Uses of Interface jsat.linear.distancemetrics.DistanceMetric (Java Statistical Analysis Tool 0.0.8 API)

Packages that use DistanceMetric
Package	Description
jsat.classifiers
jsat.classifiers.knn
jsat.classifiers.neuralnetwork
jsat.clustering
jsat.clustering.dissimilarity
jsat.clustering.evaluation
jsat.clustering.evaluation.intra
jsat.clustering.hierarchical
jsat.clustering.kmeans
jsat.datatransform.featureselection
jsat.datatransform.visualization
jsat.distributions.kernels
jsat.distributions.multivariate
jsat.linear.distancemetrics
jsat.linear.vectorcollection
jsat.linear.vectorcollection.lsh
jsat.parameters

Uses of DistanceMetric in jsat.classifiers

Constructors in jsat.classifiers with parameters of type DistanceMetric
Constructor and Description
`Rocchio(DistanceMetric dm)`

Uses of DistanceMetric in jsat.classifiers.knn

Methods in jsat.classifiers.knn that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	NearestNeighbour.`getDistanceMetric()`
`DistanceMetric`	LWL.`getDistanceMetric()` Returns the distance metric in use

Methods in jsat.classifiers.knn with parameters of type DistanceMetric
Modifier and Type	Method and Description
`void`	NearestNeighbour.`setDistanceMetric(DistanceMetric distanceMetric)`
`void`	LWL.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric that will be used for the nearest neighbor search

Constructors in jsat.classifiers.knn with parameters of type DistanceMetric
Constructor and Description
`LWL(Classifier classifier, int k, DistanceMetric dm)` Creates a new LWL classifier
`LWL(Classifier classifier, int k, DistanceMetric dm, KernelFunction kf)` Creates a new LWL classifier
`LWL(Classifier classifier, int k, DistanceMetric dm, KernelFunction kf, VectorCollectionFactory<VecPaired<Vec,Double>> vcf)` Creates a new LWL classifier
`LWL(Regressor regressor, int k, DistanceMetric dm)` Creates a new LWL Regressor
`LWL(Regressor regressor, int k, DistanceMetric dm, KernelFunction kf)` Creates a new LWL Regressor
`LWL(Regressor regressor, int k, DistanceMetric dm, KernelFunction kf, VectorCollectionFactory<VecPaired<Vec,Double>> vcf)` Creates a new LWL Regressor
`NearestNeighbour(int k, boolean weighted, DistanceMetric distanceMetric)` Constructs a new Nearest Neighbor Classifier
`NearestNeighbour(int k, boolean weighted, DistanceMetric distanceMetric, VectorCollectionFactory<VecPaired<Vec,Double>> vcf)` Constructs a new Nearest Neighbor Classifier

Uses of DistanceMetric in jsat.classifiers.neuralnetwork

Fields in jsat.classifiers.neuralnetwork declared as DistanceMetric
Modifier and Type	Field and Description
`protected DistanceMetric`	LVQ.`dm` The distance metric to use

Methods in jsat.classifiers.neuralnetwork that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	RBFNet.`getDistanceMetric()` Returns the distance metric in use
`DistanceMetric`	LVQ.`getDistanceMetric()` Returns the distance metric to use

Methods in jsat.classifiers.neuralnetwork with parameters of type DistanceMetric
Modifier and Type	Method and Description
`protected abstract double[]`	RBFNet.Phase2Learner.`estimateBandwidths(double alpha, int p, DataSet data, List<Vec> centroids, List<Double> centroidDistCache, DistanceMetric dm, ExecutorService threadpool)`
`protected abstract List<Vec>`	RBFNet.Phase1Learner.`getCentroids(DataSet data, int centroids, DistanceMetric dm, ExecutorService ex)` Obtains the centroids for the given data set
`void`	RBFNet.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric used to determine neuron activations.
`void`	LVQ.`setDistanceMetric(DistanceMetric dm)` Sets the distance used for learning

