mvpa2.kernels.np.ExponentialKernel

class mvpa2.kernels.np.ExponentialKernel(*args, **kwargs)

The 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

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
gradient(data1, data2)	Compute gradient of the kernel matrix.
reset()

Initialize instance of ExponentialKernel

Parameters:

length_scale : float or list(float), optional

The characteristic length-scale (or length-scales) of the phenomenon under investigation. Constraints: value must be convertible to type ‘float’, or value must be convertible to list(float). [Default: 1.0]

sigma_f : float, optional

Signal standard deviation. Constraints: value must be convertible to type ‘float’. [Default: 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

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
gradient(data1, data2)	Compute gradient of the kernel matrix.
reset()

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) BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS.

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. Shorter formula. Allows vector of lengthscales (ARD). BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS.

gradient(data1, data2)

Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.