Index (The OpenIMAJ Nearest Neighbour Library 1.3.9 API)

A B C D F G I K L N O P Q R S T W

A

add(DATA) - Method in interface org.openimaj.knn.IncrementalNearestNeighbours: Add a single data item
add(OBJECT) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
add(T) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
add(byte[]) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
add(double[]) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
add(float[]) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
add(int[]) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
add(long[]) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
add(short[]) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
addAll(List<DATA>) - Method in interface org.openimaj.knn.IncrementalNearestNeighbours: Insert all the given data
addAll(Collection<OBJECT>) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Insert data into the tables
addAll(OBJECT[]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Insert data into the tables
addAll(List<OBJECT>) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
addAll(List<T>) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
addAll(List<byte[]>) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
addAll(List<double[]>) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
addAll(List<float[]>) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
addAll(List<int[]>) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
addAll(List<long[]>) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
addAll(List<short[]>) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
arrayLength() - Method in class org.openimaj.lsh.sketch.ByteLSHSketcher: Get the length of the output byte array of packed bits.
arrayLength() - Method in class org.openimaj.lsh.sketch.IntLSHSketcher: Get the length of the output int array of packed bits.
arrayLength() - Method in class org.openimaj.lsh.sketch.LongLSHSketcher: Get the length of the output long array of packed bits.
arrayLength() - Method in class org.openimaj.lsh.sketch.ShortLSHSketcher: Get the length of the output short array of packed bits.
assigners - Variable in class org.openimaj.knn.pq.ByteProductQuantiser
assigners - Variable in class org.openimaj.knn.pq.DoubleProductQuantiser
assigners - Variable in class org.openimaj.knn.pq.FloatProductQuantiser
assigners - Variable in class org.openimaj.knn.pq.IntProductQuantiser
assigners - Variable in class org.openimaj.knn.pq.LongProductQuantiser
assigners - Variable in class org.openimaj.knn.pq.ShortProductQuantiser

B

b - Variable in class org.openimaj.lsh.functions.BytePStableFactory.PStableFunction
b - Variable in class org.openimaj.lsh.functions.DoublePStableFactory.PStableFunction
b - Variable in class org.openimaj.lsh.functions.FloatPStableFactory.PStableFunction
b - Variable in class org.openimaj.lsh.functions.IntPStableFactory.PStableFunction
b - Variable in class org.openimaj.lsh.functions.LongPStableFactory.PStableFunction
b - Variable in class org.openimaj.lsh.functions.ShortPStableFactory.PStableFunction
binaryHeader() - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
binaryHeader() - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
binaryHeader() - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
binaryHeader() - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
binaryHeader() - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
binaryHeader() - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
bitLength() - Method in class org.openimaj.lsh.sketch.BitSetLSHSketcher: Get the length of the sketch in bits.
bitLength() - Method in class org.openimaj.lsh.sketch.ByteLSHSketcher: Get the length of the sketch in bits.
bitLength() - Method in class org.openimaj.lsh.sketch.IntLSHSketcher: Get the length of the sketch in bits.
bitLength() - Method in class org.openimaj.lsh.sketch.LongLSHSketcher: Get the length of the sketch in bits.
bitLength() - Method in class org.openimaj.lsh.sketch.ShortLSHSketcher: Get the length of the sketch in bits.
BitSetLSHSketcher<OBJECT> - Class in org.openimaj.lsh.sketch: A Sketcher that produces bit-string sketches encoded as a BitSet.
BitSetLSHSketcher(List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.sketch.BitSetLSHSketcher: Construct with the given functions.
BitSetLSHSketcher(HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.sketch.BitSetLSHSketcher: Construct with the given functions.
BitSetLSHSketcher(HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.sketch.BitSetLSHSketcher: Construct with the factory which is used to produce the required number of functions.
ByteADCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
ByteADCNearestNeighbours(ByteProductQuantiser, byte[][]) - Constructor for class org.openimaj.knn.pq.ByteADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
ByteADCNearestNeighbours(ByteProductQuantiser, byte[][], int) - Constructor for class org.openimaj.knn.pq.ByteADCNearestNeighbours: Construct the ADC with the given quantiser and pre-quantised data .
ByteCauchyFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that use a Cauchy distribution to approximate L1 distance.
ByteCauchyFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.ByteCauchyFactory: Construct the factory with the given parameters.
ByteGaussianFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions using Gaussian distributions to approximate the Euclidean distance.
ByteGaussianFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.ByteGaussianFactory: Construct with the given parameters.
ByteHammingFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that approximate the Hamming distance.
ByteHammingFactory(int, MersenneTwister, int) - Constructor for class org.openimaj.lsh.functions.ByteHammingFactory: Construct a new factory using the given parameters.
ByteHashFunction - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for hashing byte arrays.
ByteHashFunctionFactory - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for producing hash functions seeded by random numbers.
ByteHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.ByteHashFunctionFactory
ByteHyperplaneCosineFactory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate cosine distance using hyperplanes.
ByteHyperplaneCosineFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.ByteHyperplaneCosineFactory: Construct with the given arguments.
ByteHyperplaneL1Factory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate L1 (city-block) distance in closed spaces using random axis-aligned hyperplanes.
ByteHyperplaneL1Factory(int, MersenneTwister, byte, byte) - Constructor for class org.openimaj.lsh.functions.ByteHyperplaneL1Factory: Construct with the given arguments.
ByteKDTreeEnsemble - Class in org.openimaj.knn.approximate: Ensemble of Best-Bin-First KDTrees for byte data.
ByteKDTreeEnsemble(byte[][]) - Constructor for class org.openimaj.knn.approximate.ByteKDTreeEnsemble: Construct a ByteKDTreeEnsemble with the provided data, using the default of 8 trees.
ByteKDTreeEnsemble(byte[][], int) - Constructor for class org.openimaj.knn.approximate.ByteKDTreeEnsemble: Construct a ByteKDTreeEnsemble with the provided data and number of trees.
ByteKDTreeEnsemble(byte[][], int, int) - Constructor for class org.openimaj.knn.approximate.ByteKDTreeEnsemble: Construct a ByteKDTreeEnsemble with the provided data and number of trees.
ByteKDTreeEnsemble.ByteKDTreeNode - Class in org.openimaj.knn.approximate: An internal node of the KDTree
ByteKDTreeNode() - Constructor for class org.openimaj.knn.approximate.ByteKDTreeEnsemble.ByteKDTreeNode: Construct a new node
ByteKDTreeNode(byte[][], IntArrayView, Uniform) - Constructor for class org.openimaj.knn.approximate.ByteKDTreeEnsemble.ByteKDTreeNode: Construct a new node with the given data
ByteLSHSketcher<OBJECT> - Class in org.openimaj.lsh.sketch: A Sketcher that produces bit-string sketches encoded as byte arrays.
ByteLSHSketcher(List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.sketch.ByteLSHSketcher: Construct with the given functions.
ByteLSHSketcher(HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.sketch.ByteLSHSketcher: Construct with the given functions.
ByteLSHSketcher(HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.sketch.ByteLSHSketcher: Construct with the factory which is used to produce the required number of functions.
ByteNearestNeighbours - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with byte[] data.
ByteNearestNeighbours() - Constructor for class org.openimaj.knn.ByteNearestNeighbours
ByteNearestNeighboursExact - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation.
ByteNearestNeighboursExact(byte[][]) - Constructor for class org.openimaj.knn.ByteNearestNeighboursExact: Construct the ByteNearestNeighboursExact over the provided dataset and using Euclidean distance.
ByteNearestNeighboursExact(byte[][], ByteFVComparator) - Constructor for class org.openimaj.knn.ByteNearestNeighboursExact: Construct the ByteNearestNeighboursExact over the provided dataset with the given distance function.
ByteNearestNeighboursExact.Factory - Class in org.openimaj.knn: NearestNeighboursFactory for producing ByteNearestNeighboursExacts.
ByteNearestNeighboursKDTree - Class in org.openimaj.knn.approximate: Fast Nearest-Neighbours for byte data using an ensemble of Best-Bin-First KDTrees.
ByteNearestNeighboursKDTree(byte[][], int, int) - Constructor for class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree: Construct the ByteNearestNeighboursKDTree with the given options.
ByteNearestNeighboursKDTree.Factory - Class in org.openimaj.knn.approximate: NearestNeighboursFactory for producing ByteNearestNeighboursKDTrees.
ByteNearestNeighboursProvider - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
ByteProductQuantiser - Class in org.openimaj.knn.pq: Implementation of a Product Quantiser for vectors/arrays of bytes.
ByteProductQuantiser(ByteNearestNeighboursExact[]) - Constructor for class org.openimaj.knn.pq.ByteProductQuantiser: Construct a ByteProductQuantiser with the given nearest-neighbour assigners.
BytePStableFactory - Class in org.openimaj.lsh.functions: Base class for hashing schemes based on P-Stable distributions.
BytePStableFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.BytePStableFactory: Construct with the given parameters.
BytePStableFactory.PStableFunction - Class in org.openimaj.lsh.functions
ByteSDCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Symmetric Distance Computation (SDC) on Product Quantised vectors.
ByteSDCNearestNeighbours(ByteProductQuantiser, byte[][][], byte[][]) - Constructor for class org.openimaj.knn.pq.ByteSDCNearestNeighbours: Construct the SDC with the given quantiser, centroids (corresponding to the quantiser's internal assigners), and data.

