feat: add cal_nmi();

pypots/utils/metrics.py

@@ -543,6 +543,31 @@ def cal_adjusted_rand_index(
     return aRI
 
 
+def cal_nmi(
+    class_predictions: np.ndarray,
+    targets: np.ndarray,
+) -> float:
+    """Calculate Normalized Mutual Information between two clusterings.
+
+    Parameters
+    ----------
+    class_predictions :
+        Clustering results returned by a clusterer.
+
+    targets :
+        Ground truth (correct) clustering results.
+
+    Returns
+    -------
+    NMI : float,
+        Normalized Mutual Information
+
+
+    """
+    NMI = metrics.normalized_mutual_info_score(targets, class_predictions)
+    return NMI
+
+
 def cal_cluster_purity(
     class_predictions: np.ndarray,
     targets: np.ndarray,