Packages

class GaussianMixtureModel extends Serializable with Saveable

Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective mean and covariance for each Gaussian distribution i=1..k.

Annotations
@Since( "1.3.0" )
Source
GaussianMixtureModel.scala
Linear Supertypes
Saveable, Serializable, Serializable, AnyRef, Any
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  1. GaussianMixtureModel
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Instance Constructors

  1. new GaussianMixtureModel(weights: Array[Double], gaussians: Array[MultivariateGaussian])

    weights

    Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1

    gaussians

    Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

    Annotations
    @Since( "1.3.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[lang]
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    @throws( ... ) @native()
  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
    Attributes
    protected[lang]
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    @throws( classOf[java.lang.Throwable] )
  9. val gaussians: Array[MultivariateGaussian]
    Annotations
    @Since( "1.3.0" )
  10. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
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    @native()
  11. def hashCode(): Int
    Definition Classes
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    @native()
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. def k: Int

    Number of gaussians in mixture

    Number of gaussians in mixture

    Annotations
    @Since( "1.3.0" )
  14. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. final def notify(): Unit
    Definition Classes
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    @native()
  16. final def notifyAll(): Unit
    Definition Classes
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    @native()
  17. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

    Java-friendly version of predict()

    Java-friendly version of predict()

    Annotations
    @Since( "1.4.0" )
  18. def predict(point: Vector): Int

    Maps given point to its cluster index.

    Maps given point to its cluster index.

    Annotations
    @Since( "1.5.0" )
  19. def predict(points: RDD[Vector]): RDD[Int]

    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Annotations
    @Since( "1.3.0" )
  20. def predictSoft(point: Vector): Array[Double]

    Given the input vector, return the membership values to all mixture components.

    Given the input vector, return the membership values to all mixture components.

    Annotations
    @Since( "1.4.0" )
  21. def predictSoft(points: RDD[Vector]): RDD[Array[Double]]

    Given the input vectors, return the membership value of each vector to all mixture components.

    Given the input vectors, return the membership value of each vector to all mixture components.

    Annotations
    @Since( "1.3.0" )
  22. 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
    GaussianMixtureModel → Saveable
    Annotations
    @Since( "1.4.0" )
  23. final def synchronized[T0](arg0: ⇒ T0): T0
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  24. def toString(): String
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  25. final def wait(): Unit
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  26. final def wait(arg0: Long, arg1: Int): Unit
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  27. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  28. val weights: Array[Double]
    Annotations
    @Since( "1.3.0" )

Inherited from Saveable

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