breeze.stats.distributions

ContinuousDistr

trait ContinuousDistr[T] extends Density[T] with Rand[T]

Represents a continuous Distribution. Why T? just in case.

Linear Supertypes
Rand[T], Density[T], AnyRef, Any
Known Subclasses
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Inherited
  1. ContinuousDistr
  2. Rand
  3. Density
  4. AnyRef
  5. Any
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Abstract Value Members

  1. abstract def draw(): T

    Gets one sample from the distribution.

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

    Definition Classes
    Rand
  2. abstract def logNormalizer: Double

  3. abstract def unnormalizedLogPdf(x: T): Double

Concrete Value Members

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

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

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

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

    Returns the unnormalized value of the measure

    Returns the unnormalized value of the measure

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

    Definition Classes
    Any
  6. def clone(): AnyRef

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

    Definition Classes
    Rand
  8. def drawOpt(): Option[T]

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

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

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

    Definition Classes
    Rand
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. def flatMap[E](f: (T) ⇒ 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
  14. def foreach(f: (T) ⇒ 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
  15. def get(): T

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

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

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

    Definition Classes
    Any
  19. def logApply(x: T): Double

    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes
    ContinuousDistrDensity
  20. def logPdf(x: T): Double

  21. def map[E](f: (T) ⇒ 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
  22. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  23. lazy val normalizer: Double

  24. final def notify(): Unit

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

    Definition Classes
    AnyRef
  26. def pdf(x: T): Double

    Returns the probability density function at that point.

  27. def sample(n: Int): IndexedSeq[T]

    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes
    Rand
  28. def sample(): T

    Gets one sample from the distribution.

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

    Definition Classes
    Rand
  29. def samples: Iterator[T]

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

    Return a vector of samples.

    Return a vector of samples.

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

    Definition Classes
    AnyRef
  32. def toString(): String

    Definition Classes
    AnyRef → Any
  33. def unnormalizedPdf(x: T): Double

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

  34. final def wait(): Unit

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

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

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

    Definition Classes
    Rand

Inherited from Rand[T]

Inherited from Density[T]

Inherited from AnyRef

Inherited from Any

Ungrouped