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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.  final def asInstanceOf[T0]: T0

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6.  def computeKernelMatrix[D](xs: Seq[Point[D]], k: MatrixValuedPDKernel[D]): DenseMatrix[Double]
7.  def computeKernelVectorFor[D](x: Point[D], xs: IndexedSeq[Point[D]], k: MatrixValuedPDKernel[D]): DenseMatrix[Double]

   for every domain point x in the list, we compute the kernel vector kx = (k(x, x1), ...

   for every domain point x in the list, we compute the kernel vector kx = (k(x, x1), ... k(x, xm)) since the kernel is matrix valued, kx is actually a matrix

   !! Hack - We currently return a double matrix, with the only reason that matrix multiplication (further down) is faster (breeze implementation detail). This should be replaced at some point

8.  def computeNystromApproximation[D, Value](k: MatrixValuedPDKernel[D], sampler: Sampler[D])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): KLBasis[D, Value]

   Computes the leading eigenvalues / eigenfunctions of the integral operator corresponding to kernel k.

   Computes the leading eigenvalues / eigenfunctions of the integral operator corresponding to kernel k. The number of leading eigenfunctions is at most n, where n is the number of points sampled. If the eigenvalues are decaying quickly, it can be much smaller than n.

   k

   (matrix-valued) kernel

   sampler

   A point sampler, which determines the points that are used to compute the approximation.

   returns

   The leading eigenvalue / eigenfunction pairs

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

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10.  def equals(arg0: AnyRef): Boolean

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11.  def finalize(): Unit

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

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