final case class MarkovChain[A](data: Data[A]) extends Product with Serializable
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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def
+(pair: (A, A)): MarkovChain[A]
Alias for
put
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def
++(markovChain: MarkovChain[A]): MarkovChain[A]
Alias for
join
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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- @throws( ... ) @native()
- val data: Data[A]
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final
def
eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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def
fromSeq(xs: Seq[A]): MarkovChain[A]
Create chain from sequence of items.
Create chain from sequence of items. Relationship will be created by making connections between each consecutive item.
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def
get(a: A): Option[A]
Get single item with highest probability value.
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final
def
getClass(): Class[_]
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- @native()
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def
getRandomSeq(atMost: Int)(implicit rand: RandLike): Vector[A]
Get sequence of elements selecting highest probability elements starting with random element.
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def
getRandomSeqWithProb(atMost: Int)(implicit rand: RandLike): Vector[A]
Get sequence of elements using probability distribution starting with random element.
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def
getSeq(a: A, atMost: Int): Vector[A]
Get sequence of elements using highest probability value.
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def
getSeqWithProb(a: A, atMost: Int)(implicit rand: RandLike): Vector[A]
Get sequence of elements using probability distribution.
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def
getTop(a: A, num: Int): List[A]
Get top N items with highest probability value.
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def
getWithProb(a: A)(implicit rand: RandLike): Option[A]
Get single item from probability distribution.
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final
def
isInstanceOf[T0]: Boolean
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def
join(that: MarkovChain[A]): MarkovChain[A]
Combine two instances of MarkovChain
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
put(a: A, b: A): MarkovChain[A]
Add single element to the chain
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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