keystoneml.nodes.learning

GaussianMixtureModel

case class GaussianMixtureModel(means: DenseMatrix[Double], variances: DenseMatrix[Double], weights: DenseVector[Double], weightThreshold: Double = 1.0E-4) extends Transformer[DenseVector[Double], DenseVector[Double]] with Product with Serializable

A Mixture of Gaussians, usually computed via some clustering process.

means

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

variances

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

weights

Cluster weights.

Linear Supertypes
Product, Equals, Transformer[DenseVector[Double], DenseVector[Double]], Chainable[DenseVector[Double], DenseVector[Double]], TransformerOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. GaussianMixtureModel
  2. Product
  3. Equals
  4. Transformer
  5. Chainable
  6. TransformerOperator
  7. Serializable
  8. Serializable
  9. Operator
  10. AnyRef
  11. Any
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Instance Constructors

  1. new GaussianMixtureModel(means: DenseMatrix[Double], variances: DenseMatrix[Double], weights: DenseVector[Double], weightThreshold: Double = 1.0E-4)

    means

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

    variances

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

    weights

    Cluster weights.

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
    GaussianMixtureModelTransformer
  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
    GaussianMixtureModelTransformer
  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
    TransformerChainable
  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.

Inherited from Product

Inherited from Equals

Inherited from Transformer[DenseVector[Double], DenseVector[Double]]

Inherited from Chainable[DenseVector[Double], DenseVector[Double]]

Inherited from TransformerOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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