The Rational Quadratic (RQ) 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 |
computed(*args, **kwargs) | Compute kernel and return self |
gradient(data1, data2) | Compute gradient of the kernel matrix. |
reset() | |
set_hyperparameters(hyperparameter) | Set hyperaparmeters from a vector. |
Initialize a Squared Exponential kernel instance.
Parameters : | length_scale : float or numpy.ndarray
sigma_f : float
alpha : float
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 |
computed(*args, **kwargs) | Compute kernel and return self |
gradient(data1, data2) | Compute gradient of the kernel matrix. |
reset() | |
set_hyperparameters(hyperparameter) | Set hyperaparmeters from a vector. |
Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.
Set hyperaparmeters from a vector.
Used by model selection. Note: ‘alpha’ is not considered as an hyperparameter.