case class VonMises(mu: Double, k: Double)(implicit rand: RandBasis) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
Represents a Von Mises distribution, which is a distribution over angles.
- mu
is the mean of the distribution, ~ gaussian mean
- k
is the concentration, which is like 1/gaussian variance
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- def apply(x: Double): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
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- def condition(p: (Double) => Boolean): Rand[Double]
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- Rand
- def draw(): Double
Gets one sample from the distribution.
- def drawOpt(): Option[Double]
Overridden by filter/map/flatmap for monadic invocations.
Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here
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- Rand
- def entropy: Double
- final def eq(arg0: AnyRef): Boolean
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- def filter(p: (Double) => Boolean): Rand[Double]
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- Rand
- def flatMap[E](f: (Double) => 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: (Double) => 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
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- Rand
- def get(): Double
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- Rand
- final def getClass(): Class[_ <: AnyRef]
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- val k: Double
- def logApply(x: Double): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
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- ContinuousDistr → Density
- lazy val logNormalizer: Double
- Definition Classes
- VonMises → ContinuousDistr
- def logPdf(x: Double): Double
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- ContinuousDistr
- def map[E](f: (Double) => 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.
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- Rand
- def mean: Double
- def mode: Double
- val mu: Double
- final def ne(arg0: AnyRef): Boolean
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- lazy val normalizer: Double
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- final def notify(): Unit
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- def pdf(x: Double): Double
Returns the probability density function at that point.
Returns the probability density function at that point.
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- ContinuousDistr
- def productElementNames: Iterator[String]
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- Product
- def sample(n: Int): IndexedSeq[Double]
Gets n samples from the distribution.
Gets n samples from the distribution.
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- Rand
- def sample(): Double
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
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- Rand
- def samples: Iterator[Double]
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 >: Double](size: Int)(implicit m: ClassTag[U]): DenseVector[U]
Return a vector of samples.
Return a vector of samples.
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- Rand
- final def synchronized[T0](arg0: => T0): T0
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- lazy val toString: String
- def unnormalizedLogPdf(theta: Double): Double
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- VonMises → ContinuousDistr
- def unnormalizedPdf(x: Double): 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.
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- ContinuousDistr
- def variance: Double
- final def wait(arg0: Long, arg1: Int): Unit
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- def withFilter(p: (Double) => Boolean): Rand[Double]
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- Rand