GaussianProcess

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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[D <: Dim, DO <: Dim](mean: VectorField[D, DO], cov: MatrixValuedPDKernel[D, DO])(implicit arg0: NDSpace[D], arg1: NDSpace[DO]): GaussianProcess[D, DO]

   

   Creates a new Gaussian process with given mean and covariance, which is defined on the given domain.

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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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: GaussianProcess[D, DO], trainingData: IndexedSeq[(Point[D], Vector[DO], NDimensionalNormalDistribution[DO])])(implicit arg0: NDSpace[D], arg1: NDSpace[DO]): GaussianProcess[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. final def wait(): Unit

    

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

    

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

    

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