KMeansClassifier

def != (arg0: AnyRef): Boolean

def != (arg0: Any): Boolean

def ## (): Int

def == (arg0: AnyRef): Boolean

def == (arg0: Any): Boolean

val K : Int

val N : Int

number of features

val X : M[Double]

val a : M[Int]

def asInstanceOf [T0] : T0

val assignmentLog : Seq[M[Int]]

def canEqual (arg0: Any): Boolean

def classify (observation: T): Int

def clone (): AnyRef

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

val constructor : (Seq[Double]) ⇒ T

creates a T from list of arguments of length N

val d : M[Double]

val data : Seq[T]

val distance : DistanceFunction

val distanceLog : Seq[M[Double]]

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

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

def eq (arg0: AnyRef): Boolean

def equals (arg0: Any): Boolean

def exemplar (i: Int): T

val exemplars : List[T]

val featureExtractor : (T) ⇒ Seq[Double]

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

val features : M[Double]

def finalize (): Unit

def getClass (): java.lang.Class[_]

def hashCode (): Int

def isInstanceOf [T0] : Boolean

val iterations : Int

def ne (arg0: AnyRef): Boolean

val normalizer : PCAFeatureNormalizer

def notify (): Unit

def notifyAll (): Unit

def productArity : Int

def productElement (arg0: Int): Any

def productIterator : Iterator[Any]

def productPrefix : String

def synchronized [T0] (arg0: ⇒ T0): T0

def toString (): String

def wait (): Unit

def wait (arg0: Long, arg1: Int): Unit

def wait (arg0: Long): Unit

val μ : M[Double]

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

def productElements : Iterator[Any]