def K(self, 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>K = np.zeros((X.shape[<NUM_LIT:0>], X2.shape[<NUM_LIT:0>]))<EOL><DEDENT>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>vyt = self.variance_Yt<EOL>lyt=<NUM_LIT:1>/(<NUM_LIT:2>*self.lengthscale_Yt)<EOL>a = -self.a<EOL>c = self.c<EOL>kyy = lambda tdist: np.exp(-lyt*(tdist))<EOL>k1 = lambda tdist: (<NUM_LIT:2>*lyt - <NUM_LIT:4>*lyt**<NUM_LIT:2> *(tdist) )<EOL>k4 = lambda tdist: <NUM_LIT:2>*lyt*(tdist)<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>K[ss1,ss2] = vyt*kyy(tdist[ss1,ss2])<EOL><DEDENT>elif i==<NUM_LIT:0> and j==<NUM_LIT:1>:<EOL><INDENT>K[ss1,ss2] = (k4(ttdist[ss1,ss2])+<NUM_LIT:1>)*vyt*kyy(tdist[ss1,ss2])<EOL><DEDENT>elif i==<NUM_LIT:1> and j==<NUM_LIT:1>:<EOL><INDENT>K[ss1,ss2] = ( k1(tdist[ss1,ss2]) + <NUM_LIT:1.> )*vyt* kyy(tdist[ss1,ss2])+self.ubias<EOL><DEDENT>else:<EOL><INDENT>K[ss1,ss2] = (-k4(ttdist[ss1,ss2])+<NUM_LIT:1>)*vyt*kyy(tdist[ss1,ss2])<EOL><DEDENT><DEDENT><DEDENT><DEDENT><DEDENT>return K<EOL>
Compute the covariance matrix between X and X2.