case class Wishart(df: Double, scale: DenseMatrix[Double])(implicit randBasis: RandBasis) extends ContinuousDistr[DenseMatrix[Double]] with Moments[DenseMatrix[Double], DenseMatrix[Double]] with Product with Serializable
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Instance Constructors
- new Wishart(df: Double, scale: DenseMatrix[Double])(implicit randBasis: 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: DenseMatrix[Double]): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
- Definition Classes
- ContinuousDistr → Density
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
- def condition(p: (DenseMatrix[Double]) => Boolean): Rand[DenseMatrix[Double]]
- Definition Classes
- Rand
- val df: Double
- def draw(): DenseMatrix[Double]
Gets one sample from the distribution.
- def drawOpt(): Option[DenseMatrix[Double]]
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
- def entropy: Double
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def filter(p: (DenseMatrix[Double]) => Boolean): Rand[DenseMatrix[Double]]
- Definition Classes
- Rand
- def flatMap[E](f: (DenseMatrix[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: (DenseMatrix[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
- Definition Classes
- Rand
- def get(): DenseMatrix[Double]
- 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: DenseMatrix[Double]): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
- Definition Classes
- ContinuousDistr → Density
- def logNormalizer: Double
- Definition Classes
- Wishart → ContinuousDistr
- def logPdf(x: DenseMatrix[Double]): Double
- Definition Classes
- ContinuousDistr
- def map[E](f: (DenseMatrix[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.
- Definition Classes
- Rand
- def mean: DenseMatrix[Double]
- def mode: DenseMatrix[Double]
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- lazy val normalizer: Double
- Definition Classes
- ContinuousDistr
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
- def pdf(x: DenseMatrix[Double]): 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[DenseMatrix[Double]]
Gets n samples from the distribution.
Gets n samples from the distribution.
- Definition Classes
- Rand
- def sample(): DenseMatrix[Double]
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
- Definition Classes
- Rand
- def samples: Iterator[DenseMatrix[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 >: DenseMatrix[Double]](size: Int)(implicit m: ClassTag[U]): DenseVector[U]
Return a vector of samples.
Return a vector of samples.
- Definition Classes
- Rand
- val scale: DenseMatrix[Double]
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def unnormalizedLogPdf(x: DenseMatrix[Double]): Double
- Definition Classes
- Wishart → ContinuousDistr
- def unnormalizedPdf(x: DenseMatrix[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.
- Definition Classes
- ContinuousDistr
- def variance: DenseMatrix[Double]
- 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
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- def withFilter(p: (DenseMatrix[Double]) => Boolean): Rand[DenseMatrix[Double]]
- Definition Classes
- Rand