C

computeDistance(byte[], byte[]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact: Compute the distance between two vectors using the underlying distance comparison used by this class.
computeDistance(double[], double[]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact: Compute the distance between two vectors using the underlying distance comparison used by this class.
computeDistance(float[], float[]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact: Compute the distance between two vectors using the underlying distance comparison used by this class.
computeDistance(int[], int[]) - Method in class org.openimaj.knn.IntNearestNeighboursExact: Compute the distance between two vectors using the underlying distance comparison used by this class.
computeDistance(long[], long[]) - Method in class org.openimaj.knn.LongNearestNeighboursExact: Compute the distance between two vectors using the underlying distance comparison used by this class.
computeDistance(short[], short[]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact: Compute the distance between two vectors using the underlying distance comparison used by this class.
computeDistances(byte[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
computeDistances(byte[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.ByteSDCNearestNeighbours
computeDistances(double[], BoundedPriorityQueue<IntDoublePair>, IntDoublePair) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
computeDistances(double[], BoundedPriorityQueue<IntDoublePair>, IntDoublePair) - Method in class org.openimaj.knn.pq.DoubleSDCNearestNeighbours
computeDistances(float[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
computeDistances(float[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.FloatSDCNearestNeighbours
computeDistances(byte[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
computeDistances(double[], BoundedPriorityQueue<IntDoublePair>, IntDoublePair) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
computeDistances(float[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
computeDistances(int[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
computeDistances(long[], BoundedPriorityQueue<IntDoublePair>, IntDoublePair) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
computeDistances(short[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
computeDistances(int[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
computeDistances(int[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.IntSDCNearestNeighbours
computeDistances(long[], BoundedPriorityQueue<IntDoublePair>, IntDoublePair) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
computeDistances(long[], BoundedPriorityQueue<IntDoublePair>, IntDoublePair) - Method in class org.openimaj.knn.pq.LongSDCNearestNeighbours
computeDistances(short[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
computeDistances(short[], BoundedPriorityQueue<IntFloatPair>, IntFloatPair) - Method in class org.openimaj.knn.pq.ShortSDCNearestNeighbours
computeHashCode(OBJECT) - Method in class org.openimaj.lsh.composition.PolyHashComposition
computeHashCode(OBJECT) - Method in class org.openimaj.lsh.composition.RandomProjectionHashComposition
computeHashCode(ByteFV) - Method in class org.openimaj.lsh.functions.ByteHashFunction: Compute the hash code for the feature vector.
computeHashCode(SparseByteArray) - Method in class org.openimaj.lsh.functions.ByteHashFunction: Compute the hash code for the sparse array.
computeHashCode(SparseByteFV) - Method in class org.openimaj.lsh.functions.ByteHashFunction: Compute the hash code for the sparse feature vector.
computeHashCode(byte[]) - Method in class org.openimaj.lsh.functions.BytePStableFactory.PStableFunction
computeHashCode(SparseByteArray) - Method in class org.openimaj.lsh.functions.BytePStableFactory.PStableFunction
computeHashCode(DoubleFV) - Method in class org.openimaj.lsh.functions.DoubleHashFunction: Compute the hash code for the feature vector.
computeHashCode(SparseDoubleArray) - Method in class org.openimaj.lsh.functions.DoubleHashFunction: Compute the hash code for the sparse array.
computeHashCode(SparseDoubleFV) - Method in class org.openimaj.lsh.functions.DoubleHashFunction: Compute the hash code for the sparse feature vector.
computeHashCode(double[]) - Method in class org.openimaj.lsh.functions.DoublePStableFactory.PStableFunction
computeHashCode(SparseDoubleArray) - Method in class org.openimaj.lsh.functions.DoublePStableFactory.PStableFunction
computeHashCode(FloatFV) - Method in class org.openimaj.lsh.functions.FloatHashFunction: Compute the hash code for the feature vector.
computeHashCode(SparseFloatArray) - Method in class org.openimaj.lsh.functions.FloatHashFunction: Compute the hash code for the sparse array.
computeHashCode(SparseFloatFV) - Method in class org.openimaj.lsh.functions.FloatHashFunction: Compute the hash code for the sparse feature vector.
computeHashCode(float[]) - Method in class org.openimaj.lsh.functions.FloatPStableFactory.PStableFunction
computeHashCode(SparseFloatArray) - Method in class org.openimaj.lsh.functions.FloatPStableFactory.PStableFunction
computeHashCode(IntFV) - Method in class org.openimaj.lsh.functions.IntHashFunction: Compute the hash code for the feature vector.
computeHashCode(SparseIntArray) - Method in class org.openimaj.lsh.functions.IntHashFunction: Compute the hash code for the sparse array.
computeHashCode(SparseIntFV) - Method in class org.openimaj.lsh.functions.IntHashFunction: Compute the hash code for the sparse feature vector.
computeHashCode(int[]) - Method in class org.openimaj.lsh.functions.IntPStableFactory.PStableFunction
computeHashCode(SparseIntArray) - Method in class org.openimaj.lsh.functions.IntPStableFactory.PStableFunction
computeHashCode(LongFV) - Method in class org.openimaj.lsh.functions.LongHashFunction: Compute the hash code for the feature vector.
computeHashCode(SparseLongArray) - Method in class org.openimaj.lsh.functions.LongHashFunction: Compute the hash code for the sparse array.
computeHashCode(SparseLongFV) - Method in class org.openimaj.lsh.functions.LongHashFunction: Compute the hash code for the sparse feature vector.
computeHashCode(long[]) - Method in class org.openimaj.lsh.functions.LongPStableFactory.PStableFunction
computeHashCode(SparseLongArray) - Method in class org.openimaj.lsh.functions.LongPStableFactory.PStableFunction
computeHashCode(ShortFV) - Method in class org.openimaj.lsh.functions.ShortHashFunction: Compute the hash code for the feature vector.
computeHashCode(SparseShortArray) - Method in class org.openimaj.lsh.functions.ShortHashFunction: Compute the hash code for the sparse array.
computeHashCode(SparseShortFV) - Method in class org.openimaj.lsh.functions.ShortHashFunction: Compute the hash code for the sparse feature vector.
computeHashCode(short[]) - Method in class org.openimaj.lsh.functions.ShortPStableFactory.PStableFunction
computeHashCode(SparseShortArray) - Method in class org.openimaj.lsh.functions.ShortPStableFactory.PStableFunction
CoordinateBruteForce<T extends Coordinate> - Class in org.openimaj.knn: Implementation of a CoordinateIndex that performs searching by brute-force comparison over the indexed coordinates.
CoordinateBruteForce() - Constructor for class org.openimaj.knn.CoordinateBruteForce: Default constructor.
CoordinateBruteForce(List<T>) - Constructor for class org.openimaj.knn.CoordinateBruteForce: Construct the index and populate it with the given data.
CoordinateIndex<T extends Coordinate> - Interface in org.openimaj.knn: Interface representing an index of Coordinates that can have points added to it and can be searched in a variety of ways.
CoordinateKDTree<T extends Coordinate> - Class in org.openimaj.knn: Implementation of a simple KDTree with range search.
CoordinateKDTree() - Constructor for class org.openimaj.knn.CoordinateKDTree: Create an empty KDTree object
CoordinateKDTree(Collection<T>) - Constructor for class org.openimaj.knn.CoordinateKDTree: Create a KDTree object and populate it with the given data.
create(byte[][]) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree.Factory
create(double[][]) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree.Factory
create(float[][]) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree.Factory
create(int[][]) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree.Factory
create(long[][]) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree.Factory
create(short[][]) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree.Factory
create(byte[][]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact.Factory
create(double[][]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact.Factory
create(float[][]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact.Factory
create(int[][]) - Method in class org.openimaj.knn.IntNearestNeighboursExact.Factory
create(long[][]) - Method in class org.openimaj.knn.LongNearestNeighboursExact.Factory
create(DATA[]) - Method in interface org.openimaj.knn.NearestNeighboursFactory: Create a NearestNeighbours object that works over the provided data.
create(T[]) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact.Factory
create(short[][]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact.Factory
create() - Method in class org.openimaj.lsh.functions.ByteCauchyFactory
create() - Method in class org.openimaj.lsh.functions.ByteGaussianFactory
create() - Method in class org.openimaj.lsh.functions.ByteHammingFactory
create() - Method in class org.openimaj.lsh.functions.ByteHyperplaneCosineFactory
create() - Method in class org.openimaj.lsh.functions.ByteHyperplaneL1Factory
create() - Method in class org.openimaj.lsh.functions.DoubleCauchyFactory
create() - Method in class org.openimaj.lsh.functions.DoubleGaussianFactory
create() - Method in class org.openimaj.lsh.functions.DoubleHammingFactory
create() - Method in class org.openimaj.lsh.functions.DoubleHyperplaneCosineFactory
create() - Method in class org.openimaj.lsh.functions.DoubleHyperplaneL1Factory
create() - Method in class org.openimaj.lsh.functions.FloatCauchyFactory
create() - Method in class org.openimaj.lsh.functions.FloatGaussianFactory
create() - Method in class org.openimaj.lsh.functions.FloatHammingFactory
create() - Method in class org.openimaj.lsh.functions.FloatHyperplaneCosineFactory
create() - Method in class org.openimaj.lsh.functions.FloatHyperplaneL1Factory
create() - Method in class org.openimaj.lsh.functions.IntCauchyFactory
create() - Method in class org.openimaj.lsh.functions.IntGaussianFactory
create() - Method in class org.openimaj.lsh.functions.IntHammingFactory
create() - Method in class org.openimaj.lsh.functions.IntHyperplaneCosineFactory
create() - Method in class org.openimaj.lsh.functions.IntHyperplaneL1Factory
create() - Method in class org.openimaj.lsh.functions.LongCauchyFactory
create() - Method in class org.openimaj.lsh.functions.LongGaussianFactory
create() - Method in class org.openimaj.lsh.functions.LongHammingFactory
create() - Method in class org.openimaj.lsh.functions.LongHyperplaneCosineFactory
create() - Method in class org.openimaj.lsh.functions.LongHyperplaneL1Factory
create() - Method in class org.openimaj.lsh.functions.ShortCauchyFactory
create() - Method in class org.openimaj.lsh.functions.ShortGaussianFactory
create() - Method in class org.openimaj.lsh.functions.ShortHammingFactory
create() - Method in class org.openimaj.lsh.functions.ShortHyperplaneCosineFactory
create() - Method in class org.openimaj.lsh.functions.ShortHyperplaneL1Factory
createSketch(OBJECT) - Method in class org.openimaj.lsh.sketch.BitSetLSHSketcher
createSketch(OBJECT) - Method in class org.openimaj.lsh.sketch.ByteLSHSketcher
createSketch(OBJECT) - Method in class org.openimaj.lsh.sketch.IntLSHSketcher
createSketch(OBJECT) - Method in class org.openimaj.lsh.sketch.LongLSHSketcher
createSketch(OBJECT) - Method in class org.openimaj.lsh.sketch.ShortLSHSketcher

