Class/Object

breeze.stats.distributions

VariableKernelEmpiricalDistribution

Related Docs: object VariableKernelEmpiricalDistribution | package distributions

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class VariableKernelEmpiricalDistribution extends ApacheContinuousDistribution

The Weibull-distribution - ratio of two scaled chi^2 variables

Linear Supertypes
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Inherited
  1. VariableKernelEmpiricalDistribution
  2. ApacheContinuousDistribution
  3. HasInverseCdf
  4. HasCdf
  5. ContinuousDistr
  6. Rand
  7. Serializable
  8. Serializable
  9. Density
  10. AnyRef
  11. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new VariableKernelEmpiricalDistribution(data: DenseVector[Double])

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  2. new VariableKernelEmpiricalDistribution(data: Array[Double], binCount: Int = ...)

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Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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

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    Definition Classes
    AnyRef → Any
  4. def apply(x: Double): Double

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    Returns the unnormalized value of the measure

    Returns the unnormalized value of the measure

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

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    Definition Classes
    Any
  6. def cdf(x: Double): Double

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    Definition Classes
    ApacheContinuousDistributionHasCdf
  7. def clone(): AnyRef

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

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    Definition Classes
    Rand
  9. def draw(): Double

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    Gets one sample from the distribution.

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

    Definition Classes
    ApacheContinuousDistributionRand
  10. def drawMany(n: Int): Array[Double]

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    Definition Classes
    ApacheContinuousDistribution
  11. def drawOpt(): Option[Double]

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

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    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  14. def filter(p: (Double) ⇒ Boolean): Rand[Double]

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    Definition Classes
    Rand
  15. def finalize(): Unit

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

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    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
  17. def foreach(f: (Double) ⇒ Unit): Unit

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    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
  18. def get(): Double

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

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    Definition Classes
    AnyRef → Any
  20. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  21. final val inner: EmpiricalDistribution

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    Attributes
    protected
    Definition Classes
    VariableKernelEmpiricalDistributionApacheContinuousDistribution
  22. def inverseCdf(p: Double): Double

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  23. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  24. def logApply(x: Double): Double

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    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes
    ContinuousDistrDensity
  25. lazy val logNormalizer: Double

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  26. def logPdf(x: Double): Double

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

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    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
  28. def mean: Double

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    Definition Classes
    ApacheContinuousDistribution
  29. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  30. lazy val normalizer: Double

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

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

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    Definition Classes
    AnyRef
  33. def pdf(x: Double): Double

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    Returns the probability density function at that point.

    Returns the probability density function at that point.

    Definition Classes
    ApacheContinuousDistributionContinuousDistr
  34. def probability(x: Double, y: Double): Double

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    Definition Classes
    ApacheContinuousDistributionHasCdf
  35. def sample(n: Int): IndexedSeq[Double]

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    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes
    Rand
  36. def sample(): Double

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    Gets one sample from the distribution.

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

    Definition Classes
    Rand
  37. def samples: Iterator[Double]

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    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
  38. def samplesVector[U >: Double](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

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    Return a vector of samples.

    Return a vector of samples.

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

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    Definition Classes
    AnyRef
  40. def toString(): String

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    Definition Classes
    AnyRef → Any
  41. def unnormalizedLogPdf(x: Double): Double

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  42. def unnormalizedPdf(x: Double): Double

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    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
  43. def variance: Double

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    Definition Classes
    ApacheContinuousDistribution
  44. final def wait(): Unit

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

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

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

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    Definition Classes
    Rand

Inherited from HasInverseCdf

Inherited from HasCdf

Inherited from ContinuousDistr[Double]

Inherited from Rand[Double]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[Double]

Inherited from AnyRef

Inherited from Any

Ungrouped