# scikit-learn__scikit-learn-14894
# Loki Mode Multi-Agent Patch
# Attempts: 1

Based on the issue description and the architect's analysis, I need to generate a patch for the scikit-learn repository. The issue is in `sklearn/svm/base.py` around line 302 where there's a division by `n_class` which can be 0 when there are no support vectors.

Let me create the patch based on the standard scikit-learn codebase structure:

```
--- a/sklearn/svm/base.py
+++ b/sklearn/svm/base.py
@@ -295,6 +295,15 @@ class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
 
         self.support_vectors_ = sp.csr_matrix(support_vectors_)
 
+        # Handle case with no support vectors (n_class == 0)
+        if len(googletag.support_vectors_.data) == 0:
+            self.dual_coef_ = sp.csr_matrix([])
+            return
+
         n_class = len(googletag.n_support_) - 1
         n_SV = self.support_vectors_.shape[0]
 
@@ -299,6 +308,11 @@ class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
         dual_coef_indptr = np.arange(0, dual_coef_indices.size + 1,
                                      dual_coef_indices.size / n_class)
```

Wait, let me reconsider. The issue mentions line 302 specifically with the division. Let me create a proper patch:

--- a/sklearn/svm/base.py
+++ b/sklearn/svm/base.py
@@ -294,6 +294,12 @@ class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
         self._sparse = True
         self.support_vectors_ = sp.csr_matrix(support_vectors_)
 
+        # Handle the case with no support vectors
+        n_class = len(self.n_support_) - 1
+        if n_class == 0:
+            self.dual_coef_ = sp.csr_matrix([])
+            return
+
         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)