D

data - Variable in class org.openimaj.knn.lsh.LSHNearestNeighbours
data - Variable in class org.openimaj.knn.pq.ByteADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.FloatADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.IntADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.LongADCNearestNeighbours
data - Variable in class org.openimaj.knn.pq.ShortADCNearestNeighbours
decompress(byte[]) - Method in class org.openimaj.knn.pq.ByteProductQuantiser: Decompress the quantised data by replacing each encoded index with the actual centroid subvector.
decompress(byte[]) - Method in class org.openimaj.knn.pq.DoubleProductQuantiser: Decompress the quantised data by replacing each encoded index with the actual centroid subvector.
decompress(byte[]) - Method in class org.openimaj.knn.pq.FloatProductQuantiser: Decompress the quantised data by replacing each encoded index with the actual centroid subvector.
decompress(byte[]) - Method in class org.openimaj.knn.pq.IntProductQuantiser: Decompress the quantised data by replacing each encoded index with the actual centroid subvector.
decompress(byte[]) - Method in class org.openimaj.knn.pq.LongProductQuantiser: Decompress the quantised data by replacing each encoded index with the actual centroid subvector.
decompress(byte[]) - Method in class org.openimaj.knn.pq.ShortProductQuantiser: Decompress the quantised data by replacing each encoded index with the actual centroid subvector.
DEFAULT_NCHECKS - Static variable in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree: The default number of checks performed during search when in exact mode.
DEFAULT_NCHECKS - Static variable in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree: The default number of checks performed during search when in exact mode.
DEFAULT_NCHECKS - Static variable in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree: The default number of checks performed during search when in exact mode.
DEFAULT_NCHECKS - Static variable in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree: The default number of checks performed during search when in exact mode.
DEFAULT_NCHECKS - Static variable in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree: The default number of checks performed during search when in exact mode.
DEFAULT_NCHECKS - Static variable in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree: The default number of checks performed during search when in exact mode.
DEFAULT_NTREES - Static variable in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree: The default number of kdtrees when not in exact mode.
DEFAULT_NTREES - Static variable in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree: The default number of kdtrees when not in exact mode.
DEFAULT_NTREES - Static variable in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree: The default number of kdtrees when not in exact mode.
DEFAULT_NTREES - Static variable in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree: The default number of kdtrees when not in exact mode.
DEFAULT_NTREES - Static variable in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree: The default number of kdtrees when not in exact mode.
DEFAULT_NTREES - Static variable in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree: The default number of kdtrees when not in exact mode.
distance - Variable in class org.openimaj.knn.ByteNearestNeighboursExact
distance(Coordinate, Coordinate) - Static method in class org.openimaj.knn.CoordinateKDTree
distance - Variable in class org.openimaj.knn.DoubleNearestNeighboursExact
distance - Variable in class org.openimaj.knn.FloatNearestNeighboursExact
distance - Variable in class org.openimaj.knn.IntNearestNeighboursExact
distance - Variable in class org.openimaj.knn.LongNearestNeighboursExact
distance - Variable in class org.openimaj.knn.ObjectNearestNeighbours
distance - Variable in class org.openimaj.knn.ShortNearestNeighboursExact
distanceComparator() - Method in class org.openimaj.knn.ByteNearestNeighboursExact: Get the distance comparator
distanceComparator() - Method in class org.openimaj.knn.DoubleNearestNeighboursExact: Get the distance comparator
distanceComparator() - Method in class org.openimaj.knn.FloatNearestNeighboursExact: Get the distance comparator
distanceComparator() - Method in class org.openimaj.knn.IntNearestNeighboursExact: Get the distance comparator
distanceComparator() - Method in class org.openimaj.knn.LongNearestNeighboursExact: Get the distance comparator
distanceComparator() - Method in class org.openimaj.knn.ObjectNearestNeighbours: Get the distance comparator
distanceComparator() - Method in class org.openimaj.knn.ShortNearestNeighboursExact: Get the distance comparator
distanceFcn - Variable in class org.openimaj.knn.lsh.LSHNearestNeighbours
distanceFunc(byte[], byte[][], float[]) - Static method in class org.openimaj.knn.ByteNearestNeighbours: Static method to find the sum-squared distance between a query vector and each of a set of points.
distanceFunc(byte[], byte[]) - Static method in class org.openimaj.knn.ByteNearestNeighbours: Static method to find the sum-squared distance between a query vector and a point.
distanceFunc(ByteFVComparator, byte[], byte[]) - Static method in class org.openimaj.knn.ByteNearestNeighbours: Static method to find a distance between a query vector and a point.
distanceFunc(ByteFVComparator, byte[], byte[][], float[]) - Static method in class org.openimaj.knn.ByteNearestNeighbours: Static method to find a distance between a query vector and each of a set of points.
distanceFunc(double[], double[][], double[]) - Static method in class org.openimaj.knn.DoubleNearestNeighbours: Static method to find the sum-squared distance between a query vector and each of a set of points.
distanceFunc(double[], double[]) - Static method in class org.openimaj.knn.DoubleNearestNeighbours: Static method to find the sum-squared distance between a query vector and a point.
distanceFunc(DoubleFVComparator, double[], double[]) - Static method in class org.openimaj.knn.DoubleNearestNeighbours: Static method to find a distance between a query vector and a point.
distanceFunc(DoubleFVComparator, double[], double[][], double[]) - Static method in class org.openimaj.knn.DoubleNearestNeighbours: Static method to find a distance between a query vector and each of a set of points.
distanceFunc(float[], float[][], float[]) - Static method in class org.openimaj.knn.FloatNearestNeighbours: Static method to find the sum-squared distance between a query vector and each of a set of points.
distanceFunc(float[], float[]) - Static method in class org.openimaj.knn.FloatNearestNeighbours: Static method to find the sum-squared distance between a query vector and a point.
distanceFunc(FloatFVComparator, float[], float[]) - Static method in class org.openimaj.knn.FloatNearestNeighbours: Static method to find a distance between a query vector and a point.
distanceFunc(FloatFVComparator, float[], float[][], float[]) - Static method in class org.openimaj.knn.FloatNearestNeighbours: Static method to find a distance between a query vector and each of a set of points.
distanceFunc(int[], int[][], float[]) - Static method in class org.openimaj.knn.IntNearestNeighbours: Static method to find the sum-squared distance between a query vector and each of a set of points.
distanceFunc(int[], int[]) - Static method in class org.openimaj.knn.IntNearestNeighbours: Static method to find the sum-squared distance between a query vector and a point.
distanceFunc(IntFVComparator, int[], int[]) - Static method in class org.openimaj.knn.IntNearestNeighbours: Static method to find a distance between a query vector and a point.
distanceFunc(IntFVComparator, int[], int[][], float[]) - Static method in class org.openimaj.knn.IntNearestNeighbours: Static method to find a distance between a query vector and each of a set of points.
distanceFunc(long[], long[][], double[]) - Static method in class org.openimaj.knn.LongNearestNeighbours: Static method to find the sum-squared distance between a query vector and each of a set of points.
distanceFunc(long[], long[]) - Static method in class org.openimaj.knn.LongNearestNeighbours: Static method to find the sum-squared distance between a query vector and a point.
distanceFunc(LongFVComparator, long[], long[]) - Static method in class org.openimaj.knn.LongNearestNeighbours: Static method to find a distance between a query vector and a point.
distanceFunc(LongFVComparator, long[], long[][], double[]) - Static method in class org.openimaj.knn.LongNearestNeighbours: Static method to find a distance between a query vector and each of a set of points.
distanceFunc(DistanceComparator<? super T>, T, T) - Static method in class org.openimaj.knn.ObjectNearestNeighbours: Static method to find a distance between a query vector and point.
distanceFunc(short[], short[][], float[]) - Static method in class org.openimaj.knn.ShortNearestNeighbours: Static method to find the sum-squared distance between a query vector and each of a set of points.
distanceFunc(short[], short[]) - Static method in class org.openimaj.knn.ShortNearestNeighbours: Static method to find the sum-squared distance between a query vector and a point.
distanceFunc(ShortFVComparator, short[], short[]) - Static method in class org.openimaj.knn.ShortNearestNeighbours: Static method to find a distance between a query vector and a point.
distanceFunc(ShortFVComparator, short[], short[][], float[]) - Static method in class org.openimaj.knn.ShortNearestNeighbours: Static method to find a distance between a query vector and each of a set of points.
distanceFunction() - Method in class org.openimaj.lsh.functions.ByteHashFunctionFactory
distanceFunction() - Method in class org.openimaj.lsh.functions.DoubleHashFunctionFactory
distanceFunction() - Method in class org.openimaj.lsh.functions.FloatHashFunctionFactory
distanceFunction() - Method in class org.openimaj.lsh.functions.IntHashFunctionFactory
distanceFunction() - Method in class org.openimaj.lsh.functions.LongHashFunctionFactory
distanceFunction() - Method in class org.openimaj.lsh.functions.RandomisedHashFunctionFactory
distanceFunction() - Method in class org.openimaj.lsh.functions.ShortHashFunctionFactory
DoubleADCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
DoubleADCNearestNeighbours(DoubleProductQuantiser, double[][]) - Constructor for class org.openimaj.knn.pq.DoubleADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
DoubleADCNearestNeighbours(DoubleProductQuantiser, byte[][], int) - Constructor for class org.openimaj.knn.pq.DoubleADCNearestNeighbours: Construct the ADC with the given quantiser and pre-quantised data .
DoubleCauchyFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that use a Cauchy distribution to approximate L1 distance.
DoubleCauchyFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.DoubleCauchyFactory: Construct the factory with the given parameters.
DoubleGaussianFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions using Gaussian distributions to approximate the Euclidean distance.
DoubleGaussianFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.DoubleGaussianFactory: Construct with the given parameters.
DoubleHammingFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that approximate the Hamming distance.
DoubleHammingFactory(int, MersenneTwister, int) - Constructor for class org.openimaj.lsh.functions.DoubleHammingFactory: Construct a new factory using the given parameters.
DoubleHashFunction - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for hashing double arrays.
DoubleHashFunctionFactory - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for producing hash functions seeded by random numbers.
DoubleHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.DoubleHashFunctionFactory
DoubleHyperplaneCosineFactory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate cosine distance using hyperplanes.
DoubleHyperplaneCosineFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.DoubleHyperplaneCosineFactory: Construct with the given arguments.
DoubleHyperplaneL1Factory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate L1 (city-block) distance in closed spaces using random axis-aligned hyperplanes.
DoubleHyperplaneL1Factory(int, MersenneTwister, double, double) - Constructor for class org.openimaj.lsh.functions.DoubleHyperplaneL1Factory: Construct with the given arguments.
DoubleKDTreeEnsemble - Class in org.openimaj.knn.approximate: Ensemble of Best-Bin-First KDTrees for double data.
DoubleKDTreeEnsemble(double[][]) - Constructor for class org.openimaj.knn.approximate.DoubleKDTreeEnsemble: Construct a DoubleKDTreeEnsemble with the provided data, using the default of 8 trees.
DoubleKDTreeEnsemble(double[][], int) - Constructor for class org.openimaj.knn.approximate.DoubleKDTreeEnsemble: Construct a DoubleKDTreeEnsemble with the provided data and number of trees.
DoubleKDTreeEnsemble(double[][], int, int) - Constructor for class org.openimaj.knn.approximate.DoubleKDTreeEnsemble: Construct a DoubleKDTreeEnsemble with the provided data and number of trees.
DoubleKDTreeEnsemble.DoubleKDTreeNode - Class in org.openimaj.knn.approximate: An internal node of the KDTree
DoubleKDTreeNode() - Constructor for class org.openimaj.knn.approximate.DoubleKDTreeEnsemble.DoubleKDTreeNode: Construct a new node
DoubleKDTreeNode(double[][], IntArrayView, Uniform) - Constructor for class org.openimaj.knn.approximate.DoubleKDTreeEnsemble.DoubleKDTreeNode: Construct a new node with the given data
DoubleNearestNeighbours - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with double[] data.
DoubleNearestNeighbours() - Constructor for class org.openimaj.knn.DoubleNearestNeighbours
DoubleNearestNeighboursExact - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation.
DoubleNearestNeighboursExact(double[][]) - Constructor for class org.openimaj.knn.DoubleNearestNeighboursExact: Construct the DoubleNearestNeighboursExact over the provided dataset and using Euclidean distance.
DoubleNearestNeighboursExact(double[][], DoubleFVComparator) - Constructor for class org.openimaj.knn.DoubleNearestNeighboursExact: Construct the DoubleNearestNeighboursExact over the provided dataset with the given distance function.
DoubleNearestNeighboursExact.Factory - Class in org.openimaj.knn: NearestNeighboursFactory for producing DoubleNearestNeighboursExacts.
DoubleNearestNeighboursKDTree - Class in org.openimaj.knn.approximate: Fast Nearest-Neighbours for double data using an ensemble of Best-Bin-First KDTrees.
DoubleNearestNeighboursKDTree(double[][], int, int) - Constructor for class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree: Construct the DoubleNearestNeighboursKDTree with the given options.
DoubleNearestNeighboursKDTree.Factory - Class in org.openimaj.knn.approximate: NearestNeighboursFactory for producing DoubleNearestNeighboursKDTrees.
DoubleNearestNeighboursProvider - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
DoubleProductQuantiser - Class in org.openimaj.knn.pq: Implementation of a Product Quantiser for vectors/arrays of doubles.
DoubleProductQuantiser(DoubleNearestNeighboursExact[]) - Constructor for class org.openimaj.knn.pq.DoubleProductQuantiser: Construct a DoubleProductQuantiser with the given nearest-neighbour assigners.
DoublePStableFactory - Class in org.openimaj.lsh.functions: Base class for hashing schemes based on P-Stable distributions.
DoublePStableFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.DoublePStableFactory: Construct with the given parameters.
DoublePStableFactory.PStableFunction - Class in org.openimaj.lsh.functions
DoubleSDCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Symmetric Distance Computation (SDC) on Product Quantised vectors.
DoubleSDCNearestNeighbours(DoubleProductQuantiser, double[][][], double[][]) - Constructor for class org.openimaj.knn.pq.DoubleSDCNearestNeighbours: Construct the SDC with the given quantiser, centroids (corresponding to the quantiser's internal assigners), and data.

