IVotingCategorizerLearner (Cognitive Foundry)

java.lang.Object
- gov.sandia.cognition.util.AbstractCloneableSerializable
- - gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
  - - gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>
    - - gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>
      - gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner<InputType,CategoryType,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
        
        gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner<InputType,CategoryType>

Type Parameters:

InputType - The type of the input for the categorizer to learn. This is the type passed to the internal batch learner to learn each ensemble member.

CategoryType - The type of the category that is the output for the categorizer to learn. It is also passed to the internal batch learner to learn each ensemble member. It must have a valid equals and hashCode method.

All Implemented Interfaces:

AnytimeAlgorithm<WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, IterativeAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, BatchLearnerContainer<BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>>, BagBasedCategorizerEnsembleLearner<InputType,CategoryType>, SupervisedBatchLearner<InputType,CategoryType,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

CategoryBalancedIVotingLearner
```
@PublicationReference(author="Leo Breiman",
                      title="Pasting small votes for classification in large databases and on-line",
                      year=1999,
                      type=Journal,
                      publication="Machine Learning",
                      pages={85,103},
                      url="http://www.springerlink.com/content/mnu2r28218651707/fulltext.pdf")
public class IVotingCategorizerLearner<InputType,CategoryType>
extends AbstractAnytimeSupervisedBatchLearner<InputType,CategoryType,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
implements Randomized, BatchLearnerContainer<BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>>, BagBasedCategorizerEnsembleLearner<InputType,CategoryType>
```
Learns an ensemble in a method similar to bagging except that on each iteration the bag is built from two parts, each sampled from elements from disjoint sets. The two sets are the set of examples that the ensemble currently gets correct and incorrect. In effect, ivoting has similar properties to boosting except that it does not require that the learner for each ensemble member be able to use weights on the examples.

Since:

3.0

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>` Implements a stopping criteria for IVoting that uses the out-of-bag error to determine when to stop learning the ensemble.

Field Summary

Fields
Modifier and Type	Field and Description
`protected Factory<? extends DataDistribution<CategoryType>>`	`counterFactory` Factory for counting votes.
`protected java.util.ArrayList<InputOutputPair<? extends InputType,CategoryType>>`	`currentBag` The current bag used to train the current ensemble member.
`protected java.util.ArrayList<java.lang.Integer>`	`currentCorrectIndices` The indices of examples that the ensemble currently gets correct.
`protected boolean[]`	`currentEnsembleCorrect` A boolean for each example indicating if the ensemble currently gets the example correct or incorrect.
`protected java.util.ArrayList<java.lang.Integer>`	`currentIncorrectIndices` The indices of examples that the ensemble currently gets incorrect.
`protected Evaluator<? super InputType,? extends CategoryType>`	`currentMember` The currently learned member of the ensemble.
`protected java.util.ArrayList<CategoryType>`	`currentMemberEstimates` The estimates of the current member for each example.
`protected java.util.ArrayList<DataDistribution<CategoryType>>`	`dataFullEstimates` The running estimate of the ensemble for each example.
`protected int[]`	`dataInBag` A counter for each example indicating how many times it exists in the current bag.
`protected java.util.ArrayList<? extends InputOutputPair<? extends InputType,CategoryType>>`	`dataList` The data represented as an array list.
`protected java.util.ArrayList<DataDistribution<CategoryType>>`	`dataOutOfBagEstimates` The running estimate of the ensemble for each example where an ensemble member can only vote on elements that were not in the bag used to train it.
`static int`	`DEFAULT_MAX_ITERATIONS` The default maximum number of iterations is 100.
`static double`	`DEFAULT_PERCENT_TO_SAMPLE` The default percent to sample 0.1.
`static double`	`DEFAULT_PROPORTION_INCORRECT_IN_SAMPLE` By default use 50% incorrect (and 50%) correct in the percent to sample.
`static boolean`	`DEFAULT_VOTE_OUT_OF_BAG_ONLY` The default value to vote out-of-bag.
`protected WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>`	`ensemble` The current ensemble.
`protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>`	`learner` The learner used to produce each ensemble member.
`protected int`	`numCorrectToSample` The number of correct examples to sample on each iteration.
`protected int`	`numIncorrectToSample` The number of incorrect examples to sample on each iteration.
`protected double`	`percentToSample` The percent to sample on each iteration.
`protected double`	`proportionIncorrectInSample` The proportion of incorrect examples in each sample.
`protected java.util.Random`	`random` The random number generator to use.
`protected int`	`sampleSize` The size of sample to create on each iteration.
`protected boolean`	`voteOutOfBagOnly` Controls whether or not an ensemble member can vote on items it was trained on during learning.

