class TriangularDistribution extends ApacheContinuousDistribution with Moments[Double, Double]
The Triangular-distribution - ratio of two scaled chi^2 variables
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- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
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- def apply(x: 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 cdf(x: Double): Double
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- ApacheContinuousDistribution → HasCdf
- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
- def condition(p: (Double) => Boolean): Rand[Double]
- Definition Classes
- Rand
- def draw(): Double
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to sample()
- Definition Classes
- ApacheContinuousDistribution → Rand
- def drawMany(n: Int): Array[Double]
- Definition Classes
- ApacheContinuousDistribution
- 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
- Definition Classes
- Rand
- def entropy: Double
- Definition Classes
- TriangularDistribution → Moments
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
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- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def filter(p: (Double) => Boolean): Rand[Double]
- Definition Classes
- 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
- Definition Classes
- Rand
- def get(): Double
- Definition Classes
- Rand
- final def getClass(): Class[_ <: AnyRef]
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- def hashCode(): Int
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- AnyRef → Any
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- @native() @IntrinsicCandidate()
- final val inner: org.apache.commons.math3.distribution.TriangularDistribution
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- protected
- Definition Classes
- TriangularDistribution → ApacheContinuousDistribution
- def inverseCdf(p: Double): Double
- Definition Classes
- ApacheContinuousDistribution → HasInverseCdf
- final def isInstanceOf[T0]: Boolean
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- Any
- def logApply(x: Double): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
- Definition Classes
- ContinuousDistr → Density
- lazy val logNormalizer: Double
- Definition Classes
- ApacheContinuousDistribution → ContinuousDistr
- def logPdf(x: Double): Double
- Definition Classes
- 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.
- Definition Classes
- Rand
- def mean: Double
- Definition Classes
- ApacheContinuousDistribution
- def mode: Double
- Definition Classes
- TriangularDistribution → Moments
- final def ne(arg0: AnyRef): Boolean
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- AnyRef
- lazy val normalizer: Double
- Definition Classes
- ContinuousDistr
- final def notify(): Unit
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- final def notifyAll(): 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.
- Definition Classes
- ApacheContinuousDistribution → ContinuousDistr
- def probability(x: Double, y: Double): Double
- Definition Classes
- ApacheContinuousDistribution → HasCdf
- def sample(n: Int): IndexedSeq[Double]
Gets n samples from the distribution.
Gets n samples from the distribution.
- Definition Classes
- Rand
- def sample(): Double
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
- Definition Classes
- 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.
- Definition Classes
- Rand
- final def synchronized[T0](arg0: => T0): T0
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- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- def unnormalizedLogPdf(x: Double): Double
- Definition Classes
- ApacheContinuousDistribution → 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.
- Definition Classes
- ContinuousDistr
- def variance: Double
- Definition Classes
- ApacheContinuousDistribution
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
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- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- def withFilter(p: (Double) => Boolean): Rand[Double]
- Definition Classes
- Rand