See: Description
Interface | Description |
---|---|
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 java.lang.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 | Description |
---|---|
AbstractInputOutputPair<InputType,OutputType> |
An abstract implementation of the
InputOutputPair interface. |
AbstractTargetEstimatePair<TargetType,EstimateType> |
An abstract implementation of the
TargetEstimatePair . |
AbstractValueDiscriminantPair<ValueType,DiscriminantType extends java.lang.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 java.lang.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.
|