mvpa2.kernels.sgΒΆ

PyMVPA shogun-based kernels

Provides interface to kernels defined in shogun toolbox. Commonly used kernels are provided with convenience classes: LinearSGKernel, RbfSGKernel, PolySGKernel. If you need to use some other shogun kernel, use CustomSGKernel to define one.

Inheritance diagram of mvpa2.kernels.sg

Functions

exists(dep[, force, raise_, issueWarning, ...]) Test whether a known dependency is installed on the system.

Classes

CustomSGKernel(kernel_cls[, kernel_params]) Class which can wrap any Shogun kernel and it’s kernel parameters
Kernel(\*args, \*\*kwargs) Abstract class which calculates a kernel function between datasets
LinearSGKernel([normalizer_cls, normalizer_args]) A basic linear kernel computed via Shogun: K(a,b) = a*b.T
Parameter(default[, constraints, ro, index, ...]) This class shall serve as a representation of a parameter.
PolySGKernel(\*\*kwargs) Polynomial kernel: K(a,b) = (a*b.T + c)**degree
PrecomputedSGKernel([matrix]) A kernel which is precomputed from a numpy array or a Shogun kernel
RbfSGKernel(\*\*kwargs) Radial basis function: K(a,b) = exp(-||a-b||**2/sigma)
RealFeatures shogun::CDenseFeatures< float64_t >
SGKernel(\*args, \*\*kwargs) A Kernel object with internal representation in Shogun