Constructors in jsat.classifiers.neuralnetwork with parameters of type DistanceMetric
Constructor and Description
`LVQ(DistanceMetric dm, int iterations)` Creates a new LVQ instance
`LVQ(DistanceMetric dm, int iterations, double learningRate, int representativesPerClass)` Creates a new LVQ instance
`LVQ(DistanceMetric dm, int iterations, double learningRate, int representativesPerClass, LVQ.LVQVersion lvqVersion, DecayRate learningDecay)` Creates a new LVQ instance
`LVQLLC(DistanceMetric dm, int iterations)` Creates a new LVQ-LLC instance that uses `MultivariateNormals` as the local classifier.
`LVQLLC(DistanceMetric dm, int iterations, Classifier localClasifier)` Creates a new LVQ-LLC instance
`LVQLLC(DistanceMetric dm, int iterations, Classifier localClasifier, double learningRate, int representativesPerClass)` Creates a new LVQ-LLC instance
`LVQLLC(DistanceMetric dm, int iterations, Classifier localClasifier, double learningRate, int representativesPerClass, LVQ.LVQVersion lvqVersion, DecayRate learningDecay)` Creates a new LVQ-LLC instance
`RBFNet(int numCentroids, RBFNet.Phase1Learner cl, RBFNet.Phase2Learner bl, double alpha, int p, DistanceMetric dm, Classifier baseClassifier)` Creates a new RBF Network for classification tasks.
`RBFNet(int numCentroids, RBFNet.Phase1Learner cl, RBFNet.Phase2Learner bl, double alpha, int p, DistanceMetric dm, Regressor baseRegressor)` Creates a new RBF Network for regression tasks.
`SOM(DistanceMetric dm, int somHeight, int somWeight)` Creates a new SOM using the given parameters
`SOM(DistanceMetric dm, int somHeight, int somWeight, VectorCollectionFactory<VecPaired<Vec,Integer>> vcFactory)` Creates a new SOM using the given parameters

Uses of DistanceMetric in jsat.clustering

Fields in jsat.clustering declared as DistanceMetric
Modifier and Type	Field and Description
`protected DistanceMetric`	PAM.`dm`

Methods in jsat.clustering that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	OPTICS.`getDistanceMetric()` Returns the distance metric used to compute distances in the algorithm.
`DistanceMetric`	GapStatistic.`getDistanceMetric()`
`DistanceMetric`	FLAME.`getDistanceMetric()` Returns the distance metric to use for the nearest neighbor search
`DistanceMetric`	HDBSCAN.`getDistanceMetrics()`

Methods in jsat.clustering with parameters of type DistanceMetric
Modifier and Type	Method and Description
`static void`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod)` Selects seeds from a data set to use for a clustering algorithm.
`static void`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool)` Selects seeds from a data set to use for a clustering algorithm.
`static void`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod)` Selects seeds from a data set to use for a clustering algorithm.
`static void`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int[] indices, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool)` Selects seeds from a data set to use for a clustering algorithm.
`static List<Vec>`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int k, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod)`
`static List<Vec>`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int k, DistanceMetric dm, List<Double> accelCache, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool)` Selects seeds from a data set to use for a clustering algorithm.
`static List<Vec>`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int k, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod)` Selects seeds from a data set to use for a clustering algorithm.
`static List<Vec>`	SeedSelectionMethods.`selectIntialPoints(DataSet d, int k, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection selectionMethod, ExecutorService threadpool)` Selects seeds from a data set to use for a clustering algorithm.
`void`	OPTICS.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric used to compute distances in the algorithm.
`void`	LSDBC.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric used when performing clustering.
`void`	GapStatistic.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric to use when evaluating a clustering algorithm
`void`	FLAME.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric to use for the nearest neighbor search
`void`	HDBSCAN.`setDistanceMetrics(DistanceMetric dm)` Sets the distance metric to use for determining closeness between data points

