Class

com.github.gradientgmm.components

MultivariateGaussian

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class MultivariateGaussian extends Serializable

Multivariate Gaussian distribution class

It is based on Spark's MultivariateGaussian and it implements much of the same functionality, but more of its internal objects, such as the covariance matrix determinant and the covariance matrix inverse, are public; many of its parameters are mutable as well.

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Known Subclasses
GConcaveGaussian, UpdatableGaussianComponent
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Inherited
  1. MultivariateGaussian
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Visibility
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Instance Constructors

  1. new MultivariateGaussian(mu: DenseVector[Double], sigma: DenseMatrix[Double])

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    mu

    Mean vector

Value Members

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

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    Definition Classes
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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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    Attributes
    protected[java.lang]
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    @throws( ... )

  6. val d: Int

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    data dimensionality

  7. def detSigma: Double

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    Returns the covariance matrix' determinant

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

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    Definition Classes
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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. final def getClass(): Class[_]

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  12. def getMu: DenseVector[Double]

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  13. def getSigma: DenseMatrix[Double]

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  14. def hashCode(): Int

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    Definition Classes
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  15. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  16. def logDetSigma: Double

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    Returns the covariance matrix' log-determinant

  17. def logpdf(x: Vector[Double]): Double

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    Returns the distribution's log-density function evaluated on x

  18. def logpdf(x: Vector): Double

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    Returns the distribution's log-density function evaluated on x

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

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    Definition Classes
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  20. final def notify(): Unit

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    Definition Classes
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  21. final def notifyAll(): Unit

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  22. def pdf(x: Vector[Double]): Double

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    Returns the distribution's density function evaluated on x

  23. def pdf(x: Vector): Double

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    Returns the distribution's density function evaluated on x

  24. var rootSigmaInv: DenseMatrix[Double]

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    square root of the covariance matrix inverse, and the density's constant term

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

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    Definition Classes
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  26. def toString(): String

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  27. var u: Double

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    square root of the covariance matrix inverse, and the density's constant term

  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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Inherited from Serializable

Inherited from Serializable

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