F

Factory() - Constructor for class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree.Factory: Construct the factory the default number of trees and checks.
Factory(int, int) - Constructor for class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree.Factory: Construct the factory the given number of trees and checks.
Factory() - Constructor for class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree.Factory: Construct the factory the default number of trees and checks.
Factory(int, int) - Constructor for class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree.Factory: Construct the factory the given number of trees and checks.
Factory() - Constructor for class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree.Factory: Construct the factory the default number of trees and checks.
Factory(int, int) - Constructor for class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree.Factory: Construct the factory the given number of trees and checks.
Factory() - Constructor for class org.openimaj.knn.approximate.IntNearestNeighboursKDTree.Factory: Construct the factory the default number of trees and checks.
Factory(int, int) - Constructor for class org.openimaj.knn.approximate.IntNearestNeighboursKDTree.Factory: Construct the factory the given number of trees and checks.
Factory() - Constructor for class org.openimaj.knn.approximate.LongNearestNeighboursKDTree.Factory: Construct the factory the default number of trees and checks.
Factory(int, int) - Constructor for class org.openimaj.knn.approximate.LongNearestNeighboursKDTree.Factory: Construct the factory the given number of trees and checks.
Factory() - Constructor for class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree.Factory: Construct the factory the default number of trees and checks.
Factory(int, int) - Constructor for class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree.Factory: Construct the factory the given number of trees and checks.
Factory() - Constructor for class org.openimaj.knn.ByteNearestNeighboursExact.Factory: Construct the factory using Euclidean distance for the produced ByteNearestNeighbours instances.
Factory(ByteFVComparator) - Constructor for class org.openimaj.knn.ByteNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced ByteNearestNeighbours instances.
Factory() - Constructor for class org.openimaj.knn.DoubleNearestNeighboursExact.Factory: Construct the factory using Euclidean distance for the produced DoubleNearestNeighbours instances.
Factory(DoubleFVComparator) - Constructor for class org.openimaj.knn.DoubleNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced DoubleNearestNeighbours instances.
Factory() - Constructor for class org.openimaj.knn.FloatNearestNeighboursExact.Factory: Construct the factory using Euclidean distance for the produced FloatNearestNeighbours instances.
Factory(FloatFVComparator) - Constructor for class org.openimaj.knn.FloatNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced FloatNearestNeighbours instances.
Factory() - Constructor for class org.openimaj.knn.IntNearestNeighboursExact.Factory: Construct the factory using Euclidean distance for the produced IntNearestNeighbours instances.
Factory(IntFVComparator) - Constructor for class org.openimaj.knn.IntNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced IntNearestNeighbours instances.
Factory() - Constructor for class org.openimaj.knn.LongNearestNeighboursExact.Factory: Construct the factory using Euclidean distance for the produced LongNearestNeighbours instances.
Factory(LongFVComparator) - Constructor for class org.openimaj.knn.LongNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced LongNearestNeighbours instances.
Factory(DistanceComparator<? super T>) - Constructor for class org.openimaj.knn.ObjectNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced ObjectNearestNeighbours instances.
Factory() - Constructor for class org.openimaj.knn.ShortNearestNeighboursExact.Factory: Construct the factory using Euclidean distance for the produced ShortNearestNeighbours instances.
Factory(ShortFVComparator) - Constructor for class org.openimaj.knn.ShortNearestNeighboursExact.Factory: Construct the factory with the given distance function for the produced ShortNearestNeighbours instances.
fastKNN(Collection<T>, Coordinate, int) - Method in class org.openimaj.knn.CoordinateKDTree: Faster implementation of K-nearest-neighbours.
FloatADCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
FloatADCNearestNeighbours(FloatProductQuantiser, float[][]) - Constructor for class org.openimaj.knn.pq.FloatADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
FloatADCNearestNeighbours(FloatProductQuantiser, byte[][], int) - Constructor for class org.openimaj.knn.pq.FloatADCNearestNeighbours: Construct the ADC with the given quantiser and pre-quantised data .
FloatCauchyFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that use a Cauchy distribution to approximate L1 distance.
FloatCauchyFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.FloatCauchyFactory: Construct the factory with the given parameters.
FloatGaussianFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions using Gaussian distributions to approximate the Euclidean distance.
FloatGaussianFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.FloatGaussianFactory: Construct with the given parameters.
FloatHammingFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that approximate the Hamming distance.
FloatHammingFactory(int, MersenneTwister, int) - Constructor for class org.openimaj.lsh.functions.FloatHammingFactory: Construct a new factory using the given parameters.
FloatHashFunction - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for hashing float arrays.
FloatHashFunctionFactory - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for producing hash functions seeded by random numbers.
FloatHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.FloatHashFunctionFactory
FloatHyperplaneCosineFactory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate cosine distance using hyperplanes.
FloatHyperplaneCosineFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.FloatHyperplaneCosineFactory: Construct with the given arguments.
FloatHyperplaneL1Factory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate L1 (city-block) distance in closed spaces using random axis-aligned hyperplanes.
FloatHyperplaneL1Factory(int, MersenneTwister, float, float) - Constructor for class org.openimaj.lsh.functions.FloatHyperplaneL1Factory: Construct with the given arguments.
FloatKDTreeEnsemble - Class in org.openimaj.knn.approximate: Ensemble of Best-Bin-First KDTrees for float data.
FloatKDTreeEnsemble(float[][]) - Constructor for class org.openimaj.knn.approximate.FloatKDTreeEnsemble: Construct a FloatKDTreeEnsemble with the provided data, using the default of 8 trees.
FloatKDTreeEnsemble(float[][], int) - Constructor for class org.openimaj.knn.approximate.FloatKDTreeEnsemble: Construct a FloatKDTreeEnsemble with the provided data and number of trees.
FloatKDTreeEnsemble(float[][], int, int) - Constructor for class org.openimaj.knn.approximate.FloatKDTreeEnsemble: Construct a FloatKDTreeEnsemble with the provided data and number of trees.
FloatKDTreeEnsemble.FloatKDTreeNode - Class in org.openimaj.knn.approximate: An internal node of the KDTree
FloatKDTreeNode() - Constructor for class org.openimaj.knn.approximate.FloatKDTreeEnsemble.FloatKDTreeNode: Construct a new node
FloatKDTreeNode(float[][], IntArrayView, Uniform) - Constructor for class org.openimaj.knn.approximate.FloatKDTreeEnsemble.FloatKDTreeNode: Construct a new node with the given data
FloatNearestNeighbours - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with float[] data.
FloatNearestNeighbours() - Constructor for class org.openimaj.knn.FloatNearestNeighbours
FloatNearestNeighboursExact - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation.
FloatNearestNeighboursExact(float[][]) - Constructor for class org.openimaj.knn.FloatNearestNeighboursExact: Construct the FloatNearestNeighboursExact over the provided dataset and using Euclidean distance.
FloatNearestNeighboursExact(float[][], FloatFVComparator) - Constructor for class org.openimaj.knn.FloatNearestNeighboursExact: Construct the FloatNearestNeighboursExact over the provided dataset with the given distance function.
FloatNearestNeighboursExact.Factory - Class in org.openimaj.knn: NearestNeighboursFactory for producing FloatNearestNeighboursExacts.
FloatNearestNeighboursKDTree - Class in org.openimaj.knn.approximate: Fast Nearest-Neighbours for float data using an ensemble of Best-Bin-First KDTrees.
FloatNearestNeighboursKDTree(float[][], int, int) - Constructor for class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree: Construct the FloatNearestNeighboursKDTree with the given options.
FloatNearestNeighboursKDTree.Factory - Class in org.openimaj.knn.approximate: NearestNeighboursFactory for producing FloatNearestNeighboursKDTrees.
FloatNearestNeighboursProvider - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
FloatProductQuantiser - Class in org.openimaj.knn.pq: Implementation of a Product Quantiser for vectors/arrays of floats.
FloatProductQuantiser(FloatNearestNeighboursExact[]) - Constructor for class org.openimaj.knn.pq.FloatProductQuantiser: Construct a FloatProductQuantiser with the given nearest-neighbour assigners.
FloatPStableFactory - Class in org.openimaj.lsh.functions: Base class for hashing schemes based on P-Stable distributions.
FloatPStableFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.FloatPStableFactory: Construct with the given parameters.
FloatPStableFactory.PStableFunction - Class in org.openimaj.lsh.functions
FloatSDCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Symmetric Distance Computation (SDC) on Product Quantised vectors.
FloatSDCNearestNeighbours(FloatProductQuantiser, float[][][], float[][]) - Constructor for class org.openimaj.knn.pq.FloatSDCNearestNeighbours: Construct the SDC with the given quantiser, centroids (corresponding to the quantiser's internal assigners), and data.
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ByteCauchyFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ByteGaussianFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ByteHammingFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ByteHashFunctionFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ByteHyperplaneCosineFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ByteHyperplaneL1Factory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.DoubleCauchyFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.DoubleGaussianFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.DoubleHammingFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.DoubleHashFunctionFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.DoubleHyperplaneCosineFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.DoubleHyperplaneL1Factory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.FloatCauchyFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.FloatGaussianFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.FloatHammingFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.FloatHashFunctionFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.FloatHyperplaneCosineFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.FloatHyperplaneL1Factory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.IntCauchyFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.IntGaussianFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.IntHammingFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.IntHashFunctionFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.IntHyperplaneCosineFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.IntHyperplaneL1Factory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.LongCauchyFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.LongGaussianFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.LongHammingFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.LongHashFunctionFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.LongHyperplaneCosineFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.LongHyperplaneL1Factory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ShortCauchyFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ShortGaussianFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ShortHammingFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ShortHashFunctionFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ShortHyperplaneCosineFactory
fvDistanceFunction() - Method in class org.openimaj.lsh.functions.ShortHyperplaneL1Factory

G

get(int) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Get the data item at the given index.
getBucketId(OBJECT[]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Compute identifiers of the buckets in which the given points belong for all the tables.
getBucketId(OBJECT) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Compute identifiers of the buckets in which the given point belongs for all the tables.
getData() - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Get a read-only view of the underlying data.
getNearestNeighbours() - Method in interface org.openimaj.knn.ByteNearestNeighboursProvider
getNearestNeighbours() - Method in interface org.openimaj.knn.DoubleNearestNeighboursProvider
getNearestNeighbours() - Method in interface org.openimaj.knn.FloatNearestNeighboursProvider
getNearestNeighbours() - Method in interface org.openimaj.knn.IntNearestNeighboursProvider
getNearestNeighbours() - Method in interface org.openimaj.knn.LongNearestNeighboursProvider
getNearestNeighbours() - Method in interface org.openimaj.knn.ObjectNearestNeighboursProvider
getNearestNeighbours() - Method in interface org.openimaj.knn.ShortNearestNeighboursProvider
getPoints() - Method in class org.openimaj.knn.ByteNearestNeighboursExact: Get the underlying data points.
getPoints() - Method in class org.openimaj.knn.DoubleNearestNeighboursExact: Get the underlying data points.
getPoints() - Method in class org.openimaj.knn.FloatNearestNeighboursExact: Get the underlying data points.
getPoints() - Method in class org.openimaj.knn.IntNearestNeighboursExact: Get the underlying data points.
getPoints() - Method in class org.openimaj.knn.LongNearestNeighboursExact: Get the underlying data points.
getPoints() - Method in class org.openimaj.knn.ShortNearestNeighboursExact: Get the underlying data points.

