRules() method. |
OptRule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> conditions,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
double consistencyLevel,
ApproximatedEntity approximatedEntity,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor) |
|
OptRule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> conditions,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Extends
Rule(int, int, Field, ConditionValidator, HashSet, HashSet, MemoryContainer, int[]) constructor. |
OptRule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Extends
Rule(int, int, Field, ConditionValidator, HashSet, MemoryContainer, int[]) constructor. |
OptRule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Extends
Rule(int, int, Field, ConditionValidator, MemoryContainer, int[]) constructor. |
Rule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> conditions,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
double consistencyLevel,
ApproximatedEntity approximatedEntity,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor) |
Constructs rule of given type, for given characteristic decision class
and tip for its usage, from given conditions and decisions and for given learning memory container.
|
Rule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> conditions,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Constructs rule of given type, for given characteristic decision class
and tip for its usage, from given conditions and decisions, for given learning memory container
and for given array with numbers of positive examples from learning memory container for which this rule
was induced.
|
Rule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
java.util.HashSet<Condition> decisions,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Constructs rule of given type, for given characteristic decision class
and tip for its usage, from given decisions, for given learning memory container
and for given array with numbers of positive examples from learning memory container for which this rule
was induced.
|
Rule(int type,
int characteristicDecisionClassUsageTip,
Field characteristicDecisionClass,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int[] numbersOfPositiveExamples) |
Constructs rule of given type, for given characteristic decision class
and tip for its usage, for given learning memory container and for given array with numbers of positive examples
from learning memory container for which this rule was induced.
|
RulesContainer(MemoryContainer learningMemoryContainer,
double consistencyLevel) |
Constructor for
RulesContainer class, creating rules' container for given learning memory container
and level of consistency.Calculates format of the rules ( rulesFormat field) on the basis of the description of
given learning memory container (namely on the basis of learning memory container's type and evaluation space),
stored in MemoryContainersDescriptions class. |
StandardVCDomLem(ApproximatedEntity[] approximatedEntities,
double[] variableConsistencyParameterValueThresholds,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
Constructor initializing all fields used by
VCDomLem.generateRules() method. |
StandardVCDomLem(ApproximatedEntity[] approximatedEntities,
double variableConsistencyParameterValueThreshold,
ApproximatedEntityDecisionsPredictor approximatedEntityDecisionsPredictor,
int type,
int characteristicDecisionClassUsageTip,
ConditionValidator conditionValidator,
MemoryContainer learningMemoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
Constructor initializing all fields used by
VCDomLem.generateRules() method. |
Modifier and Type | Method | Description |
---|---|---|
static MemoryContainer |
ISFLoader.loadISFIntoMemoryContainer(java.lang.String ISFFileName) |
Loads information table defined in given ISF file into memory container and returns reference to created container.
|
static MemoryContainer |
ISFLoader.loadISFIntoMemoryContainer(java.lang.String ISFFileName,
ParseLog parseLog) |
Loads information table defined in given ISF file into memory container
and returns reference to created container.
|
MemoryContainer |
None2CostGainConverter.none2CostGain(MemoryContainer memoryContainer) |
Exchanges each condition regular attribute with two condition criteria, one of cost type and the other one of gain type.
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
None2CostGainConverter.none2CostGain(MemoryContainer memoryContainer) |
Exchanges each condition regular attribute with two condition criteria, one of cost type and the other one of gain type.
|
static boolean |
ISFWriter.saveMemoryContainerIntoISF(java.lang.String ISFFileName,
MemoryContainer memoryContainer) |
Saves information table stored in given memory container into given ISF file.
|
static void |
CoverageInfoWriter.writeCoveringAtLeastAndAtMostRules(MemoryContainer memoryContainer,
RulesContainer rulesContainer,
java.lang.String fileName,
int type) |
Writes numbers of covering certain / possible at least and certain / possible at most rules (from given rules container)
for given memory container to file with given name (path)
|
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
CrossValidation.getTestDataSet(int numFold) |
Creates the test set for one fold of a cross-validation on
the dataset.
|
MemoryContainer |
CrossValidation.getTrainDataSet(int numFold) |
Creates the training set for one fold of a cross-validation
on the dataset.
