mvpa2.kernels.np.GeneralizedLinearKernel¶

-
class
mvpa2.kernels.np.
GeneralizedLinearKernel
(*args, **kwargs)¶ The linear kernel class.
Notes
Available conditional attributes:
gradients
: Dictionary of gradients per a parametergradientslog
: Dictionary of gradients per a parameter in logspace
(Conditional attributes enabled by default suffixed with
+
)Attributes
descr
Description of the object if any Methods
add_conversion
(typename, methodfull, methodraw)Adds methods to the Kernel class for new conversions as_ls
()as_np
()Converts this kernel to a Numpy-based representation as_raw_ls
()as_raw_np
()Directly return this kernel as a numpy array. as_raw_sg
()Converts directly to a Shogun kernel as_sg
()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 instance of GeneralizedLinearKernel
Parameters: sigma_0
A simple constant squared value which is broadcasted across kernel. In the case of GPR – standard deviation of the Gaussian prior probability Normal(0, sigma_0**2) of the intercept of the linear regression. [Default: 1.0]
Sigma_p
A generic scalar or vector, or diagonal matrix to scale all dimensions or associate different scaling to each dimensions while computing te kernel matrix:
. In the case of GPR – a scalar or a diagonal of covariance matrix of the Gaussian prior probability Normal(0, Sigma_p) on the weights of the linear regression. [Default: 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
Attributes
descr
Description of the object if any Methods
add_conversion
(typename, methodfull, methodraw)Adds methods to the Kernel class for new conversions as_ls
()as_np
()Converts this kernel to a Numpy-based representation as_raw_ls
()as_raw_np
()Directly return this kernel as a numpy array. as_raw_sg
()Converts directly to a Shogun kernel as_sg
()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
()-
reset
()¶