Dirichlet

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

   ContinuousDistr → Density

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 draw(): T

   

   Returns a Multinomial distribution over the iterator

   Returns a Multinomial distribution over the iterator

   Definition Classes

   Dirichlet → Rand

9. 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

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

    

    Definition Classes

    AnyRef

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. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

18. def logApply(x: T): Double

    

    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes

    ContinuousDistr → Density

19. def logDraw(): T

    

    Returns logNormalized probabilities.

    Returns logNormalized probabilities. Use this if you're worried about underflow

20. lazy val logNormalizer: Double

    

    Definition Classes

    Dirichlet → ContinuousDistr

21. def logPdf(x: T): Double

    

    Definition Classes

    ContinuousDistr

22. 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

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

    

    Definition Classes

    AnyRef

24. lazy val normalizer: Double

    

    Definition Classes

    ContinuousDistr

25. final def notify(): Unit

    

    Definition Classes

    AnyRef

26. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

27. val params: T

    

28. def pdf(x: T): Double

    

    Returns the probability density function at that point.

    Returns the probability density function at that point.

    Definition Classes

    ContinuousDistr

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

    

    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes

    Rand

30. def sample(): T

    

    Gets one sample from the distribution.

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

    Definition Classes

    Rand

31. 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

32. 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

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

    

    Definition Classes

    AnyRef

34. def unnormalizedDraw(): T

    

    Returns unnormalized probabilities for a Multinomial distribution.

35. def unnormalizedLogPdf(m: T): Double

    

    Returns the log pdf function of the Dirichlet up to a constant evaluated at m

    Returns the log pdf function of the Dirichlet up to a constant evaluated at m

    Definition Classes

    Dirichlet → ContinuousDistr

36. def unnormalizedPdf(x: T): 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

37. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

38. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

39. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

40. def withFilter(p: (T) ⇒ Boolean): Rand[T]

    

    Definition Classes

    Rand