Constructors in jsat.clustering with parameters of type DistanceMetric
Constructor and Description
`CLARA(DistanceMetric dm)`
`CLARA(DistanceMetric dm, Random rand)`
`CLARA(DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)`
`CLARA(int sampleCount, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)`
`CLARA(int sampleSize, int sampleCount, DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)`
`DBSCAN(DistanceMetric dm)`
`DBSCAN(DistanceMetric dm, VectorCollectionFactory<VecPaired<Vec,Integer>> vecFactory)`
`FLAME(DistanceMetric dm, int k, int maxIterations)` Creates a new FLAME clustering object
`GapStatistic(KClusterer base, boolean PCSampling, int B, DistanceMetric dm)` Creates a new Gap clsuterer using the base clustering algorithm given.
`HDBSCAN(DistanceMetric dm, int m_pts)` Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
`HDBSCAN(DistanceMetric dm, int m_pts, int m_clSize, VectorCollectionFactory<Vec> vcf)` Creates a new HDBSCAN using the full specification of the algorithm, where two parameters may be altered.
`HDBSCAN(DistanceMetric dm, int m_pts, VectorCollectionFactory<Vec> vcf)` Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
`LSDBC(DistanceMetric dm)` Creates a new LSDBC clustering object using the given distance metric
`LSDBC(DistanceMetric dm, double alpha)` Creates a new LSDBC clustering object using the given distance metric
`LSDBC(DistanceMetric dm, double alpha, int neighbors)` Creates a new LSDBC clustering object using the given distance metric
`MeanShift(DistanceMetric dm)` Creates a new MeanShift clustering object using a `MetricKDE` and the `GaussKF`.
`OPTICS(DistanceMetric dm, int minPts)` Creates a new OPTICS cluster object.
`OPTICS(DistanceMetric dm, int minPts, double xi)` Creates a new OPTICS cluster object.
`PAM(DistanceMetric dm)`
`PAM(DistanceMetric dm, Random rand)`
`PAM(DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)`

Uses of DistanceMetric in jsat.clustering.dissimilarity

Fields in jsat.clustering.dissimilarity declared as DistanceMetric
Modifier and Type	Field and Description
`protected DistanceMetric`	DistanceMetricDissimilarity.`dm` The distance metric that will back this dissimilarity measure.

Constructors in jsat.clustering.dissimilarity with parameters of type DistanceMetric
Constructor and Description
`AverageLinkDissimilarity(DistanceMetric dm)` Creates a new AverageLinkDissimilarity
`CentroidDissimilarity(DistanceMetric dm)` Creates a new CentroidDissimilarity
`CompleteLinkDissimilarity(DistanceMetric dm)` Creates a new CompleteLinkDissimilarity
`DistanceMetricDissimilarity(DistanceMetric dm)`
`LanceWilliamsDissimilarity(DistanceMetric dm)` Creates a new LW dissimilarity measure using the given metric as the base distance between individual points.
`MedianDissimilarity(DistanceMetric dm)`
`SingleLinkDissimilarity(DistanceMetric dm)` Creates a new SingleLinkDissimilarity

Uses of DistanceMetric in jsat.clustering.evaluation

Constructors in jsat.clustering.evaluation with parameters of type DistanceMetric
Constructor and Description
`DaviesBouldinIndex(DistanceMetric dm)` Creates a new DaviesBouldinIndex

Uses of DistanceMetric in jsat.clustering.evaluation.intra

Methods in jsat.clustering.evaluation.intra that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	SumOfSqrdPairwiseDistances.`getDistanceMetric()`

Methods in jsat.clustering.evaluation.intra with parameters of type DistanceMetric
Modifier and Type	Method and Description
`void`	SumOfSqrdPairwiseDistances.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric to be used whenever this object is called to evaluate a cluster

Constructors in jsat.clustering.evaluation.intra with parameters of type DistanceMetric
Constructor and Description
`MaxDistance(DistanceMetric dm)` Creates a new MaxDistance
`MeanCentroidDistance(DistanceMetric dm)` Creates a new MeanCentroidDistance.
`MeanDistance(DistanceMetric dm)` Creates a new MeanDistance
`SoSCentroidDistance(DistanceMetric dm)` Creates a new MeanCentroidDistance.
`SumOfSqrdPairwiseDistances(DistanceMetric dm)` Creates a new cluster evaluator using the given distance metric

Uses of DistanceMetric in jsat.clustering.hierarchical

Constructors in jsat.clustering.hierarchical with parameters of type DistanceMetric
Constructor and Description
`NNChainHAC(LanceWilliamsDissimilarity distMeasure, DistanceMetric distance)` Creates a new NNChain using the given dissimilarity measure and distance metric.

