Class/Object

ml.combust.mleap.core.clustering

GaussianMixtureModel

Related Docs: object GaussianMixtureModel | package clustering

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case class GaussianMixtureModel(gaussians: Array[MultivariateGaussian], weights: Array[Double]) extends Model with Product with Serializable

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Serializable, Serializable, Product, Equals, Model, AnyRef, Any
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Inherited
  1. GaussianMixtureModel
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Model
  7. AnyRef
  8. Any
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Instance Constructors

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

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. def apply(features: Vector): Int

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  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef

  8. def finalize(): Unit

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

  9. val gaussians: Array[MultivariateGaussian]

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

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    Definition Classes
    AnyRef → Any

  11. def inputSchema: StructType

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    Definition Classes
    GaussianMixtureModel → Model

  12. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    AnyRef

  14. final def notify(): Unit

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    Definition Classes
    AnyRef

  15. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  16. val numClusters: Int

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  17. val numFeatures: Int

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  18. def outputSchema: StructType

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    Definition Classes
    GaussianMixtureModel → Model

  19. def predict(features: Vector): Int

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  20. def predictProbability(features: Vector): Vector

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  21. def predictWithProbability(features: Vector): (Int, Double)

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  22. def predictionFromProbability(probabilities: Vector): Int

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

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    Definition Classes
    AnyRef

  24. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
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    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  27. val weights: Array[Double]

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Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Model

Inherited from AnyRef

Inherited from Any

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