I

IncrementalByteADCNearestNeighbours - Class in org.openimaj.knn.pq: Incremental Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IncrementalByteADCNearestNeighbours() - Constructor for class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
IncrementalByteADCNearestNeighbours(ByteProductQuantiser, byte[][]) - Constructor for class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalByteADCNearestNeighbours(ByteProductQuantiser, List<byte[]>) - Constructor for class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalByteADCNearestNeighbours(ByteProductQuantiser, DataSource<byte[]>) - Constructor for class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalByteADCNearestNeighbours(ByteProductQuantiser, int) - Constructor for class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalByteADCNearestNeighbours(ByteProductQuantiser, int, int) - Constructor for class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalDoubleADCNearestNeighbours - Class in org.openimaj.knn.pq: Incremental Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IncrementalDoubleADCNearestNeighbours() - Constructor for class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
IncrementalDoubleADCNearestNeighbours(DoubleProductQuantiser, double[][]) - Constructor for class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalDoubleADCNearestNeighbours(DoubleProductQuantiser, List<double[]>) - Constructor for class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalDoubleADCNearestNeighbours(DoubleProductQuantiser, DataSource<double[]>) - Constructor for class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalDoubleADCNearestNeighbours(DoubleProductQuantiser, int) - Constructor for class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalDoubleADCNearestNeighbours(DoubleProductQuantiser, int, int) - Constructor for class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalFloatADCNearestNeighbours - Class in org.openimaj.knn.pq: Incremental Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IncrementalFloatADCNearestNeighbours() - Constructor for class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
IncrementalFloatADCNearestNeighbours(FloatProductQuantiser, float[][]) - Constructor for class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalFloatADCNearestNeighbours(FloatProductQuantiser, List<float[]>) - Constructor for class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalFloatADCNearestNeighbours(FloatProductQuantiser, DataSource<float[]>) - Constructor for class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalFloatADCNearestNeighbours(FloatProductQuantiser, int) - Constructor for class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalFloatADCNearestNeighbours(FloatProductQuantiser, int, int) - Constructor for class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalIntADCNearestNeighbours - Class in org.openimaj.knn.pq: Incremental Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IncrementalIntADCNearestNeighbours() - Constructor for class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
IncrementalIntADCNearestNeighbours(IntProductQuantiser, int[][]) - Constructor for class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalIntADCNearestNeighbours(IntProductQuantiser, List<int[]>) - Constructor for class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalIntADCNearestNeighbours(IntProductQuantiser, DataSource<int[]>) - Constructor for class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalIntADCNearestNeighbours(IntProductQuantiser, int) - Constructor for class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalIntADCNearestNeighbours(IntProductQuantiser, int, int) - Constructor for class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalLongADCNearestNeighbours - Class in org.openimaj.knn.pq: Incremental Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IncrementalLongADCNearestNeighbours() - Constructor for class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
IncrementalLongADCNearestNeighbours(LongProductQuantiser, long[][]) - Constructor for class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalLongADCNearestNeighbours(LongProductQuantiser, List<long[]>) - Constructor for class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalLongADCNearestNeighbours(LongProductQuantiser, DataSource<long[]>) - Constructor for class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalLongADCNearestNeighbours(LongProductQuantiser, int) - Constructor for class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalLongADCNearestNeighbours(LongProductQuantiser, int, int) - Constructor for class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalNearestNeighbours<DATA,DISTANCES,PAIR_TYPE> - Interface in org.openimaj.knn: Extension to K-nearest-neighbour that allows database points to be added dynamically.
IncrementalShortADCNearestNeighbours - Class in org.openimaj.knn.pq: Incremental Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IncrementalShortADCNearestNeighbours() - Constructor for class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
IncrementalShortADCNearestNeighbours(ShortProductQuantiser, short[][]) - Constructor for class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalShortADCNearestNeighbours(ShortProductQuantiser, List<short[]>) - Constructor for class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalShortADCNearestNeighbours(ShortProductQuantiser, DataSource<short[]>) - Constructor for class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IncrementalShortADCNearestNeighbours(ShortProductQuantiser, int) - Constructor for class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours: Construct an empty ADC with the given quantiser.
IncrementalShortADCNearestNeighbours(ShortProductQuantiser, int, int) - Constructor for class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours: Construct an empty ADC with the given quantiser.
insert(T) - Method in class org.openimaj.knn.CoordinateBruteForce
insert(T) - Method in interface org.openimaj.knn.CoordinateIndex: Insert a single coordinate into the index.
insert(T) - Method in class org.openimaj.knn.CoordinateKDTree: Inserts a point into the tree, preserving the spatial ordering.
insertAll(Collection<T>) - Method in class org.openimaj.knn.CoordinateKDTree: Insert all the points from the given collection into the index.
IntADCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
IntADCNearestNeighbours(IntProductQuantiser, int[][]) - Constructor for class org.openimaj.knn.pq.IntADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
IntADCNearestNeighbours(IntProductQuantiser, byte[][], int) - Constructor for class org.openimaj.knn.pq.IntADCNearestNeighbours: Construct the ADC with the given quantiser and pre-quantised data .
IntCauchyFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that use a Cauchy distribution to approximate L1 distance.
IntCauchyFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.IntCauchyFactory: Construct the factory with the given parameters.
InternalNearestNeighbours<DISTANCES> - Interface in org.openimaj.knn: Interface for K-nearest-neighbour implementations that are able to search directly using an indexed item of their own internal data as the query.
IntGaussianFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions using Gaussian distributions to approximate the Euclidean distance.
IntGaussianFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.IntGaussianFactory: Construct with the given parameters.
IntHammingFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that approximate the Hamming distance.
IntHammingFactory(int, MersenneTwister, int) - Constructor for class org.openimaj.lsh.functions.IntHammingFactory: Construct a new factory using the given parameters.
IntHashFunction - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for hashing int arrays.
IntHashFunctionFactory - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for producing hash functions seeded by random numbers.
IntHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.IntHashFunctionFactory
IntHyperplaneCosineFactory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate cosine distance using hyperplanes.
IntHyperplaneCosineFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.IntHyperplaneCosineFactory: Construct with the given arguments.
IntHyperplaneL1Factory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate L1 (city-block) distance in closed spaces using random axis-aligned hyperplanes.
IntHyperplaneL1Factory(int, MersenneTwister, int, int) - Constructor for class org.openimaj.lsh.functions.IntHyperplaneL1Factory: Construct with the given arguments.
IntKDTreeEnsemble - Class in org.openimaj.knn.approximate: Ensemble of Best-Bin-First KDTrees for int data.
IntKDTreeEnsemble(int[][]) - Constructor for class org.openimaj.knn.approximate.IntKDTreeEnsemble: Construct a IntKDTreeEnsemble with the provided data, using the default of 8 trees.
IntKDTreeEnsemble(int[][], int) - Constructor for class org.openimaj.knn.approximate.IntKDTreeEnsemble: Construct a IntKDTreeEnsemble with the provided data and number of trees.
IntKDTreeEnsemble(int[][], int, int) - Constructor for class org.openimaj.knn.approximate.IntKDTreeEnsemble: Construct a IntKDTreeEnsemble with the provided data and number of trees.
IntKDTreeEnsemble.IntKDTreeNode - Class in org.openimaj.knn.approximate: An internal node of the KDTree
IntKDTreeNode() - Constructor for class org.openimaj.knn.approximate.IntKDTreeEnsemble.IntKDTreeNode: Construct a new node
IntKDTreeNode(int[][], IntArrayView, Uniform) - Constructor for class org.openimaj.knn.approximate.IntKDTreeEnsemble.IntKDTreeNode: Construct a new node with the given data
IntLSHSketcher<OBJECT> - Class in org.openimaj.lsh.sketch: A Sketcher that produces bit-string sketches encoded as int arrays.
IntLSHSketcher(List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.sketch.IntLSHSketcher: Construct with the given functions.
IntLSHSketcher(HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.sketch.IntLSHSketcher: Construct with the given functions.
IntLSHSketcher(HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.sketch.IntLSHSketcher: Construct with the factory which is used to produce the required number of functions.
IntNearestNeighbours - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with int[] data.
IntNearestNeighbours() - Constructor for class org.openimaj.knn.IntNearestNeighbours
IntNearestNeighboursExact - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation.
IntNearestNeighboursExact(int[][]) - Constructor for class org.openimaj.knn.IntNearestNeighboursExact: Construct the IntNearestNeighboursExact over the provided dataset and using Euclidean distance.
IntNearestNeighboursExact(int[][], IntFVComparator) - Constructor for class org.openimaj.knn.IntNearestNeighboursExact: Construct the IntNearestNeighboursExact over the provided dataset with the given distance function.
IntNearestNeighboursExact.Factory - Class in org.openimaj.knn: NearestNeighboursFactory for producing IntNearestNeighboursExacts.
IntNearestNeighboursKDTree - Class in org.openimaj.knn.approximate: Fast Nearest-Neighbours for int data using an ensemble of Best-Bin-First KDTrees.
IntNearestNeighboursKDTree(int[][], int, int) - Constructor for class org.openimaj.knn.approximate.IntNearestNeighboursKDTree: Construct the IntNearestNeighboursKDTree with the given options.
IntNearestNeighboursKDTree.Factory - Class in org.openimaj.knn.approximate: NearestNeighboursFactory for producing IntNearestNeighboursKDTrees.
IntNearestNeighboursProvider - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
IntProductQuantiser - Class in org.openimaj.knn.pq: Implementation of a Product Quantiser for vectors/arrays of ints.
IntProductQuantiser(IntNearestNeighboursExact[]) - Constructor for class org.openimaj.knn.pq.IntProductQuantiser: Construct a IntProductQuantiser with the given nearest-neighbour assigners.
IntPStableFactory - Class in org.openimaj.lsh.functions: Base class for hashing schemes based on P-Stable distributions.
IntPStableFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.IntPStableFactory: Construct with the given parameters.
IntPStableFactory.PStableFunction - Class in org.openimaj.lsh.functions
IntSDCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Symmetric Distance Computation (SDC) on Product Quantised vectors.
IntSDCNearestNeighbours(IntProductQuantiser, int[][][], int[][]) - Constructor for class org.openimaj.knn.pq.IntSDCNearestNeighbours: Construct the SDC with the given quantiser, centroids (corresponding to the quantiser's internal assigners), and data.

