dk.bayes.learn.lds

GenericLDSLearn

object GenericLDSLearn extends LDSMStep

Default implementation of GenericLDSMStep. It supports one dimensional linear dynamical system only, where both state and observations are 1-D real vectors.

Linear Supertypes
LDSMStep, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. GenericLDSLearn
  2. LDSMStep
  3. AnyRef
  4. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  14. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  15. def newA(sStats: IndexedSeq[DenseCanonicalGaussian]): Double

    sStats

    Sufficient statistics, marginals of transition factors (time t-1, time t)

    Definition Classes
    GenericLDSLearnLDSMStep
  16. def newC(sStats: IndexedSeq[(DenseCanonicalGaussian, Double)]): Double

    M-step

    M-step

    sStats

    Sufficient statistics. Seq of Tuple2[marginal of prior factor, observed value]

    Definition Classes
    GenericLDSLearnLDSMStep
  17. def newPi(sStats: IndexedSeq[DenseCanonicalGaussian]): Double

    sStats

    Sufficient statistics, marginals of prior factors

    Definition Classes
    GenericLDSLearnLDSMStep
  18. def newQ(sStats: IndexedSeq[DenseCanonicalGaussian]): Double

    sStats

    Sufficient statistics, marginals of transition factors (time t-1, time t)

    Definition Classes
    GenericLDSLearnLDSMStep
  19. def newR(sStats: IndexedSeq[(DenseCanonicalGaussian, Double)]): Double

    sStats

    Sufficient statistics. Seq of Tuple2[marginal of prior factor, observed value]

    Definition Classes
    GenericLDSLearnLDSMStep
  20. def newV(sStats: IndexedSeq[DenseCanonicalGaussian]): Double

    E[x2] - E[x]2

    sStats

    Sufficient statistics, marginals of prior factors

    Definition Classes
    GenericLDSLearnLDSMStep
  21. final def notify(): Unit

    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  23. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  24. def toString(): String

    Definition Classes
    AnyRef → Any
  25. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from LDSMStep

Inherited from AnyRef

Inherited from Any

Ungrouped