Package mvpa :: Package clfs :: Module kernel :: Class KernelExponential

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# Class KernelExponential

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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)

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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)

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repr(x)

Overrides: object.__repr__
  (inherited documentation)

### compute(self, data1, data2=None)

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Compute kernel matrix.

Parameters:
  - `data1` (numpy.ndarray) - data
  - `data2` (numpy.ndarray) - data (Defaults to None)
Overrides: Kernel.compute

### set_hyperparameters(self, hyperparameter)

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Set hyperaparmeters from a vector.

Used by model selection.

### compute_lml_gradient(self, alphaalphaT_Kinv, data)

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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)

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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