gov.sandia.cognition.learning.algorithm
Class AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType>
          extended by gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType>
Type Parameters:
InputType - The type of input data in the input-output pair that the learner can learn from. The Evaluator learned from the algorithm also takes this as the input parameter.
OutputType - The type of output data in the input-output pair that the learner can learn from. The Evaluator learned from the algorithm also produces this as its output.
ResultType - The type of object created by the learning algorithm. For example, a LinearBinaryCategorizer.
All Implemented Interfaces:
BatchAndIncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType>, BatchLearner<Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>, IncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType>, SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType>, SupervisedBatchLearner<InputType,OutputType,ResultType>, SupervisedIncrementalLearner<InputType,OutputType,ResultType>, CloneableSerializable, Serializable, Cloneable
Direct Known Subclasses:
AbstractOnlineKernelBinaryCategorizerLearner, AbstractOnlineLinearBinaryCategorizerLearner, AdaptiveRegularizationOfWeights, ConfidenceWeightedDiagonalDeviation, ConfidenceWeightedDiagonalVariance, OnlineBaggingCategorizerLearner, OnlineVotedPerceptron

public abstract class AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
extends AbstractBatchAndIncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType>
implements SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType>

An abstract implementation of the batch and incremental learning for an incremental supervised learner.

Since:
3.2.0
Author:
Justin Basilico
See Also:
Serialized Form

Constructor Summary
AbstractSupervisedBatchAndIncrementalLearner()
          Creates a new AbstractSupervisedBatchAndIncrementalLearner.
 
Method Summary
 void update(ResultType target, InputOutputPair<? extends InputType,OutputType> data)
          The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.
 
Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner
clone, learn, learn, update
 
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.SupervisedIncrementalLearner
update
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchAndIncrementalLearner
learn
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner
learn
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner
createInitialLearnedObject, update
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Constructor Detail

AbstractSupervisedBatchAndIncrementalLearner

public AbstractSupervisedBatchAndIncrementalLearner()
Creates a new AbstractSupervisedBatchAndIncrementalLearner.

Method Detail

update

public void update(ResultType target,
                   InputOutputPair<? extends InputType,OutputType> data)
Description copied from interface: IncrementalLearner
The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

Specified by:
update in interface IncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType extends Evaluator<? super InputType,? extends OutputType>>
Parameters:
target - The object to update.
data - The new data for the learning algorithm to use to update the object.