AbstractGPRegressionModel

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

   

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2. final def ##(): Int

   

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

   

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4. def apply[T, I](cov: LocalScalarKernel[I], noise: LocalScalarKernel[I], basisFunc: DataPipe[I, DenseVector[Double]], basis_param_prior: MultGaussianRV)(trainingdata: T, num: Int)(implicit arg0: ClassTag[I], transform: DataPipe[T, Seq[(I, Double)]]): GPBasisFuncRegressionModel[T, I]

   

   Create an instance of GPBasisFuncRegressionModel for a particular data type T

   Create an instance of GPBasisFuncRegressionModel for a particular data type T

   T

   The type of the training data

   I

   The type of the input patterns in the data set of type T

   cov

   The covariance function

   noise

   The noise covariance function

   basisFunc

   A DataPipe transforming the input features to basis function components.

   basis_param_prior

   A MultGaussianRV which is the prior distribution on basis function coefficients

   trainingdata

   The actual data set of type T

   transform

   An implicit conversion from T to Seq represented as a DataPipe

5. def apply[T, I](cov: LocalScalarKernel[I], noise: LocalScalarKernel[I], meanFunc: DataPipe[I, Double])(trainingdata: T, num: Int)(implicit arg0: ClassTag[I], transform: DataPipe[T, Seq[(I, Double)]]): AbstractGPRegressionModel[T, I]

   

   Create an instance of AbstractGPRegressionModel for a particular data type T

   Create an instance of AbstractGPRegressionModel for a particular data type T

   T

   The type of the training data

   I

   The type of the input patterns in the data set of type T

   cov

   The covariance function

   noise

   The noise covariance function

   meanFunc

   The trend or mean function

   trainingdata

   The actual data set of type T

   transform

   An implicit conversion from T to Seq represented as a DataPipe

6. def apply[M <: AbstractGPRegressionModel[Seq[(DenseVector[Double], Double)], DenseVector[Double]]](data: Seq[(DenseVector[Double], Double)], cov: LocalScalarKernel[DenseVector[Double]], noise: LocalScalarKernel[DenseVector[Double]] = new DiracKernel(1.0), order: Int = 0, ex: Int = 0, meanFunc: DataPipe[DenseVector[Double], Double] = DataPipe(_ => 0.0)): M

   

7. final def asInstanceOf[T0]: T0

   

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8. def clone(): AnyRef

   

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. def logLikelihood(trainingData: PartitionedVector, kernelMatrix: PartitionedPSDMatrix): Double

    

    Calculate the marginal log likelihood of the training data for a pre-initialized kernel and noise matrices.

    Calculate the marginal log likelihood of the training data for a pre-initialized kernel and noise matrices.

    trainingData

    The function values assimilated as a DenseVector

    kernelMatrix

    The kernel matrix of the training features

16. def logLikelihood(trainingData: DenseVector[Double], kernelMatrix: DenseMatrix[Double]): Double

    

    Calculate the marginal log likelihood of the training data for a pre-initialized kernel and noise matrices.

    Calculate the marginal log likelihood of the training data for a pre-initialized kernel and noise matrices.

    trainingData

    The function values assimilated as a DenseVector

    kernelMatrix

    The kernel matrix of the training features

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

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def solve(trainingLabels: PartitionedVector, trainingMean: PartitionedVector, priorMeanTest: PartitionedVector, smoothingMat: PartitionedPSDMatrix, kernelTest: PartitionedPSDMatrix, crossKernel: PartitionedMatrix): (PartitionedVector, PartitionedPSDMatrix)

    

    Calculate the parameters of the posterior predictive distribution for a multivariate gaussian model.

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

    

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22. def toString(): String

    

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23. final def wait(): Unit

    

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

    

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

    

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