keystoneml.nodes.learning.external

GaussianMixtureModelEstimator

class GaussianMixtureModelEstimator extends Estimator[DenseVector[Double], DenseVector[Double]]

Fit a Gaussian Mixture model to Data.

Linear Supertypes
Estimator[DenseVector[Double], DenseVector[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. GaussianMixtureModelEstimator
  2. Estimator
  3. EstimatorOperator
  4. Serializable
  5. Serializable
  6. Operator
  7. AnyRef
  8. Any
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Instance Constructors

  1. new GaussianMixtureModelEstimator(k: Int)

    k

    Number of centers to estimate.

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef → Any
  10. def execute(deps: Seq[Expression]): TransformerExpression

    Definition Classes
    EstimatorOperator → Operator
  11. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def fit(samples: Array[DenseVector[Double]]): GaussianMixtureModel

    Fit a Gaussian mixture model with k centers to a sample array.

    Fit a Gaussian mixture model with k centers to a sample array.

    samples

    Sample Array - all elements must be the same size.

    returns

    A Gaussian Mixture Model.

  13. def fit(samples: RDD[DenseVector[Double]]): GaussianMixtureModel

    Currently this model works on items that fit in local memory.

    Currently this model works on items that fit in local memory.

    samples
    returns

    A PipelineNode (Transformer) which can be called on new data.

    Definition Classes
    GaussianMixtureModelEstimatorEstimator
  14. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  17. def label: String

    Definition Classes
    Operator
  18. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  19. final def notify(): Unit

    Definition Classes
    AnyRef
  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  21. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  22. def toString(): String

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  23. final def wait(): Unit

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

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

    Definition Classes
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    Annotations
    @throws( ... )
  26. final def withData(data: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    returns

    A pipeline that fits this estimator and applies the result to inputs.

    Definition Classes
    Estimator
  27. final def withData(data: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    returns

    A pipeline that fits this estimator and applies the result to inputs.

    Definition Classes
    Estimator

Inherited from Estimator[DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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