scalismo.statisticalmodel

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class DiscreteGaussianProcess[D, DDomain <: (DiscreteDomain), Value]

A representation of a gaussian process, which is only defined on a discrete domain. While this is technically similar to a MultivariateNormalDistribution, we highlight with this class that we represent (discrete) functions, defined on the given domain.

A representation of a gaussian process, which is only defined on a discrete domain. While this is technically similar to a MultivariateNormalDistribution, we highlight with this class that we represent (discrete) functions, defined on the given domain.

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class Object
trait Matchable
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Known subtypes
class DiscreteLowRankGaussianProcess[D, DDomain, Value]
Self type
DiscreteGaussianProcess[D, DDomain, Value]

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class DiscreteLowRankGaussianProcess[D, DDomain <: (DiscreteDomain), Value] extends DiscreteGaussianProcess[D, DDomain, Value]

Represents a low-rank gaussian process, that is only defined at a finite, discrete set of points. It supports the same operations as the LowRankGaussianProcess class, but always returns instead a discrete representation. Furthermore, most operations are much more efficient, as they are implemented using fast matrix/vector operations.

Represents a low-rank gaussian process, that is only defined at a finite, discrete set of points. It supports the same operations as the LowRankGaussianProcess class, but always returns instead a discrete representation. Furthermore, most operations are much more efficient, as they are implemented using fast matrix/vector operations.

Where the modeled functions in a LowRankGaussianProcess are of type Point[D]=>Vector[D], this discretized version is of type VectorPointData.

It is possible to convert a DiscreteLowRankGaussianProcess to a LowRankGaussianProcess by calling the interpolation method.

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class DiscreteGaussianProcess[D, DDomain, Value]
class Object
trait Matchable
class Any
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class Object
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class GaussianProcess[D, Value](val mean: Field[D, Value], val cov: MatrixValuedPDKernel[D])(implicit evidence$1: NDSpace[D], val vectorizer: Vectorizer[Value])

A gaussian process from a D dimensional input space, whose input values are points, to a DO dimensional output space. The output space is a Euclidean vector space of dimensionality DO.

A gaussian process from a D dimensional input space, whose input values are points, to a DO dimensional output space. The output space is a Euclidean vector space of dimensionality DO.

Attributes

D

The dimensionality of the input space

cov

The covariance function. Needs to be positive definite

mean

The mean function

Companion:
object
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class Object
trait Matchable
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class LowRankGaussianProcess[D, Value]

Factory methods for creating Gaussian processes

Factory methods for creating Gaussian processes

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class LowRankGaussianProcess[D, Value](mean: Field[D, Value], val klBasis: KLBasis[D, Value])(implicit evidence$1: NDSpace[D], vectorizer: Vectorizer[Value]) extends GaussianProcess[D, Value]

A gaussian process which is represented in terms of a (small) finite set of basis functions. The basis functions are the orthonormal basis functions given by a mercers' decomposition.

A gaussian process which is represented in terms of a (small) finite set of basis functions. The basis functions are the orthonormal basis functions given by a mercers' decomposition.

Attributes

D

The dimensionality of the input space

Value

The output type

klBasis

A set of basis functions

mean

The mean function

Companion:
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class GaussianProcess[D, Value]
class Object
trait Matchable
class Any

Factory methods for creating Low-rank gaussian processes, as well as generic algorithms to manipulate Gaussian processes.

Factory methods for creating Low-rank gaussian processes, as well as generic algorithms to manipulate Gaussian processes.

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case class MultivariateNormalDistribution(mean: DenseVector[Double], cov: DenseMatrix[Double])

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trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any

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sealed trait NaNStrategy

Strategies for working with NaNValues.

Strategies for working with NaNValues.

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class Object
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object NaNAsMissingValue.type
object NanIsNumericValue.type

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case class PointDistributionModel[D, DDomain <: (DiscreteDomain)](gp: DiscreteLowRankGaussianProcess[D, DDomain, EuclideanVector[D]])(implicit evidence$1: NDSpace[D], warper: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]])

A StatisticalMeshModel is isomorphic to a DiscreteLowRankGaussianProcess. The difference is that while the DiscreteLowRankGaussianProcess models defomation fields, the StatisticalMeshModel applies the deformation fields to a mesh, and warps the mesh with the deformation fields to produce a new mesh.

A StatisticalMeshModel is isomorphic to a DiscreteLowRankGaussianProcess. The difference is that while the DiscreteLowRankGaussianProcess models defomation fields, the StatisticalMeshModel applies the deformation fields to a mesh, and warps the mesh with the deformation fields to produce a new mesh.

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A StatisticalMeshModel is isomorphic to a DiscreteLowRankGaussianProcess. The difference is that while the DiscreteLowRankGaussianProcess models defomation fields, the StatisticalMeshModel applies the deformation fields to a mesh, and warps the mesh with the deformation fields to produce a new mesh.

A StatisticalMeshModel is isomorphic to a DiscreteLowRankGaussianProcess. The difference is that while the DiscreteLowRankGaussianProcess models defomation fields, the StatisticalMeshModel applies the deformation fields to a mesh, and warps the mesh with the deformation fields to produce a new mesh.

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trait Serializable
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Deprecated classlikes

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case class NDimensionalNormalDistribution[D](mean: EuclideanVector[D], cov: SquareMatrix[D])(implicit evidence$1: NDSpace[D])

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