|
MemoryContainer |
CrossValidation.getWholeDataSet() |
Constructor | Description |
---|---|
CrossValidation(MemoryContainer memoryContainer) |
|
CrossValidation(MemoryContainer memoryContainer,
int numFolds) |
|
CrossValidation(MemoryContainer memoryContainer,
int decisionAttributeIndex,
int numFolds) |
Modifier and Type | Method | Description |
---|---|---|
MemoryContainer |
MonotonicVCdomLEMWrapper.getLearningMemoryContainer() |
|
MemoryContainer |
VCdomLEMWrapper.getLearningMemoryContainer() |
|
MemoryContainer |
VCdomLEMWrapperOpt.getLearningMemoryContainer() |
|
MemoryContainer |
WekaMethodWrapper.getLearningMemoryContainer() |
|
MemoryContainer |
WekaMethodWrapper.getTestMemoryContainer() |
Modifier and Type | Method | Description |
---|---|---|
void |
RulesGeneratorWrapper.build(MemoryContainer learningMemoryContainer) |
Generates rules
|
void |
SimpleClassifierWrapper.build(MemoryContainer leariningMemoryContainer) |
Builds a classifier from the learing data
|
void |
WekaMethodWrapper.build(MemoryContainer learningMemoryContainer) |
|
Classifier |
VCArcingWrapper.buildFS(MemoryContainer trainMemoryContainer,
long seed) |
|
Classifier |
VCArcingWrapper.buildXn(MemoryContainer trainMemoryContainer,
long seed) |
|
PairOfIndices[] |
JRank.calculateTestPairsThatCannotBePreservedInPreferenceRelation(MemoryContainer testInformationTable) |
Calculates test pairs of objects that cannot be preserved in preference relation
|
RulesContainer |
MonotonicVCdomLEMWrapper.generateRules(MemoryContainer memoryContainer) |
|
RulesContainer |
MonotonicVCdomLEMWrapper.generateRules(MemoryContainer memoryContainer,
int foldNumber) |
|
RulesContainer |
MonotonicVCdomLEMWrapper.generateRules(MemoryContainer memoryContainer,
java.lang.String fileName) |
|
abstract RulesContainer |
RulesGeneratorWrapper.generateRules(MemoryContainer memoryContainer) |
Generates rules for submmitted memory container.
|
abstract RulesContainer |
RulesGeneratorWrapper.generateRules(MemoryContainer memoryContainer,
int foldNumber) |
Generates rules for submmitted memory container and stores them in
file indicated by foldNumber.
|
abstract RulesContainer |
RulesGeneratorWrapper.generateRules(MemoryContainer memoryContainer,
java.lang.String fileName) |
Generates rules for submmitted memory container and stores them in
file indicated by file name.
|
RulesContainer |
VCdomLEMWrapper.generateRules(MemoryContainer memoryContainer) |
|
RulesContainer |
VCdomLEMWrapper.generateRules(MemoryContainer memoryContainer,
int foldNumber) |
|
RulesContainer |
VCdomLEMWrapper.generateRules(MemoryContainer memoryContainer,
java.lang.String fileName) |
|
RulesContainer |
VCdomLEMWrapperOpt.generateRules(MemoryContainer memoryContainer) |
|
RulesContainer |
VCdomLEMWrapperOpt.generateRules(MemoryContainer memoryContainer,
int foldNumber) |
|
RulesContainer |
VCdomLEMWrapperOpt.generateRules(MemoryContainer memoryContainer,
java.lang.String fileName) |
|
double[] |
VCArcingWrapper.getConsistencies(MemoryContainer examples,
ConsistencyMeasure consistencyMeasure) |
Computes an array of consistency measures for examples
|
boolean |
JRank.selectivelyUpdateJRankParameters(java.util.ArrayList<NamedProperties> namedPropertiesList,
MemoryContainer learningInformationTable,
MemoryContainer testInformationTable) |
SIC!
|
void |
JRank.setLearningData(MemoryContainer learningInformationTable) |
Sets learning information table.
