Uses of Interface
org.openimaj.data.DataSource

Packages that use DataSource

Package	Description
org.openimaj.data
org.openimaj.feature.local.data
org.openimaj.knn.pq
org.openimaj.ml.clustering
org.openimaj.ml.clustering.dbscan
org.openimaj.ml.clustering.kdtree
org.openimaj.ml.clustering.kmeans	K-Means in OpenIMAJ is designed to be both extremely fast and flexible.
org.openimaj.ml.clustering.rac
org.openimaj.ml.clustering.random
org.openimaj.ml.clustering.rforest
org.openimaj.tools.clusterquantiser.samplebatch
org.openimaj.workinprogress.optimisation

Uses of DataSource in org.openimaj.data

Classes in org.openimaj.data that implement DataSource

Modifier and Type	Class and Description
class	AbstractDataSource<DATATYPE> Abstract base class for DataSource implementations.
class	AbstractMultiListDataSource<DATATYPE,ELEMENTTYPE> An abstract DataSource backed by multiple lists of data.
class	ArrayBackedDataSource<T> A DataSource backed by an array.
class	ByteArrayBackedDataSource A DataSource backed by a 2D array of bytes.
class	DoubleArrayBackedDataSource A DataSource backed by a 2D array of doubles.
class	FloatArrayBackedDataSource A DataSource backed by a 2D array of floats.
class	IndexedViewDataSource<DATATYPE> This DataSource provides an indexed view of a subset of another DataSource.
class	IntArrayBackedDataSource A DataSource backed by a 2D array of ints.
class	LongArrayBackedDataSource A DataSource backed by a 2D array of longs.
class	RandomSampleDataSource<DATATYPE> This DataSource provides randomly sampled view over another DataSource.
class	ShortArrayBackedDataSource A DataSource backed by a 2D array of shorts.

Constructors in org.openimaj.data with parameters of type DataSource

Constructor and Description
IndexedViewDataSource(DataSource<DATATYPE> dataSource, int[] indexes) Construct a new IndexedViewDataSource with the given inner data and indexes into the inner data.
RandomSampleDataSource(DataSource<DATATYPE> dataSource, double proportion) Construct the view over the given DataSource such that it has the given proportion of items from the original.
RandomSampleDataSource(DataSource<DATATYPE> dataSource, int requestedSize) Construct the view over the given DataSource such that it has requestedSize items.

Uses of DataSource in org.openimaj.feature.local.data

Classes in org.openimaj.feature.local.data that implement DataSource

Modifier and Type	Class and Description
class	LocalFeatureListDataSource<T extends LocalFeature<?,? extends ArrayFeatureVector<F>>,F> A DataSource for the feature vector of one or more lists of LocalFeatures that use an ArrayFeatureVector for the feature vector.

Uses of DataSource in org.openimaj.knn.pq

Constructors in org.openimaj.knn.pq with parameters of type DataSource

Constructor and Description
IncrementalByteADCNearestNeighbours(ByteProductQuantiser pq, DataSource<byte[]> dataPoints) Construct the ADC with the given quantiser and data points.
IncrementalDoubleADCNearestNeighbours(DoubleProductQuantiser pq, DataSource<double[]> dataPoints) Construct the ADC with the given quantiser and data points.
IncrementalFloatADCNearestNeighbours(FloatProductQuantiser pq, DataSource<float[]> dataPoints) Construct the ADC with the given quantiser and data points.
IncrementalIntADCNearestNeighbours(IntProductQuantiser pq, DataSource<int[]> dataPoints) Construct the ADC with the given quantiser and data points.
IncrementalLongADCNearestNeighbours(LongProductQuantiser pq, DataSource<long[]> dataPoints) Construct the ADC with the given quantiser and data points.
IncrementalShortADCNearestNeighbours(ShortProductQuantiser pq, DataSource<short[]> dataPoints) Construct the ADC with the given quantiser and data points.

Uses of DataSource in org.openimaj.ml.clustering

Methods in org.openimaj.ml.clustering with parameters of type DataSource

Modifier and Type	Method and Description
CLUSTERTYPE	SpatialClusterer.cluster(DataSource<DATATYPE> data) Perform clustering with data from a data source.

