Class

org.apache.predictionio.e2.engine

MarkovChainModel

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case class MarkovChainModel(transitionVectors: RDD[(Int, SparseVector)], n: Int) extends Product with Serializable

Markov Chain model

transitionVectors

transition vectors

n

top N used to construct the model

Linear Supertypes
Serializable, Serializable, Product, Equals, AnyRef, Any
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  1. MarkovChainModel
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Instance Constructors

  1. new MarkovChainModel(transitionVectors: RDD[(Int, SparseVector)], n: Int)

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    transitionVectors

    transition vectors

    n

    top N used to construct the model

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )

  6. final def eq(arg0: AnyRef): Boolean

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  7. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  8. final def getClass(): Class[_]

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  9. final def isInstanceOf[T0]: Boolean

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    Any

  10. val n: Int

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    top N used to construct the model

  11. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  12. final def notify(): Unit

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    AnyRef

  13. final def notifyAll(): Unit

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    AnyRef

  14. def predict(currentState: Seq[Double]): Seq[Double]

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    Calculate the probabilities of the next state

    Calculate the probabilities of the next state

    currentState

    probabilities of the current state

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

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    Definition Classes
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  16. val transitionVectors: RDD[(Int, SparseVector)]

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    transition vectors

  17. final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

  19. final def wait(arg0: Long): Unit

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Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

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