mvpa.clfs.kernel.KernelMatern_3

Class KernelMatern_3_2

The Matern kernel class for the case ni=3/2 or ni=5/2.

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

Instance Methods

__init__(self, length_scale=1.0, sigma_f=1.0, numerator=3.0, **kwargs)
Initialize a Squared Exponential kernel instance.

source code

__repr__(self)
repr(x)

source code

compute(self, data1, data2=None)
Compute kernel matrix.

source code

gradient(self, data1, data2)
Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.

source code

set_hyperparameters(self, hyperparameter)
Set hyperaparmeters from a vector.

source code

Inherited from Kernel: compute_gradient, compute_lml_gradient, compute_lml_gradient_logscale, reset

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties

[hide private]

Inherited from object: __class__

init(self, length_scale=1.0, sigma_f=1.0, numerator=3.0, **kwargs)
(Constructor)

source code

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)
numerator, float - the numerator of parameter ni of Matern covariance functions. Currently only numerator=3.0 and numerator=5.0 are implemented. (Defaults to 3.0)

Overrides: object.__init__