Multinomial

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: I): Double

   Returns the unnormalized value of the measure

   Returns the unnormalized value of the measure

   Definition Classes

   DiscreteDistr → Density

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: (I) ⇒ Boolean): Rand[I]

   Definition Classes

   Rand

10. def draw(): I

    Gets one sample from the distribution.

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

    Definition Classes

    Multinomial → Rand

11. def drawNaive(): I

12. def drawOpt(): Option[I]

    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. final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

14. def expectedValue[U](f: (I) ⇒ U)(implicit vs: VectorSpace[U, Double]): U

15. def filter(p: (I) ⇒ Boolean): Rand[I]

    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: (I) ⇒ 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: (I) ⇒ 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(): I

    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: I): Double

    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes

    DiscreteDistr → Density

23. def logProbabilityOf(x: I): Double

    Definition Classes

    DiscreteDistr

24. def map[E](f: (I) ⇒ 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

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

    Definition Classes

    AnyRef

26. final def notify(): Unit

    Definition Classes

    AnyRef

27. final def notifyAll(): Unit

    Definition Classes

    AnyRef

28. val params: T

29. def probabilityOf(e: I): Double

    Returns the probability of that draw.

    Returns the probability of that draw.

    Definition Classes

    Multinomial → DiscreteDistr

30. def sample(n: Int): IndexedSeq[I]

    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes

    Rand

31. def sample(): I

    Gets one sample from the distribution.

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

    Definition Classes

    Rand

32. def samples: Iterator[I]

    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

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

    Return a vector of samples.

    Return a vector of samples.

    Definition Classes

    Rand

34. val sum: Double

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

    Definition Classes

    AnyRef

36. def toString(): String

    Definition Classes

    Multinomial → AnyRef → Any

37. def unnormalizedLogProbabilityOf(x: I): Double

    Definition Classes

    DiscreteDistr

38. def unnormalizedProbabilityOf(e: I): Double

    Returns the probability of that draw up to a constant

    Returns the probability of that draw up to a constant

    Definition Classes

    Multinomial → DiscreteDistr

39. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

42. def withFilter(p: (I) ⇒ Boolean): Rand[I]

    Definition Classes

    Rand