GaussianMixtureModel

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

6. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[L]): Pipeline[DenseVector[Double], C]

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

7. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: PipelineDataset[L]): Pipeline[DenseVector[Double], C]

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

8. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

9. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

10. final def andThen[C](est: Estimator[DenseVector[Double], C], data: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

    Chains an estimator onto the end of this pipeline, producing a new pipeline.

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes

    Chainable

11. final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

    Chains an estimator onto the end of this pipeline, producing a new pipeline.

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes

    Chainable

12. final def andThen[C](next: Chainable[DenseVector[Double], C]): Pipeline[DenseVector[Double], C]

    Chains a pipeline onto the end of this one, producing a new pipeline.

    Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.

    next

    the pipeline to chain

    Definition Classes

    Chainable

13. def apply(in: RDD[DenseVector[Double]]): RDD[DenseVector[Double]]

    The application of this Transformer to an RDD of input items.

    The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.

    in

    The bulk RDD input to pass into this transformer

    returns

    The bulk RDD output for the given input

    Definition Classes

    GaussianMixtureModel → Transformer

14. def apply(X: DenseMatrix[Double]): DenseMatrix[Double]

15. def apply(in: DenseVector[Double]): DenseVector[Double]

    Returns (thresholded) assignments to each cluster.

    Returns (thresholded) assignments to each cluster.

    in

    A Vector

    returns

    The thresholded assignments of the vector according to the mixture model.

    Definition Classes

    GaussianMixtureModel → Transformer

16. final def asInstanceOf[T0]: T0

    Definition Classes

    Any

17. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

18. val dim: Int

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

    Definition Classes

    AnyRef

20. def execute(deps: Seq[Expression]): Expression

    Definition Classes

    TransformerOperator → Operator

21. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes

    AnyRef → Any

23. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

24. val k: Int

25. def label: String

    Definition Classes

    Operator

26. val means: DenseMatrix[Double]

    Cluster centers.

    Cluster centers. # of Dims by # of Cluster. Each column represents a separate cluster.

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

    Definition Classes

    AnyRef

28. final def notify(): Unit

    Definition Classes

    AnyRef

29. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

31. def toPipeline: Pipeline[DenseVector[Double], DenseVector[Double]]

    A method that converts this object into a Pipeline.

    A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

    Definition Classes

    Transformer → Chainable

32. val variances: DenseMatrix[Double]

    Cluster variances (diagonal).

    Cluster variances (diagonal). # of Dims by # of Clusters. Each column represents a separate cluster.

33. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

36. val weightThreshold: Double

37. val weights: DenseVector[Double]

    Cluster weights.