Multinomial

Instance Constructors

new Multinomial(params: T)(implicit ev: (T) ⇒ QuasiTensor[I, Double], sumImpl: linalg.sum.Impl[T, Double], rand: RandBasis = Rand)

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[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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 finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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[_]

Definition Classes
AnyRef → Any
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
final def notifyAll(): Unit

Definition Classes
AnyRef
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 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(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
def withFilter(p: (I) ⇒ Boolean): Rand[I]

Definition Classes
Rand

Related Docs: object Multinomial | package distributions

case class Multinomial[T, I](params: T)(implicit ev: (T) ⇒ QuasiTensor[I, Double], sumImpl: linalg.sum.Impl[T, Double], rand: RandBasis = Rand) extends DiscreteDistr[I] with Product with Serializable

Instance Constructors

new Multinomial(params: T)(implicit ev: (T) ⇒ QuasiTensor[I, Double], sumImpl: linalg.sum.Impl[T, Double], rand: RandBasis = Rand)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply(x: I): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def condition(p: (I) ⇒ Boolean): Rand[I]

def draw(): I

def drawNaive(): I

def drawOpt(): Option[I]

final def eq(arg0: AnyRef): Boolean

def expectedValue[U](f: (I) ⇒ U)(implicit vs: VectorSpace[U, Double]): U

def filter(p: (I) ⇒ Boolean): Rand[I]

def finalize(): Unit

def flatMap[E](f: (I) ⇒ Rand[E]): Rand[E]

def foreach(f: (I) ⇒ Unit): Unit

def get(): I

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

def logApply(x: I): Double

def logProbabilityOf(x: I): Double

def map[E](f: (I) ⇒ E): Rand[E]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val params: T

def probabilityOf(e: I): Double

def sample(n: Int): IndexedSeq[I]

def sample(): I

def samples: Iterator[I]

def samplesVector[U >: I](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

val sum: Double

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def unnormalizedLogProbabilityOf(x: I): Double

def unnormalizedProbabilityOf(e: I): Double

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def withFilter(p: (I) ⇒ Boolean): Rand[I]

Inherited from Product

Inherited from Equals

Inherited from DiscreteDistr[I]

Inherited from Rand[I]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[I]

Inherited from AnyRef

Inherited from Any

Ungrouped