modelpipe

Type Members

1. class DLSSVMPipe[Source] extends ModelPipe[Source, Stream[(DenseVector[Double], Double)], DenseVector[Double], Double, DLSSVM]

   

   Created by mandar on 15/6/16.

2. class ESGPPipe4[Data, IndexSet] extends DataPipe4[DataPipe[IndexSet, Double], Double, Double, Data, ESGPModel[Data, IndexSet]]

   
   Data

   The type of the training data

   IndexSet

   The type of input features.

3. class GLMPipe[T, Source] extends ModelPipe[Source, Stream[(DenseVector[Double], Double)], DenseVector[Double], Double, GeneralizedLinearModel[T]]

   

   Created by mandar2812 on 15/6/16.

4. class GLMPipe2[T, Source] extends DataPipe2[Source, DataPipe[DenseVector[Double], DenseVector[Double]], GeneralizedLinearModel[T]]

   

5. class GPBasisFuncRegressionPipe[Source, IndexSet] extends ModelPipe[Source, Seq[(IndexSet, Double)], IndexSet, Double, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

   

   A data pipe which can spawn a Gaussian Process Basis Function regression model from a provided training data set.

   GP Basis Function Pipe

   A data pipe which can spawn a Gaussian Process Basis Function regression model from a provided training data set.

   Source

   Input data type

   IndexSet

   Type of features of each data pattern

6. class GPMixturePipe[T, I] extends MixturePipe[T, I, Double, PartitionedVector, PartitionedPSDMatrix, BlockedMultiVariateGaussian, MultGaussianPRV, AbstractGPRegressionModel[T, I]]

   

7. class GPRegressionPipe[Source, IndexSet] extends ModelPipe[Source, Seq[(IndexSet, Double)], IndexSet, Double, AbstractGPRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

   

   A data pipe which can spawn a Gaussian Process regression model from a provided training data set.

   GP Pipes

   A data pipe which can spawn a Gaussian Process regression model from a provided training data set.

   Source

   Input data type

   IndexSet

   Type of features of each data pattern

8. class GPRegressionPipe2[IndexSet] extends DataPipe2[Seq[(IndexSet, Double)], DataPipe[IndexSet, Double], AbstractGPRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

   

   A DataPipe2 which takes a data set, a trend and outputs a GP Regression model.

   GP Pipes: Alternate

   A DataPipe2 which takes a data set, a trend and outputs a GP Regression model.

   IndexSet

   Type of features of each data pattern

9. class GeneralizedLeastSquaresPipe2 extends DataPipe2[Stream[(DenseVector[Double], Double)], DataPipe[DenseVector[Double], DenseVector[Double]], GeneralizedLeastSquaresModel]

   

10. class MVStudentsTMixturePipe[T, I] extends MixturePipe[T, I, DenseVector[Double], DenseMatrix[Double], (DenseMatrix[Double], DenseMatrix[Double]), MatrixT, MatrixTRV, MVStudentsTModel[T, I]]

    

11. abstract class MixturePipe[T, I, Y, YDomain, YDomainVar, BaseDistr <: ContinuousDistr[YDomain] with Moments[YDomain, YDomainVar] with HasErrorBars[YDomain], W1 <: ContinuousRVWithDistr[YDomain, BaseDistr], BaseProcess <: ContinuousProcessModel[T, I, Y, W1] with SecondOrderProcessModel[T, I, Y, Double, DenseMatrix[Double], W1] with GloballyOptimizable] extends DataPipe2[Seq[BaseProcess], DenseVector[Double], GenContinuousMixtureModel[T, I, Y, YDomain, YDomainVar, BaseDistr, W1, BaseProcess]]

    

    Mixture Pipe takes a sequence of stochastic process models and associated probability weights and returns a mixture model.

12. trait ModelPipe[-Source, T, Q, R, +M <: Model[T, Q, R]] extends DataPipe[Source, M]

    

    Top level trait for Pipes returning ML models.

13. class ModelPredictionPipe[T, -P, Q, R, +S, M <: Model[T, Q, R]] extends DataPipe[P, S]

    

    A pipeline which encapsulates a DynaML Model.predict() functionality.

    A pipeline which encapsulates a DynaML Model.predict() functionality.

    T

    The training data type accepted by the encapsulated model

    P

    The type of unprocessed input to the pipe

    Q

    The type of input features the model accepts

    R

    The type of output returned by Model.predict()

    S

    The type of the processed output.

14. trait ModelTestPipe[Source, Destination, Data, Features, Label, M <: Model[Data, Features, Label]] extends DataPipe[(M, Source), Destination]

    

15. class StudentTMixturePipe[T, I] extends MixturePipe[T, I, Double, PartitionedVector, PartitionedPSDMatrix, BlockedMultivariateStudentsT, MultStudentsTPRV, AbstractSTPRegressionModel[T, I]]