KMeansModel

A model representing results of the K-Means clustering algorithm. Each cluster is a vector in a fixed dimensional space, and the transformer assigns the input row to its closest cluster using the Euclidean (L2) distance.

The clusters should all be of the same dimension, which should be equal to the number of input features.

clusters: The clusters of the model. These should have distinct names.
inputFeatures: A seq of numeric feature descriptions describing the input to the model.
identifier

Linear Supertypes

Product, Equals, ClusteringRowModel, CategoricalRowModel, RowModel, MLModel, Serializable, Serializable, AnyRef, Any

Instance Constructors

new KMeansModel(clusters: Seq[ClusterInfo], inputFeatures: Seq[FeatureDesc[_ <: Number]], identifier: String = "")

clusters
The clusters of the model. These should have distinct names.
inputFeatures
A seq of numeric feature descriptions describing the input to the model.
identifier

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def classLabels: Seq[String]

The total set of classes that the predicted value can take on.
The total set of classes that the predicted value can take on.
returns
The set of possible values for the predicted value.

Definition Classes
KMeansModel → CategoricalRowModel
def classesForLoading: Set[Class[_]]

This should return a representative class from every jar that is needed to load the object during deserialization.
This should return a representative class from every jar that is needed to load the object during deserialization.
Default implementation returns the class of MLModel, and the class of the model implementation.

Definition Classes
MLModel
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val clusters: Seq[ClusterInfo]

The clusters of the model.
The clusters of the model. These should have distinct names.
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

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

Definition Classes
AnyRef → Any
val identifier: String

Definition Classes
KMeansModel → RowModel
val inputFeatures: Seq[FeatureDesc[_ <: Number]]

A seq of numeric feature descriptions describing the input to the model.
A seq of numeric feature descriptions describing the input to the model.

Definition Classes
KMeansModel → RowModel
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def outputFeatures: Seq[FeatureDesc[_]]

Definition Classes
ClusteringRowModel → RowModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def transformationSchema: DetailedTransformationSchema

Definition Classes
RowModel
def transformer: KMeansTransformer

Definition Classes
KMeansModel → ClusteringRowModel → CategoricalRowModel → RowModel
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

case class KMeansModel(clusters: Seq[ClusterInfo], inputFeatures: Seq[FeatureDesc[_ <: Number]], identifier: String = "") extends ClusteringRowModel with Product with Serializable

Instance Constructors

new KMeansModel(clusters: Seq[ClusterInfo], inputFeatures: Seq[FeatureDesc[_ <: Number]], identifier: String = "")

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def classLabels: Seq[String]

def classesForLoading: Set[Class[_]]

def clone(): AnyRef

val clusters: Seq[ClusterInfo]

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

val identifier: String

val inputFeatures: Seq[FeatureDesc[_ <: Number]]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def outputFeatures: Seq[FeatureDesc[_]]

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

def transformationSchema: DetailedTransformationSchema

def transformer: KMeansTransformer

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from ClusteringRowModel

Inherited from CategoricalRowModel

Inherited from RowModel

Inherited from MLModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped