mvpa2.kernels.np.SquaredExponentialKernel

class mvpa2.kernels.np.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.

Attributes

descr	Description of the object if any
length_scale

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
compute_lml_gradient(alphaalphaT_Kinv, data)	Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
compute_lml_gradient_logscale(...)	Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
computed(\*args, \*\*kwargs)	Compute kernel and return self
reset()
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)

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
length_scale

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
compute_lml_gradient(alphaalphaT_Kinv, data)	Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
compute_lml_gradient_logscale(...)	Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
computed(\*args, \*\*kwargs)	Compute kernel and return self
reset()
set_hyperparameters(hyperparameter)	Set hyperaparmeters from a vector.

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.