Uses of DataSource in org.openimaj.ml.clustering.dbscan

Methods in org.openimaj.ml.clustering.dbscan with parameters of type DataSource

Modifier and Type	Method and Description
DoubleDBSCANClusters	DoubleNNDBSCAN.cluster(DataSource<double[]> data)

Uses of DataSource in org.openimaj.ml.clustering.kdtree

Methods in org.openimaj.ml.clustering.kdtree with parameters of type DataSource

Modifier and Type	Method and Description
KDTreeClusters	DoubleKDTreeClusterer.cluster(DataSource<double[]> data)

Uses of DataSource in org.openimaj.ml.clustering.kmeans

Methods in org.openimaj.ml.clustering.kmeans with parameters of type DataSource

Modifier and Type	Method and Description
HierarchicalByteKMeansResult	HierarchicalByteKMeans.cluster(DataSource<byte[]> data)
ByteKMeans.Result	ByteKMeans.cluster(DataSource<byte[]> ds)
void	ByteKMeans.cluster(DataSource<byte[]> data, ByteKMeans.Result result) Main clustering algorithm.
protected ByteKMeans.Result	ByteKMeans.cluster(DataSource<byte[]> data, int K) Initiate clustering with the given data and number of clusters.
SphericalKMeansResult	SphericalKMeans.cluster(DataSource<double[]> data)
HierarchicalDoubleKMeansResult	HierarchicalDoubleKMeans.cluster(DataSource<double[]> data)
DoubleKMeans.Result	DoubleKMeans.cluster(DataSource<double[]> ds)
void	DoubleKMeans.cluster(DataSource<double[]> data, DoubleKMeans.Result result) Main clustering algorithm.
protected DoubleKMeans.Result	DoubleKMeans.cluster(DataSource<double[]> data, int K) Initiate clustering with the given data and number of clusters.
FloatKMeans.Result	FloatKMeans.cluster(DataSource<float[]> ds)
HierarchicalFloatKMeansResult	HierarchicalFloatKMeans.cluster(DataSource<float[]> data)
void	FloatKMeans.cluster(DataSource<float[]> data, FloatKMeans.Result result) Main clustering algorithm.
protected FloatKMeans.Result	FloatKMeans.cluster(DataSource<float[]> data, int K) Initiate clustering with the given data and number of clusters.
HierarchicalIntKMeansResult	HierarchicalIntKMeans.cluster(DataSource<int[]> data)
IntKMeans.Result	IntKMeans.cluster(DataSource<int[]> ds)
protected IntKMeans.Result	IntKMeans.cluster(DataSource<int[]> data, int K) Initiate clustering with the given data and number of clusters.
void	IntKMeans.cluster(DataSource<int[]> data, IntKMeans.Result result) Main clustering algorithm.
HierarchicalLongKMeansResult	HierarchicalLongKMeans.cluster(DataSource<long[]> data)
LongKMeans.Result	LongKMeans.cluster(DataSource<long[]> ds)
protected LongKMeans.Result	LongKMeans.cluster(DataSource<long[]> data, int K) Initiate clustering with the given data and number of clusters.
void	LongKMeans.cluster(DataSource<long[]> data, LongKMeans.Result result) Main clustering algorithm.
ShortKMeans.Result	ShortKMeans.cluster(DataSource<short[]> ds)
HierarchicalShortKMeansResult	HierarchicalShortKMeans.cluster(DataSource<short[]> data)
protected ShortKMeans.Result	ShortKMeans.cluster(DataSource<short[]> data, int K) Initiate clustering with the given data and number of clusters.
void	ShortKMeans.cluster(DataSource<short[]> data, ShortKMeans.Result result) Main clustering algorithm.
FeatureVectorCentroidsResult<T>	FeatureVectorKMeans.cluster(DataSource<T> ds)
protected void	FeatureVectorKMeans.cluster(DataSource<T> data, FeatureVectorKMeans.Result<T> result) Main clustering algorithm.
protected FeatureVectorKMeans.Result<T>	FeatureVectorKMeans.cluster(DataSource<T> data, int K) Initiate clustering with the given data and number of clusters.
abstract void	ByteKMeansInit.initKMeans(DataSource<byte[]> bds, byte[][] clusters) Initialise the centroids based on the given data.
void	ByteKMeansInit.RANDOM.initKMeans(DataSource<byte[]> bds, byte[][] clusters)
abstract void	DoubleKMeansInit.initKMeans(DataSource<double[]> bds, double[][] clusters) Initialise the centroids based on the given data.
void	DoubleKMeansInit.RANDOM.initKMeans(DataSource<double[]> bds, double[][] clusters)
abstract void	FloatKMeansInit.initKMeans(DataSource<float[]> bds, float[][] clusters) Initialise the centroids based on the given data.
void	FloatKMeansInit.RANDOM.initKMeans(DataSource<float[]> bds, float[][] clusters)
abstract void	IntKMeansInit.initKMeans(DataSource<int[]> bds, int[][] clusters) Initialise the centroids based on the given data.
void	IntKMeansInit.RANDOM.initKMeans(DataSource<int[]> bds, int[][] clusters)
abstract void	LongKMeansInit.initKMeans(DataSource<long[]> bds, long[][] clusters) Initialise the centroids based on the given data.
void	LongKMeansInit.RANDOM.initKMeans(DataSource<long[]> bds, long[][] clusters)
abstract void	ShortKMeansInit.initKMeans(DataSource<short[]> bds, short[][] clusters) Initialise the centroids based on the given data.
void	ShortKMeansInit.RANDOM.initKMeans(DataSource<short[]> bds, short[][] clusters)
abstract void	FeatureVectorKMeansInit.initKMeans(DataSource<T> bds, T[] clusters) Initialise the centroids based on the given data.
void	FeatureVectorKMeansInit.RANDOM.initKMeans(DataSource<T> bds, T[] clusters)

