mvpa2.kernels.sg.LinearSGKernel¶
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class
mvpa2.kernels.sg.LinearSGKernel(normalizer_cls=None, normalizer_args=None, **kwargs)¶ A basic linear kernel computed via Shogun: K(a,b) = a*b.T
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()as_sg()cleanup()Wipe out internal representation compute(ds1[, ds2])Generic computation of any kernel computed(\*args, \*\*kwargs)Compute kernel and return self reset()Parameters: normalizer_cls : sg.Kernel.CKernelNormalizer
Class to use as a normalizer for the kernel. Will be instantiated upon compute(). Only supported for shogun >= 0.6.5. By default (if left None) assigns IdentityKernelNormalizer to assure no normalization.
normalizer_args : None or list
If necessary, provide a list of arguments for the normalizer.
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()as_sg()cleanup()Wipe out internal representation compute(ds1[, ds2])Generic computation of any kernel computed(\*args, \*\*kwargs)Compute kernel and return self reset()
