Index (The OpenIMAJ Clustering Library 1.3.9 API)

A B C D E F G H I K L M N O P R S T U V W

A

AbsoluteValueEigenChooser - Class in org.openimaj.ml.clustering.spectral: Attempts to automatically choose the number of eigen vectors based on the comparative value of the eigen value with the first eigen value seen.
AbsoluteValueEigenChooser(double, double) - Constructor for class org.openimaj.ml.clustering.spectral.AbsoluteValueEigenChooser
addIterationListener(Operation<SphericalKMeans.IterationResult>) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeans: Add a listener that will be called before every iteration.
analyse(Map<Double, int[][]>) - Method in class org.openimaj.experiment.evaluation.cluster.RangedDBSCANClusterEvaluator
apply(IndependentPair<double[], double[][]>) - Method in class org.openimaj.ml.clustering.spectral.SpectralClusteringConf.DefaultClustererFunction
asciiHeader() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
asciiHeader() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
asciiHeader() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
asciiHeader() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
asciiHeader() - Method in class org.openimaj.ml.clustering.IndexClusters
asciiHeader() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
asciiHeader() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
asciiHeader() - Method in class org.openimaj.ml.clustering.rac.IntRAC
asciiHeader() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
asciiHeader() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
assign(DATATYPE[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner
assign(DATATYPE) - Method in class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner
assign(byte[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
assign(byte[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
assign(double[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
assign(double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
assign(T[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
assign(T) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
assign(float[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
assign(float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
assign(int[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
assign(int[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
assign(long[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
assign(long[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
assign(short[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
assign(short[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
assign(byte[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
assign(byte[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
assign(double[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
assign(double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
assign(float[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
assign(float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
assign(int[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
assign(int[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
assign(long[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
assign(long[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
assign(short[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
assign(short[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
assign(byte[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
assign(byte[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
assign(double[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
assign(double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
assign(float[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
assign(float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
assign(int[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
assign(int[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
assign(long[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
assign(long[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
assign(short[][]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
assign(short[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
assign(DATATYPE[]) - Method in interface org.openimaj.ml.clustering.assignment.HardAssigner: Assign data to a cluster.
assign(DATATYPE) - Method in interface org.openimaj.ml.clustering.assignment.HardAssigner: Assign a single point to a cluster.
assign(byte[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
assign(byte[]) - Method in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
assign(double[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
assign(double[]) - Method in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
assign(float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
assign(float[]) - Method in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
assign(byte[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
assign(byte[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
assign(double[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
assign(double[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
assign(float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
assign(float[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
assign(int[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
assign(int[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
assign(long[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
assign(long[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
assign(short[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
assign(short[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
assign(int[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
assign(int[]) - Method in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
assign(long[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
assign(long[]) - Method in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
assign(short[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
assign(short[]) - Method in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
assign(DATATYPE[]) - Method in interface org.openimaj.ml.clustering.assignment.SoftAssigner: Assign data to clusters.
assign(DATATYPE) - Method in interface org.openimaj.ml.clustering.assignment.SoftAssigner: Assign a single point to some clusters.
assign(int[][]) - Method in class org.openimaj.ml.clustering.rac.IntRAC
assign(int[]) - Method in class org.openimaj.ml.clustering.rac.IntRAC
assign(int[][]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
assign(int[]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest: Uses the IntRandomForest.assignWord(int[]) function to construct the word representing this data point.
assignDistance(DATATYPE[], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner
assignDistance(DATATYPE) - Method in class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner
assignDistance(byte[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
assignDistance(byte[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
assignDistance(double[][], int[], double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
assignDistance(double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
assignDistance(T[], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
assignDistance(T) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
assignDistance(float[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
assignDistance(float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
assignDistance(int[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
assignDistance(int[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
assignDistance(long[][], int[], double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
assignDistance(long[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
assignDistance(short[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
assignDistance(short[]) - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
assignDistance(byte[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
assignDistance(byte[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
assignDistance(double[][], int[], double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
assignDistance(double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
assignDistance(float[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
assignDistance(float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
assignDistance(int[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
assignDistance(int[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
assignDistance(long[][], int[], double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
assignDistance(long[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
assignDistance(short[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
assignDistance(short[]) - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
assignDistance(byte[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
assignDistance(byte[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
assignDistance(double[][], int[], double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
assignDistance(double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
assignDistance(float[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
assignDistance(float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
assignDistance(int[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
assignDistance(int[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
assignDistance(long[][], int[], double[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
assignDistance(long[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
assignDistance(short[][], int[], float[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
assignDistance(short[]) - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
assignDistance(DATATYPE[], int[], DISTANCES) - Method in interface org.openimaj.ml.clustering.assignment.HardAssigner: Assign data to clusters.
assignDistance(DATATYPE) - Method in interface org.openimaj.ml.clustering.assignment.HardAssigner: Assign a single point to a cluster.
assignDistance(int[][], int[], float[]) - Method in class org.openimaj.ml.clustering.rac.IntRAC
assignDistance(int[]) - Method in class org.openimaj.ml.clustering.rac.IntRAC
assignDistance(int[][], int[], float[]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
assignDistance(int[]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
Assigner<DATATYPE> - Interface in org.openimaj.ml.clustering.assignment: Super interface for all assigners.
assigners - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
assigners - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
assigners - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
assigners - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
assigners - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
assigners - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
assignLetters(int[][]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest: Push each data point provided to a set of letters, i.e.
assignments - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult: The assignments of the training data to clusters
assignWeighted(byte[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
assignWeighted(byte[]) - Method in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
assignWeighted(double[][], int[][], double[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
assignWeighted(double[]) - Method in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
assignWeighted(float[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
assignWeighted(float[]) - Method in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
assignWeighted(byte[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
assignWeighted(byte[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
assignWeighted(double[][], int[][], double[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
assignWeighted(double[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
assignWeighted(float[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
assignWeighted(float[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
assignWeighted(int[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
assignWeighted(int[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
assignWeighted(long[][], int[][], double[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
assignWeighted(long[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
assignWeighted(short[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
assignWeighted(short[]) - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
assignWeighted(int[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
assignWeighted(int[]) - Method in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
assignWeighted(long[][], int[][], double[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
assignWeighted(long[]) - Method in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
assignWeighted(short[][], int[][], float[][]) - Method in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
assignWeighted(short[]) - Method in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
assignWeighted(DATATYPE[], int[][], DISTANCES[]) - Method in interface org.openimaj.ml.clustering.assignment.SoftAssigner: Assign data to clusters.
assignWeighted(DATATYPE) - Method in interface org.openimaj.ml.clustering.assignment.SoftAssigner: Assign a single point to some clusters.
assignWord(int[]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest: Push a single data point to a set of letters, return the letters as word.

B

bestCols(Eigenvalues) - Method in class org.openimaj.ml.clustering.spectral.PreparedSpectralClustering
binaryHeader() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
binaryHeader() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
binaryHeader() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
binaryHeader() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
binaryHeader() - Method in class org.openimaj.ml.clustering.IndexClusters
binaryHeader() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
binaryHeader() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
binaryHeader() - Method in class org.openimaj.ml.clustering.rac.IntRAC
binaryHeader() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
binaryHeader() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
blockSize - Variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The size of processing blocks for each thread
ByteCentroidsResult - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids.
ByteCentroidsResult() - Constructor for class org.openimaj.ml.clustering.ByteCentroidsResult
ByteKMeans - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
ByteKMeans(KMeansConfiguration<ByteNearestNeighbours, byte[]>) - Constructor for class org.openimaj.ml.clustering.kmeans.ByteKMeans: Construct the clusterer with the the given configuration.
ByteKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.ByteKMeans: A completely default ByteKMeans used primarily as a convenience function for reading.
ByteKMeans.Result - Class in org.openimaj.ml.clustering.kmeans: Result object for ByteKMeans, extending ByteCentroidsResult and ByteNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
ByteKMeansInit - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
ByteKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.ByteKMeansInit
ByteKMeansInit.RANDOM - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
ByteKNNAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner that picks a fixed number of nearest neighbours.
ByteKNNAssigner(CentroidsProvider<byte[]>, boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner: Construct the assigner using the given cluster data.
ByteKNNAssigner(byte[][], boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner: Construct the assigner using the given cluster data.
ByteKNNAssigner(CentroidsProvider<byte[]>, ByteFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner: Construct the assigner using the given cluster data and distance function.
ByteKNNAssigner(byte[][], ByteFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner: Construct the assigner using the given cluster data and distance function.
ByteProductQuantiserUtilities - Class in org.openimaj.knn.pq: Utility methods for easily creating a ByteProductQuantiser using (Exact) K-Means.

