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Uses of DistanceMeasure in org.apache.mahout.clustering |
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Methods in org.apache.mahout.clustering with parameters of type DistanceMeasure | |
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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)
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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 |
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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.fuzzykmeans |
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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 |
EigenSeedGenerator.buildFromEigens(org.apache.hadoop.conf.Configuration conf,
org.apache.hadoop.fs.Path input,
org.apache.hadoop.fs.Path output,
int k,
DistanceMeasure measure)
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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)
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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 | |
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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.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,
org.apache.hadoop.fs.Path tempDir,
boolean ssvd)
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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,
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 |
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Methods in org.apache.mahout.clustering.streaming.cluster that return DistanceMeasure | |
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DistanceMeasure |
StreamingKMeans.getDistanceMeasure()
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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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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)
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Uses of DistanceMeasure in org.apache.mahout.math.neighborhood |
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Fields in org.apache.mahout.math.neighborhood declared as DistanceMeasure | |
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protected DistanceMeasure |
Searcher.distanceMeasure
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Methods in org.apache.mahout.math.neighborhood that return DistanceMeasure | |
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DistanceMeasure |
Searcher.getDistanceMeasure()
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Constructors in org.apache.mahout.math.neighborhood with parameters of type DistanceMeasure | |
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BruteSearch(DistanceMeasure distanceMeasure)
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FastProjectionSearch(DistanceMeasure distanceMeasure,
int numProjections,
int searchSize)
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LocalitySensitiveHashSearch(DistanceMeasure distanceMeasure,
int searchSize)
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ProjectionSearch(DistanceMeasure distanceMeasure,
int numProjections,
int searchSize)
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Searcher(DistanceMeasure distanceMeasure)
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UpdatableSearcher(DistanceMeasure distanceMeasure)
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