def __init__(self, input_dim, F, G, variance=<NUM_LIT:1.>, weights=None):
self.input_dim = input_dim<EOL>self.F = F<EOL>self.G = G<EOL>self.G_1 ,L,Li,logdet = pdinv(G)<EOL>self.n = F.shape[<NUM_LIT:0>]<EOL>if weights is not None:<EOL><INDENT>assert weights.shape==(self.n,)<EOL><DEDENT>else:<EOL><INDENT>weights = np.ones(self.n)<EOL><DEDENT>self.num_params = self.n + <NUM_LIT:1><EOL>self.name = '<STR_LIT>'<EOL>self._set_params(np.hstack((variance,weights)))<EOL>
Argumnents
----------
input_dim: int - the number of input dimensions
F: np.array of functions with shape (n,) - the n basis functions
G: np.array with shape (n,n) - the Gram matrix associated to F
weights : np.ndarray with shape (n,)