Uses of Interface
gov.sandia.cognition.learning.algorithm.tree.DeciderLearner

Packages that use DeciderLearner

Package	Description
gov.sandia.cognition.learning.algorithm.tree	Provides decision tree learning algorithms.

Uses of DeciderLearner in gov.sandia.cognition.learning.algorithm.tree

Subinterfaces of DeciderLearner in gov.sandia.cognition.learning.algorithm.tree

Modifier and Type	Interface and Description
interface	VectorThresholdLearner<OutputType> An interface class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.

Classes in gov.sandia.cognition.learning.algorithm.tree that implement DeciderLearner

Modifier and Type	Class and Description
class	AbstractVectorThresholdMaximumGainLearner<OutputType> An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
class	RandomSubVectorThresholdLearner<OutputType> 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.
class	VectorThresholdGiniImpurityLearner<OutputType> Learns vector thresholds based on the Gini impurity measure.
class	VectorThresholdHellingerDistanceLearner<OutputType> A categorization tree decision function learner on vector data that learns a vector value threshold function using the Hellinger distance.
class	VectorThresholdInformationGainLearner<OutputType> The VectorThresholdInformationGainLearner computes the best threshold over a dataset of vectors using information gain to determine the optimal index and threshold.
class	VectorThresholdVarianceLearner The VectorThresholdVarianceLearner computes the best threshold over a dataset of vectors using the reduction in variance to determine the optimal index and threshold.

Fields in gov.sandia.cognition.learning.algorithm.tree declared as DeciderLearner

Modifier and Type	Field and Description
protected DeciderLearner<? super InputType,OutputType,?,?>	AbstractDecisionTreeLearner.deciderLearner The learning algorithm for the decision function.
protected DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer>	RandomSubVectorThresholdLearner.subLearner The decider learner for the subspace.

Methods in gov.sandia.cognition.learning.algorithm.tree that return DeciderLearner

Modifier and Type	Method and Description
DeciderLearner<? super InputType,OutputType,?,?>	AbstractDecisionTreeLearner.getDeciderLearner() Gets the learner for the decision function.
DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer>	RandomSubVectorThresholdLearner.getSubLearner() Gets the learner used to learn a threshold function over the subspace.

Methods in gov.sandia.cognition.learning.algorithm.tree with parameters of type DeciderLearner

Modifier and Type	Method and Description
void	AbstractDecisionTreeLearner.setDeciderLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner) Sets the learner for the decision function.
void	RandomSubVectorThresholdLearner.setSubLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner) Sets the learner used to learn a threshold function over the subspace.

Constructors in gov.sandia.cognition.learning.algorithm.tree with parameters of type DeciderLearner

Constructor and Description
AbstractDecisionTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner) Creates a new instance of AbstractDecisionTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner) Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner, int leafCountThreshold, int maxDepth) Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner, int leafCountThreshold, int maxDepth, java.util.Map<OutputType,java.lang.Double> priors) Creates a new instance of CategorizationTreeLearner.
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.
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner) Creates a new instance of CategorizationTreeLearner with a mean node learner
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner) Creates a new instance of CategorizationTreeLearner.
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner, int leafCountThreshold, int maxDepth) Creates a new instance of CategorizationTreeLearner.