case class Multinomial[T, I](params: T)(implicit ev: ConversionOrSubtype[T, QuasiTensor[I, Double]], sumImpl: linalg.sum.Impl[T, Double], rand: RandBasis) extends DiscreteDistr[I] with Product with Serializable
Represents a Multinomial distribution over elements. You can make a distribution over any breeze.linalg.QuasiTensor, which includes DenseVectors and Counters.
TODO: I should probably rename this to Discrete or something, since it only handles one draw.
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Instance Constructors
- new Multinomial(params: T)(implicit ev: ConversionOrSubtype[T, QuasiTensor[I, Double]], sumImpl: linalg.sum.Impl[T, 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: I): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
- Definition Classes
- DiscreteDistr → Density
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
- def condition(p: (I) => Boolean): Rand[I]
- Definition Classes
- Rand
- def draw(): I
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to sample()
- Definition Classes
- Multinomial → Rand
- def drawNaive(): I
- def drawOpt(): Option[I]
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 expectedValue[U](f: (I) => U)(implicit vs: VectorSpace[U, Double]): U
- def filter(p: (I) => Boolean): Rand[I]
- Definition Classes
- Rand
- def flatMap[E](f: (I) => 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: (I) => 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(): I
- 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: I): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
- Definition Classes
- DiscreteDistr → Density
- def logProbabilityOf(x: I): Double
- Definition Classes
- DiscreteDistr
- def map[E](f: (I) => 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
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
- val params: T
- def probabilityOf(e: I): Double
Returns the probability of that draw.
Returns the probability of that draw.
- Definition Classes
- Multinomial → DiscreteDistr
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- def sample(n: Int): IndexedSeq[I]
Gets n samples from the distribution.
Gets n samples from the distribution.
- Definition Classes
- Rand
- def sample(): I
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
- Definition Classes
- Rand
- def samples: Iterator[I]
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 >: I](size: Int)(implicit m: ClassTag[U]): DenseVector[U]
Return a vector of samples.
Return a vector of samples.
- Definition Classes
- Rand
- val sum: Double
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- Multinomial → AnyRef → Any
- def unnormalizedLogProbabilityOf(x: I): Double
- Definition Classes
- DiscreteDistr
- def unnormalizedProbabilityOf(e: I): Double
Returns the probability of that draw up to a constant
Returns the probability of that draw up to a constant
- Definition Classes
- Multinomial → DiscreteDistr
- 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: (I) => Boolean): Rand[I]
- Definition Classes
- Rand