Multivariate Pattern Analysis in Python

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# mvpa2.kernels.sg

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.

Functions

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

Classes

`Bogus` |
`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 Each instance has an internal representation self._k which might be of a different form depending on the intended use.
`LinearSGKernel`([normalizer_cls, normalizer_args]) | A basic linear kernel computed via Shogun: K(a,b) = a*b.T ..
`Parameter`(default[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter.
`PolySGKernel`(**kwargs) | Polynomial kernel: K(a,b) = (a*b.T + c)**degree c is 1 if and only if ‘inhomogenous’ is True ..
`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) ..
`SGKernel`(*args, **kwargs) | A Kernel object with internal representation in Shogun ..

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