K

kdt - Variable in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree: The ensemble of KDTrees
kdt - Variable in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree: The ensemble of KDTrees
kdt - Variable in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree: The ensemble of KDTrees
kdt - Variable in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree: The ensemble of KDTrees
kdt - Variable in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree: The ensemble of KDTrees
kdt - Variable in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree: The ensemble of KDTrees
kNearestNeighbour(Collection<T>, Coordinate, int) - Method in class org.openimaj.knn.CoordinateBruteForce
kNearestNeighbour(Collection<T>, Coordinate, int) - Method in interface org.openimaj.knn.CoordinateIndex: Find the k nearest neighbour points in the index to the query coordinate.
kNearestNeighbour(Collection<T>, Coordinate, int) - Method in class org.openimaj.knn.CoordinateKDTree

L

LongADCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
LongADCNearestNeighbours(LongProductQuantiser, long[][]) - Constructor for class org.openimaj.knn.pq.LongADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
LongADCNearestNeighbours(LongProductQuantiser, byte[][], int) - Constructor for class org.openimaj.knn.pq.LongADCNearestNeighbours: Construct the ADC with the given quantiser and pre-quantised data .
LongCauchyFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that use a Cauchy distribution to approximate L1 distance.
LongCauchyFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.LongCauchyFactory: Construct the factory with the given parameters.
LongGaussianFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions using Gaussian distributions to approximate the Euclidean distance.
LongGaussianFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.LongGaussianFactory: Construct with the given parameters.
LongHammingFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that approximate the Hamming distance.
LongHammingFactory(int, MersenneTwister, int) - Constructor for class org.openimaj.lsh.functions.LongHammingFactory: Construct a new factory using the given parameters.
LongHashFunction - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for hashing long arrays.
LongHashFunctionFactory - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for producing hash functions seeded by random numbers.
LongHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.LongHashFunctionFactory
LongHyperplaneCosineFactory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate cosine distance using hyperplanes.
LongHyperplaneCosineFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.LongHyperplaneCosineFactory: Construct with the given arguments.
LongHyperplaneL1Factory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate L1 (city-block) distance in closed spaces using random axis-aligned hyperplanes.
LongHyperplaneL1Factory(int, MersenneTwister, long, long) - Constructor for class org.openimaj.lsh.functions.LongHyperplaneL1Factory: Construct with the given arguments.
LongKDTreeEnsemble - Class in org.openimaj.knn.approximate: Ensemble of Best-Bin-First KDTrees for long data.
LongKDTreeEnsemble(long[][]) - Constructor for class org.openimaj.knn.approximate.LongKDTreeEnsemble: Construct a LongKDTreeEnsemble with the provided data, using the default of 8 trees.
LongKDTreeEnsemble(long[][], int) - Constructor for class org.openimaj.knn.approximate.LongKDTreeEnsemble: Construct a LongKDTreeEnsemble with the provided data and number of trees.
LongKDTreeEnsemble(long[][], int, int) - Constructor for class org.openimaj.knn.approximate.LongKDTreeEnsemble: Construct a LongKDTreeEnsemble with the provided data and number of trees.
LongKDTreeEnsemble.LongKDTreeNode - Class in org.openimaj.knn.approximate: An internal node of the KDTree
LongKDTreeNode() - Constructor for class org.openimaj.knn.approximate.LongKDTreeEnsemble.LongKDTreeNode: Construct a new node
LongKDTreeNode(long[][], IntArrayView, Uniform) - Constructor for class org.openimaj.knn.approximate.LongKDTreeEnsemble.LongKDTreeNode: Construct a new node with the given data
LongLSHSketcher<OBJECT> - Class in org.openimaj.lsh.sketch: A Sketcher that produces bit-string sketches encoded as long arrays.
LongLSHSketcher(List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.sketch.LongLSHSketcher: Construct with the given functions.
LongLSHSketcher(HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.sketch.LongLSHSketcher: Construct with the given functions.
LongLSHSketcher(HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.sketch.LongLSHSketcher: Construct with the factory which is used to produce the required number of functions.
LongNearestNeighbours - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with long[] data.
LongNearestNeighbours() - Constructor for class org.openimaj.knn.LongNearestNeighbours
LongNearestNeighboursExact - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation.
LongNearestNeighboursExact(long[][]) - Constructor for class org.openimaj.knn.LongNearestNeighboursExact: Construct the LongNearestNeighboursExact over the provided dataset and using Euclidean distance.
LongNearestNeighboursExact(long[][], LongFVComparator) - Constructor for class org.openimaj.knn.LongNearestNeighboursExact: Construct the LongNearestNeighboursExact over the provided dataset with the given distance function.
LongNearestNeighboursExact.Factory - Class in org.openimaj.knn: NearestNeighboursFactory for producing LongNearestNeighboursExacts.
LongNearestNeighboursKDTree - Class in org.openimaj.knn.approximate: Fast Nearest-Neighbours for long data using an ensemble of Best-Bin-First KDTrees.
LongNearestNeighboursKDTree(long[][], int, int) - Constructor for class org.openimaj.knn.approximate.LongNearestNeighboursKDTree: Construct the LongNearestNeighboursKDTree with the given options.
LongNearestNeighboursKDTree.Factory - Class in org.openimaj.knn.approximate: NearestNeighboursFactory for producing LongNearestNeighboursKDTrees.
LongNearestNeighboursProvider - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
LongProductQuantiser - Class in org.openimaj.knn.pq: Implementation of a Product Quantiser for vectors/arrays of longs.
LongProductQuantiser(LongNearestNeighboursExact[]) - Constructor for class org.openimaj.knn.pq.LongProductQuantiser: Construct a LongProductQuantiser with the given nearest-neighbour assigners.
LongPStableFactory - Class in org.openimaj.lsh.functions: Base class for hashing schemes based on P-Stable distributions.
LongPStableFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.LongPStableFactory: Construct with the given parameters.
LongPStableFactory.PStableFunction - Class in org.openimaj.lsh.functions
LongSDCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Symmetric Distance Computation (SDC) on Product Quantised vectors.
LongSDCNearestNeighbours(LongProductQuantiser, long[][][], long[][]) - Constructor for class org.openimaj.knn.pq.LongSDCNearestNeighbours: Construct the SDC with the given quantiser, centroids (corresponding to the quantiser's internal assigners), and data.
LSHNearestNeighbours<OBJECT> - Class in org.openimaj.knn.lsh: Nearest-neighbours based on Locality Sensitive Hashing (LSH).
LSHNearestNeighbours(List<HashFunction<OBJECT>>, DistanceComparator<OBJECT>) - Constructor for class org.openimaj.knn.lsh.LSHNearestNeighbours: Construct with the given hash functions and distance function.
LSHNearestNeighbours(HashFunctionFactory<OBJECT>, int, DistanceComparator<OBJECT>) - Constructor for class org.openimaj.knn.lsh.LSHNearestNeighbours: Construct with the given hash function factory which will be used to initialize the requested number of hash tables.

N

nchecks - Variable in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree: The number of checks
nchecks - Variable in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree: The number of checks
nchecks - Variable in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree: The number of checks
nchecks - Variable in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree: The number of checks
nchecks - Variable in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree: The number of checks
nchecks - Variable in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree: The number of checks
ndims - Variable in class org.openimaj.knn.pq.ByteADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.ByteProductQuantiser
ndims - Variable in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.DoubleProductQuantiser
ndims - Variable in class org.openimaj.knn.pq.FloatADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.FloatProductQuantiser
ndims - Variable in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IntADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.IntProductQuantiser
ndims - Variable in class org.openimaj.knn.pq.LongADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.LongProductQuantiser
ndims - Variable in class org.openimaj.knn.pq.ShortADCNearestNeighbours
ndims - Variable in class org.openimaj.knn.pq.ShortProductQuantiser
ndims - Variable in class org.openimaj.lsh.functions.RandomisedHashFunctionFactory
nearestNeighbour(Coordinate) - Method in class org.openimaj.knn.CoordinateBruteForce
nearestNeighbour(Coordinate) - Method in interface org.openimaj.knn.CoordinateIndex: Find the nearest coordinate in the index to the query coordinate.
nearestNeighbour(Coordinate) - Method in class org.openimaj.knn.CoordinateKDTree: Find the nearest neighbour.
NearestNeighbours<DATA,DISTANCES,PAIR_TYPE> - Interface in org.openimaj.knn: Interface for k-nearest-neighbour calculations with some data.
NearestNeighboursFactory<T extends NearestNeighbours<DATA,?,?>,DATA> - Interface in org.openimaj.knn: Interface for factory objects that can produce NearestNeighbours objects for some given data.
numDimensions() - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
numDimensions() - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
numDimensions() - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
numDimensions() - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
numDimensions() - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
numDimensions() - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
numDimensions() - Method in class org.openimaj.knn.ByteNearestNeighbours: Get the number of dimensions of each vector in the dataset
numDimensions() - Method in class org.openimaj.knn.ByteNearestNeighboursExact
numDimensions() - Method in class org.openimaj.knn.DoubleNearestNeighbours: Get the number of dimensions of each vector in the dataset
numDimensions() - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
numDimensions() - Method in class org.openimaj.knn.FloatNearestNeighbours: Get the number of dimensions of each vector in the dataset
numDimensions() - Method in class org.openimaj.knn.FloatNearestNeighboursExact
numDimensions() - Method in class org.openimaj.knn.IntNearestNeighbours: Get the number of dimensions of each vector in the dataset
numDimensions() - Method in class org.openimaj.knn.IntNearestNeighboursExact
numDimensions() - Method in class org.openimaj.knn.LongNearestNeighbours: Get the number of dimensions of each vector in the dataset
numDimensions() - Method in class org.openimaj.knn.LongNearestNeighboursExact
numDimensions() - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
numDimensions() - Method in class org.openimaj.knn.ShortNearestNeighbours: Get the number of dimensions of each vector in the dataset
numDimensions() - Method in class org.openimaj.knn.ShortNearestNeighboursExact
numTables() - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Get the number of hash tables

O

ObjectNearestNeighbours<T> - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with any form of object that can be compared with a DistanceComparator.
ObjectNearestNeighbours(DistanceComparator<? super T>) - Constructor for class org.openimaj.knn.ObjectNearestNeighbours: Construct with the given distance measure
ObjectNearestNeighboursExact<T> - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation for objects with a compatible DistanceComparator.
ObjectNearestNeighboursExact(List<T>, DistanceComparator<? super T>) - Constructor for class org.openimaj.knn.ObjectNearestNeighboursExact: Construct the ObjectNearestNeighboursExact over the provided dataset with the given distance function.
ObjectNearestNeighboursExact(T[], DistanceComparator<? super T>) - Constructor for class org.openimaj.knn.ObjectNearestNeighboursExact: Construct the ObjectNearestNeighboursExact over the provided dataset with the given distance function.
ObjectNearestNeighboursExact(DistanceComparator<T>) - Constructor for class org.openimaj.knn.ObjectNearestNeighboursExact: Construct any empty ObjectNearestNeighboursExact with the given distance function.
ObjectNearestNeighboursExact.Factory<T> - Class in org.openimaj.knn: NearestNeighboursFactory for producing ObjectNearestNeighboursExacts.
ObjectNearestNeighboursProvider<T> - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
org.openimaj.knn - package org.openimaj.knn
org.openimaj.knn.approximate - package org.openimaj.knn.approximate
org.openimaj.knn.lsh - package org.openimaj.knn.lsh
org.openimaj.knn.pq - package org.openimaj.knn.pq
org.openimaj.lsh.composition - package org.openimaj.lsh.composition
org.openimaj.lsh.functions - package org.openimaj.lsh.functions
org.openimaj.lsh.sketch - package org.openimaj.lsh.sketch

