Style: use for-loop to match MATLAB original for gradient computation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: Longkang(Loka) Li

causallearn/utils/ScoreUtils.py

@@ -504,12 +504,12 @@ def gpr_multi_new(logtheta=None, covfunc=None, x=None, y=None, xstar=None, nargo
             # Only compute gradient for the last hyperparameter (noise variance).
             # Kernel widths are fixed per the paper (Huang et al., 2018, Sec 4.2.2):
             # "the kernel widths of variables are fixed in order to work on fixed RKHSs"
-            i = len(out2) - 1
-            temp = list(covfunc.copy())
-            temp.append(logtheta)
-            temp.append(x)
-            temp.append(i)
-            out2[i] = np.sum(np.multiply(W, feval(temp))) / 2
+            for i in range(len(out2) - 1, len(out2)):
+                temp = list(covfunc.copy())
+                temp.append(logtheta)
+                temp.append(x)
+                temp.append(i)
+                out2[i] = np.sum(np.multiply(W, feval(temp))) / 2
     else:  # ... otherwise compute (marginal) test predictions ...
         temp = list(covfunc.copy())
         temp.append(logtheta)