Package gov.sandia.cognition.learning.data

Provides data set utilities for learning.

See:
          Description

Interface Summary
DataPartitioner<DataType> The DataPartitioner interface defines the functionality of an object that can create a PartitionedDataset from a collection of data.
InputOutputPair<InputType,OutputType> The InputOutputPair interface is just a container for an input and its associated output used in supervised learning.
PartitionedDataset<DataType> Interface for a dataset partitioned into training and testing sets.
RandomizedDataPartitioner<DataType> The RandomizedDataPartitioner extends a DataPartitioner to indicate that is it is randomized, which means that its partitions are based (at least in part) on an underlying random number generator.
TargetEstimatePair<TargetType,EstimateType> A Pair that encapsulates a target-estimate Pair.
ValueDiscriminantPair<ValueType,DiscriminantType extends Comparable<? super DiscriminantType>> Interface for a pair of a value and a discriminant for ordering instances that have the same value.
WeightedInputOutputPair<InputType,OutputType> The WeightedInputOutputPair class implements an additional weighting term on an InputOutputPair, typically used to inform learning algorithms of the relative weight between examples.
WeightedTargetEstimatePair<TargetType,EstimateType> Extends TargetEstimatePair with an additional weight field.
 

Class Summary
AbstractInputOutputPair<InputType,OutputType> An abstract implementation of the InputOutputPair interface.
AbstractTargetEstimatePair<TargetType,EstimateType> An abstract implementation of the TargetEstimatePair.
AbstractValueDiscriminantPair<ValueType,DiscriminantType extends Comparable<? super DiscriminantType>> An abstract implementation of the ValueDiscriminantPair interface.
DatasetUtil Static class containing utility methods for handling Collections of data in the learning package.
DefaultInputOutputPair<InputType,OutputType> A default implementation of the InputOutputPair interface.
DefaultPartitionedDataset<DataType> The PartitionedDataset class provides a simple container for the training and testing datasets to be held together.
DefaultTargetEstimatePair<TargetType,EstimateType> A default implementation of the TargetEstimatePair.
DefaultValueDiscriminantPair<ValueType,DiscriminantType extends Comparable<? super DiscriminantType>> A default implementation of the ValueDiscriminantPair interface.
DefaultWeightedInputOutputPair<InputType,OutputType> A default implementation of the WeightedInputOutputPair interface.
DefaultWeightedTargetEstimatePair<TargetType,EstimateType> Extends TargetEstimatePair with an additional weight field.
DefaultWeightedValueDiscriminant<ValueType> An implementation of ValueDiscriminantPair that stores a double as the discriminant.
RandomDataPartitioner<DataType> The RandomDataPartitioner class implements a randomized data partitioner that takes a collection of data and randomly splits it into training and testing sets based on a fixed percentage of training data.
SequentialDataMultiPartitioner This partitioner splits a Collection of data into a pre-defined number of approximately equal sequential partitions, with the nonzero remainder elements going into the final partition.
 

Package gov.sandia.cognition.learning.data Description

Provides data set utilities for learning.

Since:
2.0
Author:
Justin Basilico