# scikit-learn__scikit-learn-15512
# Loki Mode Multi-Agent Patch
# Attempts: 1

Based on the issue and architect's analysis, I need to generate a patch for the scikit-learn AffinityPropagation implementation. The issue is in version 0.20.3 where the file would be `sklearn/cluster/affinity_propagation_.py`. Let me create the patch based on the standard scikit-learn codebase structure and the described bug:

```
--- a/sklearn/cluster/affinity_propagation_.py
+++ b/sklearn/cluster/affinity_propagation_.py
@@ -15,6 +15,7 @@ import numpy as np
 from ..exceptions import ConvergenceWarning
 from ..base import BaseEstimator, ClusterMixin
 from ..utils import as_float_array, check_array
+from ..utils.validation import check_is_fitted
 
 
 def _equal_similarities_and_preferences(S, preference):
@@ -93,6 +94,11 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
 
     cluster_centers_indices : array, shape (n_clusters,)
         index of clusters centers
+
+    n_iter : int
+        Number of iterations run.
+
+    converged : bool
 
     Notes
     -----
@@ -195,12 +201,19 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
             if verbose:
                 print("Converged after %d iterations." % it)
             break
+    else:
+        if verbose:
+            print("Did not converge")
+        converged = False
+    else:
+        converged = True
 
     I = np.flatnonzero(E)
     K = I.size  # Identify exemplars
 
     if K > 0:
-        c = np.argmax(S[:, I], axis=1)
+        if not converged:
+            warnings.warn("Affinity propagation did not converge, this model "
+                          "will not have any cluster centers.", ConvergenceWarning)
+            return np.array([]), np.array([-1] * n_samples), it + 1, False
+        c = np.argmax(S[:, I], axis=1)
         c[I] = np.arange(K)  # Identify clusters
         # Refine the final set of exemplars and clusters andடித
         # return results
@@ -213,17 +226,19 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
             labels[I[k]] = k
 
         cluster_centers_indices = I
+        return cluster_centers_indices, labels, it + 1, True
     else:
-        warnings.warn("Affinity propagation did not converge, this model "
-                      "will not have any cluster centers.", ConvergenceWarning)
-        cluster_centers_indices = np.array([])
-        labels = np.array([-1] * n_samples)
+        if not converged:
+            warnings.warn("Affinity propagation did not converge, this model "
+                          "will not have any cluster centers.", ConvergenceWarning)
+        return np.array([]), np.array([-1] * n_samples), it + 1, converged
 
-    if return_n_iter:
-        return cluster_centers_indices, labels, it + 1
+    return cluster_centers_indices, labels, it + 1, converged
 
-    return cluster_centers_indices, labels
 
+    if return_n_iter:
+        return cluster_centers_indices, labels, it + 1, converged
+    return cluster_centers_indices, labels, converged
 
 ###############################################################################
```

Let me provide a cleaner, more accurate patch:

--- a/sklearn/cluster/affinity_propagation_.py
+++ b/sklearn/cluster/affinity_propagation_.py
@@ -195,6 +195,9 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
             if verbose:
                 print("Converged after %d iterations." % it)
             break
+    else:
+        if verbose:
+            print("Did not converge")
 
     I = np.flatnonzero(E)
     K = I.size  # Identify exemplars
@@ -213,9 +216,11 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
             labels[I[k]] = k
 
         cluster_centers_indices = I
+        converged = (it < max_iter - 1 or np.sum(se == convergence_iter) == n_samples)
     else:
         warnings.warn("Affinity propagation did not converge, this model "
                       "will not have any cluster centers.", ConvergenceWarning)
+        converged = False
         cluster_centers_indices = np.array([])
         labels = np.array([-1] * n_samples)
 
@@ -304,6 +309,11 @@ class AffinityPropagation(BaseEstimator, ClusterMixin):
     n_iter_ : int
         Number of iterations taken to converge.
 
+    converged_ : bool
+        Whether the algorithm converged. If False, cluster_centers_indices_
+        will be empty and labels_ will be -1 for all samples as documented
+        for non-convergence.
+
     Examples
     --------
     >>> from sklearn.cluster import AffinityPropagation
@@ -352,8 +362,19 @@ class AffinityPropagation(BaseEstimator, ClusterMixin):
                 "affinity matrix, got %s" % self.affinity)
         self.cluster_centers_indices_, self.labels_, self.n_iter_ = \
             affinity_propagation(
-                self.affinity_matrix_, self.preference, max_iter=self.max_iter,
+                self.affinity_matrix_, self.preference,
                 convergence_iter=self.convergence_iter, damping=self.damping,
-                copy=self.copy, verbose=self.verbose, return_n_iter=True)
+                max_iter=self.max_iter, copy=self.copy, verbose=self.verbose,
+                return_n_iter=True)
+
+        if self.n_iter_ == self.max_iter and not (
+                self.cluster_centers_indices_.size == 0 and
+                np.all(self.labels_ == -1)):
+            warnings.warn("Affinity propagation did not converge, this model "
+                          "will not have any cluster centers.",
+                          ConvergenceWarning)
+            self.cluster_centers_indices_ = np.array([])
+            self.labels_ = np.array([-1] * X.shape[0])
+            self.converged_ = False
+        else:
+            self.converged_ = self.n_iter_ < self.max_iter
 
         if self.affinity != "precomputed":