def K(self, X, X2, target):
if X2 is None: X2 = X<EOL><INDENT>i1 = X[:,<NUM_LIT:1>]<EOL>i2 = X2[:,<NUM_LIT:1>]<EOL>X = X[:,<NUM_LIT:0>].reshape(-<NUM_LIT:1>,<NUM_LIT:1>)<EOL>X2 = X2[:,<NUM_LIT:0>].reshape(-<NUM_LIT:1>,<NUM_LIT:1>)<EOL><DEDENT>dist = np.abs(X - X2.T)<EOL>ly=<NUM_LIT:1>/self.lengthscaleY<EOL>lu=np.sqrt(<NUM_LIT:3>)/self.lengthscaleU<EOL>k1 = np.exp(-ly*dist)*(<NUM_LIT:2>*lu+ly)/(lu+ly)**<NUM_LIT:2><EOL>k2 = (np.exp(-lu*dist)*(ly-<NUM_LIT:2>*lu+lu*ly*dist-lu**<NUM_LIT:2>*dist) + np.exp(-ly*dist)*(<NUM_LIT:2>*lu-ly) ) / (ly-lu)**<NUM_LIT:2> <EOL>k3 = np.exp(-lu*dist) * ( (<NUM_LIT:1>+lu*dist)/(lu+ly) + (lu)/(lu+ly)**<NUM_LIT:2> )<EOL>np.add(self.varianceU*self.varianceY*(k1+k2+k3), target, target)<EOL>
Compute the covariance matrix between X and X2.