C

CachedDoubleSpectralClustering - Class in org.openimaj.ml.clustering.spectral: DoubleSpectralClustering extention which knows how to write and read its eigenvectors to disk and therefore not regenerate them when calling the underlying PreparedSpectralClustering
CachedDoubleSpectralClustering(File, SpectralClusteringConf<double[]>) - Constructor for class org.openimaj.ml.clustering.spectral.CachedDoubleSpectralClustering
calculateStability(IndexClusters, IndexClusters, TIntSet) - Method in class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer
calculateThreshold(int[][], int) - Static method in class org.openimaj.ml.clustering.rac.IntRAC
centroids - Variable in class org.openimaj.ml.clustering.ByteCentroidsResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.DoubleCentroidsResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.FloatCentroidsResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.IntCentroidsResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.LongCentroidsResult: The centroids of the clusters
centroids - Variable in class org.openimaj.ml.clustering.ShortCentroidsResult: The centroids of the clusters
CentroidsProvider<DATATYPE> - Interface in org.openimaj.ml.clustering: Interface for clusterers capable of providing the centroids of the clusters.
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result
changedCentroidCount - Variable in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result
ChangeDetectingEigenChooser - Class in org.openimaj.ml.clustering.spectral: Attempts to automatically choose the number of eigen vectors based on the relative gap between eigen values.
ChangeDetectingEigenChooser(double, double) - Constructor for class org.openimaj.ml.clustering.spectral.ChangeDetectingEigenChooser
children - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult.Node: Node children (if any)
children - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult.Node: Node children (if any)
children - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult.Node: Node children (if any)
children - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult.Node: Node children (if any)
children - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult.Node: Node children (if any)
children - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult.Node: Node children (if any)
clone() - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration
clone() - Method in class org.openimaj.ml.gmm.GaussianMixtureModelEM
cluster(DATA) - Method in interface org.openimaj.ml.clustering.DataClusterer
cluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.dbscan.DistanceDBSCAN
cluster(double[][]) - Method in class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN
cluster(DataSource<double[]>) - Method in class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN
cluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.dbscan.SimilarityDBSCAN
cluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer
cluster(byte[][]) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
cluster(DataSource<byte[]>, int) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Initiate clustering with the given data and number of clusters.
cluster(byte[][], ByteKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Main clustering algorithm.
cluster(DataSource<byte[]>, ByteKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Main clustering algorithm.
cluster(DataSource<byte[]>) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
cluster(double[][]) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
cluster(DataSource<double[]>, int) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Initiate clustering with the given data and number of clusters.
cluster(double[][], DoubleKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Main clustering algorithm.
cluster(DataSource<double[]>, DoubleKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Main clustering algorithm.
cluster(DataSource<double[]>) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
cluster(List<T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Perform clustering on the given data.
cluster(T[]) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans
cluster(DataSource<T>, int) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Initiate clustering with the given data and number of clusters.
cluster(T[], FeatureVectorKMeans.Result<T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Main clustering algorithm.
cluster(DataSource<T>, FeatureVectorKMeans.Result<T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Main clustering algorithm.
cluster(DataSource<T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans
cluster(float[][]) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
cluster(DataSource<float[]>, int) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Initiate clustering with the given data and number of clusters.
cluster(float[][], FloatKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Main clustering algorithm.
cluster(DataSource<float[]>, FloatKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Main clustering algorithm.
cluster(DataSource<float[]>) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
cluster(byte[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeans
cluster(DataSource<byte[]>) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeans
cluster(double[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeans
cluster(DataSource<double[]>) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeans
cluster(float[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeans
cluster(DataSource<float[]>) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeans
cluster(int[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeans
cluster(DataSource<int[]>) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeans
cluster(long[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeans
cluster(DataSource<long[]>) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeans
cluster(short[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeans
cluster(DataSource<short[]>) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeans
cluster(int[][]) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
cluster(DataSource<int[]>, int) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Initiate clustering with the given data and number of clusters.
cluster(int[][], IntKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Main clustering algorithm.
cluster(DataSource<int[]>, IntKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Main clustering algorithm.
cluster(DataSource<int[]>) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
cluster(long[][]) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
cluster(DataSource<long[]>, int) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Initiate clustering with the given data and number of clusters.
cluster(long[][], LongKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Main clustering algorithm.
cluster(DataSource<long[]>, LongKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Main clustering algorithm.
cluster(DataSource<long[]>) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
cluster(short[][]) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
cluster(DataSource<short[]>, int) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Initiate clustering with the given data and number of clusters.
cluster(short[][], ShortKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Main clustering algorithm.
cluster(DataSource<short[]>, ShortKMeans.Result) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Main clustering algorithm.
cluster(DataSource<short[]>) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
cluster(double[][]) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
cluster(DataSource<double[]>) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
cluster(int[][]) - Method in class org.openimaj.ml.clustering.rac.ClusterLimitedIntRAC
cluster(int[][]) - Method in class org.openimaj.ml.clustering.rac.IntRAC
cluster(DataSource<int[]>) - Method in class org.openimaj.ml.clustering.rac.IntRAC
cluster(byte[][]) - Method in class org.openimaj.ml.clustering.random.RandomByteClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<byte[]>) - Method in class org.openimaj.ml.clustering.random.RandomByteClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.random.RandomClusterer
cluster(double[][]) - Method in class org.openimaj.ml.clustering.random.RandomDoubleClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<double[]>) - Method in class org.openimaj.ml.clustering.random.RandomDoubleClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(float[][]) - Method in class org.openimaj.ml.clustering.random.RandomFloatClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<float[]>) - Method in class org.openimaj.ml.clustering.random.RandomFloatClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(int[][]) - Method in class org.openimaj.ml.clustering.random.RandomIntClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<int[]>) - Method in class org.openimaj.ml.clustering.random.RandomIntClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(long[][]) - Method in class org.openimaj.ml.clustering.random.RandomLongClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<long[]>) - Method in class org.openimaj.ml.clustering.random.RandomLongClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(byte[][]) - Method in class org.openimaj.ml.clustering.random.RandomSetByteClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<byte[]>) - Method in class org.openimaj.ml.clustering.random.RandomSetByteClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(double[][]) - Method in class org.openimaj.ml.clustering.random.RandomSetDoubleClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<double[]>) - Method in class org.openimaj.ml.clustering.random.RandomSetDoubleClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(float[][]) - Method in class org.openimaj.ml.clustering.random.RandomSetFloatClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<float[]>) - Method in class org.openimaj.ml.clustering.random.RandomSetFloatClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(int[][]) - Method in class org.openimaj.ml.clustering.random.RandomSetIntClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<int[]>) - Method in class org.openimaj.ml.clustering.random.RandomSetIntClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(long[][]) - Method in class org.openimaj.ml.clustering.random.RandomSetLongClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<long[]>) - Method in class org.openimaj.ml.clustering.random.RandomSetLongClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(short[][]) - Method in class org.openimaj.ml.clustering.random.RandomSetShortClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<short[]>) - Method in class org.openimaj.ml.clustering.random.RandomSetShortClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(short[][]) - Method in class org.openimaj.ml.clustering.random.RandomShortClusterer: Selects K elements from the provided data as the centroids of the clusters.
cluster(DataSource<short[]>) - Method in class org.openimaj.ml.clustering.random.RandomShortClusterer: Selects K elements from the provided DataSource as the centroids of the clusters.
cluster(int[][]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
cluster(DataSource<int[]>) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
cluster(DATATYPE[]) - Method in interface org.openimaj.ml.clustering.SpatialClusterer: Perform clustering on the given data.
cluster(DataSource<DATATYPE>) - Method in interface org.openimaj.ml.clustering.SpatialClusterer: Perform clustering with data from a data source.
cluster(List<SparseMatrix>) - Method in class org.openimaj.ml.clustering.spectral.DoubleMultiviewSpectralClustering
cluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
cluster(Eigenvalues) - Method in class org.openimaj.ml.clustering.spectral.PreparedSpectralClustering
CLUSTER_HEADER - Static variable in interface org.openimaj.ml.clustering.Clusters: The default cluster header
clusterDistance(SparseMatrix) - Method in class org.openimaj.ml.clustering.dbscan.DistanceDBSCAN
clusterDistance(SparseMatrix) - Method in interface org.openimaj.ml.clustering.DistanceClusterer
ClusterLimitedIntRAC - Class in org.openimaj.ml.clustering.rac: Similar to IntRAC but explicitly specify the limit the number of clusters.
ClusterLimitedIntRAC() - Constructor for class org.openimaj.ml.clustering.rac.ClusterLimitedIntRAC: Sets the expected number of clusters to 100 and radius to 128.
ClusterLimitedIntRAC(double) - Constructor for class org.openimaj.ml.clustering.rac.ClusterLimitedIntRAC: Set the number of clusters to 100.
ClusterLimitedIntRAC(int[][], int, int) - Constructor for class org.openimaj.ml.clustering.rac.ClusterLimitedIntRAC: Attempt to learn the threshold and uses this as an expected number of clusters.
Clusters - Interface in org.openimaj.ml.clustering: Interface to represent the result of a clustering operation
clusters - Variable in class org.openimaj.ml.clustering.IndexClusters
clusters() - Method in class org.openimaj.ml.clustering.IndexClusters: Get the number of clusters.
clusterSimilarity(SparseMatrix) - Method in class org.openimaj.ml.clustering.dbscan.SimilarityDBSCAN
clusterSimilarity(SparseMatrix) - Method in interface org.openimaj.ml.clustering.SimilarityClusterer
clusterSimilarity(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
clz - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
codebook - Variable in class org.openimaj.ml.clustering.rac.IntRAC
computeMeanShift(double[]) - Method in class org.openimaj.ml.clustering.meanshift.ExactMeanShift
computeScore(float[]) - Method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner.ScoringScheme
computeScore(double[]) - Method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner.ScoringScheme
computeScore(float[]) - Method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner.ScoringScheme
computeScore(float[]) - Method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner.ScoringScheme
computeScore(double[]) - Method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner.ScoringScheme
computeScore(float[]) - Method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner.ScoringScheme
conf - Variable in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
ConstrainedFloatAssigner<DATATYPE> - Class in org.openimaj.ml.clustering.assignment.hard: An assigner that wraps another hard assigner and only produces valid assignments if the closest cluster is within (or outside) of a given threshold distance.
ConstrainedFloatAssigner(HardAssigner<DATATYPE, float[], IntFloatPair>, float) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner: Construct the ConstrainedFloatAssigner with the given assigner and threshold.
ConstrainedFloatAssigner(HardAssigner<DATATYPE, float[], IntFloatPair>, float, boolean) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner: Construct the ConstrainedFloatAssigner with the given assigner and threshold.
ContectedComponentSimilarityClusterer - Class in org.openimaj.ml.clustering.dbscan: Cluster based on connected components.
ContectedComponentSimilarityClusterer(double, int) - Constructor for class org.openimaj.ml.clustering.dbscan.ContectedComponentSimilarityClusterer
countActiveLeafNodes() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Count number of active leaf nodes.
countActiveLeafNodes() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Count number of active leaf nodes.
countActiveLeafNodes() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Count number of active leaf nodes.
countActiveLeafNodes() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Count number of active leaf nodes.
countActiveLeafNodes() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Count number of active leaf nodes.
countActiveLeafNodes() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Count number of active leaf nodes.
countLeafs() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Total number of leaves assuming leaves = K^depth
countLeafs() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Total number of leaves assuming leaves = K^depth
countLeafs() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Total number of leaves assuming leaves = K^depth
countLeafs() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Total number of leaves assuming leaves = K^depth
countLeafs() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Total number of leaves assuming leaves = K^depth
countLeafs() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Total number of leaves assuming leaves = K^depth
createExact(int) - Static method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Convenience method to quickly create an exact ByteKMeans.
createExact(int, int) - Static method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Convenience method to quickly create an exact ByteKMeans.
createExact(int) - Static method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Convenience method to quickly create an exact DoubleKMeans.
createExact(int, int) - Static method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Convenience method to quickly create an exact DoubleKMeans.
createExact(int, DistanceComparator<? super T>) - Static method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Convenience method to quickly create an exact ByteKMeans.
createExact(int, DistanceComparator<? super T>, int) - Static method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Convenience method to quickly create an exact ByteKMeans.
createExact(int) - Static method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Convenience method to quickly create an exact FloatKMeans.
createExact(int, int) - Static method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Convenience method to quickly create an exact FloatKMeans.
createExact(int) - Static method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Convenience method to quickly create an exact IntKMeans.
createExact(int, int) - Static method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Convenience method to quickly create an exact IntKMeans.
createExact(int) - Static method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Convenience method to quickly create an exact LongKMeans.
createExact(int, int) - Static method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Convenience method to quickly create an exact LongKMeans.
createExact(int) - Static method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Convenience method to quickly create an exact ShortKMeans.
createExact(int, int) - Static method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Convenience method to quickly create an exact ShortKMeans.
createGaussians(int, int) - Method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType
createKDTreeEnsemble(int) - Static method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Convenience method to quickly create an approximate ByteKMeans using an ensemble of KD-Trees to perform nearest-neighbour lookup.
createKDTreeEnsemble(int) - Static method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Convenience method to quickly create an approximate DoubleKMeans using an ensemble of KD-Trees to perform nearest-neighbour lookup.
createKDTreeEnsemble(int) - Static method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Convenience method to quickly create an approximate FloatKMeans using an ensemble of KD-Trees to perform nearest-neighbour lookup.
createKDTreeEnsemble(int) - Static method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Convenience method to quickly create an approximate IntKMeans using an ensemble of KD-Trees to perform nearest-neighbour lookup.
createKDTreeEnsemble(int) - Static method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Convenience method to quickly create an approximate LongKMeans using an ensemble of KD-Trees to perform nearest-neighbour lookup.
createKDTreeEnsemble(int) - Static method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Convenience method to quickly create an approximate ShortKMeans using an ensemble of KD-Trees to perform nearest-neighbour lookup.

