Custom Kernel defined by an arbitrary function
Examples
Basic linear kernel >>> k = CustomKernel(kernelfunc=lambda a,b: numpy.dot(a,b.T))
Methods
add_conversion(typename, methodfull, methodraw) | Adds methods to the Kernel class for new conversions :Parameters: typename : string Describes kernel type methodfull : function Method which converts to the new kernel object class methodraw : function Method which returns a raw kernel .. |
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 Assumptions: - ds1, ds2 are either datasets or arrays, - presumably 2D (not checked neither enforced here - _compute takes ndarrays. |
computed(*args, **kwargs) | Compute kernel and return self |
reset() |
Initialize CustomKernel with an arbitrary function.
Parameters: | kernelfunc : function
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 :Parameters: typename : string Describes kernel type methodfull : function Method which converts to the new kernel object class methodraw : function Method which returns a raw kernel .. |
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 Assumptions: - ds1, ds2 are either datasets or arrays, - presumably 2D (not checked neither enforced here - _compute takes ndarrays. |
computed(*args, **kwargs) | Compute kernel and return self |
reset() |