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm
maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
DEFAULT_ITERATION, iteration

Constructor Summary

Constructors
Constructor and Description
`IVotingCategorizerLearner()` Creates a new `IVotingCategorizerLearner`.
`IVotingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, double proportionIncorrectInSample, boolean voteOutOfBagOnly, Factory<? extends DataDistribution<CategoryType>> counterFactory, java.util.Random random)` Creates a new `IVotingCategorizerLearner`.
`IVotingCategorizerLearner(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 `IVotingCategorizerLearner`.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected void`	`cleanupAlgorithm()` Called to clean up the learning algorithm's state after learning has finished.
`protected void`	`createBag(java.util.ArrayList<java.lang.Integer> correctIndices, java.util.ArrayList<java.lang.Integer> incorrectIndices)` Create the next sample (bag) of examples to learn the next ensemble member from.
`Factory<? extends DataDistribution<CategoryType>>`	`getCounterFactory()` Gets the factory used for creating the object for counting the votes of the learned ensemble members.
`boolean[]`	`getCurrentEnsembleCorrect()` Gets whether or not the current ensemble gets each example correct.
`java.util.List<DataDistribution<CategoryType>>`	`getDataFullEstimates()` Gets the current estimates for each data point.
`int[]`	`getDataInBag()` Gets the counter for each example indicating how many times it exists in the current bag.
`java.util.List<DataDistribution<CategoryType>>`	`getDataOutOfBagEstimates()` Gets the current out-of-bag estimates for each data point.
`InputOutputPair<? extends InputType,CategoryType>`	`getExample(int index)` Gets the training example at the given index.
`BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>`	`getLearner()` Gets the learner used to learn each ensemble member.
`double`	`getPercentToSample()` Gets the percentage of the total data to sample on each iteration.
`double`	`getProportionIncorrectInSample()` Gets the proportion of incorrect examples to place in each sample.
`java.util.Random`	`getRandom()` Gets the random number generator used by this object.
`WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>`	`getResult()` Gets the current result of the algorithm.
`protected boolean`	`initializeAlgorithm()` Called to initialize the learning algorithm's state based on the data that is stored in the data field.
`boolean`	`isVoteOutOfBagOnly()` Gets whether during learning ensemble members can only vote on items that they are not in their bag (training set).
`protected static <DataType> void`	`sampleIndicesWithReplacementInto(java.util.ArrayList<java.lang.Integer> fromIndices, java.util.ArrayList<? extends DataType> baseData, int numToSample, java.util.Random random, java.util.ArrayList<DataType> output, int[] dataInBag)` Takes the given number of samples from the given list and places them in the given output list.
`void`	`setCounterFactory(Factory<? extends DataDistribution<CategoryType>> counterFactory)` Sets the factory used for creating the object for counting the votes of the learned ensemble members.
`void`	`setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)` Sets the learner used to learn each ensemble member.
`void`	`setPercentToSample(double percentToSample)` Sets the percentage of the data to sample (with replacement) on each iteration.
`void`	`setProportionIncorrectInSample(double proportionIncorrectInSample)` Sets the proportion of incorrect examples to place in each sample.
`void`	`setRandom(java.util.Random random)` Sets the random number generator used by this object.
`void`	`setVoteOutOfBagOnly(boolean voteOutOfBagOnly)` Sets whether during learning ensemble members can only vote on items that they are not in their bag (training set).
`protected boolean`	`step()` Called to take a single step of the learning algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
clone, getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm
getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.learning.algorithm.AnytimeBatchLearner
getData, getKeepGoing

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm
getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm
addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm
isResultValid, stop

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner
learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone

- Field Detail
  - DEFAULT_MAX_ITERATIONS
```
public static final int DEFAULT_MAX_ITERATIONS
```
    The default maximum number of iterations is 100.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_PERCENT_TO_SAMPLE
```
public static final double DEFAULT_PERCENT_TO_SAMPLE
```
    The default percent to sample 0.1.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_PROPORTION_INCORRECT_IN_SAMPLE
```
public static final double DEFAULT_PROPORTION_INCORRECT_IN_SAMPLE
```
    By default use 50% incorrect (and 50%) correct in the percent to sample.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_VOTE_OUT_OF_BAG_ONLY
```
public static final boolean DEFAULT_VOTE_OUT_OF_BAG_ONLY
```
    The default value to vote out-of-bag.
    