D

damped - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
data - Variable in class org.openimaj.ml.clustering.dbscan.DoubleDBSCANClusters: The data
DataClusterer<DATA,CLUSTER extends IndexClusters> - Interface in org.openimaj.ml.clustering: Clusterers can extract clusters from data types and return the data in a clustered form
DBSCAN - Class in org.openimaj.ml.clustering.dbscan: Implementation of DBSCAN (http://en.wikipedia.org/wiki/DBSCAN) using a
DBSCAN() - Constructor for class org.openimaj.ml.clustering.dbscan.DBSCAN
DBSCAN.State - Class in org.openimaj.ml.clustering.dbscan
DBSCANClusters - Class in org.openimaj.ml.clustering.dbscan
DBSCANClusters(int[], int[][]) - Constructor for class org.openimaj.ml.clustering.dbscan.DBSCANClusters
DBSCANClusters(int[], int[][], int) - Constructor for class org.openimaj.ml.clustering.dbscan.DBSCANClusters
DEFAULT_BLOCK_SIZE - Static variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The default number of samples per parallel assignment instance.
DEFAULT_NUMBER_ITERATIONS - Static variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The default number of iterations.
DefaultClustererFunction(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralClusteringConf.DefaultClustererFunction
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.dbscan.DoubleDBSCANClusters
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.rac.IntRAC
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
defaultHardAssigner() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
defaultHardAssigner() - Method in interface org.openimaj.ml.clustering.SpatialClusters: Get the default hard assigner for this clusterer.
delta - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans.IterationResult: The change in fitness from the previous iteration
detectInactive(IndexClusters, IndexClusters, TIntSet, List<int[]>) - Method in class org.openimaj.ml.clustering.incremental.IncrementalLifetimeSparseClusterer
detectInactive(IndexClusters, IndexClusters, TIntSet, List<int[]>) - Method in class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer: Given the old and new clusters, make a decision as to which rows are now inactive, and therefore which clusters are now completed
DistanceClusterer<CLUSTERS extends IndexClusters> - Interface in org.openimaj.ml.clustering: A DistanceClusterer clusters data that can be represented as a distance matrix.
DistanceDBSCAN - Class in org.openimaj.ml.clustering.dbscan: DBSCAN using a SparseMatrix of distances
DistanceDBSCAN(double, int) - Constructor for class org.openimaj.ml.clustering.dbscan.DistanceDBSCAN
distances - Static variable in class org.openimaj.ml.clustering.rac.IntRAC
DoubleCentroidsResult - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids.
DoubleCentroidsResult() - Constructor for class org.openimaj.ml.clustering.DoubleCentroidsResult
DoubleDBSCANClusters - Class in org.openimaj.ml.clustering.dbscan: DBSCANClusters which also holds the original data
DoubleDBSCANClusters(int[], int[][]) - Constructor for class org.openimaj.ml.clustering.dbscan.DoubleDBSCANClusters
DoubleDBSCANClusters(int[], int[][], int) - Constructor for class org.openimaj.ml.clustering.dbscan.DoubleDBSCANClusters
DoubleKMeans - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
DoubleKMeans(KMeansConfiguration<DoubleNearestNeighbours, double[]>) - Constructor for class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Construct the clusterer with the the given configuration.
DoubleKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.DoubleKMeans: A completely default DoubleKMeans used primarily as a convenience function for reading.
DoubleKMeans.Result - Class in org.openimaj.ml.clustering.kmeans: Result object for DoubleKMeans, extending DoubleCentroidsResult and DoubleNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
DoubleKMeansInit - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
DoubleKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.DoubleKMeansInit
DoubleKMeansInit.RANDOM - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
DoubleKNNAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner that picks a fixed number of nearest neighbours.
DoubleKNNAssigner(CentroidsProvider<double[]>, boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner: Construct the assigner using the given cluster data.
DoubleKNNAssigner(double[][], boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner: Construct the assigner using the given cluster data.
DoubleKNNAssigner(CentroidsProvider<double[]>, DoubleFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner: Construct the assigner using the given cluster data and distance function.
DoubleKNNAssigner(double[][], DoubleFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner: Construct the assigner using the given cluster data and distance function.
DoubleMultiviewSpectralClustering - Class in org.openimaj.ml.clustering.spectral
DoubleMultiviewSpectralClustering(MultiviewSpectralClusteringConf<double[]>) - Constructor for class org.openimaj.ml.clustering.spectral.DoubleMultiviewSpectralClustering
DoubleNNDBSCAN - Class in org.openimaj.ml.clustering.dbscan: Implementation of DBSCAN (http://en.wikipedia.org/wiki/DBSCAN) using a
DoubleNNDBSCAN(double, int, NearestNeighboursFactory<? extends DoubleNearestNeighbours, double[]>) - Constructor for class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN: Perform a DBScane with this configuration
DoubleNNDBSCAN(double, int) - Constructor for class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN
DoubleProductQuantiserUtilities - Class in org.openimaj.knn.pq: Utility methods for easily creating a ByteProductQuantiser using (Exact) K-Means.
DoubleSpectralClustering - Class in org.openimaj.ml.clustering.spectral: Built from a mixture of this tutorial: - http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5B0%5D.pdf And this implementation: - https://github.com/peterklipfel/AutoponicsVision/blob/master/SpectralClustering.java
DoubleSpectralClustering(SpectralClusteringConf<double[]>) - Constructor for class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
DoubleSpectralClustering() - Constructor for class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering

E

EigenChooser - Class in org.openimaj.ml.clustering.spectral: Method which makes a decision on how many eigen vectors to select
EigenChooser() - Constructor for class org.openimaj.ml.clustering.spectral.EigenChooser
eigenChooser - Variable in class org.openimaj.ml.clustering.spectral.SpectralClusteringConf: The method used to select the number of eigen vectors from the lower valued eigenvalues
eigenIterator(Eigenvalues) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian
eigenIterator(Eigenvalues) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian.Normalised
eigenIterator(Eigenvalues) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian.Warped
eigenspaceCluster(IndependentPair<double[], double[][]>) - Method in class org.openimaj.ml.clustering.spectral.PreparedSpectralClustering
eigenValues() - Method in class org.openimaj.ml.clustering.spectral.SpectralIndexedClusters
eigenValueScale - Variable in class org.openimaj.ml.clustering.spectral.SpectralClusteringConf
eigenVectors() - Method in class org.openimaj.ml.clustering.spectral.SpectralIndexedClusters
equals(Object) - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
equals(Object) - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
equals(Object) - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
equals(Object) - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
equals(Object) - Method in class org.openimaj.ml.clustering.IntCentroidsResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
equals(Object) - Method in class org.openimaj.ml.clustering.LongCentroidsResult
equals(Object) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
equals(Object) - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree
equals(Object) - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
estimate(Matrix) - Method in class org.openimaj.ml.gmm.GaussianMixtureModelEM: Estimate a new MixtureOfGaussians from the given data.
estimate(double[][]) - Method in class org.openimaj.ml.gmm.GaussianMixtureModelEM: Estimate a new MixtureOfGaussians from the given data.
evaluate() - Method in class org.openimaj.experiment.evaluation.cluster.RangedDBSCANClusterEvaluator
ExactByteAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactByteAssigner(CentroidsProvider<byte[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner: Construct the assigner using the given cluster data.
ExactByteAssigner(CentroidsProvider<byte[]>, ByteFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner: Construct the assigner using the given cluster data and distance function.
ExactByteAssigner(byte[][], ByteFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner: Construct the assigner using the given cluster data and distance function.
ExactDoubleAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactDoubleAssigner(CentroidsProvider<double[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner: Construct the assigner using the given cluster data.
ExactDoubleAssigner(CentroidsProvider<double[]>, DoubleFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner: Construct the assigner using the given cluster data and distance function.
ExactDoubleAssigner(double[][], DoubleFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner: Construct the assigner using the given cluster data and distance function.
ExactFeatureVectorAssigner<T extends org.openimaj.feature.FeatureVector> - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactFeatureVectorAssigner(CentroidsProvider<T>, DistanceComparator<? super T>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner: Construct the assigner using the given cluster data and distance function.
ExactFeatureVectorAssigner(T[], DistanceComparator<? super T>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner: Construct the assigner using the given cluster data and distance function.
ExactFeatureVectorAssigner(List<T>, DistanceComparator<? super T>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner: Construct the assigner using the given cluster data and distance function.
ExactFloatAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactFloatAssigner(CentroidsProvider<float[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner: Construct the assigner using the given cluster data.
ExactFloatAssigner(CentroidsProvider<float[]>, FloatFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner: Construct the assigner using the given cluster data and distance function.
ExactFloatAssigner(float[][], FloatFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner: Construct the assigner using the given cluster data and distance function.
ExactIntAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactIntAssigner(CentroidsProvider<int[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner: Construct the assigner using the given cluster data.
ExactIntAssigner(CentroidsProvider<int[]>, IntFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner: Construct the assigner using the given cluster data and distance function.
ExactIntAssigner(int[][], IntFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner: Construct the assigner using the given cluster data and distance function.
ExactLongAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactLongAssigner(CentroidsProvider<long[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner: Construct the assigner using the given cluster data.
ExactLongAssigner(CentroidsProvider<long[]>, LongFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner: Construct the assigner using the given cluster data and distance function.
ExactLongAssigner(long[][], LongFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner: Construct the assigner using the given cluster data and distance function.
ExactMeanShift - Class in org.openimaj.ml.clustering.meanshift: Exact mean shift implementation.
ExactMeanShift(MultivariateKernelDensityEstimate) - Constructor for class org.openimaj.ml.clustering.meanshift.ExactMeanShift: Perform the ExactMeanShift operation on the given KDE.
ExactShortAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that assigns points to the closest cluster based on the distance to the centroid.
ExactShortAssigner(CentroidsProvider<short[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner: Construct the assigner using the given cluster data.
ExactShortAssigner(CentroidsProvider<short[]>, ShortFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner: Construct the assigner using the given cluster data and distance function.
ExactShortAssigner(short[][], ShortFVComparator) - Constructor for class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner: Construct the assigner using the given cluster data and distance function.

F

factory - Variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The factory for producing the NearestNeighbours objects used in assignment.
FBEigenIterator - Class in org.openimaj.ml.clustering.spectral: A forward or backward iterator of eigen vector/value pairs
FBEigenIterator(Eigenvalues) - Constructor for class org.openimaj.ml.clustering.spectral.FBEigenIterator
feature - Variable in class org.openimaj.ml.clustering.rforest.RandomDecision: Feature index
FeatureVectorCentroidsResult<T extends org.openimaj.feature.FeatureVector> - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids in the form of FeatureVectors.
FeatureVectorCentroidsResult() - Constructor for class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
FeatureVectorKMeans<T extends org.openimaj.feature.FeatureVector> - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
FeatureVectorKMeans(KMeansConfiguration<ObjectNearestNeighbours<T>, T>) - Constructor for class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Construct the clusterer with the the given configuration.
FeatureVectorKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: A completely default ByteKMeans used primarily as a convenience function for reading.
FeatureVectorKMeans.Result<T extends org.openimaj.feature.FeatureVector> - Class in org.openimaj.ml.clustering.kmeans: Result object for FeatureVectorKMeans, extending FeatureVectorCentroidsResult and ObjectNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
FeatureVectorKMeansInit<T> - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
FeatureVectorKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeansInit
FeatureVectorKMeansInit.RANDOM<T> - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
filter(int) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult: Filter the cluster centroids be removing those with less than threshold items
FloatCentroidsResult - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids.
FloatCentroidsResult() - Constructor for class org.openimaj.ml.clustering.FloatCentroidsResult
FloatKMeans - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
FloatKMeans(KMeansConfiguration<FloatNearestNeighbours, float[]>) - Constructor for class org.openimaj.ml.clustering.kmeans.FloatKMeans: Construct the clusterer with the the given configuration.
FloatKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.FloatKMeans: A completely default FloatKMeans used primarily as a convenience function for reading.
FloatKMeans.Result - Class in org.openimaj.ml.clustering.kmeans: Result object for FloatKMeans, extending FloatCentroidsResult and FloatNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
FloatKMeansInit - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
FloatKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.FloatKMeansInit
FloatKMeansInit.RANDOM - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
FloatKNNAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner that picks a fixed number of nearest neighbours.
FloatKNNAssigner(CentroidsProvider<float[]>, boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner: Construct the assigner using the given cluster data.
FloatKNNAssigner(float[][], boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner: Construct the assigner using the given cluster data.
FloatKNNAssigner(CentroidsProvider<float[]>, FloatFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner: Construct the assigner using the given cluster data and distance function.
FloatKNNAssigner(float[][], FloatFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner: Construct the assigner using the given cluster data and distance function.
FloatProductQuantiserUtilities - Class in org.openimaj.knn.pq: Utility methods for easily creating a ByteProductQuantiser using (Exact) K-Means.

