def __init__(self, input_dim, variance=<NUM_LIT:1.>, active_dims=None, ARD=False, name='<STR_LIT>'):
super(BasisFuncKernel, self).__init__(input_dim, active_dims, name)<EOL>assert self.input_dim==<NUM_LIT:1>, "<STR_LIT>"<EOL>self.ARD = ARD<EOL>if self.ARD:<EOL><INDENT>phi_test = self._phi(np.random.normal(<NUM_LIT:0>, <NUM_LIT:1>, (<NUM_LIT:1>, self.input_dim)))<EOL>variance = variance * np.ones(phi_test.shape[<NUM_LIT:1>])<EOL><DEDENT>else:<EOL><INDENT>variance = np.array(variance)<EOL><DEDENT>self.variance = Param('<STR_LIT>', variance, Logexp())<EOL>self.link_parameter(self.variance)<EOL>
Abstract superclass for kernels with explicit basis functions for use in GPy.

This class does NOT automatically add an offset to the design matrix phi!