mvpa2.kernels.np.Matern_3_2Kernel

Inheritance diagram of Matern_3_2Kernel

class mvpa2.kernels.np.Matern_3_2Kernel(length_scale=1.0, sigma_f=1.0, numerator=3.0, **kwargs)

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.

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, 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)

numerator : float, optional

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

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: ‘numerator’ is not considered as an hyperparameter.