G

GaussianMixtureModelEM - Class in org.openimaj.ml.gmm: Gaussian mixture model learning using the EM algorithm.
GaussianMixtureModelEM(int, GaussianMixtureModelEM.CovarianceType, double, double, int, int, EnumSet<GaussianMixtureModelEM.UpdateOptions>, EnumSet<GaussianMixtureModelEM.UpdateOptions>) - Constructor for class org.openimaj.ml.gmm.GaussianMixtureModelEM: Construct with the given arguments.
GaussianMixtureModelEM(int, GaussianMixtureModelEM.CovarianceType) - Constructor for class org.openimaj.ml.gmm.GaussianMixtureModelEM: Construct with the given arguments.
GaussianMixtureModelEM.CovarianceType - Enum in org.openimaj.ml.gmm: Different forms of covariance matrix supported by the GaussianMixtureModelEM.
GaussianMixtureModelEM.EMGMM - Class in org.openimaj.ml.gmm
GaussianMixtureModelEM.UpdateOptions - Enum in org.openimaj.ml.gmm: Options for controlling what gets updated during the initialisation and/or iterations.
getAssignmentHistogram() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult: Compute the histogram of number of assignments to each cluster
getAssignments() - Method in class org.openimaj.ml.clustering.meanshift.ExactMeanShift: Get the assignments
getBlockSize() - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Get the number of samples processed in a batch by a thread.
getCentroids() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
getCentroids() - Method in interface org.openimaj.ml.clustering.CentroidsProvider
getCentroids() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
getCentroids() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
getCentroids() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
getCentroids() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
getCentroids() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
getCentroids() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
getCentroids() - Method in class org.openimaj.ml.clustering.rac.IntRAC
getCentroids() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
getClusterCentroid(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Given a path, get the cluster centroid associated with the cluster index of the path.
getClusterCentroid(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Given a path, get the cluster centroid associated with the cluster index of the path.
getClusterCentroid(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Given a path, get the cluster centroid associated with the cluster index of the path.
getClusterCentroid(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Given a path, get the cluster centroid associated with the cluster index of the path.
getClusterCentroid(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Given a path, get the cluster centroid associated with the cluster index of the path.
getClusterCentroid(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Given a path, get the cluster centroid associated with the cluster index of the path.
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Get the configuration
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Get the configuration
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Get the configuration
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Get the configuration
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Get the configuration
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Get the configuration
getConfiguration() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Get the configuration
getDepth() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Get the depth of the cluster tree
getDepth() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Get the depth of the cluster tree
getDepth() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Get the depth of the cluster tree
getDepth() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Get the depth of the cluster tree
getDepth() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Get the depth of the cluster tree
getDepth() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Get the depth of the cluster tree
getDetailReport(String, String) - Method in class org.openimaj.experiment.evaluation.cluster.RangedAnalysisResult
getDetailReport() - Method in class org.openimaj.experiment.evaluation.cluster.RangedAnalysisResult
getEps() - Method in class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN
getEps() - Method in class org.openimaj.ml.clustering.dbscan.SparseMatrixDBSCAN
getIndex(int[], int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[], int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[], int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[], int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[], int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[], int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Translates a path down the KDTree as a cluster index.
getIndex(int[]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Translates a path down the KDTree as a cluster index.
getInit() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Get the current initialisation algorithm
getInit() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Get the current initialisation algorithm
getInit() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Get the current initialisation algorithm
getInit() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Get the current initialisation algorithm
getInit() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Get the current initialisation algorithm
getInit() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Get the current initialisation algorithm
getInit() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Get the current initialisation algorithm
getK() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Get the number of clusters per node
getK() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Get the number of clusters per node
getK() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Get the number of clusters per node
getK() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Get the number of clusters per node
getK() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Get the number of clusters per node
getK() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Get the number of clusters per node
getK() - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Get the number of clusters
getLetter(int[]) - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree: The function which finds the path down this random tree for a given feature.
getMaxIterations() - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Get the maximum allowed number of iterations.
getModes() - Method in class org.openimaj.ml.clustering.meanshift.ExactMeanShift: Get the modes
getNDecisions() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
getNearestNeighbourFactory() - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Get the factory that produces the NearestNeighbours during clustering.
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result
getNearestNeighbours() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner: Get the underlying nearest-neighbour implementation.
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner: Get the underlying nearest-neighbour implementation.
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner: Get the underlying nearest-neighbour implementation.
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner: Get the underlying nearest-neighbour implementation.
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner: Get the underlying nearest-neighbour implementation.
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner: Get the underlying nearest-neighbour implementation.
getNN() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner: Get the underlying nearest-neighbour implementation.
getNoise() - Method in class org.openimaj.ml.clustering.dbscan.DBSCANClusters
getNTrees() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
getPath(int, int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int, int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int, int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int, int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int, int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int, int, int) - Static method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getPath(int) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Given an index, what was the path down the hierarchy that lead to it.
getRoot() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult: Get the root node of the tree
getRoot() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult: Get the root node of the tree
getRoot() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult: Get the root node of the tree
getRoot() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult: Get the root node of the tree
getRoot() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult: Get the root node of the tree
getRoot() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult: Get the root node of the tree
getSummaryReport(String, String) - Method in class org.openimaj.experiment.evaluation.cluster.RangedAnalysisResult
getSummaryReport() - Method in class org.openimaj.experiment.evaluation.cluster.RangedAnalysisResult
getTrees() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
getValVect() - Method in class org.openimaj.ml.clustering.spectral.SpectralIndexedClusters
GraphLaplacian - Class in org.openimaj.ml.clustering.spectral: Functions which turn a graph weight adjacency matrix into the Laplacian matrix.
GraphLaplacian() - Constructor for class org.openimaj.ml.clustering.spectral.GraphLaplacian
GraphLaplacian.Normalised - Class in org.openimaj.ml.clustering.spectral: The inverted symmetric normalised Laplacian is defined as: L = D^-1/2 A D^-1/2
GraphLaplacian.Unnormalised - Class in org.openimaj.ml.clustering.spectral: The symmetric normalised Laplacian is defined as: L = D - W
GraphLaplacian.Warped - Class in org.openimaj.ml.clustering.spectral: The inverted symmetric normalised Laplacian is defined as: L = D^-1 .

H

HardAssigner<DATATYPE,DISTANCES,DISTANCE_INDEX> - Interface in org.openimaj.ml.clustering.assignment: The HardAssigner interface describes classes that assign a spatial point to a single cluster.
HardCoded(int) - Constructor for class org.openimaj.ml.clustering.spectral.StoppingCondition.HardCoded
HardCodedEigenChooser - Class in org.openimaj.ml.clustering.spectral
HardCodedEigenChooser(int) - Constructor for class org.openimaj.ml.clustering.spectral.HardCodedEigenChooser
hasConverged() - Method in class org.openimaj.ml.gmm.GaussianMixtureModelEM: Get's the convergence state of the algorithm.
hasNext() - Method in class org.openimaj.ml.clustering.spectral.FBEigenIterator
HierarchicalByteHardAssigner - Class in org.openimaj.ml.clustering.assignment.hard: The HierarchicalByteHardAssigner is a HardAssigner for HierarchicalByteKMeansResult instances.
HierarchicalByteHardAssigner(HierarchicalByteKMeansResult, HierarchicalByteHardAssigner.ScoringScheme) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner: Construct with the given hierarchical KMeans clusterer and scoring scheme.
HierarchicalByteHardAssigner(HierarchicalByteKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner: Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalByteHardAssigner.ScoringScheme - Enum in org.openimaj.ml.clustering.assignment.hard: The HierarchicalByteHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.
HierarchicalByteKMeans - Class in org.openimaj.ml.clustering.kmeans: Hierarchical Byte K-Means clustering (HierarchicalByteKMeans) is a simple hierarchical version of ByteKMeans.
HierarchicalByteKMeans(KMeansConfiguration<ByteNearestNeighbours, byte[]>, int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeans: Construct a new HierarchicalByteKMeans with the given parameters.
HierarchicalByteKMeans(int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeans: Construct a new HierarchicalByteKMeans with the given parameters.
HierarchicalByteKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a HierarchicalByteKMeans clustering operation.
HierarchicalByteKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
HierarchicalByteKMeansResult.Node - Class in org.openimaj.ml.clustering.kmeans: HierarchicalByteKMeans tree node The number of children is not bigger than the HierarchicalByteKMeans K parameter
HierarchicalBytePathAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner for gathering the clusters assigned to a point from a hierarchical clustering.
HierarchicalBytePathAssigner(HierarchicalByteKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner: Construct with the given HierarchicalByteKMeansResult instance.
HierarchicalDoubleHardAssigner - Class in org.openimaj.ml.clustering.assignment.hard: The HierarchicalDoubleHardAssigner is a HardAssigner for HierarchicalDoubleKMeansResult instances.
HierarchicalDoubleHardAssigner(HierarchicalDoubleKMeansResult, HierarchicalDoubleHardAssigner.ScoringScheme) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner: Construct with the given hierarchical KMeans clusterer and scoring scheme.
HierarchicalDoubleHardAssigner(HierarchicalDoubleKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner: Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalDoubleHardAssigner.ScoringScheme - Enum in org.openimaj.ml.clustering.assignment.hard: The HierarchicalDoubleHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.
HierarchicalDoubleKMeans - Class in org.openimaj.ml.clustering.kmeans: Hierarchical Double K-Means clustering (HierarchicalDoubleKMeans) is a simple hierarchical version of DoubleKMeans.
HierarchicalDoubleKMeans(KMeansConfiguration<DoubleNearestNeighbours, double[]>, int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeans: Construct a new HierarchicalDoubleKMeans with the given parameters.
HierarchicalDoubleKMeans(int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeans: Construct a new HierarchicalDoubleKMeans with the given parameters.
HierarchicalDoubleKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a HierarchicalDoubleKMeans clustering operation.
HierarchicalDoubleKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
HierarchicalDoubleKMeansResult.Node - Class in org.openimaj.ml.clustering.kmeans: HierarchicalDoubleKMeans tree node The number of children is not bigger than the HierarchicalDoubleKMeans K parameter
HierarchicalDoublePathAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner for gathering the clusters assigned to a point from a hierarchical clustering.
HierarchicalDoublePathAssigner(HierarchicalDoubleKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner: Construct with the given HierarchicalDoubleKMeansResult instance.
HierarchicalFloatHardAssigner - Class in org.openimaj.ml.clustering.assignment.hard: The HierarchicalFloatHardAssigner is a HardAssigner for HierarchicalFloatKMeansResult instances.
HierarchicalFloatHardAssigner(HierarchicalFloatKMeansResult, HierarchicalFloatHardAssigner.ScoringScheme) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner: Construct with the given hierarchical KMeans clusterer and scoring scheme.
HierarchicalFloatHardAssigner(HierarchicalFloatKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner: Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalFloatHardAssigner.ScoringScheme - Enum in org.openimaj.ml.clustering.assignment.hard: The HierarchicalFloatHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.
HierarchicalFloatKMeans - Class in org.openimaj.ml.clustering.kmeans: Hierarchical Float K-Means clustering (HierarchicalFloatKMeans) is a simple hierarchical version of FloatKMeans.
HierarchicalFloatKMeans(KMeansConfiguration<FloatNearestNeighbours, float[]>, int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeans: Construct a new HierarchicalFloatKMeans with the given parameters.
HierarchicalFloatKMeans(int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeans: Construct a new HierarchicalFloatKMeans with the given parameters.
HierarchicalFloatKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a HierarchicalFloatKMeans clustering operation.
HierarchicalFloatKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
HierarchicalFloatKMeansResult.Node - Class in org.openimaj.ml.clustering.kmeans: HierarchicalFloatKMeans tree node The number of children is not bigger than the HierarchicalFloatKMeans K parameter
HierarchicalFloatPathAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner for gathering the clusters assigned to a point from a hierarchical clustering.
HierarchicalFloatPathAssigner(HierarchicalFloatKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner: Construct with the given HierarchicalFloatKMeansResult instance.
HierarchicalIntHardAssigner - Class in org.openimaj.ml.clustering.assignment.hard: The HierarchicalIntHardAssigner is a HardAssigner for HierarchicalIntKMeansResult instances.
HierarchicalIntHardAssigner(HierarchicalIntKMeansResult, HierarchicalIntHardAssigner.ScoringScheme) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner: Construct with the given hierarchical KMeans clusterer and scoring scheme.
HierarchicalIntHardAssigner(HierarchicalIntKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner: Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalIntHardAssigner.ScoringScheme - Enum in org.openimaj.ml.clustering.assignment.hard: The HierarchicalIntHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.
HierarchicalIntKMeans - Class in org.openimaj.ml.clustering.kmeans: Hierarchical Integer K-Means clustering (HierarchicalIntKMeans) is a simple hierarchical version of IntKMeans.
HierarchicalIntKMeans(KMeansConfiguration<IntNearestNeighbours, int[]>, int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeans: Construct a new HierarchicalIntKMeans with the given parameters.
HierarchicalIntKMeans(int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeans: Construct a new HierarchicalIntKMeans with the given parameters.
HierarchicalIntKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a HierarchicalIntKMeans clustering operation.
HierarchicalIntKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
HierarchicalIntKMeansResult.Node - Class in org.openimaj.ml.clustering.kmeans: HierarchicalIntKMeans tree node The number of children is not bigger than the HierarchicalIntKMeans K parameter
HierarchicalIntPathAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner for gathering the clusters assigned to a point from a hierarchical clustering.
HierarchicalIntPathAssigner(HierarchicalIntKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner: Construct with the given HierarchicalIntKMeansResult instance.
HierarchicalLongHardAssigner - Class in org.openimaj.ml.clustering.assignment.hard: The HierarchicalLongHardAssigner is a HardAssigner for HierarchicalLongKMeansResult instances.
HierarchicalLongHardAssigner(HierarchicalLongKMeansResult, HierarchicalLongHardAssigner.ScoringScheme) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner: Construct with the given hierarchical KMeans clusterer and scoring scheme.
HierarchicalLongHardAssigner(HierarchicalLongKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner: Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalLongHardAssigner.ScoringScheme - Enum in org.openimaj.ml.clustering.assignment.hard: The HierarchicalLongHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.
HierarchicalLongKMeans - Class in org.openimaj.ml.clustering.kmeans: Hierarchical Long K-Means clustering (HierarchicalLongKMeans) is a simple hierarchical version of LongKMeans.
HierarchicalLongKMeans(KMeansConfiguration<LongNearestNeighbours, long[]>, int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeans: Construct a new HierarchicalLongKMeans with the given parameters.
HierarchicalLongKMeans(int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeans: Construct a new HierarchicalLongKMeans with the given parameters.
HierarchicalLongKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a HierarchicalLongKMeans clustering operation.
HierarchicalLongKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
HierarchicalLongKMeansResult.Node - Class in org.openimaj.ml.clustering.kmeans: HierarchicalLongKMeans tree node The number of children is not bigger than the HierarchicalLongKMeans K parameter
HierarchicalLongPathAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner for gathering the clusters assigned to a point from a hierarchical clustering.
HierarchicalLongPathAssigner(HierarchicalLongKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner: Construct with the given HierarchicalLongKMeansResult instance.
HierarchicalShortHardAssigner - Class in org.openimaj.ml.clustering.assignment.hard: The HierarchicalShortHardAssigner is a HardAssigner for HierarchicalShortKMeansResult instances.
HierarchicalShortHardAssigner(HierarchicalShortKMeansResult, HierarchicalShortHardAssigner.ScoringScheme) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner: Construct with the given hierarchical KMeans clusterer and scoring scheme.
HierarchicalShortHardAssigner(HierarchicalShortKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner: Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalShortHardAssigner.ScoringScheme - Enum in org.openimaj.ml.clustering.assignment.hard: The HierarchicalShortHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.
HierarchicalShortKMeans - Class in org.openimaj.ml.clustering.kmeans: Hierarchical Short K-Means clustering (HierarchicalShortKMeans) is a simple hierarchical version of ShortKMeans.
HierarchicalShortKMeans(KMeansConfiguration<ShortNearestNeighbours, short[]>, int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeans: Construct a new HierarchicalShortKMeans with the given parameters.
HierarchicalShortKMeans(int, int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeans: Construct a new HierarchicalShortKMeans with the given parameters.
HierarchicalShortKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a HierarchicalShortKMeans clustering operation.
HierarchicalShortKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
HierarchicalShortKMeansResult.Node - Class in org.openimaj.ml.clustering.kmeans: HierarchicalShortKMeans tree node The number of children is not bigger than the HierarchicalShortKMeans K parameter
HierarchicalShortPathAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner for gathering the clusters assigned to a point from a hierarchical clustering.
HierarchicalShortPathAssigner(HierarchicalShortKMeansResult) - Constructor for class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner: Construct with the given HierarchicalShortKMeansResult instance.

