Package | Description |
---|---|
pl.poznan.put.cs.idss.jrs.apriori | |
pl.poznan.put.cs.idss.jrs.jmaf.parser | |
pl.poznan.put.cs.idss.jrs.rules |
Modifier and Type | Method | Description |
---|---|---|
Condition |
ElementaryCondition.toCondition() |
Modifier and Type | Method | Description |
---|---|---|
Condition |
RulesParser.parseRulesCondition() |
Parses the rule's condition
|
Condition |
RulesParser.parseRulesDecision() |
Parses the rule's decision
|
Modifier and Type | Class | Description |
---|---|---|
class |
PairCondition |
Class representing condition created on the basis of example's field of type
PairField . |
class |
SimilarityCondition |
Class representing similarity condition.
|
class |
SingleCondition |
Class representing single condition.
|
class |
SingleConditionForPairOfValues |
Class representing single condition defined for attribute whose values are pairs of simple values (objects of type
PairField ). |
Modifier and Type | Method | Description |
---|---|---|
abstract Condition |
Condition.duplicate() |
Returns duplicate of this condition
|
Condition |
PairCondition.duplicate() |
Returns duplicate of this condition.
|
Condition |
SimilarityCondition.duplicate() |
Returns duplicate of this condition.
|
Condition |
SingleCondition.duplicate() |
Returns duplicate of this condition.
|
Condition |
SingleConditionForPairOfValues.duplicate() |
Returns duplicate of this condition.
|
Condition[] |
Rule.getConditionsAsArray() |
Gets array with all "regular" conditions present in this rule.
|
Condition[] |
Rule.getDecisions() |
Gets array (
Condition[] ) with all decisions present in this rule.Because of efficiency, reference to the field of this class, not its copy, is returned. |
Condition |
Rule.getTemporarilyDeletedCondition() |
Gets temporarily deleted condition, belonging to "regular" conditions returned by
getConditions method. |
Condition |
Rule.getTemporaryCondition() |
Gets temporary additional condition, independent of "regular" conditions returned by
getConditions method. |
Modifier and Type | Method | Description |
---|---|---|
java.util.HashSet<Condition> |
ApproximatedEntityDecisionsPredictor.getDecisionsForRuleInducedFromBoundary(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from boundary
of given approximated entity.
|
java.util.HashSet<Condition> |
DecisionClassDecisionsPredictor.getDecisionsForRuleInducedFromBoundary(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from boundary
of given approximated entity.
|
java.util.HashSet<Condition> |
UnionDecisionsPredictor.getDecisionsForRuleInducedFromBoundary(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from boundary
of given approximated entity.
|
java.util.HashSet<Condition> |
ApproximatedEntityDecisionsPredictor.getDecisionsForRuleInducedFromLowerApproximation(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from lower approximation
of given approximated entity.
|
java.util.HashSet<Condition> |
DecisionClassDecisionsPredictor.getDecisionsForRuleInducedFromLowerApproximation(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from lower approximation
of given approximated entity.
|
java.util.HashSet<Condition> |
UnionDecisionsPredictor.getDecisionsForRuleInducedFromLowerApproximation(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from lower approximation
of given approximated entity.
|
java.util.HashSet<Condition> |
ApproximatedEntityDecisionsPredictor.getDecisionsForRuleInducedFromUpperApproximation(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from upper approximation
of given approximated entity.
|
java.util.HashSet<Condition> |
DecisionClassDecisionsPredictor.getDecisionsForRuleInducedFromUpperApproximation(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from upper approximation
of given approximated entity.
|
java.util.HashSet<Condition> |
UnionDecisionsPredictor.getDecisionsForRuleInducedFromUpperApproximation(ApproximatedEntity approximatedEntity) |
Gets hash set with decisions for right (decision) side of decision rule induced from upper approximation
of given approximated entity.
|
Modifier and Type | Method | Description |
---|---|---|
void |
Rule.addCondition(Condition condition) |
Adds "regular" condition to the left (condition) side of this rule and deletes temporary additional condition.
|
void |
Rule.addDecision(Condition decision) |
Adds decision to the right (decision) side of this rule.