Uses of DistanceMetric in jsat.clustering.kmeans

Fields in jsat.clustering.kmeans declared as DistanceMetric
Modifier and Type	Field and Description
`protected DistanceMetric`	KMeans.`dm`

Methods in jsat.clustering.kmeans that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	MiniBatchKMeans.`getDistanceMetric()` Returns the distance metric used for determining the nearest cluster center
`DistanceMetric`	KMeans.`getDistanceMetric()` Returns the distance metric in use

Methods in jsat.clustering.kmeans with parameters of type DistanceMetric
Modifier and Type	Method and Description
`void`	MiniBatchKMeans.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric used for determining the nearest cluster center

Constructors in jsat.clustering.kmeans with parameters of type DistanceMetric
Constructor and Description
`ElkanKMeans(DistanceMetric dm)` Creates a new KMeans instance
`ElkanKMeans(DistanceMetric dm, Random rand)` Creates a new KMeans instance
`ElkanKMeans(DistanceMetric dm, Random rand, SeedSelectionMethods.SeedSelection seedSelection)` Creates a new KMeans instance.
`HamerlyKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection)` Creates a new k-Means object
`HamerlyKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand)` Creates a new k-Means object
`KMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand)`
`MiniBatchKMeans(DistanceMetric dm, int batchSize, int iterations)` Creates a new Mini-Batch k-Means object that uses `k-means++` for seed selection.
`MiniBatchKMeans(DistanceMetric dm, int batchSize, int iterations, SeedSelectionMethods.SeedSelection seedSelection)` Creates a new Mini-Batch k-Means object
`NaiveKMeans(DistanceMetric dm)` Creates a new naive k-Means cluster using `k-means++` for the seed selection.
`NaiveKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection)` Creates a new naive k-Means cluster
`NaiveKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand)` Creates a new naive k-Means cluster

Uses of DistanceMetric in jsat.datatransform.featureselection

Methods in jsat.datatransform.featureselection that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	ReliefF.`getDistanceMetric()` Returns the distance metric to use

Methods in jsat.datatransform.featureselection with parameters of type DistanceMetric
Modifier and Type	Method and Description
`void`	ReliefF.`setDistanceMetric(DistanceMetric dm)` Sets the distance metric to infer the feature importance with

Constructors in jsat.datatransform.featureselection with parameters of type DistanceMetric
Constructor and Description
`ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm)` Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
`ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm, ExecutorService threadPool)` Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
`ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm, VectorCollectionFactory<Vec> vcf)` Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
`ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm, VectorCollectionFactory<Vec> vcf, ExecutorService threadPool)` Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
`ReliefF(int featureCount, int m, int n, DistanceMetric dm)` Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
`ReliefF(int featureCount, int m, int n, DistanceMetric dm, VectorCollectionFactory<Vec> vcf)` Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.

Uses of DistanceMetric in jsat.datatransform.visualization

Methods in jsat.datatransform.visualization that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	MDS.`getEmbeddingMetric()`

Methods in jsat.datatransform.visualization with parameters of type DistanceMetric
Modifier and Type	Method and Description
`protected static void`	TSNE.`computeP(DataSet d, ExecutorService ex, Random rand, int knn, int[][] nearMe, double[][] nearMePij, DistanceMetric dm, double perplexity)`
`void`	LargeViz.`setDistanceMetricEmbedding(DistanceMetric dm)` Sets the distance metric to use for the embedded space.
`void`	LargeViz.`setDistanceMetricSource(DistanceMetric dm)` Sets the distance metric to use for the original space.
`void`	MDS.`setEmbeddingMetric(DistanceMetric embedMetric)` Sets the distance metric to use when creating the initial dissimilarity matrix of a new dataset.

Uses of DistanceMetric in jsat.distributions.kernels

Fields in jsat.distributions.kernels declared as DistanceMetric
Modifier and Type	Field and Description
`protected DistanceMetric`	DistanceMetricBasedKernel.`d` the distance metric to use for the Kernel

Methods in jsat.distributions.kernels with parameters of type DistanceMetric
Modifier and Type	Method and Description
`static Distribution`	GeneralRBFKernel.`guessSigma(DataSet d, DistanceMetric dist)` Guess the distribution to use for the kernel width term `σ` in the General RBF kernel.

