mvpa2.kernels.np.RationalQuadraticKernel¶
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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
gradient
(data1, data2)Compute gradient of the kernel matrix. 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
gradient
(data1, data2)Compute gradient of the kernel matrix. 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.
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set_hyperparameters
(hyperparameter)¶ Set hyperaparmeters from a vector.
Used by model selection. Note: ‘alpha’ is not considered as an hyperparameter.
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