Method parameters in org.apache.mahout.clustering.fuzzykmeans with type arguments of type SoftCluster |
protected void |
FuzzyKMeansClusterer.addPointToClusters(java.util.List<SoftCluster> clusterList,
Vector point)
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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. |
Vector |
FuzzyKMeansClusterer.computePi(java.util.List<SoftCluster> clusters,
java.util.List<java.lang.Double> clusterDistanceList)
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void |
FuzzyKMeansClusterer.emitPointProbToCluster(Vector point,
java.util.List<SoftCluster> clusters,
org.apache.hadoop.mapreduce.Mapper.Context context)
Emit the point and its probability of belongingness to each cluster |
void |
FuzzyKMeansClusterer.emitPointToClusters(VectorWritable point,
java.util.List<SoftCluster> clusters,
org.apache.hadoop.mapreduce.Mapper.Context context)
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void |
FuzzyKMeansClusterer.emitPointToClusters(VectorWritable point,
java.util.List<SoftCluster> clusters,
org.apache.hadoop.io.SequenceFile.Writer writer)
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protected static boolean |
FuzzyKMeansClusterer.runFuzzyKMeansIteration(java.lang.Iterable<Vector> points,
java.util.List<SoftCluster> clusterList,
FuzzyKMeansClusterer clusterer)
Perform a single iteration over the points and clusters, assigning points to clusters and returning if
the iterations are completed. |
void |
FuzzyKMeansReducer.setup(java.util.List<SoftCluster> clusters,
org.apache.hadoop.conf.Configuration conf)
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protected boolean |
FuzzyKMeansClusterer.testConvergence(java.lang.Iterable<SoftCluster> clusters)
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