The Squared Exponential kernel class.
Note that it can handle a length scale for each dimension for Automtic Relevance Determination.
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. |
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
sigma_f : float, optional
enable_ca : None or list of str
disable_ca : None or list of str
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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. |
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 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 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).
Set hyperaparmeters from a vector.
Used by model selection.