0c8b83454f5ca772d2d87180ccbe891a295fcf8b,GPy/kern/prod_orthogonal.py,prod_orthogonal,K,#prod_orthogonal#Any#Any#Any#,41

Before Change

    def K(self,X,X2,target):
        Compute the covariance matrix between X and X2.
        self._K_computations(X,X2)
        target += self._K1*self._K2

    def _K_computations(self,X,X2):
        
        Compute the two kernel matrices.

After Change

        return parameter names.
        return [self.k1.name + "_" + param_name for param_name in self.k1._get_param_names()] + [self.k2.name + "_" + param_name for param_name in self.k2._get_param_names()]

    def K(self,X,X2,target):
        Compute the covariance matrix between X and X2.
        if X2 is None: X2 = X
        target1 = np.zeros_like(target)
        target2 = np.zeros_like(target)
        self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],target1)
        self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2)
        target += target1 * target2

    def dK_dtheta(self,dL_dK,X,X2,target):

Instances