    See Also:
    
    Constant Field Values
  - learner
```
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner
```
    The learner used to produce each ensemble member.
  - percentToSample
```
protected double percentToSample
```
    The percent to sample on each iteration.
  - proportionIncorrectInSample
```
protected double proportionIncorrectInSample
```
    The proportion of incorrect examples in each sample. Must be between 0.0 and 1.0 (inclusive).
  - voteOutOfBagOnly
```
protected boolean voteOutOfBagOnly
```
    Controls whether or not an ensemble member can vote on items it was trained on during learning. By default, the ensemble member can only vote on out-of-bag values.
  - counterFactory
```
protected Factory<? extends DataDistribution<CategoryType>> counterFactory
```
    Factory for counting votes.
  - random
```
protected java.util.Random random
```
    The random number generator to use.
  - ensemble
```
protected transient WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> ensemble
```
    The current ensemble.
  - dataList
```
protected transient java.util.ArrayList<? extends InputOutputPair<? extends InputType,CategoryType>> dataList
```
    The data represented as an array list.
  - dataFullEstimates
```
protected transient java.util.ArrayList<DataDistribution<CategoryType>> dataFullEstimates
```
    The running estimate of the ensemble for each example. Updated in each iteration with the ensemble member created for that iteration. This is used instead of evaluating the ensemble in each iteration to make it so that each ensemble member is only evaluated once on each training example.
  - dataOutOfBagEstimates
```
protected transient java.util.ArrayList<DataDistribution<CategoryType>> dataOutOfBagEstimates
```
    The running estimate of the ensemble for each example where an ensemble member can only vote on elements that were not in the bag used to train it.
  - currentEnsembleCorrect
```
protected transient boolean[] currentEnsembleCorrect
```
    A boolean for each example indicating if the ensemble currently gets the example correct or incorrect.
  - currentCorrectIndices
```
protected transient java.util.ArrayList<java.lang.Integer> currentCorrectIndices
```
    The indices of examples that the ensemble currently gets correct.
  - currentIncorrectIndices
```
protected transient java.util.ArrayList<java.lang.Integer> currentIncorrectIndices
```
    The indices of examples that the ensemble currently gets incorrect.
  - sampleSize
```
protected transient int sampleSize
```
    The size of sample to create on each iteration.
  - numCorrectToSample
```
protected transient int numCorrectToSample
```
    The number of correct examples to sample on each iteration.
  - numIncorrectToSample
```
protected transient int numIncorrectToSample
```
    The number of incorrect examples to sample on each iteration.
  - currentBag
```
protected transient java.util.ArrayList<InputOutputPair<? extends InputType,CategoryType>> currentBag
```
    The current bag used to train the current ensemble member.
  - dataInBag
```
protected transient int[] dataInBag
```
    A counter for each example indicating how many times it exists in the current bag.
  - currentMember
```
protected transient Evaluator<? super InputType,? extends CategoryType> currentMember
```
    The currently learned member of the ensemble.
  - currentMemberEstimates
```
protected transient java.util.ArrayList<CategoryType> currentMemberEstimates
```
    The estimates of the current member for each example.
- Constructor Detail
  - IVotingCategorizerLearner
```
public IVotingCategorizerLearner()
```
    Creates a new IVotingCategorizerLearner.
  - IVotingCategorizerLearner
```
public IVotingCategorizerLearner(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 IVotingCategorizerLearner.
    
    Parameters:
    
    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.
  - IVotingCategorizerLearner
```
public IVotingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
                                 int maxIterations,
                                 double percentToSample,
                                 double proportionIncorrectInSample,
                                 boolean voteOutOfBagOnly,
                                 Factory<? extends DataDistribution<CategoryType>> counterFactory,
                                 java.util.Random random)
```
    Creates a new IVotingCategorizerLearner.
    
    Parameters:
    
    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.
    
    proportionIncorrectInSample - The percentage of incorrect examples to put in each sample. Must be between 0.0 and 1.0 (inclusive).
    
    voteOutOfBagOnly - Controls whether or not in-bag or out-of-bag votes are used to determine accuracy.
    
    counterFactory - The factory for counting votes.
    
    random - The random number generator to use.
- Method Detail
  - initializeAlgorithm
```
protected boolean initializeAlgorithm()
```
    Description copied from class: AbstractAnytimeBatchLearner
    
    Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.
    
    Specified by:
    
    initializeAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
    
    Returns:
    