I

IncrementalLifetimeSparseClusterer - Class in org.openimaj.ml.clustering.incremental: An IncrementalSparseClusterer which also has a notion of a lifetime.
IncrementalLifetimeSparseClusterer(SparseMatrixClusterer<? extends IndexClusters>, int) - Constructor for class org.openimaj.ml.clustering.incremental.IncrementalLifetimeSparseClusterer
IncrementalLifetimeSparseClusterer(SparseMatrixClusterer<? extends IndexClusters>, int, int) - Constructor for class org.openimaj.ml.clustering.incremental.IncrementalLifetimeSparseClusterer
IncrementalLifetimeSparseClusterer(SparseMatrixClusterer<? extends IndexClusters>, int, double, int) - Constructor for class org.openimaj.ml.clustering.incremental.IncrementalLifetimeSparseClusterer
IncrementalSparseClusterer - Class in org.openimaj.ml.clustering.incremental: An incremental clusterer which holds old SparseMatrix instances internally, only forgetting rows once they have been clustered and are relatively stable.
IncrementalSparseClusterer(SparseMatrixClusterer<? extends IndexClusters>, int) - Constructor for class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer
IncrementalSparseClusterer(SparseMatrixClusterer<? extends IndexClusters>, int, double) - Constructor for class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer
IndexClusters - Class in org.openimaj.ml.clustering: Class to describe objects that are the result of the clustering where the training data is implicitly clustered
IndexClusters() - Constructor for class org.openimaj.ml.clustering.IndexClusters: Used only to initailise for IOUtils
IndexClusters(int[][], int) - Constructor for class org.openimaj.ml.clustering.IndexClusters
IndexClusters(int[][]) - Constructor for class org.openimaj.ml.clustering.IndexClusters
IndexClusters(int[]) - Constructor for class org.openimaj.ml.clustering.IndexClusters
IndexClusters(List<int[]>) - Constructor for class org.openimaj.ml.clustering.IndexClusters
initKMeans(DataSource<byte[]>, byte[][]) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<byte[]>, byte[][]) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeansInit.RANDOM
initKMeans(DataSource<double[]>, double[][]) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<double[]>, double[][]) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeansInit.RANDOM
initKMeans(DataSource<T>, T[]) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<T>, T[]) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeansInit.RANDOM
initKMeans(DataSource<float[]>, float[][]) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<float[]>, float[][]) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeansInit.RANDOM
initKMeans(DataSource<int[]>, int[][]) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<int[]>, int[][]) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeansInit.RANDOM
initKMeans(DataSource<long[]>, long[][]) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<long[]>, long[][]) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeansInit.RANDOM
initKMeans(DataSource<short[]>, short[][]) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeansInit: Initialise the centroids based on the given data.
initKMeans(DataSource<short[]>, short[][]) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeansInit.RANDOM
IntCentroidsResult - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids.
IntCentroidsResult() - Constructor for class org.openimaj.ml.clustering.IntCentroidsResult
IntKMeans - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
IntKMeans(KMeansConfiguration<IntNearestNeighbours, int[]>) - Constructor for class org.openimaj.ml.clustering.kmeans.IntKMeans: Construct the clusterer with the the given configuration.
IntKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.IntKMeans: A completely default IntKMeans used primarily as a convenience function for reading.
IntKMeans.Result - Class in org.openimaj.ml.clustering.kmeans: Result object for IntKMeans, extending IntCentroidsResult and IntNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
IntKMeansInit - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
IntKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.IntKMeansInit
IntKMeansInit.RANDOM - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
IntKNNAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner that picks a fixed number of nearest neighbours.
IntKNNAssigner(CentroidsProvider<int[]>, boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner: Construct the assigner using the given cluster data.
IntKNNAssigner(int[][], boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner: Construct the assigner using the given cluster data.
IntKNNAssigner(CentroidsProvider<int[]>, IntFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner: Construct the assigner using the given cluster data and distance function.
IntKNNAssigner(int[][], IntFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner: Construct the assigner using the given cluster data and distance function.
IntProductQuantiserUtilities - Class in org.openimaj.knn.pq: Utility methods for easily creating a ByteProductQuantiser using (Exact) K-Means.
IntRAC - Class in org.openimaj.ml.clustering.rac: An implementation of the RAC algorithm proposed by Ramanan and Niranjan.
IntRAC() - Constructor for class org.openimaj.ml.clustering.rac.IntRAC: Sets the threshold to 128
IntRAC(double) - Constructor for class org.openimaj.ml.clustering.rac.IntRAC: Define the threshold at which point a new cluster will be made.
IntRAC(int[][], int, int) - Constructor for class org.openimaj.ml.clustering.rac.IntRAC: Iteratively select subSamples from bKeys and try to choose a threshold which results in nClusters.
IntRandomForest - Class in org.openimaj.ml.clustering.rforest: An implementation of the RandomForest clustering algorithm proposed by Jurie et al.
IntRandomForest() - Constructor for class org.openimaj.ml.clustering.rforest.IntRandomForest: Makes a default random forest with 32 trees each with 32 decisions.
IntRandomForest(int, int) - Constructor for class org.openimaj.ml.clustering.rforest.IntRandomForest: Makes a random forest with nTrees each with nDecisions.
iteration - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans.IterationResult: The iteration number, starting from 0
iterationListeners - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
IterationResult() - Constructor for class org.openimaj.ml.clustering.kmeans.SphericalKMeans.IterationResult
iterations - Variable in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result
iterations - Variable in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result
iterations - Variable in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result
iterations - Variable in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result
iterations - Variable in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result
iterations - Variable in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result
iterations - Variable in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result

K

K - Variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The number of clusters
k - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
K - Variable in class org.openimaj.ml.clustering.random.RandomByteClusterer
K - Variable in class org.openimaj.ml.clustering.random.RandomDoubleClusterer
K - Variable in class org.openimaj.ml.clustering.random.RandomFloatClusterer
K - Variable in class org.openimaj.ml.clustering.random.RandomIntClusterer
K - Variable in class org.openimaj.ml.clustering.random.RandomLongClusterer
K - Variable in class org.openimaj.ml.clustering.random.RandomShortClusterer
KDTreeByteEuclideanAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that uses a ByteNearestNeighboursKDTree to generate approximately correct cluster assignments.
KDTreeByteEuclideanAssigner(CentroidsProvider<byte[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeByteEuclideanAssigner(byte[][]) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeDoubleEuclideanAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that uses a DoubleNearestNeighboursKDTree to generate approximately correct cluster assignments.
KDTreeDoubleEuclideanAssigner(CentroidsProvider<double[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeDoubleEuclideanAssigner(double[][]) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeFloatEuclideanAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that uses a FloatNearestNeighboursKDTree to generate approximately correct cluster assignments.
KDTreeFloatEuclideanAssigner(CentroidsProvider<float[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeFloatEuclideanAssigner(float[][]) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeIntEuclideanAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that uses a IntNearestNeighboursKDTree to generate approximately correct cluster assignments.
KDTreeIntEuclideanAssigner(CentroidsProvider<int[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeIntEuclideanAssigner(int[][]) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeLongEuclideanAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that uses a LongNearestNeighboursKDTree to generate approximately correct cluster assignments.
KDTreeLongEuclideanAssigner(CentroidsProvider<long[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeLongEuclideanAssigner(long[][]) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeShortEuclideanAssigner - Class in org.openimaj.ml.clustering.assignment.hard: A HardAssigner that uses a ShortNearestNeighboursKDTree to generate approximately correct cluster assignments.
KDTreeShortEuclideanAssigner(CentroidsProvider<short[]>) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner: Construct the assigner using the given cluster data.
KDTreeShortEuclideanAssigner(short[][]) - Constructor for class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner: Construct the assigner using the given cluster data.
KMeansConfiguration<NN extends NearestNeighbours<DATA,?,?>,DATA> - Class in org.openimaj.ml.clustering.kmeans: Configuration for the K-Means algorithm.
KMeansConfiguration(int, NearestNeighboursFactory<? extends NN, DATA>) - Constructor for class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Create configuration for data that will create K clusters.
KMeansConfiguration(int, NearestNeighboursFactory<? extends NN, DATA>, int) - Constructor for class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Create configuration for data that will create K clusters.
KMeansConfiguration(int, NearestNeighboursFactory<? extends NN, DATA>, int, ExecutorService) - Constructor for class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Create configuration for data that will create K clusters.
KMeansConfiguration(int, NearestNeighboursFactory<? extends NN, DATA>, int, int, ExecutorService) - Constructor for class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Create configuration for data with M dimensions that will create K clusters.
KMeansConfiguration() - Constructor for class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: A completely default configuration used primarily as a convenience function for reading.

