InputType - The input type for supervised learning. Passed on to the internal
learning algorithm. Also the input type for the learned ensemble.CategoryType - The output type for supervised learning. Passed on to the internal
learning algorithm. Also the output type of the learned ensemble.@PublicationReference(title="Bagging Predictors", author="Leo Breiman", year=1996, type=Journal, publication="Machine Learning", pages={123,140}, url="http://www.springerlink.com/index/L4780124W2874025.pdf") public class BaggingCategorizerLearner<InputType,CategoryType> extends AbstractBaggingLearner<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>> implements BagBasedCategorizerEnsembleLearner<InputType,CategoryType>
| Modifier and Type | Class and Description |
|---|---|
static class |
BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>
Implements a stopping criteria for bagging that uses the out-of-bag
error to determine when to stop learning the ensemble.
|
bag, dataInBag, dataList, DEFAULT_MAX_ITERATIONS, DEFAULT_PERCENT_TO_SAMPLE, ensemble, learner, percentToSample, randomdata, keepGoingmaxIterationsDEFAULT_ITERATION, iteration| Constructor and Description |
|---|
BaggingCategorizerLearner()
Creates a new instance of BaggingCategorizerLearner.
|
BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
Creates a new instance of BaggingCategorizerLearner.
|
BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new instance of BaggingCategorizerLearner.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
addEnsembleMember(Evaluator<? super InputType,? extends CategoryType> member)
Adds a new member to the ensemble.
|
protected WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> |
createInitialEnsemble()
Create the initial, empty ensemble for the algorithm to use.
|
int[] |
getDataInBag()
Gets the array of counts of the number of samples of each example in
the current bag.
|
InputOutputPair<? extends InputType,CategoryType> |
getExample(int index)
Gets the training example at the given index.
|
cleanupAlgorithm, fillBag, getBag, getDataList, getEnsemble, getLearner, getPercentToSample, getRandom, getResult, initializeAlgorithm, setBag, setDataInBag, setDataList, setEnsemble, setLearner, setPercentToSample, setRandom, stepclone, getData, getKeepGoing, learn, setData, setKeepGoing, stopgetMaxIterations, isResultValid, setMaxIterationsaddIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListenersequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetData, getKeepGoinggetMaxIterations, getResult, setMaxIterationsaddIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListenerisResultValid, stoplearnclonepublic BaggingCategorizerLearner()
public BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
learner - The learner to use to create the categorizer on each iteration.public BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, java.util.Random random)
learner - The learner to use to create the categorizer on each iteration.maxIterations - The maximum number of iterations to run for, which is also the
number of learners to create.percentToSample - The percentage of the total size of the data to sample on each
iteration. Must be positive.random - The random number generator to use.protected WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> createInitialEnsemble()
AbstractBaggingLearnercreateInitialEnsemble in class AbstractBaggingLearner<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>protected void addEnsembleMember(Evaluator<? super InputType,? extends CategoryType> member)
AbstractBaggingLearneraddEnsembleMember in class AbstractBaggingLearner<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>member - The new member to add to the ensemble.public int[] getDataInBag()
AbstractBaggingLearnergetDataInBag in interface BagBasedCategorizerEnsembleLearner<InputType,CategoryType>getDataInBag in class AbstractBaggingLearner<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>public InputOutputPair<? extends InputType,CategoryType> getExample(int index)
BagBasedCategorizerEnsembleLearnergetExample in interface BagBasedCategorizerEnsembleLearner<InputType,CategoryType>index - The 0-based index to lookup.