scalismo.statisticalmodel
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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.
Attributes
- Companion:
- object
- Graph
- Supertypes
- class Objecttrait Matchableclass Any
- Known subtypes
- Self type
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- Companion:
- class
- Graph
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- class Objecttrait Matchableclass Any
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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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- Companion:
- object
- Graph
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- Self type
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- Companion:
- class
- Graph
- Supertypes
- class Objecttrait Matchableclass Any
- Self type
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- Graph
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- class Objecttrait Matchableclass Any
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- Graph
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- class Objecttrait Matchableclass Any
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- Graph
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- class Objecttrait Matchableclass Any
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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
- Graph
- Supertypes
- class Objecttrait Matchableclass Any
- Known subtypes
Factory methods for creating Gaussian processes
Factory methods for creating Gaussian processes
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- class
- Graph
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- class Objecttrait Matchableclass Any
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- GaussianProcess.type
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- Graph
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- class Objecttrait Matchableclass Any
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- GaussianProcess1D.type
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- Graph
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- class Objecttrait Matchableclass Any
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- GaussianProcess2D.type
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- Graph
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- class Objecttrait Matchableclass Any
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- GaussianProcess3D.type
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:
- object
- Graph
- Supertypes
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.
Attributes
- Companion:
- class
- Graph
- Supertypes
- class Objecttrait Matchableclass Any
- Self type
Attributes
- Companion:
- object
- Graph
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- trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass Any
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- class
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- trait Producttrait Mirrorclass Objecttrait Matchableclass Any
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Strategies for working with NaNValues.
Strategies for working with NaNValues.
Attributes
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- object
- Graph
- Supertypes
- class Objecttrait Matchableclass Any
- Known subtypes
- object NaNAsMissingValue.typeobject NanIsNumericValue.type
Attributes
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- trait
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- trait Sumtrait Mirrorclass Objecttrait Matchableclass Any
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- NaNStrategy.type
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.
Attributes
- See also:
- Companion:
- object
- Graph
- Supertypes
- trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass Any
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- Companion:
- class
- Graph
- Supertypes
- class Objecttrait Matchableclass Any
- Self type
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- Graph
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- class Objecttrait Matchableclass Any
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- Graph
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- class Objecttrait Matchableclass Any
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- Graph
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- class Objecttrait Matchableclass Any
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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.
Attributes
- See also:
- Companion:
- object
- Graph
- Supertypes
- trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass Any
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- Companion:
- class
- Graph
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- trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
- StatisticalMeshModel.type
Deprecated classlikes
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- Deprecated
- true
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- class Objecttrait Matchableclass Any
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- Companion:
- object
- Deprecated
- true
- Graph
- Supertypes
- trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass Any