GaussianMixtureModel

Instance Constructors

new GaussianMixtureModel(weights: Array[Double], gaussians: Array[MultivariateGaussian])

weights
Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1
gaussians
Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

Annotations
@Since( "1.3.0" )

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

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final def ==(arg0: Any): Boolean

Definition Classes
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final def asInstanceOf[T0]: T0

Definition Classes
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def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def formatVersion: String

Current version of model save/load format.
Current version of model save/load format.

Attributes
protected
Definition Classes
GaussianMixtureModel → Saveable
val gaussians: Array[MultivariateGaussian]

Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i
Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

Annotations
@Since( "1.3.0" )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
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final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def k: Int

Number of gaussians in mixture
Number of gaussians in mixture

Annotations
@Since( "1.3.0" )
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: JavaRDD[Vector]): JavaRDD[Integer]

Java-friendly version of predict()
Java-friendly version of predict()

Annotations
@Since( "1.4.0" )
def predict(point: Vector): Int

Maps given point to its cluster index.
Maps given point to its cluster index.

Annotations
@Since( "1.5.0" )
def predict(points: RDD[Vector]): RDD[Int]

Maps given points to their cluster indices.
Maps given points to their cluster indices.

Annotations
@Since( "1.3.0" )
def predictSoft(point: Vector): Array[Double]

Given the input vector, return the membership values to all mixture components.
Given the input vector, return the membership values to all mixture components.

Annotations
@Since( "1.4.0" )
def predictSoft(points: RDD[Vector]): RDD[Array[Double]]

Given the input vectors, return the membership value of each vector to all mixture components.
Given the input vectors, return the membership value of each vector to all mixture components.

Annotations
@Since( "1.3.0" )
def save(sc: SparkContext, path: String): Unit

Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using Loader.load.
sc
Spark context used to save model data.
path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

Definition Classes
GaussianMixtureModel → Saveable
Annotations
@Since( "1.4.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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

Definition Classes
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Annotations
@throws( ... )
val weights: Array[Double]

Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1
Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1

Annotations
@Since( "1.3.0" )

Related Docs: object GaussianMixtureModel | package clustering

class GaussianMixtureModel extends Serializable with Saveable

Instance Constructors

new GaussianMixtureModel(weights: Array[Double], gaussians: Array[MultivariateGaussian])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatVersion: String

val gaussians: Array[MultivariateGaussian]

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def k: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

def predict(point: Vector): Int

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

def predictSoft(point: Vector): Array[Double]

def predictSoft(points: RDD[Vector]): RDD[Array[Double]]

def save(sc: SparkContext, path: String): Unit

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

val weights: Array[Double]

Inherited from Saveable

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped