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PyMVPA: Python MultiVariate Pattern Analysis
Package mvpa :: Package clfs :: Module kernel :: Class KernelExponential
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Class KernelExponential

source code

The Exponential kernel class.

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

Instance Methods [hide private]
 
__init__(self, length_scale=1.0, sigma_f=1.0, **kwargs)
Initialize an 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
 
compute_lml_gradient(self, 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.		 source code
 
compute_lml_gradient_logscale(self, 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.		 source code

Inherited from Kernel: compute_gradient, reset

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

Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, length_scale=1.0, sigma_f=1.0, **kwargs)
(Constructor)

source code 
Initialize an 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)
Overrides: object.__init__

__repr__(self)
(Representation operator)

source code 
repr(x)
Overrides: object.__repr__
(inherited documentation)

compute(self, data1, data2=None)

source code 
Compute kernel matrix.
Parameters:
  • data1 (numpy.ndarray) - data
  • data2 (numpy.ndarray) - data (Defaults to None)
Overrides: Kernel.compute

set_hyperparameters(self, hyperparameter)

source code 

Set hyperaparmeters from a vector.

Used by model selection.

compute_lml_gradient(self, alphaalphaT_Kinv, data)

source code 
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.
Overrides: Kernel.compute_lml_gradient

compute_lml_gradient_logscale(self, alphaalphaT_Kinv, data)

source code 
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.
Overrides: Kernel.compute_lml_gradient_logscale

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PyMVPA: Python MultiVariate Pattern Analysis
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