KMedoidsModel

Related Docs: object KMedoidsModel | package cluster

class KMedoidsModel[T] extends Serializable

Represents a K-Medoids clustering model

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Instance Constructors

1. new KMedoidsModel(medoids: Seq[T], metric: (T, T) ⇒ Double)

medoids

The collection of cluster medoids that embodies the model

metric

The metric function over data elements asumed by the model

Value Members

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

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2. final def ##(): Int

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

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4. final def asInstanceOf[T0]: T0

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5. def clone(): AnyRef

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6. def computeCost(data: RDD[T], normalized: Boolean = false): Double

Return the model cost with respect to the given data

Return the model cost with respect to the given data

Model cost is defined as the sum of closest-distances over the data elements

data

The input data to compute the cost over

normalized

If true, compute cost normalized by number of data elements. Defaults to false.

returns

The sum of closest-distances over the data elements

7. def cost(data: RDD[T], normalized: Boolean = false): Double

Return the model cost with respect to the given data

Return the model cost with respect to the given data

Model cost is defined as the sum of closest-distances over the data elements

data

The input data to compute the cost over

normalized

If true, compute cost normalized by number of data elements. Defaults to false.

returns

The sum of closest-distances over the data elements

8. lazy val distance: (T) ⇒ Double

The model distance function: maps an element to its distance to the closest medoid

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

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10. def equals(arg0: Any): Boolean

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11. def finalize(): Unit

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

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13. def hashCode(): Int

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14. final def isInstanceOf[T0]: Boolean

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15. def k: Int

The number of medoids in the model

16. val medoids: Seq[T]

The collection of cluster medoids that embodies the model

17. val metric: (T, T) ⇒ Double

The metric function over data elements asumed by the model

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

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. def predict(points: RDD[T]): RDD[Int]

Return an RDD produced by predicting the closest medoid to each row

Return an RDD produced by predicting the closest medoid to each row

points

An RDD whose rows are elements of the data space

returns

An RDD whose rows are the corresponding indices of the closest medoids

22. def predict(point: T): Int

Return the index of the medoid closest to the input

Return the index of the medoid closest to the input

point

An element of the data space

returns

The index of the medoid closest to the input

23. def predictBy[O, V](obj: O)(f: (O) ⇒ (T, V)): (Int, V)

Extracts a data object and a tag value from another data structure, and returns the index of closest cluster, paired with the tag value

Extracts a data object and a tag value from another data structure, and returns the index of closest cluster, paired with the tag value

obj

An object containing a data point and an associated tag value

f

Function to extract data point and the tag value from 'obj'

returns

A pair value (j, v) where (j) is index of closest cluster and (v) is the associated tag value

24. def predictWithDistance(point: T): (Int, Double)

Returns the index of closest cluster, paired with corresponding distance

Returns the index of closest cluster, paired with corresponding distance

point

A data object

returns

Pair (j, d) with (j) the closest cluster index and (d) the corresponding distance

25. def predictWithDistanceBy[O, V](obj: O)(f: (O) ⇒ (T, V)): (Int, Double, V)

Extracts a data object and a tag value from another data structure, and returns the index of closest cluster, with the corresponding distance and associated tag value

Extracts a data object and a tag value from another data structure, and returns the index of closest cluster, with the corresponding distance and associated tag value

obj

An object containing a data point and an associated tag value

f

Function to extract data point and tag value from 'obj'

returns

A tuple (j, d, v) where (j) is index of closest cluster, (d) is corresponding distance, and (v) is the associated tag value

26. lazy val predictor: (T) ⇒ Int

The model prediction function: maps an element to the index of the closest medoid

27. lazy val predictorWithDistance: (T) ⇒ (Int, Double)

Returns index of closest medoid, paired with its distance to that medoid

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

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29. def toString(): String

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30. final def wait(): Unit

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

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

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