Kernels#
The Radial Basis Function (RBF) kernel. |
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Computes an approximation of the kernel using Random Fourier Features. |
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Base kernel class. |
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The ArCosine kernel. |
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Compute engine class for finite basis function approximations to a kernel. |
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A constant kernel. |
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Computation engine for constant diagonal kernels. |
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Dense kernel computation class. |
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Diagonal kernel computation class. |
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Eigen kernel computation class. |
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A base kernel whose lengthscale changes with location. |
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The Gneiting nonseparable space–time kernel. |
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The Matérn graph kernel defined on the vertex set of a graph. |
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Intrinsic Coregionalization Model kernel. |
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Linear Model of Coregionalization kernel. |
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The linear kernel. |
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The Matérn kernel with smoothness parameter fixed at 0.5. |
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The Matérn kernel with smoothness parameter fixed at 1.5. |
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The Matérn kernel with smoothness parameter fixed at 2.5. |
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Base class for multi-output kernels. |
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Compute engine for multi-output kernels. |
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Orthogonal Additive Kernel (OAK). |
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The periodic kernel. |
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The Polynomial kernel with variable degree. |
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The powered exponential family of kernels. |
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A kernel that is the product of a set of kernels. |
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The Rational Quadratic kernel. |
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A kernel that is the sum of a set of kernels. |
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A base kernel whose standard deviation changes with location. |
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The White noise kernel. |
Location functions#
A location function gives a kernel parameter that changes with input location,
such as the standard deviation of VaryingAmplitude or
the lengthscale of Gibbs. It is not a mean function: a
mean function describes the Gaussian process, but a location function describes
its covariance. A location function evaluates one input point, selects its own
columns with active_dims, and returns a value on the log scale.
Base class for location functions. |
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A location function with the same value at all locations. |
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A location function that is log-linear in selected input columns. |