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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, PointRepr[D] <: DiscreteDomain[D]](reference: PointRepr[D], gp: LowRankGaussianProcess[D, EuclideanVector[D]])(implicit arg0: NDSpace[D], canWarp: DomainWarp[D, PointRepr], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, PointRepr]

   creates a StatisticalMeshModel by discretizing the given Gaussian Process on the points of the reference mesh.

5.  final def asInstanceOf[T0]: T0

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6.  def augmentModel[D, DDomain[D] <: DiscreteDomain[D]](model: PointDistributionModel[D, DDomain], biasModel: LowRankGaussianProcess[D, EuclideanVector[D]])(implicit arg0: NDSpace[D], canWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, DDomain]

   Adds a bias model to the given statistical shape model

7.  def clone(): AnyRef

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8.  def createUsingPCA[D, DDomain[D] <: DiscreteDomain[D]](reference: DDomain[D], fields: Seq[Field[D, EuclideanVector[D]]], stoppingCriterion: StoppingCriterion)(implicit arg0: NDSpace[D], canWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, DDomain]

   Creates a new Statistical mesh model, with its mean and covariance matrix estimated from the given fields.

   Creates a new Statistical mesh model, with its mean and covariance matrix estimated from the given fields.

   Per default, the resulting mesh model will have rank (i.e. number of principal components) corresponding to the number of linearly independent fields. By providing an explicit stopping criterion, one can, however, compute only the leading principal components. See PivotedCholesky.StoppingCriterion for more details.

9.  def createUsingPCA[D, DDomain[D] <: DiscreteDomain[D]](dc: DataCollection[D, DDomain, EuclideanVector[D]], stoppingCriterion: StoppingCriterion = PivotedCholesky.RelativeTolerance(0))(implicit arg0: NDSpace[D], canWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, DDomain]

   Returns a PCA model with given reference mesh and a set of items in correspondence.

   Returns a PCA model with given reference mesh and a set of items in correspondence. All points of the reference mesh are considered for computing the PCA

   Per default, the resulting mesh model will have rank (i.e. number of principal components) corresponding to the number of linearly independent fields. By providing an explicit stopping criterion, one can, however, compute only the leading principal components. See PivotedCholesky.StoppingCriterion for more details.

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

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

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12.  def finalize(): Unit

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14.  def hashCode(): Int

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15.  final def isInstanceOf[T0]: Boolean

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16.  final def ne(arg0: AnyRef): Boolean

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17.  final def notify(): Unit

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18.  final def notifyAll(): Unit

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19.  final def synchronized[T0](arg0: => T0): T0

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21.  final def wait(): Unit

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

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

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