mvpa2.kernels.CustomKernel¶
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class
mvpa2.kernels.CustomKernel(kernelfunc=None, *args, **kwargs)¶ Custom Kernel defined by an arbitrary function
Examples
Basic linear kernel >>> k = CustomKernel(kernelfunc=lambda a,b: numpy.dot(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(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 reset()Initialize CustomKernel with an arbitrary function.
Parameters: kernelfunc : function
Any callable function which takes two numpy arrays and calculates a kernel function, treating the rows as samples and the columns as features. It is called from compute(d1, d2) -> func(d1,d2) and should return a numpy matrix K(i,j) which holds the kernel evaluated from d1 sample i and d2 sample j
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
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 reset()