    True if the learning algorithm can be run and false if it cannot.
  - step
```
protected boolean step()
```
    Description copied from class: AbstractAnytimeBatchLearner
    
    Called to take a single step of the learning algorithm.
    
    Specified by:
    
    step in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
    
    Returns:
    
    True if another step can be taken and false it the algorithm should halt.
  - createBag
```
protected void createBag(java.util.ArrayList<java.lang.Integer> correctIndices,
                         java.util.ArrayList<java.lang.Integer> incorrectIndices)
```
    Create the next sample (bag) of examples to learn the next ensemble member from.
    
    Parameters:
    
    correctIndices - The list of indices the ensemble is currently getting correct.
    
    incorrectIndices - The list of indices the ensemble is currently getting incorrect.
  - sampleIndicesWithReplacementInto
```
protected static <DataType> void sampleIndicesWithReplacementInto(java.util.ArrayList<java.lang.Integer> fromIndices,
                                                                  java.util.ArrayList<? extends DataType> baseData,
                                                                  int numToSample,
                                                                  java.util.Random random,
                                                                  java.util.ArrayList<DataType> output,
                                                                  int[] dataInBag)
```
    Takes the given number of samples from the given list and places them in the given output list. It samples with replacement, which means that a given item may appear multiple times in the bag. It also keeps track of how many times each item was sampled.
    
    Type Parameters:
    
    DataType - The data type to sample.
    
    Parameters:
    
    fromIndices - The indices into the given base data to sample from.
    
    baseData - The list to sample from using the given list of indices.
    
    numToSample - The number to sample. Must be non-negative.
    
    random - The random number generator to use.
    
    output - The list to add the samples to.
    
    dataInBag - The array of counters for the number of times each example is sampled.
  - cleanupAlgorithm
```
protected void cleanupAlgorithm()
```
    Description copied from class: AbstractAnytimeBatchLearner
    
    Called to clean up the learning algorithm's state after learning has finished.
    
    Specified by:
    
    cleanupAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
  - getResult
```
public WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> getResult()
```
    Description copied from interface: AnytimeAlgorithm
    
    Gets the current result of the algorithm.
    
    Specified by:
    
    getResult in interface AnytimeAlgorithm<WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
    
    Returns:
    
    Current result of the algorithm.
  - getDataInBag
```
public int[] getDataInBag()
```
    Description copied from interface: BagBasedCategorizerEnsembleLearner
    
    Gets the counter for each example indicating how many times it exists in the current bag.
    
    Specified by:
    
    getDataInBag in interface BagBasedCategorizerEnsembleLearner<InputType,CategoryType>
    
    Returns:
    
    The counter per example of how many times it is in the current bag. Note that this may be an internal state array for the algorithm so it shouldn't be modified. It is provided for efficient access.
  - getExample
```
public InputOutputPair<? extends InputType,CategoryType> getExample(int index)
```
    Description copied from interface: BagBasedCategorizerEnsembleLearner
    
    Gets the training example at the given index.
    
    Specified by:
    
    getExample in interface BagBasedCategorizerEnsembleLearner<InputType,CategoryType>
    
    Parameters:
    
    index - The 0-based index to lookup.
    
    Returns:
    
    The training example at that index.
  - getLearner
```
public BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> getLearner()
```
    Gets the learner used to learn each ensemble member.
    
    Specified by:
    
    getLearner in interface BatchLearnerContainer<BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>>
    
    Returns:
    
    The learner used for each ensemble member.
  - setLearner
```
public void setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
```
    Sets the learner used to learn each ensemble member. Must be a supervised learning algorithm that takes in a collection of input-output pairs of the given data types and produces an evaluator for those data types.
    
    Parameters:
    
    learner - The learner used for each ensemble member.
  - getPercentToSample
```
public double getPercentToSample()
```
    Gets the percentage of the total data to sample on each iteration.
    
    Returns:
    
    The percentage of the total data to sample on each iteration.
  - setPercentToSample
```
public void setPercentToSample(double percentToSample)
```
    Sets the percentage of the data to sample (with replacement) on each iteration. Must be greater than zero. The percent is represented as a floating point number with 1.0 representing 100%.
    
