mvpa2.clfs.gpr.SquaredExponentialKernel

class mvpa2.clfs.gpr.SquaredExponentialKernel(length_scale=1.0, sigma_f=1.0, **kwargs)

The Squared Exponential kernel class.

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

Methods

Initialize a Squared Exponential kernel instance.

Parameters:

length_scale : float or numpy.ndarray, optional

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

sigma_f : float, optional

Signal standard deviation. (Defaults to 1.0)

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

Methods

compute_lml_gradient(alphaalphaT_Kinv, data)

Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD).

compute_lml_gradient_logscale(alphaalphaT_Kinv, data)

Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Hyperparameters are in log scale which is sometimes more stable. Shorter formula. Allows vector of lengthscales (ARD).

length_scale

reset()

set_hyperparameters(hyperparameter)

Set hyperaparmeters from a vector.

Used by model selection.