Packages

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
StreamingKMeansModel
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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])

    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])
    Annotations
    @Since( "1.1.0" )
  3. new KMeansModel(clusterCenters: Array[Vector], distanceMeasure: String, trainingCost: Double, numIter: Int)

Value Members

  1. val clusterCenters: Array[Vector]
    Annotations
    @Since( "1.0.0" )
  2. def computeCost(data: RDD[Vector]): Double

    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" )
  3. val distanceMeasure: String
    Annotations
    @Since( "2.4.0" )
  4. def k: Int

    Total number of clusters.

    Total number of clusters.

    Annotations
    @Since( "0.8.0" )
  5. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

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

    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

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

    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" )
  8. def save(sc: SparkContext, path: String): Unit

    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
    KMeansModel → Saveable
    Annotations
    @Since( "1.4.0" )
  9. def toPMML(): String

    Export the model to a String in PMML format

    Export the model to a String in PMML format

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

    Export the model to the OutputStream in PMML format

    Export the model to the OutputStream in PMML format

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

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

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

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

    Export the model to a local file in PMML format

    Export the model to a local file in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  13. val trainingCost: Double
    Annotations
    @Since( "2.4.0" )