Constructors in jsat.distributions.kernels with parameters of type DistanceMetric
Constructor and Description
`DistanceMetricBasedKernel(DistanceMetric d)` Creates a new distance based kerenel
`GeneralRBFKernel(DistanceMetric d, double sigma)` Creates a new Generic RBF Kernel

Uses of DistanceMetric in jsat.distributions.multivariate

Methods in jsat.distributions.multivariate that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	MetricKDE.`getDistanceMetric()` Returns the distance metric that is used for density estimation

Methods in jsat.distributions.multivariate with parameters of type DistanceMetric
Modifier and Type	Method and Description
`void`	MetricKDE.`setDistanceMetric(DistanceMetric distanceMetric)` Sets the distance metric that is used for density estimation

Constructors in jsat.distributions.multivariate with parameters of type DistanceMetric
Constructor and Description
`MetricKDE(DistanceMetric distanceMetric)` Creates a new KDE object that still needs a data set to model the distribution of
`MetricKDE(DistanceMetric distanceMetric, VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)` Creates a new KDE object that still needs a data set to model the distribution of
`MetricKDE(KernelFunction kf, DistanceMetric distanceMetric)`
`MetricKDE(KernelFunction kf, DistanceMetric distanceMetric, VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)` Creates a new KDE object that still needs a data set to model the distribution of
`MetricKDE(KernelFunction kf, DistanceMetric distanceMetric, VectorCollectionFactory<VecPaired<Vec,Integer>> vcf, int defaultK, double defaultStndDev)` Creates a new KDE object that still needs a data set to model the distribution of

Uses of DistanceMetric in jsat.linear.distancemetrics

Subinterfaces of DistanceMetric in jsat.linear.distancemetrics
Modifier and Type	Interface and Description
`interface`	`DenseSparseMetric` Many algorithms require computing the distances from a small set of points to many other points.

Classes in jsat.linear.distancemetrics that implement DistanceMetric
Modifier and Type	Class and Description
`class`	`ChebyshevDistance` Chebyshev Distance is the L_∞ norm.
`class`	`CosineDistance` The Cosine Distance is a adaption of the Cosine Similarity's range from [-1, 1] into the range [0, 1].
`class`	`CosineDistanceNormalized` This distance metric returns the same cosine distance as `CosineDistance`.
`class`	`DistanceCounter` This class exists primarily as a sanity/benchmarking utility.
`class`	`EuclideanDistance` Euclidean Distance is the L₂ norm.
`class`	`KernelDistance` Creates a distance metric from a given kernel trick.
`class`	`MahalanobisDistance` The Mahalanobis Distance is a metric that takes into account the variance of the data.
`class`	`ManhattanDistance` Manhattan Distance is the L₁ norm.
`class`	`MinkowskiDistance` Minkowski Distance is the L_p norm.
`class`	`NormalizedEuclideanDistance` Implementation of the Normalized Euclidean Distance Metric.
`class`	`PearsonDistance` A valid distance metric formed from the Pearson Correlation between two vectors.
`class`	`SquaredEuclideanDistance` In many applications, the squared `EuclideanDistance` is used because it avoids an expensive `Math.sqrt(double)` operation.
`class`	`TrainableDistanceMetric` Some Distance Metrics require information that can be learned from the data set.
`class`	`WeightedEuclideanDistance` Implements the weighted Euclidean distance such that d(a, b) = ∑_{∀ i ∈ \|w\|} w_i (x_i-y_i)² When used with a weight vector of ones, it degenerates into the `EuclideanDistance`.

