org.apache.spark.mllib.clustering

GaussianMixtureModel

class GaussianMixtureModel extends Serializable

:: 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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@Experimental()
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Instance Constructors

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

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

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  10. def finalize(): Unit

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  11. val gaussians: Array[MultivariateGaussian]

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def k: Int

    Number of gaussians in mixture

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def predict(points: RDD[Vector]): RDD[Int]

    Maps given points to their cluster indices.

  20. def predictSoft(points: RDD[Vector]): RDD[Array[Double]]

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

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

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  22. def toString(): String

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  23. final def wait(): Unit

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

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  26. val weights: Array[Double]

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