Class

org.apache.spark.mllib.stat.distribution

MultivariateGaussian

Related Doc: package distribution

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class MultivariateGaussian extends Serializable

:: DeveloperApi :: 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 Degenerate case in Multivariate normal distribution (Wikipedia))

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@Since( "1.3.0" ) @DeveloperApi()
Source
MultivariateGaussian.scala
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Instance Constructors

  1. new MultivariateGaussian(mu: Vector, sigma: Matrix)

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    mu

    The mean vector of the distribution

    sigma

    The covariance matrix of the distribution

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    @Since( "1.3.0" )

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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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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  12. def logpdf(x: Vector): Double

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

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

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    @Since( "1.3.0" )
  13. val mu: Vector

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    The mean vector of the distribution

    The mean vector of the distribution

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    @Since( "1.3.0" )
  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def pdf(x: Vector): Double

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    Returns density of this multivariate Gaussian at given point, x

    Returns density of this multivariate Gaussian at given point, x

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    @Since( "1.3.0" )
  18. val sigma: Matrix

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    The covariance matrix of the distribution

    The covariance matrix of the distribution

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    @Since( "1.3.0" )
  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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  23. final def wait(arg0: Long): Unit

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