keystoneml.nodes.images.external

EncEvalGMMFisherVectorEstimator

case class EncEvalGMMFisherVectorEstimator(k: Int) extends Estimator[DenseMatrix[Float], DenseMatrix[Float]] with Product with Serializable

Trains an enceval Fisher Vector implementation, via estimating a GMM by treating each column of the inputs as a separate DenseVector input to GaussianMixtureModelEstimator

TODO: Pending philosophical discussions on how to best make it so you can swap in GMM, KMeans++, etc. for Fisher Vectors. For now just hard-codes GMM here

k

Number of centers to estimate.

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Product, Equals, Estimator[DenseMatrix[Float], DenseMatrix[Float]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. EncEvalGMMFisherVectorEstimator
  2. Product
  3. Equals
  4. Estimator
  5. EstimatorOperator
  6. Serializable
  7. Serializable
  8. Operator
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Instance Constructors

  1. new EncEvalGMMFisherVectorEstimator(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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  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 execute(deps: Seq[Expression]): TransformerExpression

    Definition Classes
    EstimatorOperator → Operator

  10. def finalize(): Unit

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

  11. def fit(data: RDD[DenseMatrix[Float]]): FisherVector

    The type-safe method that ML developers need to implement when writing new Estimators.

    The type-safe method that ML developers need to implement when writing new Estimators.

    data

    The estimator's training data.

    returns

    A new transformer

    Definition Classes
    EncEvalGMMFisherVectorEstimator → Estimator

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

    Definition Classes
    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  14. val k: Int

    Number of centers to estimate.

  15. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  20. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  23. final def withData(data: PipelineDataset[DenseMatrix[Float]]): Pipeline[DenseMatrix[Float], DenseMatrix[Float]]

    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

  24. final def withData(data: RDD[DenseMatrix[Float]]): Pipeline[DenseMatrix[Float], DenseMatrix[Float]]

    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 Product

Inherited from Equals

Inherited from Estimator[DenseMatrix[Float], DenseMatrix[Float]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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