| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.clustering.cluster |
Provides implementations of different types of clusters.
|
| gov.sandia.cognition.learning.algorithm.clustering.divergence |
Provides divergence functions for use in clustering.
|
| gov.sandia.cognition.learning.algorithm.clustering.initializer |
Provides implementations of methods for selecting initial clusters.
|
| gov.sandia.cognition.learning.algorithm.nearest |
Provides algorithms for Nearest-Neighbor memory-based functions.
|
| gov.sandia.cognition.learning.function.distance |
Provides distance functions.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MedoidClusterCreator<DataType>
The
MedoidClusterCreator class creates a
CentroidCluster at the sample that minimizes the sum
of the divergence to the objects assigned to the cluster. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
The AbstractClusterToClusterDivergenceFunction class is an abstract class
that helps out implementations of ClusterToClusterDivergenceFunction
implementations by holding a DivergenceFunction between elements of a
cluster.
|
class |
CentroidClusterDivergenceFunction<DataType>
The CentroidClusterDivergenceFunction class implements a divergence function
between a cluster and an object by computing the divergence between the
center of the cluster and the object.
|
class |
ClusterCentroidDivergenceFunction<DataType>
The ClusterCentroidDivergenceFunction class implements the distance
between two clusters by computing the distance between the cluster's
centroid.
|
class |
ClusterCompleteLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
The ClusterCompleteLinkDivergenceFunction class implements the complete
linkage distance metric between two clusters.
|
class |
ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
The ClusterMeanLinkDivergenceFunction class implements the mean linkage
distance metric between two clusters.
|
class |
ClusterSingleLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
The ClusterSingleLinkDivergenceFunction class implements the complete
linkage distance metric between two clusters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
Implements an abstract FixedClusterInitializer that works by using the
minimum distance from a point to the cluster.
|
class |
DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
Implements
FixedClusterInitializer that initializes clusters by
first selecting a random point for the first cluster and then randomly
sampling each successive cluster based on the squared minimum distance from
the point to the existing selected clusters. |
class |
GreedyClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
Implements a FixedClusterInitializer that greedily attempts to create the
initial clusters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKNearestNeighbor<InputType,OutputType>
Partial implementation of KNearestNeighbor.
|
class |
AbstractNearestNeighbor<InputType,OutputType>
Partial implementation of KNearestNeighbor.
|
class |
KNearestNeighborExhaustive<InputType,OutputType>
A generic k-nearest-neighbor classifier.
|
static class |
KNearestNeighborExhaustive.Learner<InputType,OutputType>
This is a BatchLearner interface for creating a new KNearestNeighborExhaustive
from a given dataset, simply a pass-through to the constructor of
KNearestNeighborExhaustive
|
class |
KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the k-nearest neighbor algorithm.
|
static class |
KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
This is a BatchLearner interface for creating a new KNearestNeighbor
from a given dataset, simply a pass-through to the constructor of
KNearestNeighbor
|
class |
NearestNeighborExhaustive<InputType,OutputType>
The
NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input. |
static class |
NearestNeighborExhaustive.Learner<InputType,OutputType>
The
NearestNeighborExhaustive.Learner class implements a batch learner for
the NearestNeighborExhaustive class. |
class |
NearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the nearest neighbor algorithm.
|
static class |
NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
This is a BatchLearner interface for creating a new NearestNeighbor
from a given dataset, simply a pass-through to the constructor of
NearestNeighbor
|
| Modifier and Type | Method and Description |
|---|---|
DefaultDivergenceFunctionContainer<FirstType,SecondType> |
DefaultDivergenceFunctionContainer.clone() |
| Constructor and Description |
|---|
DefaultDivergenceFunctionContainer(DefaultDivergenceFunctionContainer<? super FirstType,? super SecondType> other)
Creates a new instance of
DefaultDivergenceFunctionContainer. |