Class

org.apache.spark.ml.stat.distribution

MultivariateGaussian

Related Doc: package distribution

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class MultivariateGaussian extends Serializable

This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution. In the event that the covariance matrix is singular, the density will be computed in a reduced dimensional subspace under which the distribution is supported. (see http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Degenerate_case)

Source
MultivariateGaussian.scala
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  1. MultivariateGaussian
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Instance Constructors

  1. new MultivariateGaussian(mean: Vector, cov: Matrix)

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    mean

    The mean vector of the distribution

    cov

    The covariance matrix of the distribution

Value Members

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

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  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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  6. val cov: Matrix

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    The covariance matrix of the distribution

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. def logpdf(x: Vector): Double

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    Returns the log-density of this multivariate Gaussian at given point, x

  14. val mean: Vector

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    The mean vector of the distribution

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def pdf(x: Vector): Double

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    Returns density of this multivariate Gaussian at given point, x

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

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  20. def toString(): String

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  21. final def wait(): Unit

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

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

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Inherited from Serializable

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