breeze.stats.distributions

MultivariateGaussian

case class MultivariateGaussian(mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Moments[DenseVector[Double], DenseMatrix[Double]] with Product with Serializable

Represents a Gaussian distribution over a single real variable.

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Inherited
  1. MultivariateGaussian
  2. Product
  3. Equals
  4. Moments
  5. ContinuousDistr
  6. Rand
  7. Serializable
  8. Serializable
  9. Density
  10. AnyRef
  11. Any
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Instance Constructors

  1. new MultivariateGaussian(mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit rand: RandBasis = Rand)

Value Members

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

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

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. def apply(x: DenseVector[Double]): Double

    Returns the unnormalized value of the measure

    Returns the unnormalized value of the measure

    Definition Classes
    ContinuousDistrDensity
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def condition(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

    Definition Classes
    Rand
  10. val covariance: DenseMatrix[Double]

  11. def draw(): DenseVector[Double]

    Gets one sample from the distribution.

    Gets one sample from the distribution. Equivalent to sample()

    Definition Classes
    MultivariateGaussianRand
  12. def drawOpt(): Option[DenseVector[Double]]

    Overridden by filter/map/flatmap for monadic invocations.

    Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here

    Definition Classes
    Rand
  13. lazy val entropy: Double

    Definition Classes
    MultivariateGaussianMoments
  14. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  15. def filter(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

    Definition Classes
    Rand
  16. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  17. def flatMap[E](f: (DenseVector[Double]) ⇒ Rand[E]): Rand[E]

    Converts a random sampler of one type to a random sampler of another type.

    Converts a random sampler of one type to a random sampler of another type. Examples: randInt(10).flatMap(x => randInt(3 * x.asInstanceOf[Int]) gives a Rand[Int] in the range [0,30] Equivalently, for(x <- randInt(10); y <- randInt(30 *x)) yield y

    f

    the transform to apply to the sampled value.

    Definition Classes
    Rand
  18. def foreach(f: (DenseVector[Double]) ⇒ Unit): Unit

    Samples one element and qpplies the provided function to it.

    Samples one element and qpplies the provided function to it. Despite the name, the function is applied once. Sample usage:

     for(x <- Rand.uniform) { println(x) } 
    

    f

    the function to be applied

    Definition Classes
    Rand
  19. def get(): DenseVector[Double]

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

    Definition Classes
    AnyRef → Any
  21. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  22. def logApply(x: DenseVector[Double]): Double

    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes
    ContinuousDistrDensity
  23. lazy val logNormalizer: Double

    Definition Classes
    MultivariateGaussianContinuousDistr
  24. def logPdf(x: DenseVector[Double]): Double

    Definition Classes
    ContinuousDistr
  25. def map[E](f: (DenseVector[Double]) ⇒ E): Rand[E]

    Converts a random sampler of one type to a random sampler of another type.

    Converts a random sampler of one type to a random sampler of another type. Examples: uniform.map(_*2) gives a Rand[Double] in the range [0,2] Equivalently, for(x <- uniform) yield 2*x

    f

    the transform to apply to the sampled value.

    Definition Classes
    Rand
  26. val mean: DenseVector[Double]

    Definition Classes
    MultivariateGaussianMoments
  27. def mode: DenseVector[Double]

    Definition Classes
    MultivariateGaussianMoments
  28. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  29. lazy val normalizer: Double

    Definition Classes
    ContinuousDistr
  30. final def notify(): Unit

    Definition Classes
    AnyRef
  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  32. def pdf(x: DenseVector[Double]): Double

    Returns the probability density function at that point.

    Returns the probability density function at that point.

    Definition Classes
    ContinuousDistr
  33. def sample(n: Int): IndexedSeq[DenseVector[Double]]

    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes
    Rand
  34. def sample(): DenseVector[Double]

    Gets one sample from the distribution.

    Gets one sample from the distribution. Equivalent to get()

    Definition Classes
    Rand
  35. def samples: Iterator[DenseVector[Double]]

    An infinitely long iterator that samples repeatedly from the Rand

    An infinitely long iterator that samples repeatedly from the Rand

    returns

    an iterator that repeatedly samples

    Definition Classes
    Rand
  36. def samplesVector[U >: DenseVector[Double]](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

    Return a vector of samples.

    Return a vector of samples.

    Definition Classes
    Rand
  37. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  38. def toString(): String

    Definition Classes
    MultivariateGaussian → AnyRef → Any
  39. def unnormalizedLogPdf(t: DenseVector[Double]): Double

    Definition Classes
    MultivariateGaussianContinuousDistr
  40. def unnormalizedPdf(x: DenseVector[Double]): Double

    Returns the probability density function up to a constant at that point.

    Returns the probability density function up to a constant at that point.

    Definition Classes
    ContinuousDistr
  41. def variance: DenseMatrix[Double]

    Definition Classes
    MultivariateGaussianMoments
  42. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. def withFilter(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

    Definition Classes
    Rand

Inherited from Product

Inherited from Equals

Inherited from Rand[DenseVector[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[DenseVector[Double]]

Inherited from AnyRef

Inherited from Any

Ungrouped