mvpa2.kernels.np.RationalQuadraticKernel

Inheritance diagram of RationalQuadraticKernel

class mvpa2.kernels.np.RationalQuadraticKernel(length_scale=1.0, sigma_f=1.0, alpha=0.5, **kwargs)

The Rational Quadratic (RQ) kernel class.

Note that it can handle a length scale for each dimension for Automtic Relevance Determination.

Attributes

descr Description of the object if any

Methods

gradient(data1, data2) Compute gradient of the kernel matrix.
set_hyperparameters(hyperparameter) Set hyperaparmeters from a vector.

Initialize a Squared Exponential kernel instance.

Parameters:

length_scale : float or numpy.ndarray

the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0)

sigma_f : float

Signal standard deviation. (Defaults to 1.0)

alpha : float

The parameter of the RQ functions family. (Defaults to 2.0)

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

Attributes

descr Description of the object if any

Methods

gradient(data1, data2) Compute gradient of the kernel matrix.
set_hyperparameters(hyperparameter) Set hyperaparmeters from a vector.
gradient(data1, data2)

Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.

set_hyperparameters(hyperparameter)

Set hyperaparmeters from a vector.

Used by model selection. Note: ‘alpha’ is not considered as an hyperparameter.