Uses of Interface
org.apache.mahout.common.distance.DistanceMeasure

Packages that use DistanceMeasure
org.apache.mahout.clustering This package provides several clustering algorithm implementations. 
org.apache.mahout.clustering.canopy   
org.apache.mahout.clustering.dirichlet.models   
org.apache.mahout.clustering.fuzzykmeans   
org.apache.mahout.clustering.kmeans This package provides an implementation of the k-means clustering algorithm. 
org.apache.mahout.clustering.meanshift   
org.apache.mahout.clustering.spectral.kmeans   
org.apache.mahout.common.distance   
 

Uses of DistanceMeasure in org.apache.mahout.clustering
 

Fields in org.apache.mahout.clustering declared as DistanceMeasure
protected  DistanceMeasure DistanceMeasureCluster.measure
           
 

Methods in org.apache.mahout.clustering that return DistanceMeasure
 DistanceMeasure JsonDistanceMeasureAdapter.deserialize(com.google.gson.JsonElement json, java.lang.reflect.Type typeOfT, com.google.gson.JsonDeserializationContext context)
           
 DistanceMeasure DistanceMeasureCluster.getMeasure()
           
 

Methods in org.apache.mahout.clustering with parameters of type DistanceMeasure
 com.google.gson.JsonElement JsonDistanceMeasureAdapter.serialize(DistanceMeasure src, java.lang.reflect.Type typeOfSrc, com.google.gson.JsonSerializationContext context)
           
 void DistanceMeasureCluster.setMeasure(DistanceMeasure measure)
           
 

Constructors in org.apache.mahout.clustering with parameters of type DistanceMeasure
DistanceMeasureCluster(Vector point, int id, DistanceMeasure measure)
           
 

Uses of DistanceMeasure in org.apache.mahout.clustering.canopy
 

Methods in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure
static org.apache.hadoop.fs.Path CanopyDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runSequential)
          Build a directory of Canopy clusters from the input vectors and other arguments.
static void CanopyDriver.clusterData(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path points, org.apache.hadoop.fs.Path canopies, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runSequential)
           
 void CanopyClusterer.config(DistanceMeasure aMeasure, double aT1, double aT2)
          Configure the Canopy for unit tests
static java.util.List<Canopy> CanopyClusterer.createCanopies(java.util.List<Vector> points, DistanceMeasure measure, double t1, double t2)
          Iterate through the points, adding new canopies.
static void CanopyDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runClustering, boolean runSequential)
          Build a directory of Canopy clusters from the input arguments and, if requested, cluster the input vectors using these clusters
static void CanopyDriver.run(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runClustering, boolean runSequential)
          Convenience method creates new Configuration() Build a directory of Canopy clusters from the input arguments and, if requested, cluster the input vectors using these clusters
 

Constructors in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure
Canopy(Vector center, int canopyId, DistanceMeasure measure)
          Create a new Canopy containing the given point and canopyId
CanopyClusterer(DistanceMeasure measure, double t1, double t2)
           
 

Uses of DistanceMeasure in org.apache.mahout.clustering.dirichlet.models
 

Methods in org.apache.mahout.clustering.dirichlet.models that return DistanceMeasure
 DistanceMeasure DistanceMeasureClusterDistribution.getMeasure()
           
 

Methods in org.apache.mahout.clustering.dirichlet.models with parameters of type DistanceMeasure
 void DistanceMeasureClusterDistribution.setMeasure(DistanceMeasure measure)
           
 

Constructors in org.apache.mahout.clustering.dirichlet.models with parameters of type DistanceMeasure
DistanceMeasureClusterDistribution(VectorWritable modelPrototype, DistanceMeasure measure)
           
 

Uses of DistanceMeasure in org.apache.mahout.clustering.fuzzykmeans
 

Methods in org.apache.mahout.clustering.fuzzykmeans that return DistanceMeasure
 DistanceMeasure FuzzyKMeansClusterer.getMeasure()
           
 

Methods in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure
static org.apache.hadoop.fs.Path FuzzyKMeansDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, float m, boolean runSequential)
          Iterate over the input vectors to produce cluster directories for each iteration
static void FuzzyKMeansDriver.clusterData(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, float m, boolean emitMostLikely, double threshold, boolean runSequential)
          Run the job using supplied arguments
static java.util.List<java.util.List<SoftCluster>> FuzzyKMeansClusterer.clusterPoints(java.lang.Iterable<Vector> points, java.util.List<SoftCluster> clusters, DistanceMeasure measure, double threshold, double m, int numIter)
          This is the reference k-means implementation.
static void FuzzyKMeansDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, float m, boolean runClustering, boolean emitMostLikely, double threshold, boolean runSequential)
          Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.
static void FuzzyKMeansDriver.run(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, float m, boolean runClustering, boolean emitMostLikely, double threshold, boolean runSequential)
          Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.
 

