axle.ml.KMeans

KMeansClassifier

case class KMeansClassifier [T] (data: Seq[T], N: Int, featureExtractor: (T) ⇒ Seq[Double], constructor: (Seq[Double]) ⇒ T, distance: DistanceFunction, K: Int, iterations: Int) extends Product with Serializable

KMeansClassifier[T]

T

type of the objects being classified

N

number of features

featureExtractor

creates a list of features (Doubles) of length N given a T

constructor

creates a T from list of arguments of length N

Linear Supertypes
Serializable, Serializable, Product, Equals, AnyRef, Any
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  1. KMeansClassifier
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. AnyRef
  7. Any
Visibility
  1. Public
  2. All

Instance Constructors

  1. new KMeansClassifier (data: Seq[T], N: Int, featureExtractor: (T) ⇒ Seq[Double], constructor: (Seq[Double]) ⇒ T, distance: DistanceFunction, K: Int, iterations: Int)

    N

    number of features

    featureExtractor

    creates a list of features (Doubles) of length N given a T

    constructor

    creates a T from list of arguments of length N

Value Members

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

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

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

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

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

    Attributes
    final
    Definition Classes
    Any
  6. val K : Int

  7. val N : Int

    number of features

  8. val X : M[Double]

  9. val a : M[Int]

  10. def asInstanceOf [T0] : T0

    Attributes
    final
    Definition Classes
    Any
  11. val assignmentLog : Seq[M[Int]]

  12. def assignmentsAndDistances (distance: DistanceFunction, X: M[Double], μ: M[Double]): (M[Int], M[Double])

    assignmentsAndDistances

    assignmentsAndDistances

    X
    μ

    Returns: N x 1 matrix: indexes of centroids closest to xi N x 1 matrix: distances to those centroids

  13. def canEqual (arg0: Any): Boolean

    Definition Classes
    KMeansClassifier → Equals
  14. def centroidIndexAndDistanceClosestTo (distance: DistanceFunction, μ: M[Double], x: M[Double]): (Int, Double)

    centroidIndexAndDistanceClosestTo

    centroidIndexAndDistanceClosestTo

    μ
    x

  15. def centroids (X: M[Double], K: Int, assignments: M[Int]): (M[Double], Seq[Int])

    centroids

    centroids

    X

    M x N scaled feature matrix

    K

    number of centroids

  16. def classify (observation: T): Int

  17. def clone (): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  18. def clusterLA (X: M[Double], distance: DistanceFunction, K: Int, iterations: Int): Seq[(M[Double], M[Int], M[Double])]

    clusterLA

    clusterLA

    X

    (normalized feature matrix)

    distance
    K
    iterations

  19. def confusionMatrix [L] (data: Seq[T], labelExtractor: (T) ⇒ L): ConfusionMatrix[T, L]

  20. val constructor : (Seq[Double]) ⇒ T

    creates a T from list of arguments of length N

  21. val d : M[Double]

  22. val data : Seq[T]

  23. val distance : DistanceFunction

  24. val distanceLog : Seq[M[Double]]

  25. def distanceLogSeries (): Seq[(String, SortedMap[Int, Double])]

  26. def distanceTreeMap (centroidId: Int): SortedMap[Int, Double]

  27. def eq (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef
  28. def equals (arg0: Any): Boolean

    Definition Classes
    KMeansClassifier → Equals → AnyRef → Any
  29. def exemplar (i: Int): T

  30. val exemplars : List[T]

  31. val featureExtractor : (T) ⇒ Seq[Double]

    creates a list of features (Doubles) of length N given a T

  32. val features : M[Double]

  33. def finalize (): Unit

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

    Attributes
    final
    Definition Classes
    AnyRef → Any
  35. def hashCode (): Int

    Definition Classes
    KMeansClassifier → AnyRef → Any
  36. def isInstanceOf [T0] : Boolean

    Attributes
    final
    Definition Classes
    Any
  37. val iterations : Int

  38. def ne (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef
  39. val normalizer : PCAFeatureNormalizer

  40. def notify (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
  41. def notifyAll (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
  42. def productArity : Int

    Definition Classes
    KMeansClassifier → Product
  43. def productElement (arg0: Int): Any

    Definition Classes
    KMeansClassifier → Product
  44. def productIterator : Iterator[Any]

    Definition Classes
    Product
  45. def productPrefix : String

    Definition Classes
    KMeansClassifier → Product
  46. def synchronized [T0] (arg0: ⇒ T0): T0

    Attributes
    final
    Definition Classes
    AnyRef
  47. def toString (): String

    Definition Classes
    KMeansClassifier → AnyRef → Any
  48. def wait (): Unit

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

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

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()
  51. val μ : M[Double]

  52. val μads : Seq[(M[Double], M[Int], M[Double])]

Deprecated Value Members

  1. def productElements : Iterator[Any]

    Definition Classes
    Product
    Annotations
    @deprecated
    Deprecated

    use productIterator instead

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any