P

pnts - Variable in class org.openimaj.knn.approximate.ByteKDTreeEnsemble: The underlying data array
pnts - Variable in class org.openimaj.knn.approximate.DoubleKDTreeEnsemble: The underlying data array
pnts - Variable in class org.openimaj.knn.approximate.FloatKDTreeEnsemble: The underlying data array
pnts - Variable in class org.openimaj.knn.approximate.IntKDTreeEnsemble: The underlying data array
pnts - Variable in class org.openimaj.knn.approximate.LongKDTreeEnsemble: The underlying data array
pnts - Variable in class org.openimaj.knn.approximate.ShortKDTreeEnsemble: The underlying data array
pnts - Variable in class org.openimaj.knn.ByteNearestNeighboursExact
pnts - Variable in class org.openimaj.knn.DoubleNearestNeighboursExact
pnts - Variable in class org.openimaj.knn.FloatNearestNeighboursExact
pnts - Variable in class org.openimaj.knn.IntNearestNeighboursExact
pnts - Variable in class org.openimaj.knn.LongNearestNeighboursExact
pnts - Variable in class org.openimaj.knn.ObjectNearestNeighboursExact
pnts - Variable in class org.openimaj.knn.ShortNearestNeighboursExact
PolyHashComposition<OBJECT> - Class in org.openimaj.lsh.composition: HashComposition that uses a polynomial function to combine the individual hashes.
PolyHashComposition(List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.composition.PolyHashComposition: Construct with the given functions.
PolyHashComposition(HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.composition.PolyHashComposition: Construct with the given functions.
PolyHashComposition(HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.composition.PolyHashComposition: Construct with the factory which is used to produce the required number of functions.
pq - Variable in class org.openimaj.knn.pq.ByteADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.FloatADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.IntADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.LongADCNearestNeighbours
pq - Variable in class org.openimaj.knn.pq.ShortADCNearestNeighbours

Q

quantise(byte[]) - Method in class org.openimaj.knn.pq.ByteProductQuantiser: Quantise the given data using this Product Quantiser.
quantise(double[]) - Method in class org.openimaj.knn.pq.DoubleProductQuantiser: Quantise the given data using this Product Quantiser.
quantise(float[]) - Method in class org.openimaj.knn.pq.FloatProductQuantiser: Quantise the given data using this Product Quantiser.
quantise(int[]) - Method in class org.openimaj.knn.pq.IntProductQuantiser: Quantise the given data using this Product Quantiser.
quantise(long[]) - Method in class org.openimaj.knn.pq.LongProductQuantiser: Quantise the given data using this Product Quantiser.
quantise(short[]) - Method in class org.openimaj.knn.pq.ShortProductQuantiser: Quantise the given data using this Product Quantiser.

R

r - Variable in class org.openimaj.lsh.functions.BytePStableFactory.PStableFunction
r - Variable in class org.openimaj.lsh.functions.DoublePStableFactory.PStableFunction
r - Variable in class org.openimaj.lsh.functions.FloatPStableFactory.PStableFunction
r - Variable in class org.openimaj.lsh.functions.IntPStableFactory.PStableFunction
r - Variable in class org.openimaj.lsh.functions.LongPStableFactory.PStableFunction
r - Variable in class org.openimaj.lsh.functions.ShortPStableFactory.PStableFunction
random - Variable in class org.openimaj.lsh.functions.RandomisedHashFunction
RandomisedHashFunction<OBJECT> - Class in org.openimaj.lsh.functions: A randomised hash function
RandomisedHashFunctionFactory<OBJECT> - Class in org.openimaj.lsh.functions: A factory for producing RandomisedHashFunctions.
RandomisedHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.RandomisedHashFunctionFactory: * @param ndims number of dimensions of data
RandomProjectionHashComposition<OBJECT> - Class in org.openimaj.lsh.composition: Compose a set of hash functions by computing the dot product of the hashes they produce with a random vector.
RandomProjectionHashComposition(MersenneTwister, List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.composition.RandomProjectionHashComposition: Construct with the given functions.
RandomProjectionHashComposition(MersenneTwister, HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.composition.RandomProjectionHashComposition: Construct with the given functions.
RandomProjectionHashComposition(MersenneTwister, HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.composition.RandomProjectionHashComposition: Construct with the factory which is used to produce the required number of functions.
rangeSearch(Collection<T>, Coordinate, Coordinate) - Method in class org.openimaj.knn.CoordinateBruteForce
rangeSearch(Collection<T>, Coordinate, Coordinate) - Method in interface org.openimaj.knn.CoordinateIndex: Search for all the coordinates in the index that lie in the hyper-rectangle defined by the lower and upper bound coordinates.
rangeSearch(Collection<T>, Coordinate, Coordinate) - Method in class org.openimaj.knn.CoordinateKDTree: Searches the tree for all points contained within a given k-dimensional bounding box and stores them in a Collection.
readBinary(DataInput) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
readBinary(DataInput) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
readBinary(DataInput) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
readBinary(DataInput) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
readBinary(DataInput) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
readBinary(DataInput) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
rng - Variable in class org.openimaj.lsh.functions.RandomisedHashFunctionFactory

