BisectingKMeansModel (Spark 3.5.5 JavaDoc) (original) (raw)

• Object

• • org.apache.spark.mllib.clustering.BisectingKMeansModel

• All Implemented Interfaces:
  java.io.Serializable, org.apache.spark.internal.Logging, Saveable

public class BisectingKMeansModel
extends Object
implements scala.Serializable, Saveable, org.apache.spark.internal.Logging
Clustering model produced by BisectingKMeans. The prediction is done level-by-level from the root node to a leaf node, and at each node among its children the closest to the input point is selected.
param: root the root node of the clustering tree
See Also:
Serialized Form

• • Nested Class Summary

  Nested Classes

  Modifier and Type	Class and Description
  static class	BisectingKMeansModel.SaveLoadV1_0$
  static class	BisectingKMeansModel.SaveLoadV2_0$
  static class	BisectingKMeansModel.SaveLoadV3_0$

     * ### Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging  
     `org.apache.spark.internal.Logging.SparkShellLoggingFilter`  

  • Constructor Summary

  Constructors

  Constructor and Description
  BisectingKMeansModel(org.apache.spark.mllib.clustering.ClusteringTreeNode root)

  • Method Summary

  All Methods Static Methods Instance Methods Concrete Methods

  Modifier and Type	Method and Description
  Vector[]	clusterCenters() Leaf cluster centers.
  double	computeCost(JavaRDD<Vector> data) Java-friendly version of computeCost().
  double	computeCost(RDD<Vector> data) Computes the sum of squared distances between the input points and their corresponding cluster centers.
  double	computeCost(Vector point) Computes the squared distance between the input point and the cluster center it belongs to.
  String	distanceMeasure()
  int	k()
  static BisectingKMeansModel	load(SparkContext sc, String path)
  JavaRDD	predict(JavaRDD<Vector> points) Java-friendly version of predict().
  RDD	predict(RDD<Vector> points) Predicts the indices of the clusters that the input points belong to.
  int	predict(Vector point) Predicts the index of the cluster that the input point belongs to.
  void	save(SparkContext sc, String path) Save this model to the given path.
  double	trainingCost()

     * ### Methods inherited from class Object  
     `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`  
     * ### Methods inherited from interface org.apache.spark.internal.Logging  
     `$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize`

• • Constructor Detail

     * #### BisectingKMeansModel  
     public BisectingKMeansModel(org.apache.spark.mllib.clustering.ClusteringTreeNode root)  

  • Method Detail

    * #### load  
    public static [BisectingKMeansModel](../../../../../org/apache/spark/mllib/clustering/BisectingKMeansModel.html "class in org.apache.spark.mllib.clustering") load([SparkContext](../../../../../org/apache/spark/SparkContext.html "class in org.apache.spark") sc,  
                                            String path)  
    * #### distanceMeasure  
    public String distanceMeasure()  
    * #### trainingCost  
    public double trainingCost()  
    * #### clusterCenters  
    public [Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")[] clusterCenters()  
    Leaf cluster centers.  
    Returns:  
    (undocumented)  
    * #### k  
    public int k()  
    * #### predict  
    public int predict([Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg") point)  
    Predicts the index of the cluster that the input point belongs to.  
    Parameters:  
    `point` \- (undocumented)  
    Returns:  
    (undocumented)  
    * #### predict  
    public [RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<Object> predict([RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> points)  
    Predicts the indices of the clusters that the input points belong to.  
    Parameters:  
    `points` \- (undocumented)  
    Returns:  
    (undocumented)  
    * #### predict  
    public [JavaRDD](../../../../../org/apache/spark/api/java/JavaRDD.html "class in org.apache.spark.api.java")<Integer> predict([JavaRDD](../../../../../org/apache/spark/api/java/JavaRDD.html "class in org.apache.spark.api.java")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> points)  
    Java-friendly version of `predict()`.  
    Parameters:  
    `points` \- (undocumented)  
    Returns:  
    (undocumented)  
    * #### computeCost  
    public double computeCost([Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg") point)  
    Computes the squared distance between the input point and the cluster center it belongs to.  
    Parameters:  
    `point` \- (undocumented)  
    Returns:  
    (undocumented)  
    * #### computeCost  
    public double computeCost([RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> data)  
    Computes the sum of squared distances between the input points and their corresponding cluster centers.  
    Parameters:  
    `data` \- (undocumented)  
    Returns:  
    (undocumented)  
    * #### computeCost  
    public double computeCost([JavaRDD](../../../../../org/apache/spark/api/java/JavaRDD.html "class in org.apache.spark.api.java")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> data)  
    Java-friendly version of `computeCost()`.  
    Parameters:  
    `data` \- (undocumented)  
    Returns:  
    (undocumented)  
    * #### save  
    public void save([SparkContext](../../../../../org/apache/spark/SparkContext.html "class in org.apache.spark") sc,  
                     String path)  
    Description copied from interface: `[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)`  
    Save this model to the given path.  
     This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/  
     The model may be loaded using `Loader.load`.  
    Specified by:  
    `[save](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)` in interface `[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html "interface in org.apache.spark.mllib.util")`  
    Parameters:  
    `sc` \- Spark context used to save model data.  
    `path` \- Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.