org.apache.spark.mllib.clustering

GaussianMixtureModel

class GaussianMixtureModel extends Serializable with Saveable

:: Experimental ::

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.

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@Since( "1.3.0" ) @Experimental()
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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: AnyRef): Boolean

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  11. def formatVersion: String

    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    GaussianMixtureModel → Saveable

  12. val gaussians: Array[MultivariateGaussian]

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

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

    Annotations
    @Since( "1.3.0" )

  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def k: Int

    Number of gaussians in mixture

    Number of gaussians in mixture

    Annotations
    @Since( "1.3.0" )

  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

    Java-friendly version of predict()

    Java-friendly version of predict()

    Annotations
    @Since( "1.4.0" )

  21. def predict(point: Vector): Int

    Maps given point to its cluster index.

    Maps given point to its cluster index.

    Annotations
    @Since( "1.5.0" )

  22. 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" )

  23. 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" )

  24. 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" )

  25. 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" )

  26. final def synchronized[T0](arg0: ⇒ T0): T0

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  27. def toString(): String

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  28. final def wait(): Unit

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  29. final def wait(arg0: Long, arg1: Int): Unit

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  30. final def wait(arg0: Long): Unit

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  31. val weights: Array[Double]

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

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

    Annotations
    @Since( "1.3.0" )

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