Uses of DataSource in org.openimaj.ml.clustering.rac

Methods in org.openimaj.ml.clustering.rac with parameters of type DataSource

Modifier and Type	Method and Description
IntRAC	IntRAC.cluster(DataSource<int[]> data)

Uses of DataSource in org.openimaj.ml.clustering.random

Methods in org.openimaj.ml.clustering.random with parameters of type DataSource

Modifier and Type	Method and Description
ByteCentroidsResult	RandomSetByteClusterer.cluster(DataSource<byte[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
ByteCentroidsResult	RandomByteClusterer.cluster(DataSource<byte[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
DoubleCentroidsResult	RandomSetDoubleClusterer.cluster(DataSource<double[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
DoubleCentroidsResult	RandomDoubleClusterer.cluster(DataSource<double[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
FloatCentroidsResult	RandomSetFloatClusterer.cluster(DataSource<float[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
FloatCentroidsResult	RandomFloatClusterer.cluster(DataSource<float[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
IntCentroidsResult	RandomIntClusterer.cluster(DataSource<int[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
IntCentroidsResult	RandomSetIntClusterer.cluster(DataSource<int[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
LongCentroidsResult	RandomLongClusterer.cluster(DataSource<long[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
LongCentroidsResult	RandomSetLongClusterer.cluster(DataSource<long[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
ShortCentroidsResult	RandomSetShortClusterer.cluster(DataSource<short[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.
ShortCentroidsResult	RandomShortClusterer.cluster(DataSource<short[]> data) Selects K elements from the provided DataSource as the centroids of the clusters.

Uses of DataSource in org.openimaj.ml.clustering.rforest

Methods in org.openimaj.ml.clustering.rforest with parameters of type DataSource

Modifier and Type	Method and Description
IntRandomForest	IntRandomForest.cluster(DataSource<int[]> data)

Uses of DataSource in org.openimaj.tools.clusterquantiser.samplebatch

Classes in org.openimaj.tools.clusterquantiser.samplebatch that implement DataSource

Modifier and Type	Class and Description
class	SampleBatchByteDataSource A batched datasource
class	SampleBatchIntDataSource A batched datasource

Uses of DataSource in org.openimaj.workinprogress.optimisation

Methods in org.openimaj.workinprogress.optimisation with parameters of type DataSource

Modifier and Type	Method and Description
void	SGD.train(DataSource<DATATYPE> data)