KMedoidsModel

Instance Constructors

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

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
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
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
lazy val distance: (T) ⇒ Double

The model distance function: maps an element to its distance to the closest medoid
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

Attributes
protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

Definition Classes
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def k: Int

The number of medoids in the model
val medoids: Seq[T]

The collection of cluster medoids that embodies the model
val metric: (T, T) ⇒ Double

The metric function over data elements asumed by the model
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
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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
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
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
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
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
lazy val predictor: (T) ⇒ Int

The model prediction function: maps an element to the index of the closest medoid
lazy val predictorWithDistance: (T) ⇒ (Int, Double)

Returns index of closest medoid, paired with its distance to that medoid
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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@throws( ... )
final def wait(arg0: Long): Unit

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@throws( ... )

Related Docs: object KMedoidsModel | package cluster

class KMedoidsModel[T] extends Serializable

Instance Constructors

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

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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

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

lazy val distance: (T) ⇒ Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def k: Int

val medoids: Seq[T]

val metric: (T, T) ⇒ Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def predict(point: T): Int

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

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

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

lazy val predictor: (T) ⇒ Int

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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