Methods in jsat.linear.distancemetrics that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	DistanceMetric.`clone()`

Methods in jsat.linear.distancemetrics with parameters of type DistanceMetric
Modifier and Type	Method and Description
`static void`	TrainableDistanceMetric.`trainIfNeeded(DistanceMetric dm, DataSet dataset)` Static helper method for training a distance metric only if it is needed.
`static void`	TrainableDistanceMetric.`trainIfNeeded(DistanceMetric dm, DataSet dataset, ExecutorService threadpool)` Static helper method for training a distance metric only if it is needed.
`static <V extends Vec> void`	TrainableDistanceMetric.`trainIfNeeded(DistanceMetric dm, List<V> dataset)` Static helper method for training a distance metric only if it is needed.
`static <V extends Vec> void`	TrainableDistanceMetric.`trainIfNeeded(DistanceMetric dm, List<V> dataset, ExecutorService threadpool)`

Constructors in jsat.linear.distancemetrics with parameters of type DistanceMetric
Constructor and Description
`DistanceCounter(DistanceMetric base)` Creates a new distance counter to wrap the given base metric

Uses of DistanceMetric in jsat.linear.vectorcollection

Methods in jsat.linear.vectorcollection that return DistanceMetric
Modifier and Type	Method and Description
`DistanceMetric`	VectorArray.`getDistanceMetric()`

Methods in jsat.linear.vectorcollection with parameters of type DistanceMetric
Modifier and Type	Method and Description
`VectorCollection<V>`	VPTreeMV.VPTreeMVFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	VPTree.VPTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	VectorCollectionFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)` Creates a new Vector Collection from the given source using the provided metric.
`VectorCollection<V>`	VectorArray.VectorArrayFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	RTree.RTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	RandomBallCoverOneShot.RandomBallCoverOneShotFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	RandomBallCover.RandomBallCoverFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	KDTree.KDTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	EuclideanCollection.EuclideanCollectionFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	DefaultVectorCollectionFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	CoverTree.CoverTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	VPTreeMV.VPTreeMVFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	VPTree.VPTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	VectorCollectionFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)` Creates a new Vector Collection from the given source using the provided metric.
`VectorCollection<V>`	VectorArray.VectorArrayFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	RTree.RTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	RandomBallCoverOneShot.RandomBallCoverOneShotFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	RandomBallCover.RandomBallCoverFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	KDTree.KDTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	EuclideanCollection.EuclideanCollectionFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	DefaultVectorCollectionFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`VectorCollection<V>`	CoverTree.CoverTreeFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`
`void`	VectorArray.`setDistanceMetric(DistanceMetric distanceMetric)`

Constructors in jsat.linear.vectorcollection with parameters of type DistanceMetric
Constructor and Description
`CoverTree(DistanceMetric dm)`
`CoverTree(List<V> source, DistanceMetric dm)`
`CoverTree(List<V> source, DistanceMetric dm, ExecutorService threadpool)`
`CoverTree(List<V> source, DistanceMetric dm, ExecutorService threadpool, boolean looseBounds)`
`KDTree(List<V> vecs, DistanceMetric distanceMetric)` Creates a new KDTree with the given data and methods.
`KDTree(List<V> vecs, DistanceMetric distanceMetric, KDTree.PivotSelection pvSelection)` Creates a new KDTree with the given data and methods.
`KDTree(List<V> vecs, DistanceMetric distanceMetric, KDTree.PivotSelection pvSelection, ExecutorService threadpool)` Creates a new KDTree with the given data and methods.
`RandomBallCover(DistanceMetric dm)`
`RandomBallCover(List<V> vecs, DistanceMetric dm)` Creates a new Random Ball Cover
`RandomBallCover(List<V> vecs, DistanceMetric dm, ExecutorService execServ)` Creates a new Random Ball Cover
`RandomBallCoverOneShot(List<V> vecs, DistanceMetric dm)` Creates a new one-shot version of the Random Cover Ball.
`RandomBallCoverOneShot(List<V> vecs, DistanceMetric dm, ExecutorService execServ)` Creates a new one-shot version of the Random Cover Ball.
`RandomBallCoverOneShot(List<V> vecs, DistanceMetric dm, int s)` Creates a new one-shot version of the Random Cover Ball.
`RandomBallCoverOneShot(List<V> vecs, DistanceMetric dm, int s, ExecutorService execServ)` Creates a new one-shot version of the Random Cover Ball.
`RTree(int dimensions, DistanceMetric dm)`
`RTree(int dimensions, DistanceMetric dm, int max)`
`RTree(int dimensions, DistanceMetric dm, int max, int min)`
`VectorArray(DistanceMetric distanceMetric)`
`VectorArray(DistanceMetric distanceMetric, Collection<? extends V> c)`
`VectorArray(DistanceMetric distanceMetric, int initialCapacity)`
`VPTree(DistanceMetric dm)`
`VPTree(List<V> list, DistanceMetric dm)`
`VPTree(List<V> list, DistanceMetric dm, ExecutorService threadpool)`
`VPTree(List<V> list, DistanceMetric dm, VPTree.VPSelection vpSelection)`
`VPTree(List<V> list, DistanceMetric dm, VPTree.VPSelection vpSelection, Random rand, int sampleSize, int searchIterations)`
`VPTree(List<V> list, DistanceMetric dm, VPTree.VPSelection vpSelection, Random rand, int sampleSize, int searchIterations, ExecutorService threadpool)`
`VPTreeMV(DistanceMetric dm)`
`VPTreeMV(List<V> list, DistanceMetric dm)`
`VPTreeMV(List<V> list, DistanceMetric dm, ExecutorService threadpool)`
`VPTreeMV(List<V> list, DistanceMetric dm, VPTree.VPSelection vpSelection)`
`VPTreeMV(List<V> list, DistanceMetric dm, VPTree.VPSelection vpSelection, Random rand, int sampleSize, int searchIterations)`
`VPTreeMV(List<V> list, DistanceMetric dm, VPTree.VPSelection vpSelection, Random rand, int sampleSize, int searchIterations, ExecutorService threadpool)`

