Kriging

abstract def createPredictorInit(numberOfPoints: Int): (Double, Double) ⇒ (Double, Double)

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def absArray(arr: Array[Double]): Array[Double]

Returns the absolute values of a given array

def apply(x: Double, y: Double): (Double, Double)

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def curried: (Double) ⇒ (Double) ⇒ (Double, Double)

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getPointDistancesSorted(points: Array[PointFeature[Double]], minPoints: Int, bandwidth: Double, point: Point): Array[(Int, Double)]

Returns the indices of points close to the point for prediction within the given bandwidth

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(pointMatrix: Array[Point]): Array[(Double, Double)]

Kriging Prediction for an Array of points

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

def toString(): String

def tupled: ((Double, Double)) ⇒ (Double, Double)

def varianceMatrixGen(sv: Semivariogram, points: Array[PointFeature[Double]]): RealMatrix

final def wait(): Unit

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

final def wait(arg0: Long): Unit