sand.gcs.optimize

NelderMead

trait NelderMead[CoordType] extends NonLinearOptimizer[CoordType]

This optimizer uses the NelderMead simplex method of optimization

Convenience wrapper trait around Apache's implementation of the Nelder Mead optimizer (aka Downhill Simplex or Amoeba).

The type parameter passed into the trait will need to have a typeclass implementation of sand.gcs.coordinate.DistanceComputable in scope upon instantiation.

CoordType

A type that can be used as a coordinate via sand.gcs.coordinate.DistanceComputable

Linear Supertypes
NonLinearOptimizer[CoordType], AnyRef, Any
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Abstract Value Members

  1. implicit abstract def requireDistanceComputable: DistanceComputable[CoordType]

    Require an implicit sand.gcs.coordinate.DistanceComputable of the type parameter in scope.

    Require an implicit sand.gcs.coordinate.DistanceComputable of the type parameter in scope.

    This essentially puts a context bound on CoordType, allowing for a Typeclass Pattern on a trait. We keep this a trait instead of an abstract class to use the Cake Pattern as well.

    returns

    Typeclass implementation of sand.gcs.coordinate.DistanceComputable

    Attributes
    protected
    Definition Classes
    NonLinearOptimizer

Concrete 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. lazy val distanceCalculator: DistanceComputable[CoordType]

    Attributes
    protected
    Definition Classes
    NonLinearOptimizer
  9. final def eq(arg0: AnyRef): Boolean

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

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

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

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

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

    Definition Classes
    Any
  15. def landmarkCostFunction(ids: IndexedSeq[Int])(implicit store: DistanceStore, dim: Int): ObjectiveFunction

    Measure how much an embeding of landmarks deviates from optimum

    Measure how much an embeding of landmarks deviates from optimum

    (|estimated - groundtruth| / groundtruth)^2 is the function used for the difference between one node and landmark

    ids

    The ids of landmarks to embed

    returns

    An ObjectiveFunction to be used with an optimizer

    Attributes
    protected
    Definition Classes
    NonLinearOptimizer
  16. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  17. def nodeCostFunction(distances: IndexedSeq[Double], points: IndexedSeq[CoordType]): ObjectiveFunction

    Measure how much distances from a node to landmarks deviate

    Measure how much distances from a node to landmarks deviate

    (|estimated - groundtruth| / groundtruth)^2 is the function used for the difference between one node and landmark

    distances

    The distances between a node and a landmark

    points

    The actual coordinates of the landmarks

    returns

    An ObjectiveFunction to be used with an optimizer

    Attributes
    protected
    Definition Classes
    NonLinearOptimizer
  18. final def notify(): Unit

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

    Definition Classes
    AnyRef
  20. def optimizeLandmarks(ids: IndexedSeq[Int])(implicit store: DistanceStore, dim: Int): Seq[CoordType]

    Optimize a set of landmarks against each other

    Optimize a set of landmarks against each other

    ids

    The ids of landmarks to embed

    store

    The distance store

    dim

    The dimension of the space we are working in

    returns

    A sequence of points where the distances between any two points is as close as possible to the desired distance

    Definition Classes
    NelderMeadNonLinearOptimizer
  21. def optimizeNode(neighbors: IndexedSeq[(Int, CoordType)], id: Int)(implicit store: DistanceStore, dim: Int): CoordType

    Optimize a node against a set of landmarks

    Optimize a node against a set of landmarks

    neighbors

    A sequence of pairs of ids and points

    dim

    The dimension of the space we are working in

    returns

    A point who's distance to each point is as close as possible to the distance associated with the point

    Definition Classes
    NelderMeadNonLinearOptimizer
  22. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  23. def toString(): String

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

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from NonLinearOptimizer[CoordType]

Inherited from AnyRef

Inherited from Any

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