Uses of DistanceMetric in jsat.linear.vectorcollection.lsh

Methods in jsat.linear.vectorcollection.lsh with parameters of type DistanceMetric
Modifier and Type	Method and Description
`VectorCollection<V>`	RandomProjectionLSH.RandomProjectionLSHFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric)`
`VectorCollection<V>`	RandomProjectionLSH.RandomProjectionLSHFactory.`getVectorCollection(List<V> source, DistanceMetric distanceMetric, ExecutorService threadpool)`

Constructors in jsat.linear.vectorcollection.lsh with parameters of type DistanceMetric
Constructor and Description
`E2LSH(List<V> vecs, double radius, double eps, int w, int k, double delta, DistanceMetric dm)` Creates a new LSH scheme for a given distance metric
`E2LSH(List<V> vecs, double radius, double eps, int w, int k, double delta, DistanceMetric dm, List<Double> distCache)` Creates a new LSH scheme for a given distance metric

Uses of DistanceMetric in jsat.parameters

Methods in jsat.parameters that return DistanceMetric
Modifier and Type	Method and Description
`abstract DistanceMetric`	MetricParameter.`getMetric()` Returns the distance metric that was used for this learner

Methods in jsat.parameters with parameters of type DistanceMetric
Modifier and Type	Method and Description
`abstract boolean`	MetricParameter.`setMetric(DistanceMetric val)` Sets the distance metric that should be sued

Uses of Interfacejsat.linear.distancemetrics.DistanceMetric

Uses of DistanceMetric in jsat.classifiers

Uses of DistanceMetric in jsat.classifiers.knn

Uses of DistanceMetric in jsat.classifiers.neuralnetwork

Uses of DistanceMetric in jsat.clustering

Uses of DistanceMetric in jsat.clustering.dissimilarity

Uses of DistanceMetric in jsat.clustering.evaluation

Uses of DistanceMetric in jsat.clustering.evaluation.intra

Uses of DistanceMetric in jsat.clustering.hierarchical

Uses of DistanceMetric in jsat.clustering.kmeans

Uses of DistanceMetric in jsat.datatransform.featureselection

Uses of DistanceMetric in jsat.datatransform.visualization

Uses of DistanceMetric in jsat.distributions.kernels

Uses of DistanceMetric in jsat.distributions.multivariate

Uses of DistanceMetric in jsat.linear.distancemetrics

Uses of DistanceMetric in jsat.linear.vectorcollection

Uses of DistanceMetric in jsat.linear.vectorcollection.lsh

Uses of DistanceMetric in jsat.parameters

Uses of Interface
jsat.linear.distancemetrics.DistanceMetric