L

lambda - Variable in class org.openimaj.ml.clustering.spectral.MultiviewSpectralClusteringConf: regularisation parameter
laplacian(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
laplacian(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian
laplacian(SparseMatrix, DiagonalMatrix) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian
laplacian(SparseMatrix, DiagonalMatrix) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian.Normalised
laplacian(SparseMatrix, DiagonalMatrix) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian.Unnormalised
laplacian(SparseMatrix, DiagonalMatrix) - Method in class org.openimaj.ml.clustering.spectral.GraphLaplacian.Warped
laplacian - Variable in class org.openimaj.ml.clustering.spectral.SpectralClusteringConf: The graph laplacian creator
laplacianEigenVectors(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
LongCentroidsResult - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids.
LongCentroidsResult() - Constructor for class org.openimaj.ml.clustering.LongCentroidsResult
LongKMeans - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
LongKMeans(KMeansConfiguration<LongNearestNeighbours, long[]>) - Constructor for class org.openimaj.ml.clustering.kmeans.LongKMeans: Construct the clusterer with the the given configuration.
LongKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.LongKMeans: A completely default LongKMeans used primarily as a convenience function for reading.
LongKMeans.Result - Class in org.openimaj.ml.clustering.kmeans: Result object for LongKMeans, extending LongCentroidsResult and LongNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
LongKMeansInit - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
LongKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.LongKMeansInit
LongKMeansInit.RANDOM - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
LongKNNAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner that picks a fixed number of nearest neighbours.
LongKNNAssigner(CentroidsProvider<long[]>, boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner: Construct the assigner using the given cluster data.
LongKNNAssigner(long[][], boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner: Construct the assigner using the given cluster data.
LongKNNAssigner(CentroidsProvider<long[]>, LongFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner: Construct the assigner using the given cluster data and distance function.
LongKNNAssigner(long[][], LongFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner: Construct the assigner using the given cluster data and distance function.
LongProductQuantiserUtilities - Class in org.openimaj.knn.pq: Utility methods for easily creating a ByteProductQuantiser using (Exact) K-Means.

M

M - Variable in class org.openimaj.ml.clustering.random.RandomByteClusterer
M - Variable in class org.openimaj.ml.clustering.random.RandomDoubleClusterer
M - Variable in class org.openimaj.ml.clustering.random.RandomFloatClusterer
M - Variable in class org.openimaj.ml.clustering.random.RandomIntClusterer
M - Variable in class org.openimaj.ml.clustering.random.RandomLongClusterer
M - Variable in class org.openimaj.ml.clustering.random.RandomShortClusterer
maxIters - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
mergeModes(double[][]) - Method in class org.openimaj.ml.clustering.meanshift.ExactMeanShift
mstep(GaussianMixtureModelEM.EMGMM, GaussianMixtureModelEM, Matrix, Matrix, Matrix, double[]) - Method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType: Mode specific maximisation-step.
mstep(GaussianMixtureModelEM.EMGMM, double[][], double[][]) - Method in class org.openimaj.ml.gmm.GaussianMixtureModelEM
MultiviewSimilarityClusterer<CLUSTERS extends IndexClusters> - Interface in org.openimaj.ml.clustering: A MultiviewSimilarityClusterer clusters data that can be represented as multiple similarity matrices.
MultiviewSpectralClusteringConf<DATATYPE> - Class in org.openimaj.ml.clustering.spectral
MultiviewSpectralClusteringConf(double, SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>, GraphLaplacian) - Constructor for class org.openimaj.ml.clustering.spectral.MultiviewSpectralClusteringConf
MultiviewSpectralClusteringConf(double, SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>, int) - Constructor for class org.openimaj.ml.clustering.spectral.MultiviewSpectralClusteringConf
MultiviewSpectralClusteringConf(double, SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>) - Constructor for class org.openimaj.ml.clustering.spectral.MultiviewSpectralClusteringConf

N

ndims - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
nDims - Variable in class org.openimaj.ml.clustering.rac.IntRAC
nEigenVectors(Iterator<DoubleObjectPair<Vector>>, int) - Method in class org.openimaj.ml.clustering.spectral.AbsoluteValueEigenChooser
nEigenVectors(Iterator<DoubleObjectPair<Vector>>, int) - Method in class org.openimaj.ml.clustering.spectral.ChangeDetectingEigenChooser
nEigenVectors(Iterator<DoubleObjectPair<Vector>>, int) - Method in class org.openimaj.ml.clustering.spectral.EigenChooser
nEigenVectors(Iterator<DoubleObjectPair<Vector>>, int) - Method in class org.openimaj.ml.clustering.spectral.HardCodedEigenChooser
nEntries - Variable in class org.openimaj.ml.clustering.IndexClusters
next() - Method in class org.openimaj.ml.clustering.spectral.FBEigenIterator
niters - Variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The max number of iterations
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
nn - Variable in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
nn - Variable in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result
nn - Variable in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result
nn - Variable in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result
nn - Variable in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result
nn - Variable in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result
nn - Variable in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result
nn - Variable in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result
Node() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult.Node
Node() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult.Node
Node() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult.Node
Node() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult.Node
Node() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult.Node
Node() - Constructor for class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult.Node
noiseAsClusters - Variable in class org.openimaj.ml.clustering.dbscan.DBSCAN
Normalised() - Constructor for class org.openimaj.ml.clustering.spectral.GraphLaplacian.Normalised
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result: Get the number of changed centroids in the last iteration.
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result: Get the number of changed centroids in the last iteration.
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result: Get the number of changed centroids in the last iteration.
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result: Get the number of changed centroids in the last iteration.
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result: Get the number of changed centroids in the last iteration.
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result: Get the number of changed centroids in the last iteration.
numChangedCentroids() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result: Get the number of changed centroids in the last iteration.
numClusters() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
numClusters() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
numClusters() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
numClusters() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
numClusters() - Method in class org.openimaj.ml.clustering.IndexClusters: Get the number of clusters.
numClusters() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Get the number of clusters
numClusters() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
numClusters() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
numClusters() - Method in class org.openimaj.ml.clustering.rac.IntRAC
numClusters() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
numClusters() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
numClusters() - Method in interface org.openimaj.ml.clustering.SpatialClusters: Get the number of clusters.
numDimensions() - Method in interface org.openimaj.ml.clustering.assignment.Assigner: Get the number of dimensions of the input vectors.
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
numDimensions() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
numDimensions() - Method in class org.openimaj.ml.clustering.dbscan.DoubleDBSCANClusters
numDimensions() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
numDimensions() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
numDimensions() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
numDimensions() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
numDimensions() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
numDimensions() - Method in class org.openimaj.ml.clustering.rac.IntRAC
numDimensions() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
numDimensions() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
numDimensions() - Method in interface org.openimaj.ml.clustering.SpatialClusters: Get the data dimensionality
numEntries() - Method in class org.openimaj.ml.clustering.IndexClusters: Get the number of data entries
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result: Get the number of K-Means iterations that produced this result.
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result: Get the number of K-Means iterations that produced this result.
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result: Get the number of K-Means iterations that produced this result.
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result: Get the number of K-Means iterations that produced this result.
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans.Result: Get the number of K-Means iterations that produced this result.
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans.Result: Get the number of K-Means iterations that produced this result.
numIterations() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result: Get the number of K-Means iterations that produced this result.
numNeighbours - Variable in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
numNeighbours - Variable in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
numNeighbours - Variable in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
numNeighbours - Variable in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
numNeighbours - Variable in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
numNeighbours - Variable in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner

O

org.openimaj.experiment.evaluation.cluster - package org.openimaj.experiment.evaluation.cluster
org.openimaj.knn.pq - package org.openimaj.knn.pq
org.openimaj.ml.clustering - package org.openimaj.ml.clustering
org.openimaj.ml.clustering.assignment - package org.openimaj.ml.clustering.assignment
org.openimaj.ml.clustering.assignment.hard - package org.openimaj.ml.clustering.assignment.hard
org.openimaj.ml.clustering.assignment.soft - package org.openimaj.ml.clustering.assignment.soft
org.openimaj.ml.clustering.dbscan - package org.openimaj.ml.clustering.dbscan
org.openimaj.ml.clustering.dbscan.neighbourhood - package org.openimaj.ml.clustering.dbscan.neighbourhood
org.openimaj.ml.clustering.incremental - package org.openimaj.ml.clustering.incremental
org.openimaj.ml.clustering.kmeans - package org.openimaj.ml.clustering.kmeans: K-Means in OpenIMAJ is designed to be both extremely fast and flexible.
org.openimaj.ml.clustering.meanshift - package org.openimaj.ml.clustering.meanshift
org.openimaj.ml.clustering.rac - package org.openimaj.ml.clustering.rac
org.openimaj.ml.clustering.random - package org.openimaj.ml.clustering.random
org.openimaj.ml.clustering.rforest - package org.openimaj.ml.clustering.rforest
org.openimaj.ml.clustering.spectral - package org.openimaj.ml.clustering.spectral
org.openimaj.ml.gmm - package org.openimaj.ml.gmm

P

path - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
path - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
path - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
path - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
path - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
path - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
performClustering(double[][]) - Method in class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN
performClustering(SparseMatrix) - Method in class org.openimaj.ml.clustering.dbscan.SparseMatrixDBSCAN
performClustering(SparseMatrix) - Method in class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer
performClustering(byte[][]) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
performClustering(double[][]) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
performClustering(T[]) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans
performClustering(List<T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Perform clustering on the given data.
performClustering(float[][]) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
performClustering(byte[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeans
performClustering(double[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeans
performClustering(float[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeans
performClustering(int[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeans
performClustering(long[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeans
performClustering(short[][]) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeans
performClustering(int[][]) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
performClustering(long[][]) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
performClustering(short[][]) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
performClustering(double[][]) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
performClustering(int[][]) - Method in class org.openimaj.ml.clustering.rac.IntRAC
performClustering(byte[][]) - Method in class org.openimaj.ml.clustering.random.RandomByteClusterer
performClustering(SparseMatrix) - Method in class org.openimaj.ml.clustering.random.RandomClusterer
performClustering(double[][]) - Method in class org.openimaj.ml.clustering.random.RandomDoubleClusterer
performClustering(float[][]) - Method in class org.openimaj.ml.clustering.random.RandomFloatClusterer
performClustering(int[][]) - Method in class org.openimaj.ml.clustering.random.RandomIntClusterer
performClustering(long[][]) - Method in class org.openimaj.ml.clustering.random.RandomLongClusterer
performClustering(short[][]) - Method in class org.openimaj.ml.clustering.random.RandomShortClusterer
performClustering(int[][]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
performClustering(List<SparseMatrix>) - Method in class org.openimaj.ml.clustering.spectral.DoubleMultiviewSpectralClustering
performClustering(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
performClustering(Eigenvalues) - Method in class org.openimaj.ml.clustering.spectral.PreparedSpectralClustering
performMeanShift() - Method in class org.openimaj.ml.clustering.meanshift.ExactMeanShift
prepare(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.AbsoluteValueEigenChooser
prepare(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.ChangeDetectingEigenChooser
prepare(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.EigenChooser: Make a coarse decision of the number of eigen vectors to extract in the first place with the knowledge of the eigen values that will likely be important
prepare(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.HardCodedEigenChooser
PreparedSpectralClustering - Class in org.openimaj.ml.clustering.spectral: For a given set of Eigenvalues perform the stages of spectral clustering which involve the selection of the best eigen values and the calling of an internal clustering algorithm
PreparedSpectralClustering(SpectralClusteringConf<double[]>) - Constructor for class org.openimaj.ml.clustering.spectral.PreparedSpectralClustering

