| Modifier and Type | Method and Description |
|---|---|
static AMapping |
MLPipeline.execute(ACache source,
ACache target,
Configuration configuration,
String mlAlgorithmName,
MLImplementationType mlImplementationType,
List<LearningParameter> learningParameters,
String trainingDataFile,
EvaluatorType pfmType,
int maxIt) |
| Modifier and Type | Method and Description |
|---|---|
static void |
Evaluator.main(String[] args) |
| Modifier and Type | Method and Description |
|---|---|
MLResults |
ActiveMLAlgorithm.activeLearn() |
protected abstract MLResults |
ACoreMLAlgorithm.activeLearn()
Learning method for supervised active core ML algorithm implementations
Normally, it is used as a first step to initialize the ML model
before going through the active learning process
|
protected MLResults |
ACIDS.activeLearn() |
protected MLResults |
WombatComplete.activeLearn() |
protected MLResults |
Eagle.activeLearn() |
MLResults |
ActiveMLAlgorithm.activeLearn(AMapping oracleMapping) |
protected abstract MLResults |
ACoreMLAlgorithm.activeLearn(AMapping oracleMapping)
Learning method for supervised active core ML algorithm implementations.
|
protected MLResults |
WombatComplete.activeLearn(AMapping oracleMapping) |
protected MLResults |
WombatSimple.activeLearn(AMapping oracleMapping) |
protected MLResults |
Eagle.activeLearn(AMapping oracleMapping) |
static AMLAlgorithm |
MLAlgorithmFactory.createMLAlgorithm(Class<? extends ACoreMLAlgorithm> clazz,
MLImplementationType mlType) |
AMapping |
ActiveMLAlgorithm.getNextExamples(int size) |
protected abstract AMapping |
ACoreMLAlgorithm.getNextExamples(int size)
Get a set of examples to be added to the mapping.
|
protected AMapping |
WombatComplete.getNextExamples(int size) |
protected AMapping |
WombatSimple.getNextExamples(int size) |
protected AMapping |
Eagle.getNextExamples(int size) |
protected abstract MLResults |
ACoreMLAlgorithm.learn(AMapping trainingData)
Learning method for supervised core ML algorithm implementations, where
the confidence values for each pair in the trainingData determine its
truth degree.
|
MLResults |
SupervisedMLAlgorithm.learn(AMapping trainingData) |
protected abstract MLResults |
ACoreMLAlgorithm.learn(PseudoFMeasure pfm)
Learning method for unsupervised core ML algorithm implementations.
|
MLResults |
UnsupervisedMLAlgorithm.learn(PseudoFMeasure pfm) |
protected MLResults |
ACIDS.learn(PseudoFMeasure pfm) |
| Constructor and Description |
|---|
ActiveMLAlgorithm(Class<? extends ACoreMLAlgorithm> clazz) |
SupervisedMLAlgorithm(Class<? extends ACoreMLAlgorithm> clazz) |
UnsupervisedMLAlgorithm(Class<? extends ACoreMLAlgorithm> clazz) |
| Modifier and Type | Method and Description |
|---|---|
protected MLResults |
Dragon.activeLearn()
generates an initial training set and calls
Dragon.activeLearn(AMapping) |
protected MLResults |
Dragon.activeLearn(AMapping oracleMapping)
Creates a training set out of the oracleMapping and uses
J48 to
build a decision tree The decision tree gets parsed to a
LinkSpecification by TreeParser |
protected AMapping |
Dragon.getNextExamples(int size) |
protected MLResults |
Dragon.learn(AMapping trainingData) |
protected MLResults |
Dragon.learn(PseudoFMeasure pfm) |
| Modifier and Type | Method and Description |
|---|---|
protected MLResults |
LinearEuclid.activeLearn() |
protected MLResults |
LinearEuclid.activeLearn(AMapping oracleMapping) |
protected AMapping |
LinearEuclid.getNextExamples(int size) |
protected MLResults |
LinearEuclid.learn(AMapping trainingData) |
protected MLResults |
BooleanEuclid.learn(PseudoFMeasure pfm) |
protected MLResults |
LinearEuclid.learn(PseudoFMeasure pfm) |
protected MLResults |
MeshEuclid.learn(PseudoFMeasure pfm) |
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