axle.ml.KMeansModule

KMeansClassifier

case class KMeansClassifier[T](data: Seq[T], N: Int, featureExtractor: (T) ⇒ Seq[Double], constructor: (Seq[Double]) ⇒ T, K: Int, iterations: Int)(implicit evidence$2: Eq[T], space: MetricSpace[KMeansModule.Matrix[Double], Double]) extends Classifier[T, Int] with 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, Classifier[T, Int], (T) ⇒ Int, AnyRef, Any
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  1. KMeansClassifier
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Classifier
  7. Function1
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Instance Constructors

  1. new KMeansClassifier(data: Seq[T], N: Int, featureExtractor: (T) ⇒ Seq[Double], constructor: (Seq[Double]) ⇒ T, K: Int, iterations: Int)(implicit arg0: Eq[T], space: MetricSpace[KMeansModule.Matrix[Double], Double])

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

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef → Any

  4. val K: Int

  5. val N: Int

    number of features

  6. val X: KMeansModule.Matrix[Double]

  7. val a: KMeansModule.Matrix[Int]

  8. def andThen[A](g: (Int) ⇒ A): (T) ⇒ A

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  9. def apply(observation: T): Int

    Definition Classes
    KMeansClassifier → Classifier → Function1

  10. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  11. val assignmentLog: Seq[KMeansModule.Matrix[Int]]

  12. def assignmentsAndDistances(space: MetricSpace[KMeansModule.Matrix[Double], Double], X: KMeansModule.Matrix[Double], μ: KMeansModule.Matrix[Double]): (KMeansModule.Matrix[Int], KMeansModule.Matrix[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 centroidIndexAndDistanceClosestTo(space: MetricSpace[KMeansModule.Matrix[Double], Double], μ: KMeansModule.Matrix[Double], x: KMeansModule.Matrix[Double]): (Int, Double)

    centroidIndexAndDistanceClosestTo

    centroidIndexAndDistanceClosestTo

    μ
    x

  14. def centroids(X: KMeansModule.Matrix[Double], K: Int, assignments: KMeansModule.Matrix[Int]): (KMeansModule.Matrix[Double], Seq[Int])

    centroids

    centroids

    X

    M x N scaled feature matrix

    K

    number of centroids

  15. def classes(): Range

    Definition Classes
    KMeansClassifier → Classifier

  16. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  17. def clusterLA(X: KMeansModule.Matrix[Double], space: MetricSpace[KMeansModule.Matrix[Double], Double], K: Int, iterations: Int): Seq[(KMeansModule.Matrix[Double], KMeansModule.Matrix[Int], KMeansModule.Matrix[Double])]

    clusterLA

    clusterLA

    X

    (normalized feature matrix)

    K
    iterations

  18. def compose[A](g: (A) ⇒ T): (A) ⇒ Int

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  19. def confusionMatrix[L](data: Seq[T], labelExtractor: (T) ⇒ L)(implicit arg0: Order[L]): ConfusionMatrix[T, Int, L]

    Definition Classes
    Classifier

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

    creates a T from list of arguments of length N

  21. val d: KMeansModule.Matrix[Double]

  22. val data: Seq[T]

  23. val distanceLog: Seq[KMeansModule.Matrix[Double]]

  24. def distanceLogSeries(): List[(String, TreeMap[Int, Double])]

  25. def distanceTreeMap(centroidId: Int): TreeMap[Int, Double]

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

    Definition Classes
    AnyRef

  27. def exemplar(i: Int): T

  28. val exemplars: List[T]

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

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

  30. val features: KMeansModule.Matrix[Double]

  31. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  33. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  34. val iterations: Int

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

    Definition Classes
    AnyRef

  36. val normalizer: KMeansModule.PCAFeatureNormalizer

  37. final def notify(): Unit

    Definition Classes
    AnyRef

  38. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  39. def performance(data: Seq[T], classExtractor: (T) ⇒ Int, k: Int): ClassifierPerformance[Rational]

    Definition Classes
    Classifier

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

    Definition Classes
    AnyRef

  41. def toString(): String

    Definition Classes
    Function1 → AnyRef → Any

  42. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  45. val μ: KMeansModule.Matrix[Double]

  46. val μads: Seq[(KMeansModule.Matrix[Double], KMeansModule.Matrix[Int], KMeansModule.Matrix[Double])]

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Classifier[T, Int]

Inherited from (T) ⇒ Int

Inherited from AnyRef

Inherited from Any

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