NAME¶
mlpack::kernel -
Kernel functions.
SYNOPSIS¶
Classes¶
class
CosineDistance
The cosine distance (or cosine similarity). class
EpanechnikovKernel
The Epanechnikov kernel, defined as. class
ExampleKernel
An example kernel function. class
GaussianKernel
The standard Gaussian kernel. class
HyperbolicTangentKernel
Hyperbolic tangent kernel. class
KernelTraits
This is a template class that can provide information about various kernels.
class
KernelTraits< CosineDistance >
Kernel traits for the cosine distance. class
KernelTraits<
EpanechnikovKernel >
Kernel traits for the Epanechnikov kernel. class
KernelTraits<
GaussianKernel >
Kernel traits for the Gaussian kernel. class
KernelTraits<
LaplacianKernel >
Kernel traits of the Laplacian kernel. class
KernelTraits<
SphericalKernel >
Kernel traits for the spherical kernel. class
KernelTraits<
TriangularKernel >
Kernel traits for the triangular kernel. class
KMeansSelection
class
LaplacianKernel
The standard Laplacian kernel. class
LinearKernel
The simple linear kernel (dot product). class
NystroemMethod
class
OrderedSelection
class
PolynomialKernel
The simple polynomial kernel. class
PSpectrumStringKernel
The p-spectrum string kernel. class
RandomSelection
class
SphericalKernel
class
TriangularKernel
The trivially simple triangular kernel, defined by.
Detailed Description¶
Kernel functions.
This namespace contains kernel functions, which evaluate some kernel function $
K(x, y) $ for some arbitrary vectors $ x $ and $ y $ of the same dimension.
The single restriction on the function $ K(x, y) $ is that it must satisfy
Mercer's condition:
for all square integrable functions $ g(x) $.
The kernels in this namespace all implement the same methods as the
ExampleKernel class. Any additional custom kernels should implement all
the methods that class implements; in addition, any method using a kernel
should rely on any arbitrary kernel function class having a default
constructor and a function
double Evaluate(arma::vec&, arma::vec&);
Author¶
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