Based on the research, I can now generate the patch. The fix is to check if `n_SV` (number of support vectors) is 0 before performing the division that causes the ZeroDivisionError.

--- a/sklearn/svm/base.py
+++ b/sklearn/svm/base.py
@@ -297,10 +297,14 @@ class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
             n_class = 1
         n_SV = self.support_vectors_.shape[0]
 
         dual_coef_indices = np.tile(np.arange(n_SV), n_class)
-        dual_coef_indptr = np.arange(0, dual_coef_indices.size + 1,
-                                     dual_coef_indices.size / n_class)
-        self.dual_coef_ = sp.csr_matrix(
-            (dual_coef_data, dual_coef_indices, dual_coef_indptr),
-            (n_class, n_SV))
+        if not n_SV:
+            self.dual_coef_ = sp.csr_matrix([])
+        else:
+            dual_coef_indptr = np.arange(0, dual_coef_indices.size + 1,
+                                         dual_coef_indices.size / n_class)
+            self.dual_coef_ = sp.csr_matrix(
+                (dual_coef_data, dual_coef_indices, dual_coef_indptr),
+                (n_class, n_SV))
 
         def predict(self, X):