gov.sandia.cognition.learning.algorithm.tree

Class RandomSubVectorThresholdLearner<OutputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.util.AbstractRandomized

gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner<OutputType>

Type Parameters:

OutputType - The output type for the decider.

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>>,VectorElementThresholdCategorizer>, DimensionFilterableLearner, DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer>, VectorThresholdLearner<OutputType>, VectorFactoryContainer, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

public class RandomSubVectorThresholdLearner<OutputType>
extends AbstractRandomized
implements VectorThresholdLearner<OutputType>, VectorFactoryContainer

Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner. This component is typically used along with a decision tree learner to create random forests of decision trees.

Since:

3.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_PERCENT_TO_SAMPLE The default percent to sample is 0.1.
protected int[]	dimensionsToConsider The dimensions to sample from in the learner.
protected double	percentToSample The percentage of the dimensionality to sample.
protected DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer>	subLearner The decider learner for the subspace.
protected VectorFactory<? extends Vector>	vectorFactory The vector factory to use.

Fields inherited from class gov.sandia.cognition.util.AbstractRandomized

random

Constructor Summary

Constructors

Constructor and Description
RandomSubVectorThresholdLearner() Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner, double percentToSample, int[] dimensionsToConsider, java.util.Random random, VectorFactory<? extends Vector> vectorFactory) Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner, double percentToSample, java.util.Random random) Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner, double percentToSample, java.util.Random random, VectorFactory<? extends Vector> vectorFactory) Creates a new RandomSubVectorThresholdLearner.

Method Summary

Modifier and Type	Method and Description
RandomSubVectorThresholdLearner<OutputType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
int[]	getDimensionsToConsider() Gets the dimensions that the learner is to consider.
double	getPercentToSample() Gets the percent of the dimensionality to sample.
int	getSubDimensionality(int dimensionality) Gets the dimensionality of the subspace based on the full dimensionality.
DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer>	getSubLearner() Gets the learner used to learn a threshold function over the subspace.
VectorFactory<? extends Vector>	getVectorFactory() Gets the vector factory.
VectorElementThresholdCategorizer	learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setDimensionsToConsider(int... dimensionsToConsider) Gets the dimensions that the learner is to consider.
void	setPercentToSample(double percentToSample) Sets the percent of the dimensionality to sample.
void	setSubLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner) Sets the learner used to learn a threshold function over the subspace.
void	setVectorFactory(VectorFactory<? extends Vector> vectorFactory) Sets the vector factory.

Methods inherited from class gov.sandia.cognition.util.AbstractRandomized

getRandom, setRandom

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_PERCENT_TO_SAMPLE

public static final double DEFAULT_PERCENT_TO_SAMPLE

The default percent to sample is 0.1.

See Also:

Constant Field Values

subLearner

protected DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner

The decider learner for the subspace.

percentToSample

protected double percentToSample

The percentage of the dimensionality to sample.

dimensionsToConsider

protected int[] dimensionsToConsider

The dimensions to sample from in the learner.

vectorFactory

protected VectorFactory<? extends Vector> vectorFactory

The vector factory to use.

Constructor Detail

RandomSubVectorThresholdLearner

public RandomSubVectorThresholdLearner()

Creates a new RandomSubVectorThresholdLearner.

RandomSubVectorThresholdLearner

public RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner,
                                       double percentToSample,
                                       java.util.Random random)

Creates a new RandomSubVectorThresholdLearner.

Parameters:

subLearner - The threshold decision function learner to use over the subspace.

percentToSample - The percentage of the dimensionality to sample (must be between 0.0 (exclusive) and 1.0 (inclusive).

random - The random number generator.

RandomSubVectorThresholdLearner

public RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner,
                                       double percentToSample,
                                       java.util.Random random,
                                       VectorFactory<? extends Vector> vectorFactory)

Creates a new RandomSubVectorThresholdLearner.

Parameters:

subLearner - The threshold decision function learner to use over the subspace.

percentToSample - The percentage of the dimensionality to sample (must be between 0.0 and 1.0.

random - The random number generator.

vectorFactory - The vector factory to use.

RandomSubVectorThresholdLearner

public RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner,
                                       double percentToSample,
                                       int[] dimensionsToConsider,
                                       java.util.Random random,
                                       VectorFactory<? extends Vector> vectorFactory)

Creates a new RandomSubVectorThresholdLearner.

Parameters:

subLearner - The threshold decision function learner to use over the subspace.

percentToSample - The percentage of the dimensionality to sample (must be between 0.0 and 1.0.

dimensionsToConsider - The array of vector dimensions to consider. Null means all of them are considered.

random - The random number generator.

vectorFactory - The vector factory to use.

Method Detail

clone

public RandomSubVectorThresholdLearner<OutputType> 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 AbstractRandomized

Returns:

A clone of this object.

learn

public VectorElementThresholdCategorizer learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> 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<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>>,VectorElementThresholdCategorizer>

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.

getSubDimensionality

public int getSubDimensionality(int dimensionality)

Gets the dimensionality of the subspace based on the full dimensionality.

Parameters:

dimensionality - The full dimensionality

Returns:

The dimensionality of the subspace. Will always be greater than or equal to 1.

getSubLearner

public DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> getSubLearner()

Gets the learner used to learn a threshold function over the subspace.

Returns:

The learner for the subspace.

setSubLearner

public void setSubLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner)

Sets the learner used to learn a threshold function over the subspace.

Parameters:

subLearner - The learner for the subspace.

getPercentToSample

public double getPercentToSample()

Gets the percent of the dimensionality to sample. Must be between 0.0 and 1.0.

Returns:

The percent of the dimensionality to sample.

setPercentToSample

public void setPercentToSample(double percentToSample)

Sets the percent of the dimensionality to sample. Must be between 0.0 and 1.0.

Parameters:

percentToSample - The percent of the dimensionality to sample.

getDimensionsToConsider

public int[] getDimensionsToConsider()

Description copied from interface: DimensionFilterableLearner

Gets the dimensions that the learner is to consider. Null means that all of them are included.

Specified by:

getDimensionsToConsider in interface DimensionFilterableLearner

Returns:

The array of vector dimensions to consider. Null means all of them are considered.

setDimensionsToConsider

public void setDimensionsToConsider(int... dimensionsToConsider)

Description copied from interface: DimensionFilterableLearner

Gets the dimensions that the learner is to consider. Null means that all of them are included.

Specified by:

setDimensionsToConsider in interface DimensionFilterableLearner

Parameters:

dimensionsToConsider - The array of vector dimensions to consider. Null means all of them are considered.

getVectorFactory

public VectorFactory<? extends Vector> getVectorFactory()

Gets the vector factory.

Specified by:

getVectorFactory in interface VectorFactoryContainer

Returns:

The vector factory.

setVectorFactory

public void setVectorFactory(VectorFactory<? extends Vector> vectorFactory)

Sets the vector factory.

Parameters:

vectorFactory - The vector factory.