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

add_conversion(typename, methodfull, methodraw) Adds methods to the Kernel class for new conversions
as_ls(kernel)
as_np() Converts this kernel to a Numpy-based representation
as_raw_ls(kernel)
as_raw_np() Directly return this kernel as a numpy array.
as_raw_sg(kernel) Converts directly to a Shogun kernel
as_sg(kernel) Converts this kernel to a Shogun-based representation
cleanup() Wipe out internal representation
compute(ds1[, ds2]) Generic computation of any kernel
computed(\*args, \*\*kwargs) Compute kernel and return self
gradient(data1, data2) Compute gradient of the kernel matrix.
reset()
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

add_conversion(typename, methodfull, methodraw) Adds methods to the Kernel class for new conversions
as_ls(kernel)
as_np() Converts this kernel to a Numpy-based representation
as_raw_ls(kernel)
as_raw_np() Directly return this kernel as a numpy array.
as_raw_sg(kernel) Converts directly to a Shogun kernel
as_sg(kernel) Converts this kernel to a Shogun-based representation
cleanup() Wipe out internal representation
compute(ds1[, ds2]) Generic computation of any kernel
computed(\*args, \*\*kwargs) Compute kernel and return self
gradient(data1, data2) Compute gradient of the kernel matrix.
reset()
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.