Constructors in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure
FuzzyKMeansClusterer(DistanceMeasure measure, double convergenceDelta, double m)
          Init the fuzzy k-means clusterer with the distance measure to use for comparison.
SoftCluster(Vector center, int clusterId, DistanceMeasure measure)
          Construct a new SoftCluster with the given point as its center
 

Uses of DistanceMeasure in org.apache.mahout.clustering.kmeans
 

Methods in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
static org.apache.hadoop.fs.Path KMeansDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, int maxIterations, java.lang.String delta, boolean runSequential)
          Iterate over the input vectors to produce cluster directories for each iteration
static org.apache.hadoop.fs.Path RandomSeedGenerator.buildRandom(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int k, DistanceMeasure measure)
           
static void KMeansDriver.clusterData(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, java.lang.String convergenceDelta, boolean runSequential)
          Run the job using supplied arguments
static java.util.List<java.util.List<Cluster>> KMeansClusterer.clusterPoints(java.lang.Iterable<Vector> points, java.util.List<Cluster> clusters, DistanceMeasure measure, int maxIter, double distanceThreshold)
          This is the reference k-means implementation.
 boolean Cluster.computeConvergence(DistanceMeasure measure, double convergenceDelta)
          Return if the cluster is converged by comparing its center and centroid.
static void KMeansDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, boolean runClustering, boolean runSequential)
          Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.
static void KMeansDriver.run(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, boolean runClustering, boolean runSequential)
          Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.
protected static boolean KMeansClusterer.runKMeansIteration(java.lang.Iterable<Vector> points, java.lang.Iterable<Cluster> clusters, DistanceMeasure measure, double distanceThreshold)
          Perform a single iteration over the points and clusters, assigning points to clusters and returning if the iterations are completed.
 void KMeansReducer.setup(java.util.List<Cluster> clusters, DistanceMeasure measure)
           
 

Constructors in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
Cluster(Vector center, int clusterId, DistanceMeasure measure)
          Construct a new cluster with the given point as its center
KMeansClusterer(DistanceMeasure measure)
          Init the k-means clusterer with the distance measure to use for comparison.
 

Uses of DistanceMeasure in org.apache.mahout.clustering.meanshift
 

Methods in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure
 org.apache.hadoop.fs.Path MeanShiftCanopyDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, double convergenceDelta, int maxIterations, boolean runSequential)
          Iterate over the input clusters to produce the next cluster directories for each iteration
static java.util.List<MeanShiftCanopy> MeanShiftCanopyClusterer.clusterPoints(java.lang.Iterable<Vector> points, DistanceMeasure measure, double convergenceThreshold, double t1, double t2, int numIter)
          This is the reference mean-shift implementation.
static void MeanShiftCanopyDriver.createCanopyFromVectors(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, boolean runSequential)
          Convert input vectors to MeanShiftCanopies for further processing
 void MeanShiftCanopyDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, double convergenceDelta, int maxIterations, boolean inputIsCanopies, boolean runClustering, boolean runSequential)
          Run the job where the input format can be either Vectors or Canopies.
 

Constructors in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure
MeanShiftCanopy(Vector point, int id, DistanceMeasure measure)
          Create a new Canopy containing the given point
MeanShiftCanopyClusterer(DistanceMeasure aMeasure, double aT1, double aT2, double aDelta)
           
 

Uses of DistanceMeasure in org.apache.mahout.clustering.spectral.kmeans
 

Methods in org.apache.mahout.clustering.spectral.kmeans with parameters of type DistanceMeasure
static void SpectralKMeansDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int numDims, int clusters, DistanceMeasure measure, double convergenceDelta, int maxIterations)
          Run the Spectral KMeans clustering on the supplied arguments
 

Uses of DistanceMeasure in org.apache.mahout.common.distance
 

Classes in org.apache.mahout.common.distance that implement DistanceMeasure
 class CosineDistanceMeasure
          This class implements a cosine distance metric by dividing the dot product of two vectors by the product of their lengths
 class EuclideanDistanceMeasure
          This class implements a Euclidean distance metric by summing the square root of the squared differences between each coordinate.
 class MahalanobisDistanceMeasure
           
 class ManhattanDistanceMeasure
          This class implements a "manhattan distance" metric by summing the absolute values of the difference between each coordinate
 class SquaredEuclideanDistanceMeasure
          Like EuclideanDistanceMeasure but it does not take the square root.
 class TanimotoDistanceMeasure
          Tanimoto coefficient implementation.
 class WeightedDistanceMeasure
          Abstract implementation of DistanceMeasure with support for weights.
 class WeightedEuclideanDistanceMeasure
          This class implements a Euclidean distance metric by summing the square root of the squared differences between each coordinate, optionally adding weights.
 class WeightedManhattanDistanceMeasure
          This class implements a "Manhattan distance" metric by summing the absolute values of the difference between each coordinate, optionally with weights.
 



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