    Parameters:
    
    percentToSample - The percent of the data to sample on each iteration. Must be greater than zero. Defaults to 100%.
  - getProportionIncorrectInSample
```
public double getProportionIncorrectInSample()
```
    Gets the proportion of incorrect examples to place in each sample.
    
    Returns:
    
    The proportion of incorrect examples in each sample.
  - setProportionIncorrectInSample
```
public void setProportionIncorrectInSample(double proportionIncorrectInSample)
```
    Sets the proportion of incorrect examples to place in each sample. Must be between 0.0 and 1.0 (inclusive). The rest of the examples in the sample will be filled from the correct examples.
    
    Parameters:
    
    proportionIncorrectInSample - The proportion of incorrect examples in each sample. Must be between 0.0 and 1.0 (inclusive).
  - isVoteOutOfBagOnly
```
public boolean isVoteOutOfBagOnly()
```
    Gets whether during learning ensemble members can only vote on items that they are not in their bag (training set).
    
    Returns:
    
    If out-of-bag-only voting is enabled.
  - setVoteOutOfBagOnly
```
public void setVoteOutOfBagOnly(boolean voteOutOfBagOnly)
```
    Sets whether during learning ensemble members can only vote on items that they are not in their bag (training set). In the vast majority of cases, this should be enabled. It is enabled by default.
    
    Parameters:
    
    voteOutOfBagOnly - If out-of-bag-only voting should be enabled.
  - getCounterFactory
```
public Factory<? extends DataDistribution<CategoryType>> getCounterFactory()
```
    Gets the factory used for creating the object for counting the votes of the learned ensemble members.
    
    Returns:
    
    The factory used to create the vote counting objects.
  - setCounterFactory
```
public void setCounterFactory(Factory<? extends DataDistribution<CategoryType>> counterFactory)
```
    Sets the factory used for creating the object for counting the votes of the learned ensemble members.
    
    Parameters:
    
    counterFactory - The factory used to create the vote counting objects.
  - getRandom
```
public java.util.Random getRandom()
```
    Description copied from interface: Randomized
    
    Gets the random number generator used by this object.
    
    Specified by:
    
    getRandom in interface Randomized
    
    Returns:
    
    The random number generator used by this object.
  - setRandom
```
public void setRandom(java.util.Random random)
```
    Description copied from interface: Randomized
    
    Sets the random number generator used by this object.
    
    Specified by:
    
    setRandom in interface Randomized
    
    Parameters:
    
    random - The random number generator for this object to use.
  - getDataFullEstimates
```
public java.util.List<DataDistribution<CategoryType>> getDataFullEstimates()
```
    Gets the current estimates for each data point. Do not modify these counts as they will change the algorithm.
    
    Returns:
    
    The current estimates for each data point.
  - getDataOutOfBagEstimates
```
public java.util.List<DataDistribution<CategoryType>> getDataOutOfBagEstimates()
```
    Gets the current out-of-bag estimates for each data point. Do not modify these counts as they will change the algorithm.
    
    Returns:
    
    The current out-of-bag estimates for each data point.
  - getCurrentEnsembleCorrect
```
public boolean[] getCurrentEnsembleCorrect()
```
    Gets whether or not the current ensemble gets each example correct. Do not modify these values, as they will change the algorithm.
    
    Returns:
    
    The array of booleans regarding whether or not the ensemble gets an example correct.

Class IVotingCategorizerLearner<InputType,CategoryType>

Nested Class Summary

Field Summary

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

Constructor Summary

Method Summary

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

Methods inherited from class java.lang.Object

Methods inherited from interface gov.sandia.cognition.learning.algorithm.AnytimeBatchLearner

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

Field Detail

DEFAULT_MAX_ITERATIONS

DEFAULT_PERCENT_TO_SAMPLE

DEFAULT_PROPORTION_INCORRECT_IN_SAMPLE

DEFAULT_VOTE_OUT_OF_BAG_ONLY

learner

percentToSample

proportionIncorrectInSample

voteOutOfBagOnly

counterFactory

random

ensemble

dataList

dataFullEstimates

dataOutOfBagEstimates

currentEnsembleCorrect

currentCorrectIndices

currentIncorrectIndices

sampleSize

numCorrectToSample

numIncorrectToSample

currentBag

dataInBag

currentMember

currentMemberEstimates

Constructor Detail

IVotingCategorizerLearner

IVotingCategorizerLearner

IVotingCategorizerLearner

Method Detail

initializeAlgorithm

step

createBag

sampleIndicesWithReplacementInto

cleanupAlgorithm

getResult

getDataInBag

getExample

getLearner

setLearner

getPercentToSample

setPercentToSample

getProportionIncorrectInSample

setProportionIncorrectInSample

isVoteOutOfBagOnly

setVoteOutOfBagOnly

getCounterFactory

setCounterFactory

getRandom

setRandom

getDataFullEstimates

getDataOutOfBagEstimates

getCurrentEnsembleCorrect