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Kernels
A kernel is a function that maps from two row vectors to a scalar which is used to express the similarity between the vectors.
To learn more about the properties of the provided kernels, we recommend the Usual_covariance_functions Wikipedia page and the kernel-functions-for-machine-learning-applications article.
User-defined kernels should implement the Kernel trait. To learn more about the implementation of kernels adapted to a particular problem, we recommend the chapter two (Expressing Structure with Kernels) and three (Automatic Model Construction) of the very good Automatic Model Construction with Gaussian Processes.
This implementation is inspired by rusty-machines’.
Structs
The Exponential Kernel.
The Hyperbolic Tangent Kernel.
A wrapper tuple struct used for kernel arithmetic
The point-wise product of two kernels.
The sum of two kernels.
The Linear Kernel.
The Matèrn1 kernel which is 1 differentiable and correspond to a classical Matèrn kernel with nu=3/2.
The Matèrn2 kernel which is 2 differentiable and correspond to a classical Matèrn kernel with nu=5/2.
The Multiquadric Kernel.
The Polynomial Kernel.
The Rational Quadratic Kernel.
Squared exponential kernel
Traits
The Kernel trait.
Type Definitions
Gaussian kernel.