gov.sandia.cognition.learning.function.distance

Class DivergencesEvaluator<InputType,ValueType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.data.convert.AbstractDataConverter<InputType,Vector>

gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder<InputType>

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

Type Parameters:

InputType - The type of input value that the class evaluates. It is the second parameter passed to the divergence function. Typically a type like a Vector.

ValueType - The type of value that the divergence is computed from. It is the first parameter passed to the divergence function. It is typically a type like Vector or CenteroidCluster.

All Implemented Interfaces:

DataConverter<InputType,Vector>, DataToVectorEncoder<InputType>, Evaluator<InputType,Vector>, DivergenceFunctionContainer<ValueType,InputType>, VectorFactoryContainer, VectorOutputEvaluator<InputType,Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class DivergencesEvaluator<InputType,ValueType>
extends AbstractToVectorEncoder<InputType>
implements VectorOutputEvaluator<InputType,Vector>, DivergenceFunctionContainer<ValueType,InputType>

Evaluates the divergence (distance) between an input and a list of values, storing the resulting divergence values in a vector. This can be used as a feature representation built from something like a clustering algorithm or from a set of prototype/basis elements.

Since:

3.3.3

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DivergencesEvaluator.Learner<DataType,InputType,ValueType> A learner adapter for the DivergencesEvaluator.

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 java.util.Collection<ValueType>	values The data to evaluate the divergence from.

Fields inherited from class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder

vectorFactory

Constructor Summary

Constructors

Constructor and Description
DivergencesEvaluator() Creates a new DivergencesEvaluator with a null divergence function and an empty set of values.
DivergencesEvaluator(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, java.util.Collection<ValueType> values) Creates a new DivergencesEvaluator with the given divergence and values.
DivergencesEvaluator(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, java.util.Collection<ValueType> values, VectorFactory<?> vectorFactory) Creates a new DivergencesEvaluator with the given divergence and values.

Method Summary

Modifier and Type	Method and Description
DivergencesEvaluator<InputType,ValueType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
static <InputType,ValueType> DivergencesEvaluator<InputType,ValueType>	create(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, java.util.Collection<ValueType> values) Convenience method for creation a DivergeceEvaluator.
void	encode(InputType input, Vector result, int startIndex) Encodes the given object into the given Vector, starting at the given index.
DivergenceFunction<? super ValueType,? super InputType>	getDivergenceFunction() Gets the divergence function used by this object.
int	getOutputDimensionality() Gets the expected dimensionality of the output vector of the evaluator, if it is known.
java.util.Collection<ValueType>	getValues() Gets the values that the divergence is computed from using the divergence function to the input.
void	setDivergenceFunction(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Sets the divergence function to use from the values to the inputs.
void	setValues(java.util.Collection<ValueType> values) Sets the values that the divergence is computed from using the divergence function to the input.

Methods inherited from class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder

encode, evaluate, getVectorFactory, setVectorFactory

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator

evaluate

Field Detail

divergenceFunction

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

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

values

protected java.util.Collection<ValueType> values

The data to evaluate the divergence from.

Constructor Detail

DivergencesEvaluator

public DivergencesEvaluator()

Creates a new DivergencesEvaluator with a null divergence function and an empty set of values.

DivergencesEvaluator

public DivergencesEvaluator(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction,
                            java.util.Collection<ValueType> values)

Creates a new DivergencesEvaluator with the given divergence and values.

Parameters:

divergenceFunction - The divergence function to use.

values - The values to calculate the divergence from.

DivergencesEvaluator

public DivergencesEvaluator(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction,
                            java.util.Collection<ValueType> values,
                            VectorFactory<?> vectorFactory)

Creates a new DivergencesEvaluator with the given divergence and values.

Parameters:

divergenceFunction - The divergence function to use.

values - The values to calculate the divergence from.

vectorFactory - The vector factory to use.

Method Detail

clone

public DivergencesEvaluator<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 AbstractCloneableSerializable

Returns:

A clone of this object.

encode

public void encode(InputType input,
                   Vector result,
                   int startIndex)

Description copied from interface: DataToVectorEncoder

Encodes the given object into the given Vector, starting at the given index.

Specified by:

encode in interface DataToVectorEncoder<InputType>

Parameters:

input - The object to encode.

result - The vector to encode the object into.

startIndex - The index to start the encoding at.

getOutputDimensionality

public int getOutputDimensionality()

Description copied from interface: VectorOutputEvaluator

Gets the expected dimensionality of the output vector of the evaluator, if it is known. If it is not known, -1 is returned.

Specified by:

getOutputDimensionality in interface DataToVectorEncoder<InputType>

Specified by:

getOutputDimensionality in interface VectorOutputEvaluator<InputType,Vector>

Returns:

The expected dimensionality of the output vector of the evaluator, or -1 if it is not known.

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.

getValues

public java.util.Collection<ValueType> getValues()

Gets the values that the divergence is computed from using the divergence function to the input.

Returns:

The values that the distance is computed from.

setValues

public void setValues(java.util.Collection<ValueType> values)

Sets the values that the divergence is computed from using the divergence function to the input.

Parameters:

values - The values that the distance is computed from.

create

public static <InputType,ValueType> DivergencesEvaluator<InputType,ValueType> create(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction,
                                                                                     java.util.Collection<ValueType> values)

Convenience method for creation a DivergeceEvaluator.

Type Parameters:

InputType - The type of input value that the class evaluates.

ValueType - The type of value that the divergence is computed from.

Parameters:

divergenceFunction - The divergence function to use.

values - The values to calculate the divergence from.

Returns:

A new evaluator.