def update_gradients_full(self, dL_dK, X, X2=None):
X,slices = X[:,:-<NUM_LIT:1>],index_to_slices(X[:,-<NUM_LIT:1>])<EOL>if X2 is None:<EOL><INDENT>X2,slices2 = X,slices<EOL>K = np.zeros((X.shape[<NUM_LIT:0>], X.shape[<NUM_LIT:0>]))<EOL><DEDENT>else:<EOL><INDENT>X2,slices2 = X2[:,:-<NUM_LIT:1>],index_to_slices(X2[:,-<NUM_LIT:1>])<EOL><DEDENT>vyt = self.variance_Yt<EOL>lyt = <NUM_LIT:1.>/(<NUM_LIT:2>*self.lengthscale_Yt)<EOL>tdist = (X[:,<NUM_LIT:0>][:,None] - X2[:,<NUM_LIT:0>][None,:])**<NUM_LIT:2><EOL>ttdist = (X[:,<NUM_LIT:0>][:,None] - X2[:,<NUM_LIT:0>][None,:])<EOL>rd=tdist.shape[<NUM_LIT:0>]<EOL>dka = np.zeros([rd,rd])<EOL>dkc = np.zeros([rd,rd])<EOL>dkYdvart = np.zeros([rd,rd])<EOL>dkYdlent = np.zeros([rd,rd])<EOL>dkdubias = np.zeros([rd,rd])<EOL>kyy = lambda tdist: np.exp(-lyt*(tdist))<EOL>dkyydlyt = lambda tdist: kyy(tdist)*(-tdist)<EOL>k1 = lambda tdist: (<NUM_LIT:2>*lyt - <NUM_LIT:4>*lyt**<NUM_LIT:2> * (tdist) )<EOL>k4 = lambda ttdist: <NUM_LIT:2>*lyt*(ttdist)<EOL>dk1dlyt = lambda tdist: <NUM_LIT> - <NUM_LIT:4>*<NUM_LIT>*lyt*tdist<EOL>dk4dlyt = lambda ttdist: <NUM_LIT:2>*(ttdist)<EOL>for i, s1 in enumerate(slices):<EOL><INDENT>for j, s2 in enumerate(slices2):<EOL><INDENT>for ss1 in s1:<EOL><INDENT>for ss2 in s2:<EOL><INDENT>if i==<NUM_LIT:0> and j==<NUM_LIT:0>:<EOL><INDENT>dkYdvart[ss1,ss2] = kyy(tdist[ss1,ss2])<EOL>dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])<EOL>dkdubias[ss1,ss2] = <NUM_LIT:0><EOL><DEDENT>elif i==<NUM_LIT:0> and j==<NUM_LIT:1>:<EOL><INDENT>dkYdvart[ss1,ss2] = (k4(ttdist[ss1,ss2])+<NUM_LIT:1>)*kyy(tdist[ss1,ss2])<EOL>dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (k4(ttdist[ss1,ss2])+<NUM_LIT:1.>)+vyt*kyy(tdist[ss1,ss2])*(dk4dlyt(ttdist[ss1,ss2]))<EOL>dkdubias[ss1,ss2] = <NUM_LIT:0><EOL><DEDENT>elif i==<NUM_LIT:1> and j==<NUM_LIT:1>:<EOL><INDENT>dkYdvart[ss1,ss2] = (k1(tdist[ss1,ss2]) + <NUM_LIT:1.> )* kyy(tdist[ss1,ss2])<EOL>dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])*( k1(tdist[ss1,ss2]) + <NUM_LIT:1.> ) +vyt*kyy(tdist[ss1,ss2])*dk1dlyt(tdist[ss1,ss2])<EOL>dkdubias[ss1,ss2] = <NUM_LIT:1><EOL><DEDENT>else:<EOL><INDENT>dkYdvart[ss1,ss2] = (-k4(ttdist[ss1,ss2])+<NUM_LIT:1>)*kyy(tdist[ss1,ss2])<EOL>dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (-k4(ttdist[ss1,ss2])+<NUM_LIT:1.>)+vyt*kyy(tdist[ss1,ss2])*(-dk4dlyt(ttdist[ss1,ss2]) )<EOL>dkdubias[ss1,ss2] = <NUM_LIT:0><EOL><DEDENT><DEDENT><DEDENT><DEDENT><DEDENT>self.variance_Yt.gradient = np.sum(dkYdvart * dL_dK)<EOL>self.lengthscale_Yt.gradient =  np.sum(dkYdlent*(-<NUM_LIT:0.5>*self.lengthscale_Yt**(-<NUM_LIT:2>)) * dL_dK)<EOL>self.ubias.gradient = np.sum(dkdubias * dL_dK)<EOL>
derivative of the covariance matrix with respect to the parameters.