|
void |
Rule.addOrExchangeTemporaryCondition(Condition condition) |
Adds or exchange temporary additional condition, independent of "regular" conditions stored in
conditions hash table. |
boolean |
ConditionValidator.conditionValid(Condition condition,
int conditionType) |
Checks if given condition is valid.
|
boolean |
SimpleConditionValidator.conditionValid(Condition condition,
int conditionType) |
Checks if given condition is valid.
|
boolean |
Rule.containsCondition(Condition condition) |
Checks if this rule already contains "regular" condition equal to given one
|
void |
Rule.deleteCondition(Condition condition) |
Permanently deletes from this rule "regular" condition equal to given one (to given pattern).
|
double |
RuleStatistics.getConfidenceAssumingThatConditionIsAddedToRule(Condition condition) |
Gets confidence (credibility / certainty factor) of the rule in the context of rule's learning memory container, assuming that given condition is added to the rule.
|
double |
RuleStatistics.getEpsilonPrimMeasureValueAssumingThatConditionIsAddedToRule(Condition condition) |
Gets value of the inconsistency measure $\epsilon'$, in the context of rule's learning memory container,
assuming that given condition is added to the rule.
|
double[] |
RuleStatisticsOpt.getFastStatisticsAssumingThatConditionIsAddedToRule(Condition condition) |
Calculates support, quantity of covered examples and confidence assuming that condition is added to rule
|
double |
RuleStatistics.getInconsistencyMeasureValueAssumingThatConditionIsAddedToRule(Condition condition) |
Gets value of the inconsistency measure $\epsilon$, in the context of rule's learning memory container,
assuming that given condition is added to the rule.
|
int[] |
RuleStatistics.getNumbersOfCoveredExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
Gets numbers of examples covered by the rule, in the context of its learning memory container, assuming that given condition is added to the rule.
|
int[] |
RuleStatisticsOpt.getNumbersOfCoveredExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
Gets numbers of examples covered by the rule, in the context of its learning memory container, assuming that given condition is added to the rule.
|
int[] |
RuleStatisticsOpt.getNumbersOfStrongCoveredExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
TODO - add description
|
int[] |
RuleStatisticsOpt.getNumbersOfStrongSupportingExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
TODO - add description
|
int[] |
RuleStatistics.getNumbersOfSupportingExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
Gets numbers of examples supporting the rule, in the context of its learning memory container, assuming that given condition is added to the rule.
|
int[] |
RuleStatisticsOpt.getNumbersOfSupportingExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
Gets numbers of examples supporting the rule, in the context of its learning memory container, assuming that given condition is added to the rule.
|
int |
RuleStatistics.getQuantityOfCoveredExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
Gets quantity of examples covered by the rule, in the context of its learning memory container, assuming that given condition is added to the rule.
|
int |
RuleStatistics.getQuantityOfNotNeutralCoveredExamplesAssumingThatConditionIsAddedToRule(Condition condition) |
Gets quantity of not neutral examples covered by the rule, in the context of its learning memory container, assuming that given condition is added to the rule.
|
int |
RuleStatistics.getSupportAssumingThatConditionIsAddedToRule(Condition condition) |
Gets support of the rule in the context of its learning memory container, assuming that given condition is added to the rule.
|
void |
Rule.setOrExchangeTemporarilyDeletedCondition(Condition condition) |
Sets or exchanges temporarily deleted "regular" condition from this rule.
|
Modifier and Type | Method | Description |
---|---|---|
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! |
void |
Rule.setConditions(java.util.HashSet<Condition> conditions) |
Sets all conditions from given hash set on the left (condition) side of this rule,
deletes temporary additional condition and sets reference to temporarily deleted condition to
null . |
void |
Rule.setDecisions(java.util.HashSet<Condition> decisions) |
Sets all decisions from given hash set on the right (decision) side of this rule.
|
Constructor | Description |
---|---|
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> conditions,
java.util.HashSet<Condition> decisions,
Attribute[] learningAttributes) |
Extends
Rule(int, int, Field, ConditionValidator, HashSet, HashSet, Attribute[]) 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. |
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> conditions,
java.util.HashSet<Condition> decisions,
Attribute[] learningAttributes) |
Constructs rule of given type, for given characteristic decision class
and tip for its usage, from given conditions and decisions and for given attributes from learning memory container.
Stores references to condition validator and learning attributes, without coping. |
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.
|