R

RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.ByteKMeansInit.RANDOM
RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.DoubleKMeansInit.RANDOM
RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeansInit.RANDOM
RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.FloatKMeansInit.RANDOM
RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.IntKMeansInit.RANDOM
RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.LongKMeansInit.RANDOM
RANDOM() - Constructor for class org.openimaj.ml.clustering.kmeans.ShortKMeansInit.RANDOM
random - Variable in class org.openimaj.ml.clustering.random.RandomByteClusterer
random - Variable in class org.openimaj.ml.clustering.random.RandomDoubleClusterer
random - Variable in class org.openimaj.ml.clustering.random.RandomFloatClusterer
random - Variable in class org.openimaj.ml.clustering.random.RandomIntClusterer
random - Variable in class org.openimaj.ml.clustering.random.RandomLongClusterer
random - Variable in class org.openimaj.ml.clustering.random.RandomShortClusterer
RandomByteClusterer - Class in org.openimaj.ml.clustering.random: A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
RandomByteClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomByteClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomByteClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomByteClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomClusterer - Class in org.openimaj.ml.clustering.random: Given a similarity or distance matrix, this clusterer randomly selects a number of clusters and randomly assigned each row to each cluster.
RandomClusterer() - Constructor for class org.openimaj.ml.clustering.random.RandomClusterer: unseeded random
RandomClusterer(long) - Constructor for class org.openimaj.ml.clustering.random.RandomClusterer: seeded random
RandomClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomClusterer: seeded random
RandomClusterer(int, long) - Constructor for class org.openimaj.ml.clustering.random.RandomClusterer: seeded random
RandomDecision - Class in org.openimaj.ml.clustering.rforest: A single decision node of a RandomForest tree.
RandomDecision(int, int[], int[]) - Constructor for class org.openimaj.ml.clustering.rforest.RandomDecision
RandomDecision(int, int[], int[], Random) - Constructor for class org.openimaj.ml.clustering.rforest.RandomDecision
RandomDecision() - Constructor for class org.openimaj.ml.clustering.rforest.RandomDecision: Emtpy contructor provided to allow reading of the decision
RandomDecisionTree - Class in org.openimaj.ml.clustering.rforest: A tree of RandomDecision nodes used for constructing a string of bits which represent a cluster point for a single data point
RandomDecisionTree(int, int, int[], int[]) - Constructor for class org.openimaj.ml.clustering.rforest.RandomDecisionTree: Construct a new RandomDecisionTree setting the number of decisions and the values needed to choose a random index and min/max values for each feature vector index.
RandomDecisionTree(int, int, int[], int[], Random) - Constructor for class org.openimaj.ml.clustering.rforest.RandomDecisionTree: Construct a new RandomDecisionTree setting the number of decisions and the values needed to choose a random index and min/max values for each feature vector index.
RandomDecisionTree() - Constructor for class org.openimaj.ml.clustering.rforest.RandomDecisionTree: A convenience function allowing the RandomDecisionTree to be written and read.
RandomDoubleClusterer - Class in org.openimaj.ml.clustering.random: A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
RandomDoubleClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomDoubleClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomDoubleClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomDoubleClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomFloatClusterer - Class in org.openimaj.ml.clustering.random: A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
RandomFloatClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomFloatClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomFloatClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomFloatClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomIntClusterer - Class in org.openimaj.ml.clustering.random: A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
RandomIntClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomIntClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomIntClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomIntClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomLongClusterer - Class in org.openimaj.ml.clustering.random: A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
RandomLongClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomLongClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomLongClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomLongClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomSetByteClusterer - Class in org.openimaj.ml.clustering.random: A similar strategy to RandomSetByteClusterer however it is guaranteed that the same training vector will not be sampled more than once.
RandomSetByteClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetByteClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetByteClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetByteClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomSetDoubleClusterer - Class in org.openimaj.ml.clustering.random: A similar strategy to RandomSetDoubleClusterer however it is guaranteed that the same training vector will not be sampled more than once.
RandomSetDoubleClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetDoubleClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetDoubleClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetDoubleClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomSetFloatClusterer - Class in org.openimaj.ml.clustering.random: A similar strategy to RandomSetFloatClusterer however it is guaranteed that the same training vector will not be sampled more than once.
RandomSetFloatClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetFloatClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetFloatClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetFloatClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomSetIntClusterer - Class in org.openimaj.ml.clustering.random: A similar strategy to RandomSetIntClusterer however it is guaranteed that the same training vector will not be sampled more than once.
RandomSetIntClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetIntClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetIntClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetIntClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomSetLongClusterer - Class in org.openimaj.ml.clustering.random: A similar strategy to RandomSetLongClusterer however it is guaranteed that the same training vector will not be sampled more than once.
RandomSetLongClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetLongClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetLongClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetLongClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomSetShortClusterer - Class in org.openimaj.ml.clustering.random: A similar strategy to RandomSetShortClusterer however it is guaranteed that the same training vector will not be sampled more than once.
RandomSetShortClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetShortClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomSetShortClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomSetShortClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RandomShortClusterer - Class in org.openimaj.ml.clustering.random: A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
RandomShortClusterer(int, int) - Constructor for class org.openimaj.ml.clustering.random.RandomShortClusterer: Creates a new random byte cluster used to create K centroids with data containing M elements.
RandomShortClusterer(int) - Constructor for class org.openimaj.ml.clustering.random.RandomShortClusterer: Creates a new random byte cluster used to create centroids with data containing M elements.
RangedAnalysisResult<KEY,ANA extends AnalysisResult> - Class in org.openimaj.experiment.evaluation.cluster
RangedAnalysisResult() - Constructor for class org.openimaj.experiment.evaluation.cluster.RangedAnalysisResult
RangedDBSCANClusterEvaluator<D,T extends AnalysisResult> - Class in org.openimaj.experiment.evaluation.cluster
RangedDBSCANClusterEvaluator(UniformDoubleRangeIterable, SparseMatrixDBSCAN, SparseMatrix, int[][], ClusterAnalyser<T>) - Constructor for class org.openimaj.experiment.evaluation.cluster.RangedDBSCANClusterEvaluator
RangedDBSCANClusterEvaluator(UniformDoubleRangeIterable, SparseMatrixDBSCAN, SparseMatrix, Map<A, ? extends List<B>>, ClusterAnalyser<T>) - Constructor for class org.openimaj.experiment.evaluation.cluster.RangedDBSCANClusterEvaluator
RangedDBSCANClusterEvaluator(UniformDoubleRangeIterable, SparseMatrixDBSCAN, SparseMatrix, Function<B, Integer>, Map<A, ? extends List<B>>, ClusterAnalyser<T>) - Constructor for class org.openimaj.experiment.evaluation.cluster.RangedDBSCANClusterEvaluator
RangedDBSCANClusterEvaluator(UniformDoubleRangeIterable, SparseMatrixDBSCAN, Map<A, ? extends List<B>>, Function<List<B>, SparseMatrix>, ClusterAnalyser<T>) - Constructor for class org.openimaj.experiment.evaluation.cluster.RangedDBSCANClusterEvaluator
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.IndexClusters
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.IntCentroidsResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.LongCentroidsResult
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.rac.IntRAC
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree: Read/Write RandomDecisionTree (including decision nodes)
readASCII(Scanner) - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.IndexClusters
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.IntCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.LongCentroidsResult
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.rac.IntRAC
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.rforest.RandomDecision: Read decision
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree: Read/Write RandomDecisionTree (including decision nodes)
readBinary(DataInput) - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
readString(String) - Method in class org.openimaj.ml.clustering.rforest.RandomDecision: Read decision from a string
RegionMode<PAIRTYPE> - Interface in org.openimaj.ml.clustering.dbscan.neighbourhood: RegionMode instances can provide Neighbours of the n'th data point given all the data points
regionQuery(int) - Method in interface org.openimaj.ml.clustering.dbscan.neighbourhood.RegionMode
remove() - Method in class org.openimaj.ml.clustering.spectral.FBEigenIterator
result - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
result - Variable in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.ByteKMeans.Result
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.DoubleKMeans.Result
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans.Result
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.FloatKMeans.Result
result - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult.Node: ByteCentroidsResult for this node
result - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult.Node: DoubleCentroidsResult for this node
result - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult.Node: FloatCentroidsResult for this node
result - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult.Node: IntCentroidsResult for this node
result - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult.Node: LongCentroidsResult for this node
result - Variable in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult.Node: ShortCentroidsResult for this node
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.IntKMeans.Result
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.LongKMeans.Result
Result() - Constructor for class org.openimaj.ml.clustering.kmeans.ShortKMeans.Result
result - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans.IterationResult: The current results
rng - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
roundDouble(double) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
roundDouble(double) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
roundDouble(double) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
roundDouble(double) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
roundDouble(double) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
roundDouble(double) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
roundFloat(double) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
roundFloat(double) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
roundFloat(double) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
roundFloat(double) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
roundFloat(double) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
roundFloat(double) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
roundInt(double) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
roundInt(double) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
roundInt(double) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
roundInt(double) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
roundInt(double) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
roundInt(double) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
roundLong(double) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
roundLong(double) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
roundLong(double) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
roundLong(double) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
roundLong(double) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
roundLong(double) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans

