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.  def addBinaryDistanceBandColumn(dataframe: DataFrame, threshold: Double, includeZeroDistanceNeighbors: Boolean = true, includeSelf: Boolean = false, geometry: String = null, useSpheroid: Boolean = false, savedAttributes: Seq[String] = null, resultName: String = "weights"): DataFrame

   Annotates a dataframe with a weights column for each data record containing the other members within the threshold and their weight.

   Annotates a dataframe with a weights column for each data record containing the other members within the threshold and their weight. Weights will always be 1.0. The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named 'geometry' will be used. If more than one are present and neither is named 'geometry', the column name must be provided. The new column will be named 'cluster'.

   dataframe

   DataFrame with geometry column

   threshold

   Distance threshold for considering neighbors

   includeZeroDistanceNeighbors

   whether to include neighbors that are 0 distance. If 0 distance neighbors are included and binary is false, values are infinity as per the floating point spec (divide by 0)

   includeSelf

   whether to include self in the list of neighbors

   geometry

   name of the geometry column

   useSpheroid

   whether to use a cartesian or spheroidal distance calculation. Default is false

   savedAttributes

   the attributes to save in the neighbor column. Default is all columns.

   resultName

   the name of the resulting column. Default is 'weights'.

   returns

   The input DataFrame with a weight column added containing neighbors and their weights (always 1) added to each row.

5.  def addDistanceBandColumn(dataframe: DataFrame, threshold: Double, binary: Boolean = true, alpha: Double = -1.0, includeZeroDistanceNeighbors: Boolean = false, includeSelf: Boolean = false, selfWeight: Double = 1.0, geometry: String = null, useSpheroid: Boolean = false, savedAttributes: Seq[String] = null, resultName: String = "weights"): DataFrame

   Annotates a dataframe with a weights column for each data record containing the other members within the threshold and their weight.

   Annotates a dataframe with a weights column for each data record containing the other members within the threshold and their weight. The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named 'geometry' will be used. If more than one are present and neither is named 'geometry', the column name must be provided. The new column will be named 'cluster'.

   dataframe

   DataFrame with geometry column

   threshold

   Distance threshold for considering neighbors

   binary

   whether to use binary weights or inverse distance weights for neighbors (dist^alpha)

   alpha

   alpha to use for inverse distance weights ignored when binary is true

   includeZeroDistanceNeighbors

   whether to include neighbors that are 0 distance. If 0 distance neighbors are included and binary is false, values are infinity as per the floating point spec (divide by 0)

   includeSelf

   whether to include self in the list of neighbors

   selfWeight

   the value to use for the self weight

   geometry

   name of the geometry column

   useSpheroid

   whether to use a cartesian or spheroidal distance calculation. Default is false

   savedAttributes

   the attributes to save in the neighbor column. Default is all columns.

   resultName

   the name of the resulting column. Default is 'weights'.

   returns

   The input DataFrame with a weight column added containing neighbors and their weights added to each row.

6.  def addWeightedDistanceBandColumn(dataframe: DataFrame, threshold: Double, alpha: Double = -1.0, includeZeroDistanceNeighbors: Boolean = false, includeSelf: Boolean = false, selfWeight: Double = 1.0, geometry: String = null, useSpheroid: Boolean = false, savedAttributes: Seq[String] = null, resultName: String = "weights"): DataFrame

   Annotates a dataframe with a weights column for each data record containing the other members within the threshold and their weight.

   Annotates a dataframe with a weights column for each data record containing the other members within the threshold and their weight. Weights will be dist^alpha. The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named 'geometry' will be used. If more than one are present and neither is named 'geometry', the column name must be provided. The new column will be named 'cluster'.

   dataframe

   DataFrame with geometry column

   threshold

   Distance threshold for considering neighbors

   alpha

   alpha to use for inverse distance weights. Computation is dist^alpha. Default is -1.0

   includeZeroDistanceNeighbors

   whether to include neighbors that are 0 distance. If 0 distance neighbors are included and binary is false, values are infinity as per the floating point spec (divide by 0)

   includeSelf

   whether to include self in the list of neighbors

   selfWeight

   the weight to provide for the self as its own neighbor. Default is 1.0

   geometry

   name of the geometry column

   useSpheroid

   whether to use a cartesian or spheroidal distance calculation. Default is false

   savedAttributes

   the attributes to save in the neighbor column. Default is all columns.

   resultName

   the name of the resulting column. Default is 'weights'.

   returns

   The input DataFrame with a weight column added containing neighbors and their weights (dist^alpha) added to each row.

7.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... ) @native()

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[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

12.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

13.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

14.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

15.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

16.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

17.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

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

    Definition Classes

    AnyRef

19.  def toString(): String

    Definition Classes

    AnyRef → Any

20.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

22.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()