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Packages that use DistanceMeasure | |
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org.apache.mahout.clustering.canopy | |
org.apache.mahout.clustering.dirichlet.models | |
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.meanshift | |
org.apache.mahout.clustering.spectral.kmeans | |
org.apache.mahout.common.distance | |
org.apache.mahout.math.hadoop.similarity |
Uses of DistanceMeasure in org.apache.mahout.clustering.canopy |
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Methods in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure | |
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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 | |
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Canopy(Vector center,
int canopyId,
DistanceMeasure measure)
Create a new Canopy containing the given point and canopyId |
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CanopyClusterer(DistanceMeasure measure,
double t1,
double t2)
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Uses of DistanceMeasure in org.apache.mahout.clustering.dirichlet.models |
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Methods in org.apache.mahout.clustering.dirichlet.models that return DistanceMeasure | |
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DistanceMeasure |
DistanceMeasureClusterDistribution.getMeasure()
|
Methods in org.apache.mahout.clustering.dirichlet.models with parameters of type DistanceMeasure | |
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void |
DistanceMeasureClusterDistribution.setMeasure(DistanceMeasure measure)
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Constructors in org.apache.mahout.clustering.dirichlet.models with parameters of type DistanceMeasure | |
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DistanceMeasureClusterDistribution(VectorWritable modelPrototype,
DistanceMeasure measure)
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Uses of DistanceMeasure in org.apache.mahout.clustering.fuzzykmeans |
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Methods in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure | |
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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 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 | |
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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 |
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Methods in org.apache.mahout.clustering.iterator that return DistanceMeasure | |
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DistanceMeasure |
DistanceMeasureCluster.getMeasure()
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Methods in org.apache.mahout.clustering.iterator with parameters of type DistanceMeasure | |
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void |
DistanceMeasureCluster.setMeasure(DistanceMeasure measure)
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Constructors in org.apache.mahout.clustering.iterator with parameters of type DistanceMeasure | |
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DistanceMeasureCluster(Vector point,
int id,
DistanceMeasure measure)
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Uses of DistanceMeasure in org.apache.mahout.clustering.kmeans |
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Methods in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure | |
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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,
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.conf.Configuration conf,
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,
double clusterClassificationThreshold,
boolean runSequential)
Run the job using supplied arguments |
boolean |
Kluster.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,
double clusterClassificationThreshold,
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,
double clusterClassificationThreshold,
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.kmeans with parameters of type DistanceMeasure | |
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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.meanshift |
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Methods in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure | |
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static 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,
IKernelProfile kernelProfile,
double t1,
double t2,
double convergenceDelta,
int maxIterations,
boolean runSequential,
boolean runClustering)
Iterate over the input clusters to produce the next cluster directories for each iteration |
static List<MeanShiftCanopy> |
MeanShiftCanopyClusterer.clusterPoints(Iterable<Vector> points,
DistanceMeasure measure,
IKernelProfile aKernelProfileDerivative,
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 |
static MeanShiftCanopy |
MeanShiftCanopy.initialCanopy(Vector point,
int id,
DistanceMeasure measure)
Create an initial Canopy, retaining the original type of the given point (e.g. |
static void |
MeanShiftCanopyDriver.run(org.apache.hadoop.conf.Configuration conf,
org.apache.hadoop.fs.Path input,
org.apache.hadoop.fs.Path output,
DistanceMeasure measure,
IKernelProfile kernelProfile,
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 | |
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MeanShiftCanopy(Vector point,
int id,
DistanceMeasure measure)
Create a new Canopy containing the given point |
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MeanShiftCanopyClusterer(DistanceMeasure aMeasure,
IKernelProfile aKernelProfileDerivative,
double aT1,
double aT2,
double aDelta,
boolean runClustering)
|
Uses of DistanceMeasure in org.apache.mahout.clustering.spectral.kmeans |
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Methods in org.apache.mahout.clustering.spectral.kmeans with parameters of type DistanceMeasure | |
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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 |
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Classes in org.apache.mahout.common.distance that implement DistanceMeasure | |
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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 |
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Methods in org.apache.mahout.math.hadoop.similarity with parameters of type DistanceMeasure | |
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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)
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