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

Packages that use DistanceMeasure
org.apache.mahout.clustering  
org.apache.mahout.clustering.canopy   
org.apache.mahout.clustering.fuzzykmeans   
org.apache.mahout.clustering.iterator   
org.apache.mahout.clustering.kmeans This package provides an implementation of the k-means clustering algorithm. 
org.apache.mahout.clustering.spectral.kmeans   
org.apache.mahout.clustering.streaming.cluster   
org.apache.mahout.common.distance   
org.apache.mahout.math.hadoop.similarity   
org.apache.mahout.math.neighborhood   
 

Uses of DistanceMeasure in org.apache.mahout.clustering
 

Methods in org.apache.mahout.clustering with parameters of type DistanceMeasure
static double ClusteringUtils.daviesBouldinIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure, List<OnlineSummarizer> clusterDistanceSummaries)
          Computes the Davies-Bouldin Index for a given clustering.
static double ClusteringUtils.dunnIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure, List<OnlineSummarizer> clusterDistanceSummaries)
          Computes the Dunn Index of a given clustering.
static double ClusteringUtils.estimateDistanceCutoff(Iterable<? extends Vector> data, DistanceMeasure distanceMeasure, int sampleLimit)
           
static double ClusteringUtils.estimateDistanceCutoff(List<? extends Vector> data, DistanceMeasure distanceMeasure)
          Estimates the distance cutoff.
static Matrix ClusteringUtils.getConfusionMatrix(List<? extends Vector> rowCentroids, List<? extends Vector> columnCentroids, Iterable<? extends Vector> datapoints, DistanceMeasure distanceMeasure)
          Creates a confusion matrix by searching for the closest cluster of both the row clustering and column clustering of a point and adding its weight to that cell of the matrix.
static List<OnlineSummarizer> ClusteringUtils.summarizeClusterDistances(Iterable<? extends Vector> datapoints, Iterable<? extends Vector> centroids, DistanceMeasure distanceMeasure)
          Computes the summaries for the distances in each cluster.
 

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, double t3, double t4, int clusterFilter, boolean runSequential)
          Build a directory of Canopy clusters from the input vectors and other arguments.
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, int clusterFilter, boolean runSequential)
          Convenience method for backwards compatibility
 void CanopyClusterer.config(DistanceMeasure aMeasure, double aT1, double aT2)
          Configure the Canopy for unit tests
static List<Canopy> CanopyClusterer.createCanopies(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, double clusterClassificationThreshold, boolean runSequential)
          Convenience method to provide backward compatibility
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, double t3, double t4, int clusterFilter, boolean runClustering, double clusterClassificationThreshold, 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, double clusterClassificationThreshold, 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.fuzzykmeans
 

Constructors in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure
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.iterator
 

Methods in org.apache.mahout.clustering.iterator that return DistanceMeasure
 DistanceMeasure DistanceMeasureCluster.getMeasure()
           
 

Methods in org.apache.mahout.clustering.iterator with parameters of type DistanceMeasure
 void DistanceMeasureCluster.setMeasure(DistanceMeasure measure)
           
 

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

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 EigenSeedGenerator.buildFromEigens(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int k, DistanceMeasure measure)
           
static org.apache.hadoop.fs.Path RandomSeedGenerator.buildRandom(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int k, DistanceMeasure measure)
           
 boolean Kluster.computeConvergence(DistanceMeasure measure, double convergenceDelta)
          Return if the cluster is converged by comparing its center and centroid.
 

Constructors in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
Kluster(Vector center, int clusterId, DistanceMeasure measure)
          Construct a new cluster with the given point as its center
 

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, org.apache.hadoop.fs.Path tempDir, boolean ssvd)
           
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, org.apache.hadoop.fs.Path tempDir, boolean ssvd, int numReducers, int blockHeight, int oversampling, int poweriters)
          Run the Spectral KMeans clustering on the supplied arguments
 

Uses of DistanceMeasure in org.apache.mahout.clustering.streaming.cluster
 

Methods in org.apache.mahout.clustering.streaming.cluster that return DistanceMeasure
 DistanceMeasure StreamingKMeans.getDistanceMeasure()
           
 

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

Classes in org.apache.mahout.common.distance that implement DistanceMeasure
 class ChebyshevDistanceMeasure
          This class implements a "Chebyshev distance" metric by finding the maximum difference between each coordinate.
 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 MinkowskiDistanceMeasure
          Implement Minkowski distance, a real-valued generalization of the integral L(n) distances: Manhattan = L1, Euclidean = L2.
 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.
 

Uses of DistanceMeasure in org.apache.mahout.math.hadoop.similarity
 

Methods in org.apache.mahout.math.hadoop.similarity with parameters of type DistanceMeasure
static void VectorDistanceSimilarityJob.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path seeds, org.apache.hadoop.fs.Path output, DistanceMeasure measure, String outType)
           
static void VectorDistanceSimilarityJob.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path seeds, org.apache.hadoop.fs.Path output, DistanceMeasure measure, String outType, Double maxDistance)
           
 

Uses of DistanceMeasure in org.apache.mahout.math.neighborhood
 

Fields in org.apache.mahout.math.neighborhood declared as DistanceMeasure
protected  DistanceMeasure Searcher.distanceMeasure
           
 

Methods in org.apache.mahout.math.neighborhood that return DistanceMeasure
 DistanceMeasure Searcher.getDistanceMeasure()
           
 

Constructors in org.apache.mahout.math.neighborhood with parameters of type DistanceMeasure
BruteSearch(DistanceMeasure distanceMeasure)
           
FastProjectionSearch(DistanceMeasure distanceMeasure, int numProjections, int searchSize)
           
LocalitySensitiveHashSearch(DistanceMeasure distanceMeasure, int searchSize)
           
ProjectionSearch(DistanceMeasure distanceMeasure, int numProjections, int searchSize)
           
Searcher(DistanceMeasure distanceMeasure)
           
UpdatableSearcher(DistanceMeasure distanceMeasure)
           
 



Copyright © 2008–2014 The Apache Software Foundation. All rights reserved.