object DiscreteLowRankGaussianProcess extends Serializable
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- case class Eigenpair[D, DDomain[DD] <: DiscreteDomain[DD], Value](eigenvalue: Double, eigenfunction: DiscreteField[D, DDomain, Value]) extends Product with Serializable
- type KLBasis[D, DDomain[D] <: DiscreteDomain[D], Value] = Seq[Eigenpair[D, DDomain, Value]]
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- def apply[D, DDomain[D] <: DiscreteDomain[D], Value](mean: DiscreteField[D, DDomain, Value], klBasis: KLBasis[D, DDomain, Value])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): DiscreteLowRankGaussianProcess[D, DDomain, Value]
- def apply[D, DDomain[D] <: DiscreteDomain[D], Value](domain: DDomain[D], gp: LowRankGaussianProcess[D, Value])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): DiscreteLowRankGaussianProcess[D, DDomain, Value]
Creates a new DiscreteLowRankGaussianProcess by discretizing the given gaussian process at the domain points.
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- def createUsingPCA[D, DDomain[D] <: DiscreteDomain[D], Value](domain: DDomain[D], fields: Seq[Field[D, Value]], stoppingCriterion: StoppingCriterion)(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): DiscreteLowRankGaussianProcess[D, DDomain, Value]
Creates a new DiscreteLowRankGaussianProcess, where the mean and covariance matrix are estimated from the given sample of continuous vector fields using Principal Component Analysis.
- def createUsingPCA[D, DDomain[D] <: DiscreteDomain[D], Value](dc: DataCollection[D, DDomain, Value], stoppingCriterion: StoppingCriterion = PivotedCholesky.RelativeTolerance(0))(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): DiscreteLowRankGaussianProcess[D, DDomain, Value]
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- def regression[D, DDomain[D] <: DiscreteDomain[D], Value](gp: DiscreteLowRankGaussianProcess[D, DDomain, Value], trainingData: IndexedSeq[(PointId, Value, MultivariateNormalDistribution)])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): DiscreteLowRankGaussianProcess[D, DDomain, Value]
Discrete implementation of LowRankGaussianProcess.regression
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