case class Dirichlet[T, I](params: T)(implicit space: EnumeratedCoordinateField[T, I, Double], rand: RandBasis) extends ContinuousDistr[T] with Product with Serializable
Represents a Dirichlet distribution, the conjugate prior to the multinomial.
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Instance Constructors
- new Dirichlet(params: T)(implicit space: EnumeratedCoordinateField[T, I, Double], rand: RandBasis)
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- def apply(x: T): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
- Definition Classes
- ContinuousDistr → Density
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
- def condition(p: (T) => Boolean): Rand[T]
- Definition Classes
- Rand
- def draw(): T
Returns a Multinomial distribution over the iterator
- 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
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def filter(p: (T) => Boolean): Rand[T]
- Definition Classes
- Rand
- 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
- 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
- def get(): T
- Definition Classes
- Rand
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @IntrinsicCandidate()
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- 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
- def logDraw(): T
Returns logNormalized probabilities.
Returns logNormalized probabilities. Use this if you're worried about underflow
- lazy val logNormalizer: Double
- Definition Classes
- Dirichlet → ContinuousDistr
- def logPdf(x: T): Double
- Definition Classes
- ContinuousDistr
- 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
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- lazy val normalizer: Double
- Definition Classes
- ContinuousDistr
- final def notify(): Unit
- Definition Classes
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- Annotations
- @native() @IntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
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- Annotations
- @native() @IntrinsicCandidate()
- val params: T
- def pdf(x: T): Double
Returns the probability density function at that point.
Returns the probability density function at that point.
- Definition Classes
- ContinuousDistr
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- def sample(n: Int): IndexedSeq[T]
Gets n samples from the distribution.
Gets n samples from the distribution.
- Definition Classes
- Rand
- def sample(): T
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
- Definition Classes
- Rand
- 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
- 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
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def unnormalizedDraw(): T
Returns unnormalized probabilities for a Multinomial distribution.
- 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
- 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
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
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- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- def withFilter(p: (T) => Boolean): Rand[T]
- Definition Classes
- Rand