Class/Object

org.apache.spark.mllib.clustering

KMeansModel

Related Docs: object KMeansModel | package clustering

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class KMeansModel extends Saveable with Serializable with PMMLExportable

A clustering model for K-means. Each point belongs to the cluster with the closest center.

Annotations
@Since( "0.8.0" )
Source
KMeansModel.scala
Linear Supertypes
PMMLExportable, Serializable, Serializable, Saveable, AnyRef, Any
Known Subclasses
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Inherited
  1. KMeansModel
  2. PMMLExportable
  3. Serializable
  4. Serializable
  5. Saveable
  6. AnyRef
  7. Any
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Visibility
  1. Public
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Instance Constructors

  1. new KMeansModel(centers: Iterable[Vector])

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    A Java-friendly constructor that takes an Iterable of Vectors.

    A Java-friendly constructor that takes an Iterable of Vectors.

    Annotations
    @Since( "1.4.0" )
  2. new KMeansModel(clusterCenters: Array[Vector])

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    Annotations
    @Since( "1.1.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. val clusterCenters: Array[Vector]

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    Annotations
    @Since( "1.0.0" )
  7. def computeCost(data: RDD[Vector]): Double

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    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Annotations
    @Since( "0.8.0" )
  8. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. def formatVersion: String

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    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    KMeansModelSaveable
  12. final def getClass(): Class[_]

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    Definition Classes
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  13. def hashCode(): Int

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    Definition Classes
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  14. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  15. def k: Int

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    Total number of clusters.

    Total number of clusters.

    Annotations
    @Since( "0.8.0" )
  16. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  17. final def notify(): Unit

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    Definition Classes
    AnyRef
  18. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  19. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

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    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Annotations
    @Since( "1.0.0" )
  20. def predict(points: RDD[Vector]): RDD[Int]

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    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Annotations
    @Since( "1.0.0" )
  21. def predict(point: Vector): Int

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    Returns the cluster index that a given point belongs to.

    Returns the cluster index that a given point belongs to.

    Annotations
    @Since( "0.8.0" )
  22. def save(sc: SparkContext, path: String): Unit

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    Save this model to the given path.

    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.

    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.

    Definition Classes
    KMeansModelSaveable
    Annotations
    @Since( "1.4.0" )
  23. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  24. def toPMML(): String

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    :: Experimental :: Export the model to a String in PMML format

    :: Experimental :: Export the model to a String in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Experimental() @Since( "1.4.0" )
  25. def toPMML(outputStream: OutputStream): Unit

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    :: Experimental :: Export the model to the OutputStream in PMML format

    :: Experimental :: Export the model to the OutputStream in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Experimental() @Since( "1.4.0" )
  26. def toPMML(sc: SparkContext, path: String): Unit

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    :: Experimental :: Export the model to a directory on a distributed file system in PMML format

    :: Experimental :: Export the model to a directory on a distributed file system in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Experimental() @Since( "1.4.0" )
  27. def toPMML(localPath: String): Unit

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    :: Experimental :: Export the model to a local file in PMML format

    :: Experimental :: Export the model to a local file in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Experimental() @Since( "1.4.0" )
  28. def toString(): String

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    Definition Classes
    AnyRef → Any
  29. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
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    Annotations
    @throws( ... )
  31. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from PMMLExportable

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

Inherited from Any

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