LowRankGaussianProcess

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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 approximateGP[D <: Dim, DO <: Dim](gp: GaussianProcess[D, DO], sampler: Sampler[D], numBasisFunctions: Int)(implicit arg0: NDSpace[D], arg1: NDSpace[DO]): LowRankGaussianProcess[D, DO]

   

   Perform a low-rank approximation of the Gaussian process using the Nystrom method.

   Perform a low-rank approximation of the Gaussian process using the Nystrom method. The sample points used for the nystrom method are sampled using the given sample.

   gp

   The gaussian process to approximate

   sampler

   determines which points will be used as samples for the nystrom approximation.

   numBasisFunctions

   The number of basis functions to approximate.

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. def regression[D <: Dim, DO <: Dim](gp: LowRankGaussianProcess[D, DO], trainingData: IndexedSeq[(Point[D], Vector[DO], NDimensionalNormalDistribution[DO])])(implicit arg0: NDSpace[D], arg1: NDSpace[DO]): LowRankGaussianProcess[D, DO]

    

    * Performs a Gaussian process regression, where we assume that each training point (vector) is subject to zero-mean noise with given variance.

    * Performs a Gaussian process regression, where we assume that each training point (vector) is subject to zero-mean noise with given variance.

    gp

    The gaussian process

    trainingData

    Point/value pairs where that the sample should approximate, together with an error model (the uncertainty) at each point.

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

    

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18. def toString(): String

    

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19. def transform[D <: Dim](gp: LowRankGaussianProcess[D, D], rigidTransform: RigidTransformation[D])(implicit arg0: NDSpace[D]): LowRankGaussianProcess[D, D]

    

    perform a rigid transformation of the gaussian process, i.e.

    perform a rigid transformation of the gaussian process, i.e. it is later defined on the transformed domain and its vectors are transformed along the domain.

20. final def wait(): Unit

    

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

    

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

    

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