|
void |
MonotonicVCdomLEMWrapper.setLearningMemoryContainer(MemoryContainer memoryContainer) |
|
void |
VCdomLEMWrapper.setLearningMemoryContainer(MemoryContainer memoryContainer) |
|
void |
VCdomLEMWrapperOpt.setLearningMemoryContainer(MemoryContainer memoryContainer) |
|
void |
RulesGeneratorWrapper.setMemoryContainer(MemoryContainer memoryContainer) |
Sets memory container
|
void |
JRank.setTestData(MemoryContainer testInformationTable) |
Sets test information table.
|
void |
RulesGeneratorWrapper.setTestMemoryContainer(MemoryContainer testMemoryContainer) |
|
ClassificationResultsValidationContainer |
BaggingWrapper.validate(MemoryContainer testMemoryContainer,
long seed) |
Validates the bagged classifier generated on learning data set.
|
ClassificationResultsValidationContainer |
RulesGeneratorWrapper.validate(RulesClassificationMethod method,
MemoryContainer testMemoryContainer) |
Validates the rule classifier that is bulid on rules from
rulesContainer on a given test container. |
ClassificationResultsValidationContainer |
RulesGeneratorWrapper.validate(RulesClassificationMethod method,
MemoryContainer testMemoryContainer,
int decisionAttributeIndex) |
Validates the rule classifier that is bulid on rules from
rulesContainer on a given test container. |
ClassificationResultsContainer |
RulesGeneratorWrapper.validate(MemoryContainer testContainer) |
|
ClassificationResultsContainer |
SimpleClassifierWrapper.validate(MemoryContainer testContainer) |
Validates the classifer on the test container
testContainer . |
ClassificationResultsValidationContainer |
VCArcingWrapper.validate(MemoryContainer testMemoryContainer,
long seed) |
Validates the bagged classifier generated on learning data set.
|
ClassificationResultsValidationContainer |
WekaMethodWrapper.validate(WekaClassificationMethod baseMethod,
MemoryContainer testMemoryContainer,
boolean methodValid) |
Validates the weka classifier generated on learning data set.
|
ClassificationResultsValidationContainer |
WekaMethodWrapper.validate(WekaClassificationMethod baseMethod,
MemoryContainer testMemoryContainer,
int decisionAttributeIndex) |
Validates the weka classifier generated on learning data set.
|
ClassificationResultsContainer |
WekaMethodWrapper.validate(MemoryContainer testContainer) |
Constructor | Description |
---|---|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment,
boolean allPositiveExamples) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment,
boolean allPositiveExamples,
RulesClassificationMethod method) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int consistencyMeasure,
int conditionsSelectionMethod,
int negativeExamplesTreatment,
boolean allPositiveExamples) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int consistencyMeasure,
int conditionsSelectionMethod,
int negativeExamplesTreatment,
boolean allPositiveExamples,
RulesClassificationMethod method) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment,
RulesClassificationMethod method) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
java.lang.String fileName,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
MonotonicVCdomLEMWrapper(MemoryContainer memoryContainer,
java.lang.String fileName,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment,
RulesClassificationMethod method) |
|
RulesGeneratorWrapper(MemoryContainer learningMemoryContainer) |
|
RulesGeneratorWrapper(MemoryContainer learningMemoryContainer,
MemoryContainer testMemoryContainer) |
|
VCdomLEMWrapper(MemoryContainer memoryContainer) |
|
VCdomLEMWrapper(MemoryContainer memoryContainer,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
VCdomLEMWrapper(MemoryContainer memoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
VCdomLEMWrapper(MemoryContainer memoryContainer,
java.lang.String fileName,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
VCdomLEMWrapperOpt(MemoryContainer memoryContainer) |
|
VCdomLEMWrapperOpt(MemoryContainer memoryContainer,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
VCdomLEMWrapperOpt(MemoryContainer memoryContainer,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
VCdomLEMWrapperOpt(MemoryContainer memoryContainer,
java.lang.String fileName,
double confidenceLevel,
int conditionsSelectionMethod,
int negativeExamplesTreatment) |
|
WekaMethodWrapper(MemoryContainer learningMemoryContainer) |
|
WekaMethodWrapper(MemoryContainer learningMemoryContainer,
MemoryContainer testMemoryContainer) |