Modifier and Type | Field and Description |
---|---|
static com.google.common.base.Predicate<Rule> |
Filters.CompatibilityRule |
static com.google.common.base.Predicate<Rule> |
Filters.ConstraintRule |
protected org.apache.commons.collections4.SetValuedMap<Rule,GroundRule> |
MemoryGroundRuleStore.groundRules |
Modifier and Type | Method and Description |
---|---|
int |
GroundRuleStore.count(Rule rule) |
int |
MemoryGroundRuleStore.count(Rule rule) |
Iterable<GroundRule> |
GroundRuleStore.getGroundRules(Rule rule)
Returns every GroundRule that was instantiated by a given Rule.
|
Iterable<GroundRule> |
MemoryGroundRuleStore.getGroundRules(Rule rule) |
void |
GroundRuleStore.removeGroundRules(Rule rule)
Removes all GroundRules that was instantiated by a given rule.
|
void |
MemoryGroundRuleStore.removeGroundRules(Rule rule) |
void |
AtomRegisterGroundRuleStore.removeGroundRules(Rule rule) |
Modifier and Type | Method and Description |
---|---|
static void |
LazyMPEInference.inference(List<Rule> rules,
Reasoner reasoner,
GroundRuleStore groundRuleStore,
TermStore termStore,
TermGenerator termGenerator,
LazyAtomManager lazyAtomManager,
int maxRounds)
Do the full MPE inference process.
|
Modifier and Type | Field and Description |
---|---|
protected List<Rule> |
WeightLearningApplication.allRules |
Modifier and Type | Method and Description |
---|---|
static WeightLearningApplication |
WeightLearningApplication.getWLA(String className,
List<Rule> rules,
Database randomVariableDatabase,
Database observedTruthDatabase)
Construct a weight learning application given the data.
|
Constructor and Description |
---|
VotedPerceptron(List<Rule> rules,
Database rvDB,
Database observedDB,
boolean supportsLatentVariables) |
WeightLearningApplication(List<Rule> rules,
Database rvDB,
Database observedDB,
boolean supportsLatentVariables) |
Constructor and Description |
---|
ExpectationMaximization(List<Rule> rules,
Database rvDB,
Database observedDB) |
HardEM(List<Rule> rules,
Database rvDB,
Database observedDB) |
PairedDualLearner(List<Rule> rules,
Database rvDB,
Database observedDB) |
Constructor and Description |
---|
LazyMaxLikelihoodMPE(List<Rule> rules,
Database rvDB,
Database observedDB) |
MaxLikelihoodMPE(List<Rule> rules,
Database rvDB,
Database observedDB) |
MaxPiecewisePseudoLikelihood(List<Rule> rules,
Database rvDB,
Database observedDB) |
MaxPseudoLikelihood(List<Rule> rules,
Database rvDB,
Database observedDB) |
Constructor and Description |
---|
Hyperband(List<Rule> rules,
Database rvDB,
Database observedDB) |
InitialWeightHyperband(List<Rule> rules,
Database rvDB,
Database observedDB) |
Constructor and Description |
---|
BaseGridSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
ContinuousRandomGridSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
GridSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
GuidedRandomGridSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
InitialWeightGridSearch(List<Rule> rules,
WeightLearningApplication internalWLA,
Database rvDB,
Database observedDB)
The WeightLearningApplication should not have had initGroundModel() called yet.
|
InitialWeightRandomGridSearch(List<Rule> rules,
WeightLearningApplication internalWLA,
Database rvDB,
Database observedDB) |
InitialWeightRankSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
InitialWeightRankSearch(List<Rule> rules,
WeightLearningApplication internalWLA,
Database rvDB,
Database observedDB)
The WeightLearningApplication should not have had initGroundModel() called yet.
|
RandomGridSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
RankSearch(List<Rule> rules,
Database rvDB,
Database observedDB) |
Modifier and Type | Method and Description |
---|---|
static int |
Grounding.groundAll(List<Rule> rules,
AtomManager atomManager,
GroundRuleStore groundRuleStore) |
Modifier and Type | Method and Description |
---|---|
int |
LazyAtomManager.activateAtoms(List<Rule> rules,
GroundRuleStore groundRuleStore)
Activate any lazy atoms above the threshold.
|
int |
LazyAtomManager.activateAtoms(Set<RandomVariableAtom> atoms,
List<Rule> rules,
GroundRuleStore groundRuleStore)
Activate a specific set of lazy atoms.
|
Modifier and Type | Field and Description |
---|---|
protected List<Rule> |
Model.rules |
protected Set<Rule> |
Model.ruleSet
Redundant set for fast membership checks
|
Modifier and Type | Method and Description |
---|---|
List<Rule> |
Model.getRules() |
Modifier and Type | Method and Description |
---|---|
void |
Model.addRule(Rule rule)
Adds a Rule to this Model.
|
void |
Model.removeRule(Rule rule)
Removes a Rule from this Model.
|
Modifier and Type | Interface and Description |
---|---|
interface |
UnweightedRule
A template for
UnweightedGroundRules ,
which constrain the values that GroundAtoms can take. |
interface |
WeightedRule |
Modifier and Type | Class and Description |
---|---|
class |
AbstractRule
Base class for all (first order, i.e., not ground) rules.
|
Modifier and Type | Method and Description |
---|---|
Rule |
GroundRule.getRule() |
Modifier and Type | Class and Description |
---|---|
class |
AbstractArithmeticRule
Base class for all (first order, i.e., not ground) arithmetic rules.
|
class |
UnweightedArithmeticRule
A template for
UnweightedGroundArithmeticRules . |
class |
WeightedArithmeticRule |
Modifier and Type | Method and Description |
---|---|
Rule |
AbstractGroundArithmeticRule.getRule() |
Modifier and Type | Class and Description |
---|---|
class |
AbstractLogicalRule
Base class for all (first order, i.e., not ground) logical rules.
|
class |
UnweightedLogicalRule |
class |
WeightedLogicalRule |
Modifier and Type | Method and Description |
---|---|
static Rule |
ModelLoader.loadRule(DataStore data,
String input)
Parse and return a single rule.
|
Rule |
RulePartial.toRule()
Shortcut for toRule(null, null), which will create an unweighted rule.
|
Rule |
RulePartial.toRule(Double weight,
Boolean squared)
Create a rule from the partial given the weight and squared.
|
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