mvpa2.kernels.np.Matern_5_2Kernel¶
-
class
mvpa2.kernels.np.Matern_5_2Kernel(**kwargs)¶ The Matern kernel class for the case ni=5/2.
This kernel is just Matern_3_2Kernel(numerator=5.0).
Attributes
descrDescription of the object if any 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. as_raw_sg(kernel)Converts directly to a Shogun kernel as_sg(kernel)Converts this kernel to a Shogun-based representation 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
the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0)
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
sigma_f : float, optional
Signal standard deviation. (Defaults to 1.0)
numerator : float, optional
the numerator of parameter ni of Matern covariance functions. Currently only numerator=3.0 and numerator=5.0 are implemented. (Defaults to 3.0)
Attributes
descrDescription of the object if any 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. as_raw_sg(kernel)Converts directly to a Shogun kernel as_sg(kernel)Converts this kernel to a Shogun-based representation 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.