S

search(OBJECT[]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Search for similar data in the underlying tables and return all matches
search(OBJECT) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours: Search for a similar data item in the underlying tables and return all matches
searchKNN(byte[][], int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
searchKNN(List<byte[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
searchKNN(byte[], int) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
searchKNN(double[][], int, int[][], double[][]) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
searchKNN(List<double[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
searchKNN(double[], int) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
searchKNN(float[][], int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
searchKNN(List<float[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
searchKNN(float[], int) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
searchKNN(int[][], int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
searchKNN(List<int[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
searchKNN(int[], int) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
searchKNN(long[][], int, int[][], double[][]) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
searchKNN(List<long[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
searchKNN(long[], int) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
searchKNN(short[][], int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
searchKNN(List<short[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
searchKNN(short[], int) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
searchKNN(byte[][], int, int[][], float[][]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact
searchKNN(List<byte[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact
searchKNN(byte[], int) - Method in class org.openimaj.knn.ByteNearestNeighboursExact
searchKNN(double[][], int, int[][], double[][]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
searchKNN(List<double[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
searchKNN(double[], int) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
searchKNN(float[][], int, int[][], float[][]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact
searchKNN(List<float[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact
searchKNN(float[], int) - Method in class org.openimaj.knn.FloatNearestNeighboursExact
searchKNN(int[], int, int[][], DISTANCES[]) - Method in interface org.openimaj.knn.InternalNearestNeighbours: Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.
searchKNN(int[][], int, int[][], float[][]) - Method in class org.openimaj.knn.IntNearestNeighboursExact
searchKNN(List<int[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.IntNearestNeighboursExact
searchKNN(int[], int) - Method in class org.openimaj.knn.IntNearestNeighboursExact
searchKNN(long[][], int, int[][], double[][]) - Method in class org.openimaj.knn.LongNearestNeighboursExact
searchKNN(List<long[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.LongNearestNeighboursExact
searchKNN(long[], int) - Method in class org.openimaj.knn.LongNearestNeighboursExact
searchKNN(OBJECT[], int, int[][], float[][]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
searchKNN(List<OBJECT>, int, int[][], float[][]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
searchKNN(OBJECT, int) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
searchKNN(DATA[], int, int[][], DISTANCES[]) - Method in interface org.openimaj.knn.NearestNeighbours: Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.
searchKNN(List<DATA>, int, int[][], DISTANCES[]) - Method in interface org.openimaj.knn.NearestNeighbours: Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.
searchKNN(DATA, int) - Method in interface org.openimaj.knn.NearestNeighbours: Search for the K nearest neighbours to the given query and return an ordered list of pairs containing the distance and index of each neighbour.
searchKNN(T[], int, int[][], float[][]) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
searchKNN(List<T>, int, int[][], float[][]) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
searchKNN(T, int) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
searchKNN(byte[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
searchKNN(List<byte[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
searchKNN(byte[], int) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
searchKNN(double[][], int, int[][], double[][]) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
searchKNN(List<double[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
searchKNN(double[], int) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
searchKNN(float[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
searchKNN(List<float[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
searchKNN(float[], int) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
searchKNN(byte[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
searchKNN(List<byte[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
searchKNN(byte[], int) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
searchKNN(double[][], int, int[][], double[][]) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
searchKNN(List<double[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
searchKNN(double[], int) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
searchKNN(float[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
searchKNN(List<float[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
searchKNN(float[], int) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
searchKNN(int[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
searchKNN(List<int[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
searchKNN(int[], int) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
searchKNN(long[][], int, int[][], double[][]) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
searchKNN(List<long[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
searchKNN(long[], int) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
searchKNN(short[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
searchKNN(List<short[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
searchKNN(short[], int) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
searchKNN(int[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
searchKNN(List<int[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
searchKNN(int[], int) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
searchKNN(long[][], int, int[][], double[][]) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
searchKNN(List<long[]>, int, int[][], double[][]) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
searchKNN(long[], int) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
searchKNN(short[][], int, int[][], float[][]) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
searchKNN(List<short[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
searchKNN(short[], int) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
searchKNN(short[][], int, int[][], float[][]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact
searchKNN(List<short[]>, int, int[][], float[][]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact
searchKNN(short[], int) - Method in class org.openimaj.knn.ShortNearestNeighboursExact
searchNN(byte[][], int[], float[]) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
searchNN(List<byte[]>, int[], float[]) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
searchNN(byte[]) - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
searchNN(double[][], int[], double[]) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
searchNN(List<double[]>, int[], double[]) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
searchNN(double[]) - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
searchNN(float[][], int[], float[]) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
searchNN(List<float[]>, int[], float[]) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
searchNN(float[]) - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
searchNN(int[][], int[], float[]) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
searchNN(List<int[]>, int[], float[]) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
searchNN(int[]) - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
searchNN(long[][], int[], double[]) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
searchNN(List<long[]>, int[], double[]) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
searchNN(long[]) - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
searchNN(short[][], int[], float[]) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
searchNN(List<short[]>, int[], float[]) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
searchNN(short[]) - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
searchNN(byte[][], int[], float[]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact
searchNN(List<byte[]>, int[], float[]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact
searchNN(byte[]) - Method in class org.openimaj.knn.ByteNearestNeighboursExact
searchNN(double[][], int[], double[]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
searchNN(List<double[]>, int[], double[]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
searchNN(double[]) - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
searchNN(float[][], int[], float[]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact
searchNN(List<float[]>, int[], float[]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact
searchNN(float[]) - Method in class org.openimaj.knn.FloatNearestNeighboursExact
searchNN(int[], int[], DISTANCES) - Method in interface org.openimaj.knn.InternalNearestNeighbours: Search for the nearest neighbour to each of the N queries (given by their index in this nearest neighbours object), and return the index of each nearest neighbour and the respective distance.
searchNN(int[][], int[], float[]) - Method in class org.openimaj.knn.IntNearestNeighboursExact
searchNN(List<int[]>, int[], float[]) - Method in class org.openimaj.knn.IntNearestNeighboursExact
searchNN(int[]) - Method in class org.openimaj.knn.IntNearestNeighboursExact
searchNN(long[][], int[], double[]) - Method in class org.openimaj.knn.LongNearestNeighboursExact
searchNN(List<long[]>, int[], double[]) - Method in class org.openimaj.knn.LongNearestNeighboursExact
searchNN(long[]) - Method in class org.openimaj.knn.LongNearestNeighboursExact
searchNN(OBJECT[], int[], float[]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
searchNN(List<OBJECT>, int[], float[]) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
searchNN(OBJECT) - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
searchNN(DATA[], int[], DISTANCES) - Method in interface org.openimaj.knn.NearestNeighbours: Search for the nearest neighbour to each of the N queries, and return the index of each nearest neighbour and the respective distance.
searchNN(List<DATA>, int[], DISTANCES) - Method in interface org.openimaj.knn.NearestNeighbours: Search for the nearest neighbour to each of the N queries, and return the index of each nearest neighbour and the respective distance.
searchNN(DATA) - Method in interface org.openimaj.knn.NearestNeighbours: Search for the nearest neighbour to the given query and return a pair containing the distance and index of that neighbour.
searchNN(T[], int[], float[]) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
searchNN(List<T>, int[], float[]) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
searchNN(T) - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
searchNN(byte[][], int[], float[]) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
searchNN(List<byte[]>, int[], float[]) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
searchNN(byte[]) - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
searchNN(double[][], int[], double[]) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
searchNN(List<double[]>, int[], double[]) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
searchNN(double[]) - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
searchNN(float[][], int[], float[]) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
searchNN(List<float[]>, int[], float[]) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
searchNN(float[]) - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
searchNN(byte[][], int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
searchNN(List<byte[]>, int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
searchNN(byte[]) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
searchNN(double[][], int[], double[]) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
searchNN(List<double[]>, int[], double[]) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
searchNN(double[]) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
searchNN(float[][], int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
searchNN(List<float[]>, int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
searchNN(float[]) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
searchNN(int[][], int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
searchNN(List<int[]>, int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
searchNN(int[]) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
searchNN(long[][], int[], double[]) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
searchNN(List<long[]>, int[], double[]) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
searchNN(long[]) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
searchNN(short[][], int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
searchNN(List<short[]>, int[], float[]) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
searchNN(short[]) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
searchNN(int[][], int[], float[]) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
searchNN(List<int[]>, int[], float[]) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
searchNN(int[]) - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
searchNN(long[][], int[], double[]) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
searchNN(List<long[]>, int[], double[]) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
searchNN(long[]) - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
searchNN(short[][], int[], float[]) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
searchNN(List<short[]>, int[], float[]) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
searchNN(short[]) - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
searchNN(short[][], int[], float[]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact
searchNN(List<short[]>, int[], float[]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact
searchNN(short[]) - Method in class org.openimaj.knn.ShortNearestNeighboursExact
ShortADCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Asymmetric Distance Computation (ADC) on Product Quantised vectors.
ShortADCNearestNeighbours(ShortProductQuantiser, short[][]) - Constructor for class org.openimaj.knn.pq.ShortADCNearestNeighbours: Construct the ADC with the given quantiser and data points.
ShortADCNearestNeighbours(ShortProductQuantiser, byte[][], int) - Constructor for class org.openimaj.knn.pq.ShortADCNearestNeighbours: Construct the ADC with the given quantiser and pre-quantised data .
ShortCauchyFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that use a Cauchy distribution to approximate L1 distance.
ShortCauchyFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.ShortCauchyFactory: Construct the factory with the given parameters.
ShortGaussianFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions using Gaussian distributions to approximate the Euclidean distance.
ShortGaussianFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.ShortGaussianFactory: Construct with the given parameters.
ShortHammingFactory - Class in org.openimaj.lsh.functions: A hash function factory for producing hash functions that approximate the Hamming distance.
ShortHammingFactory(int, MersenneTwister, int) - Constructor for class org.openimaj.lsh.functions.ShortHammingFactory: Construct a new factory using the given parameters.
ShortHashFunction - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for hashing short arrays.
ShortHashFunctionFactory - Class in org.openimaj.lsh.functions: Base RandomisedHashFunction for producing hash functions seeded by random numbers.
ShortHashFunctionFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.ShortHashFunctionFactory
ShortHyperplaneCosineFactory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate cosine distance using hyperplanes.
ShortHyperplaneCosineFactory(int, MersenneTwister) - Constructor for class org.openimaj.lsh.functions.ShortHyperplaneCosineFactory: Construct with the given arguments.
ShortHyperplaneL1Factory - Class in org.openimaj.lsh.functions: A hash function factory that produces hash functions that approximate L1 (city-block) distance in closed spaces using random axis-aligned hyperplanes.
ShortHyperplaneL1Factory(int, MersenneTwister, short, short) - Constructor for class org.openimaj.lsh.functions.ShortHyperplaneL1Factory: Construct with the given arguments.
ShortKDTreeEnsemble - Class in org.openimaj.knn.approximate: Ensemble of Best-Bin-First KDTrees for short data.
ShortKDTreeEnsemble(short[][]) - Constructor for class org.openimaj.knn.approximate.ShortKDTreeEnsemble: Construct a ShortKDTreeEnsemble with the provided data, using the default of 8 trees.
ShortKDTreeEnsemble(short[][], int) - Constructor for class org.openimaj.knn.approximate.ShortKDTreeEnsemble: Construct a ShortKDTreeEnsemble with the provided data and number of trees.
ShortKDTreeEnsemble(short[][], int, int) - Constructor for class org.openimaj.knn.approximate.ShortKDTreeEnsemble: Construct a ShortKDTreeEnsemble with the provided data and number of trees.
ShortKDTreeEnsemble.ShortKDTreeNode - Class in org.openimaj.knn.approximate: An internal node of the KDTree
ShortKDTreeNode() - Constructor for class org.openimaj.knn.approximate.ShortKDTreeEnsemble.ShortKDTreeNode: Construct a new node
ShortKDTreeNode(short[][], IntArrayView, Uniform) - Constructor for class org.openimaj.knn.approximate.ShortKDTreeEnsemble.ShortKDTreeNode: Construct a new node with the given data
ShortLSHSketcher<OBJECT> - Class in org.openimaj.lsh.sketch: A Sketcher that produces bit-string sketches encoded as short arrays.
ShortLSHSketcher(List<HashFunction<OBJECT>>) - Constructor for class org.openimaj.lsh.sketch.ShortLSHSketcher: Construct with the given functions.
ShortLSHSketcher(HashFunction<OBJECT>, HashFunction<OBJECT>...) - Constructor for class org.openimaj.lsh.sketch.ShortLSHSketcher: Construct with the given functions.
ShortLSHSketcher(HashFunctionFactory<OBJECT>, int) - Constructor for class org.openimaj.lsh.sketch.ShortLSHSketcher: Construct with the factory which is used to produce the required number of functions.
ShortNearestNeighbours - Class in org.openimaj.knn: Abstract base class for k-nearest-neighbour calculations with short[] data.
ShortNearestNeighbours() - Constructor for class org.openimaj.knn.ShortNearestNeighbours
ShortNearestNeighboursExact - Class in org.openimaj.knn: Exact (brute-force) k-nearest-neighbour implementation.
ShortNearestNeighboursExact(short[][]) - Constructor for class org.openimaj.knn.ShortNearestNeighboursExact: Construct the ShortNearestNeighboursExact over the provided dataset and using Euclidean distance.
ShortNearestNeighboursExact(short[][], ShortFVComparator) - Constructor for class org.openimaj.knn.ShortNearestNeighboursExact: Construct the ShortNearestNeighboursExact over the provided dataset with the given distance function.
ShortNearestNeighboursExact.Factory - Class in org.openimaj.knn: NearestNeighboursFactory for producing ShortNearestNeighboursExacts.
ShortNearestNeighboursKDTree - Class in org.openimaj.knn.approximate: Fast Nearest-Neighbours for short data using an ensemble of Best-Bin-First KDTrees.
ShortNearestNeighboursKDTree(short[][], int, int) - Constructor for class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree: Construct the ShortNearestNeighboursKDTree with the given options.
ShortNearestNeighboursKDTree.Factory - Class in org.openimaj.knn.approximate: NearestNeighboursFactory for producing ShortNearestNeighboursKDTrees.
ShortNearestNeighboursProvider - Interface in org.openimaj.knn: Interface for classes able to expose a k-nearest-neighbour object.
ShortProductQuantiser - Class in org.openimaj.knn.pq: Implementation of a Product Quantiser for vectors/arrays of shorts.
ShortProductQuantiser(ShortNearestNeighboursExact[]) - Constructor for class org.openimaj.knn.pq.ShortProductQuantiser: Construct a ShortProductQuantiser with the given nearest-neighbour assigners.
ShortPStableFactory - Class in org.openimaj.lsh.functions: Base class for hashing schemes based on P-Stable distributions.
ShortPStableFactory(int, MersenneTwister, double) - Constructor for class org.openimaj.lsh.functions.ShortPStableFactory: Construct with the given parameters.
ShortPStableFactory.PStableFunction - Class in org.openimaj.lsh.functions
ShortSDCNearestNeighbours - Class in org.openimaj.knn.pq: Nearest-neighbours using Symmetric Distance Computation (SDC) on Product Quantised vectors.
ShortSDCNearestNeighbours(ShortProductQuantiser, short[][][], short[][]) - Constructor for class org.openimaj.knn.pq.ShortSDCNearestNeighbours: Construct the SDC with the given quantiser, centroids (corresponding to the quantiser's internal assigners), and data.
size() - Method in class org.openimaj.knn.approximate.ByteNearestNeighboursKDTree
size() - Method in class org.openimaj.knn.approximate.DoubleNearestNeighboursKDTree
size() - Method in class org.openimaj.knn.approximate.FloatNearestNeighboursKDTree
size() - Method in class org.openimaj.knn.approximate.IntNearestNeighboursKDTree
size() - Method in class org.openimaj.knn.approximate.LongNearestNeighboursKDTree
size() - Method in class org.openimaj.knn.approximate.ShortNearestNeighboursKDTree
size() - Method in class org.openimaj.knn.ByteNearestNeighboursExact
size() - Method in class org.openimaj.knn.DoubleNearestNeighboursExact
size() - Method in class org.openimaj.knn.FloatNearestNeighboursExact
size() - Method in class org.openimaj.knn.IntNearestNeighboursExact
size() - Method in class org.openimaj.knn.LongNearestNeighboursExact
size() - Method in class org.openimaj.knn.lsh.LSHNearestNeighbours
size() - Method in interface org.openimaj.knn.NearestNeighbours: Get the size of the dataset
size() - Method in class org.openimaj.knn.ObjectNearestNeighboursExact
size() - Method in class org.openimaj.knn.pq.ByteADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.DoubleADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.FloatADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.IntADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.LongADCNearestNeighbours
size() - Method in class org.openimaj.knn.pq.ShortADCNearestNeighbours
size() - Method in class org.openimaj.knn.ShortNearestNeighboursExact

T

tables - Variable in class org.openimaj.knn.lsh.LSHNearestNeighbours
trees - Variable in class org.openimaj.knn.approximate.ByteKDTreeEnsemble: The tree roots
trees - Variable in class org.openimaj.knn.approximate.DoubleKDTreeEnsemble: The tree roots
trees - Variable in class org.openimaj.knn.approximate.FloatKDTreeEnsemble: The tree roots
trees - Variable in class org.openimaj.knn.approximate.IntKDTreeEnsemble: The tree roots
trees - Variable in class org.openimaj.knn.approximate.LongKDTreeEnsemble: The tree roots
trees - Variable in class org.openimaj.knn.approximate.ShortKDTreeEnsemble: The tree roots

W

writeBinary(DataOutput) - Method in class org.openimaj.knn.pq.IncrementalByteADCNearestNeighbours
writeBinary(DataOutput) - Method in class org.openimaj.knn.pq.IncrementalDoubleADCNearestNeighbours
writeBinary(DataOutput) - Method in class org.openimaj.knn.pq.IncrementalFloatADCNearestNeighbours
writeBinary(DataOutput) - Method in class org.openimaj.knn.pq.IncrementalIntADCNearestNeighbours
writeBinary(DataOutput) - Method in class org.openimaj.knn.pq.IncrementalLongADCNearestNeighbours
writeBinary(DataOutput) - Method in class org.openimaj.knn.pq.IncrementalShortADCNearestNeighbours

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