S

scorer - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
scorer - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
scorer - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
scorer - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
scorer - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
scorer - Variable in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Set the seed for the internal random number generator.
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Set the seed for the internal random number generator.
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Set the seed for the internal random number generator.
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Set the seed for the internal random number generator.
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Set the seed for the internal random number generator.
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Set the seed for the internal random number generator.
seed(long) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Set the seed for the internal random number generator.
seed - Variable in class org.openimaj.ml.clustering.random.RandomByteClusterer
seed - Variable in class org.openimaj.ml.clustering.random.RandomDoubleClusterer
seed - Variable in class org.openimaj.ml.clustering.random.RandomFloatClusterer
seed - Variable in class org.openimaj.ml.clustering.random.RandomIntClusterer
seed - Variable in class org.openimaj.ml.clustering.random.RandomLongClusterer
seed - Variable in class org.openimaj.ml.clustering.random.RandomShortClusterer
setBlockSize(int) - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Set the number of samples processed in a batch by a thread.
setConfiguration(KMeansConfiguration<ByteNearestNeighbours, byte[]>) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Set the configuration
setConfiguration(KMeansConfiguration<DoubleNearestNeighbours, double[]>) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Set the configuration
setConfiguration(KMeansConfiguration<ObjectNearestNeighbours<T>, T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Set the configuration
setConfiguration(KMeansConfiguration<FloatNearestNeighbours, float[]>) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Set the configuration
setConfiguration(KMeansConfiguration<IntNearestNeighbours, int[]>) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Set the configuration
setConfiguration(KMeansConfiguration<LongNearestNeighbours, long[]>) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Set the configuration
setConfiguration(KMeansConfiguration<ShortNearestNeighbours, short[]>) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Set the configuration
setCovariances(MultivariateGaussian[], Matrix) - Method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType
setEps(double) - Method in class org.openimaj.ml.clustering.dbscan.SparseMatrixDBSCAN
setInit(ByteKMeansInit) - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans: Set the current initialisation algorithm
setInit(DoubleKMeansInit) - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans: Set the current initialisation algorithm
setInit(FeatureVectorKMeansInit<T>) - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans: Set the current initialisation algorithm
setInit(FloatKMeansInit) - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans: Set the current initialisation algorithm
setInit(IntKMeansInit) - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans: Set the current initialisation algorithm
setInit(LongKMeansInit) - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans: Set the current initialisation algorithm
setInit(ShortKMeansInit) - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans: Set the current initialisation algorithm
setK(int) - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Set the number of clusters
setMaxIterations(int) - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Set the maximum allowed number of iterations.
setMinMax(int[], int[]) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest: The maximum and minimum values for the various dimentions against which random decisions will be based.
setNearestNeighbourFactory(NearestNeighboursFactory<? extends NN, DATA>) - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Set the factory that produces the NearestNeighbours during clustering.
setNoiseAsClusters(boolean) - Method in class org.openimaj.ml.clustering.dbscan.DBSCAN: Treat noise as clusters on their own
setNumClusters(int) - Method in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: Set the number of clusters
setRandomSeed(int) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
setRandomSeed(int) - Method in class org.openimaj.ml.clustering.rforest.RandomDecision: Random seed upon which a java Random object is seeded and used to choose random indecies and thresholds.
setSeed(long) - Method in class org.openimaj.ml.clustering.random.RandomByteClusterer
setSeed(long) - Method in class org.openimaj.ml.clustering.random.RandomDoubleClusterer
setSeed(long) - Method in class org.openimaj.ml.clustering.random.RandomFloatClusterer
setSeed(long) - Method in class org.openimaj.ml.clustering.random.RandomIntClusterer
setSeed(long) - Method in class org.openimaj.ml.clustering.random.RandomLongClusterer
setSeed(long) - Method in class org.openimaj.ml.clustering.random.RandomShortClusterer
ShortCentroidsResult - Class in org.openimaj.ml.clustering: The result of a SpatialClusterer that just produces a flat set of centroids.
ShortCentroidsResult() - Constructor for class org.openimaj.ml.clustering.ShortCentroidsResult
ShortKMeans - Class in org.openimaj.ml.clustering.kmeans: Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
ShortKMeans(KMeansConfiguration<ShortNearestNeighbours, short[]>) - Constructor for class org.openimaj.ml.clustering.kmeans.ShortKMeans: Construct the clusterer with the the given configuration.
ShortKMeans() - Constructor for class org.openimaj.ml.clustering.kmeans.ShortKMeans: A completely default ShortKMeans used primarily as a convenience function for reading.
ShortKMeans.Result - Class in org.openimaj.ml.clustering.kmeans: Result object for ShortKMeans, extending ShortCentroidsResult and ShortNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
ShortKMeansInit - Class in org.openimaj.ml.clustering.kmeans: Initialisation for K-Means clustering.
ShortKMeansInit() - Constructor for class org.openimaj.ml.clustering.kmeans.ShortKMeansInit
ShortKMeansInit.RANDOM - Class in org.openimaj.ml.clustering.kmeans: Simple kmeans initialized on randomly selected samples.
ShortKNNAssigner - Class in org.openimaj.ml.clustering.assignment.soft: A SoftAssigner that picks a fixed number of nearest neighbours.
ShortKNNAssigner(CentroidsProvider<short[]>, boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner: Construct the assigner using the given cluster data.
ShortKNNAssigner(short[][], boolean, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner: Construct the assigner using the given cluster data.
ShortKNNAssigner(CentroidsProvider<short[]>, ShortFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner: Construct the assigner using the given cluster data and distance function.
ShortKNNAssigner(short[][], ShortFVComparison, int) - Constructor for class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner: Construct the assigner using the given cluster data and distance function.
ShortProductQuantiserUtilities - Class in org.openimaj.knn.pq: Utility methods for easily creating a ByteProductQuantiser using (Exact) K-Means.
SimilarityClusterer<CLUSTERS extends IndexClusters> - Interface in org.openimaj.ml.clustering: A SimilarityClusterer clusters data that can be represented as a similarity matrix.
SimilarityDBSCAN - Class in org.openimaj.ml.clustering.dbscan: DBSCAN using a SparseMatrix of similarities
SimilarityDBSCAN(double, int) - Constructor for class org.openimaj.ml.clustering.dbscan.SimilarityDBSCAN
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ConstrainedFloatAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactByteAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactDoubleAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFeatureVectorAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactIntAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactLongAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.ExactShortAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeByteEuclideanAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeDoubleEuclideanAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeFloatEuclideanAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeIntEuclideanAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.hard.KDTreeShortEuclideanAssigner
size() - Method in interface org.openimaj.ml.clustering.assignment.HardAssigner: The number of centroids or unique ids that can be generated.
size() - Method in class org.openimaj.ml.clustering.assignment.soft.ByteKNNAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.DoubleKNNAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.FloatKNNAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalBytePathAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalDoublePathAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalFloatPathAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalIntPathAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.HierarchicalShortPathAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.IntKNNAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.LongKNNAssigner
size() - Method in class org.openimaj.ml.clustering.assignment.soft.ShortKNNAssigner
size() - Method in interface org.openimaj.ml.clustering.assignment.SoftAssigner: The number of clusters.
size() - Method in class org.openimaj.ml.clustering.rac.IntRAC: The number of centroids; this potentially grows as assignments are made.
size() - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
skipEigenVectors - Variable in class org.openimaj.ml.clustering.spectral.SpectralClusteringConf
SoftAssigner<DATATYPE,DISTANCES> - Interface in org.openimaj.ml.clustering.assignment: The SoftAssigner interface describes classes that assign a spatial point to multiple clusters, possibly with weighting.
SparseMatrixClusterer<CLUSTERS extends IndexClusters> - Interface in org.openimaj.ml.clustering: A matrix clusterer can cluster a matrix of data in some way
SparseMatrixDBSCAN - Class in org.openimaj.ml.clustering.dbscan: Implementation of DBSCAN (http://en.wikipedia.org/wiki/DBSCAN) using a
SparseMatrixDBSCAN(double, int) - Constructor for class org.openimaj.ml.clustering.dbscan.SparseMatrixDBSCAN: Perform a DBScane with this configuration
SpatialClusterer<CLUSTERTYPE extends SpatialClusters<DATATYPE>,DATATYPE> - Interface in org.openimaj.ml.clustering: A SpatialClusterer clusters data that can be represented as points in a space.
SpatialClusters<DATATYPE> - Interface in org.openimaj.ml.clustering: Interface to describe objects that are the result of the clustering performed by a SpatialClusterer.
spectralCluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.CachedDoubleSpectralClustering
spectralCluster(SparseMatrix) - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
SpectralClusteringConf<DATATYPE> - Class in org.openimaj.ml.clustering.spectral
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>, int) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralClusteringConf
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralClusteringConf: The underlying EigenChooser is set to an ChangeDetectingEigenChooser which looks for a 100x gap between eigen vectors to select number of clusters.
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>, GraphLaplacian, int) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralClusteringConf
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>, GraphLaplacian) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralClusteringConf: The underlying EigenChooser is set to an ChangeDetectingEigenChooser which looks for a 100x gap between eigen vectors to select number of clusters.
SpectralClusteringConf(SpectralClusteringConf.ClustererProvider<DATATYPE>) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralClusteringConf: The underlying EigenChooser is set to an ChangeDetectingEigenChooser which looks for a 100x gap between eigen vectors to select number of clusters.
SpectralClusteringConf.ClustererProvider<DATATYPE> - Interface in org.openimaj.ml.clustering.spectral: A function which can represent itself as a string
SpectralClusteringConf.DefaultClustererFunction<DATATYPE> - Class in org.openimaj.ml.clustering.spectral
SpectralIndexedClusters - Class in org.openimaj.ml.clustering.spectral: IndexClusters which also hold the eigenvector/value pairs which created them
SpectralIndexedClusters(IndexClusters, IndependentPair<double[], double[][]>) - Constructor for class org.openimaj.ml.clustering.spectral.SpectralIndexedClusters
SphericalKMeans - Class in org.openimaj.ml.clustering.kmeans: Multithreaded (optionally) damped spherical k-means with support for bigger-than-memory data.
SphericalKMeans(int, int) - Constructor for class org.openimaj.ml.clustering.kmeans.SphericalKMeans: Construct with the given parameters.
SphericalKMeans(int, int, boolean) - Constructor for class org.openimaj.ml.clustering.kmeans.SphericalKMeans: Construct with the given parameters.
SphericalKMeans(int) - Constructor for class org.openimaj.ml.clustering.kmeans.SphericalKMeans: Construct with the given parameters.
SphericalKMeans.IterationResult - Class in org.openimaj.ml.clustering.kmeans: Object storing the result of the previous iteration of spherical kmeans.
SphericalKMeansResult - Class in org.openimaj.ml.clustering.kmeans: The result of a SpatialClusterer that just produces a flat set of centroids.
SphericalKMeansResult() - Constructor for class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
State(int, RegionMode<IntDoublePair>) - Constructor for class org.openimaj.ml.clustering.dbscan.DBSCAN.State
State(int, RegionMode<IntDoublePair>, boolean) - Constructor for class org.openimaj.ml.clustering.dbscan.DBSCAN.State
stop - Variable in class org.openimaj.ml.clustering.spectral.MultiviewSpectralClusteringConf: when to stop iterating
stop(List<IndependentPair<double[], double[][]>>) - Method in class org.openimaj.ml.clustering.spectral.StoppingCondition.HardCoded
stop(List<IndependentPair<double[], double[][]>>) - Method in interface org.openimaj.ml.clustering.spectral.StoppingCondition: Called once at the beggining of each full iteration
StoppingCondition - Interface in org.openimaj.ml.clustering.spectral: The stopping condition for a multiview spectral clustering algorithm
StoppingCondition.HardCoded - Class in org.openimaj.ml.clustering.spectral: Counts the iterations

T

terminationEps - Variable in class org.openimaj.ml.clustering.kmeans.SphericalKMeans
threadpool - Variable in class org.openimaj.ml.clustering.kmeans.KMeansConfiguration: The threadpool for parallel processing
threshold - Variable in class org.openimaj.ml.clustering.incremental.IncrementalSparseClusterer
threshold - Variable in class org.openimaj.ml.clustering.rac.IntRAC
threshold - Variable in class org.openimaj.ml.clustering.rforest.RandomDecision: Feature threshold
toString() - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.dbscan.DBSCANClusters
toString() - Method in class org.openimaj.ml.clustering.dbscan.DoubleNNDBSCAN
toString() - Method in class org.openimaj.ml.clustering.dbscan.SparseMatrixDBSCAN
toString() - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.IndexClusters
toString() - Method in class org.openimaj.ml.clustering.IntCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.ByteKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.DoubleKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.FeatureVectorKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.FloatKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
toString() - Method in class org.openimaj.ml.clustering.kmeans.IntKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.LongKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.ShortKMeans
toString() - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
toString() - Method in class org.openimaj.ml.clustering.LongCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.rforest.RandomDecision
toString() - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree
toString() - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
toString() - Method in class org.openimaj.ml.clustering.spectral.AbsoluteValueEigenChooser
toString() - Method in class org.openimaj.ml.clustering.spectral.DoubleSpectralClustering
toString() - Method in class org.openimaj.ml.clustering.spectral.HardCodedEigenChooser
toString() - Method in interface org.openimaj.ml.clustering.spectral.SpectralClusteringConf.ClustererProvider
toString() - Method in class org.openimaj.ml.clustering.spectral.SpectralClusteringConf.DefaultClustererFunction
totalSamples - Variable in class org.openimaj.ml.clustering.rac.IntRAC
train(byte[][], int, int, int) - Static method in class org.openimaj.knn.pq.ByteProductQuantiserUtilities: Learn a ByteProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(byte[][], int, int) - Static method in class org.openimaj.knn.pq.ByteProductQuantiserUtilities: Learn a ByteProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(double[][], int, int, int) - Static method in class org.openimaj.knn.pq.DoubleProductQuantiserUtilities: Learn a DoubleProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(double[][], int, int) - Static method in class org.openimaj.knn.pq.DoubleProductQuantiserUtilities: Learn a DoubleProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(float[][], int, int, int) - Static method in class org.openimaj.knn.pq.FloatProductQuantiserUtilities: Learn a FloatProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(float[][], int, int) - Static method in class org.openimaj.knn.pq.FloatProductQuantiserUtilities: Learn a FloatProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(int[][], int, int, int) - Static method in class org.openimaj.knn.pq.IntProductQuantiserUtilities: Learn a IntProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(int[][], int, int) - Static method in class org.openimaj.knn.pq.IntProductQuantiserUtilities: Learn a IntProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(long[][], int, int, int) - Static method in class org.openimaj.knn.pq.LongProductQuantiserUtilities: Learn a LongProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(long[][], int, int) - Static method in class org.openimaj.knn.pq.LongProductQuantiserUtilities: Learn a LongProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(short[][], int, int, int) - Static method in class org.openimaj.knn.pq.ShortProductQuantiserUtilities: Learn a ShortProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.
train(short[][], int, int) - Static method in class org.openimaj.knn.pq.ShortProductQuantiserUtilities: Learn a ShortProductQuantiser by applying exact K-Means to sub-vectors extracted from the given data.

U

Unnormalised() - Constructor for class org.openimaj.ml.clustering.spectral.GraphLaplacian.Unnormalised

V

validRegion(List<PAIRTYPE>) - Method in interface org.openimaj.ml.clustering.dbscan.neighbourhood.RegionMode
valueOf(String) - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner.ScoringScheme: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner.ScoringScheme: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner.ScoringScheme: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner.ScoringScheme: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner.ScoringScheme: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner.ScoringScheme: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.UpdateOptions: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalByteHardAssigner.ScoringScheme: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner.ScoringScheme: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalFloatHardAssigner.ScoringScheme: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalIntHardAssigner.ScoringScheme: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalLongHardAssigner.ScoringScheme: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.clustering.assignment.hard.HierarchicalShortHardAssigner.ScoringScheme: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.openimaj.ml.gmm.GaussianMixtureModelEM.UpdateOptions: Returns an array containing the constants of this enum type, in the order they are declared.

W

Warped() - Constructor for class org.openimaj.ml.clustering.spectral.GraphLaplacian.Warped
write(DataOutput) - Method in class org.openimaj.ml.clustering.rforest.RandomDecision: Write decision to a binary stream, threshold followed by feature.
write(DataOutput) - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree: Read/Write RandomDecisionTree (including decision nodes)
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.IndexClusters
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.IntCentroidsResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.LongCentroidsResult
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.rac.IntRAC
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.rforest.RandomDecision: write decision in a human readable form
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.rforest.RandomDecisionTree: Read/Write RandomDecisionTree (including decision nodes)
writeASCII(PrintWriter) - Method in class org.openimaj.ml.clustering.ShortCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.ByteCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.DoubleCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.FeatureVectorCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.FloatCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.IndexClusters
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.IntCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalByteKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalDoubleKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalFloatKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalIntKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.HierarchicalShortKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.kmeans.SphericalKMeansResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.LongCentroidsResult
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.rac.IntRAC
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.rforest.IntRandomForest
writeBinary(DataOutput) - Method in class org.openimaj.ml.clustering.ShortCentroidsResult

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