gov.sandia.cognition.learning.function.distance

Class DivergencesEvaluator.Learner<DataType,InputType,ValueType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.AbstractBatchLearnerContainer<BatchLearner<? super DataType,? extends java.util.Collection<ValueType>>>

gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner<DataType,InputType,ValueType>

Type Parameters:

DataType - The data type for learning. Passed to the wrapped learner.

InputType - The input type for the evaluator.

ValueType - The value type that is the output of learning and is used as the values in the learned evaluator.

All Implemented Interfaces:

BatchLearner<DataType,DivergencesEvaluator<InputType,ValueType>>, BatchLearnerContainer<BatchLearner<? super DataType,? extends java.util.Collection<ValueType>>>, DivergenceFunctionContainer<ValueType,InputType>, VectorFactoryContainer, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

DivergencesEvaluator<InputType,ValueType>

public static class DivergencesEvaluator.Learner<DataType,InputType,ValueType>
extends AbstractBatchLearnerContainer<BatchLearner<? super DataType,? extends java.util.Collection<ValueType>>>
implements BatchLearner<DataType,DivergencesEvaluator<InputType,ValueType>>, DivergenceFunctionContainer<ValueType,InputType>, VectorFactoryContainer

A learner adapter for the DivergencesEvaluator. It calls a base learner and then wraps learned collection of values in an evaluator that uses the given divergence function.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected DivergenceFunction<? super ValueType,? super InputType>	divergenceFunction The divergence function to apply between the data and the input.
protected VectorFactory<?>	vectorFactory The vector factory to use.

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

learner

Constructor Summary

Constructors

Constructor and Description
Learner() Creates a new DivergenceFunction.Learner with null base learner and divergence functions.
Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Creates a new DivergenceFunction.Learner with the given properties.
Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, VectorFactory<?> vectorFactory) Creates a new DivergenceFunction.Learner with the given properties.

Method Summary

Modifier and Type	Method and Description
DivergencesEvaluator.Learner<DataType,InputType,ValueType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
static <DataType,InputType,ValueType> DivergencesEvaluator.Learner<DataType,InputType,ValueType>	create(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Convenience method for creating a DivergencesEvaluator.Learner.
DivergenceFunction<? super ValueType,? super InputType>	getDivergenceFunction() Gets the divergence function used by this object.
VectorFactory<? extends Vector>	getVectorFactory() Gets the vector factory the object to use to create new vectors.
DivergencesEvaluator<InputType,ValueType>	learn(DataType data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setDivergenceFunction(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Sets the divergence function to use from the values to the inputs.
void	setVectorFactory(VectorFactory<?> vectorFactory) Sets the vector factory to use.

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

getLearner, setLearner

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

divergenceFunction

protected DivergenceFunction<? super ValueType,? super InputType> divergenceFunction

The divergence function to apply between the data and the input.

vectorFactory

protected VectorFactory<?> vectorFactory

The vector factory to use.

Constructor Detail

Learner

public Learner()

Creates a new DivergenceFunction.Learner with null base learner and divergence functions.

Learner

public Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner,
               DivergenceFunction<? super ValueType,? super InputType> divergenceFunction)

Creates a new DivergenceFunction.Learner with the given properties.

Parameters:

learner - The base learner to use.

divergenceFunction - The divergence function to use.

Learner

public Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner,
               DivergenceFunction<? super ValueType,? super InputType> divergenceFunction,
               VectorFactory<?> vectorFactory)

Creates a new DivergenceFunction.Learner with the given properties.

Parameters:

learner - The base learner to use.

divergenceFunction - The divergence function to use.

vectorFactory - The vector factory to use.

Method Detail

clone

public DivergencesEvaluator.Learner<DataType,InputType,ValueType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractBatchLearnerContainer<BatchLearner<? super DataType,? extends java.util.Collection<ValueType>>>

Returns:

A clone of this object.

learn

public DivergencesEvaluator<InputType,ValueType> learn(DataType data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<DataType,DivergencesEvaluator<InputType,ValueType>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

getDivergenceFunction

public DivergenceFunction<? super ValueType,? super InputType> getDivergenceFunction()

Description copied from interface: DivergenceFunctionContainer

Gets the divergence function used by this object.

Specified by:

getDivergenceFunction in interface DivergenceFunctionContainer<ValueType,InputType>

Returns:

The divergence function.

setDivergenceFunction

public void setDivergenceFunction(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction)

Sets the divergence function to use from the values to the inputs.

Parameters:

divergenceFunction - The divergence function to use.

getVectorFactory

public VectorFactory<? extends Vector> getVectorFactory()

Description copied from interface: VectorFactoryContainer

Gets the vector factory the object to use to create new vectors.

Specified by:

getVectorFactory in interface VectorFactoryContainer

Returns:

The vector factory.

setVectorFactory

public void setVectorFactory(VectorFactory<?> vectorFactory)

Sets the vector factory to use.

Parameters:

vectorFactory - The vector factory to use.

create

public static <DataType,InputType,ValueType> DivergencesEvaluator.Learner<DataType,InputType,ValueType> create(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner,
                                                                                                               DivergenceFunction<? super ValueType,? super InputType> divergenceFunction)

Convenience method for creating a DivergencesEvaluator.Learner.

Type Parameters:

DataType - The data type for learning. Passed to the wrapped learner.

InputType - The input type for the evaluator.

ValueType - The value type that is the output of learning and is used as the values in the learned evaluator.

Parameters:

learner - The base learner to use.

divergenceFunction - The divergence function to use.

Returns:

A new learner.