com.cra.figaro.algorithm.filtering

ParticleFilter

object ParticleFilter

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

Type Members

  1. type BeliefState = Array[State]

    A representation of the current beliefs of the particle filter.

    A representation of the current beliefs of the particle filter. A BeliefState should not be confused with a State, which is a particular configuration of the system. A BeliefState represents a distribution over States, and in a particle filter, it is implemented as a collection of representative States.

  2. type WeightedParticle = (Double, State)

    Weighted particles, consisting of a weight and a state.

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. def apply(initial: Universe, transition: (Universe) ⇒ Universe, numParticles: Int): OneTimeParticleFilter

    A one-time particle filter in which the static universe is empty.

    A one-time particle filter in which the static universe is empty.

    initial

    The universe describing the distribution over the initial state of the system

    transition

    The transition model describing how the current state of the system depends on the previous

    numParticles

    Number of particles to use at each time step

  7. def apply(static: Universe, initial: Universe, transition: (Universe) ⇒ Universe, numParticles: Int): OneTimeParticleFilter

    A one-time particle filter.

    A one-time particle filter.

    static

    The universe of elements whose values do not change over time

    initial

    The universe describing the distribution over the initial state of the system

    transition

    The transition model describing how the current state of the system depends on the previous

    numParticles

    Number of particles to use at each time step

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def clone(): AnyRef

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

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

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

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

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

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

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

    Definition Classes
    AnyRef
  17. final def notify(): Unit

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

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

    Definition Classes
    AnyRef
  20. def toString(): String

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

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped