trait DiscreteDistr[T] extends Density[T] with Rand[T]
Represents a discrete Distribution.
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- final def !=(arg0: Any): Boolean
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- def apply(x: T): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
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- DiscreteDistr → Density
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- def condition(p: (T) => Boolean): Rand[T]
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- 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
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- Rand
- final def eq(arg0: AnyRef): Boolean
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- def filter(p: (T) => Boolean): Rand[T]
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- 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.
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- 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
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- Rand
- def get(): T
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- Rand
- final def getClass(): Class[_ <: AnyRef]
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- def hashCode(): Int
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- def logApply(x: T): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
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- DiscreteDistr → Density
- def logProbabilityOf(x: T): Double
- 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.
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- Rand
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- def sample(n: Int): IndexedSeq[T]
Gets n samples from the distribution.
Gets n samples from the distribution.
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- Rand
- def sample(): T
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
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- 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.
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- Rand
- final def synchronized[T0](arg0: => T0): T0
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- def toString(): String
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- def unnormalizedLogProbabilityOf(x: T): Double
- def unnormalizedProbabilityOf(x: T): Double
Returns the probability of that draw up to a constant
- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(): Unit
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- def withFilter(p: (T) => Boolean): Rand[T]
- Definition Classes
- Rand