Update utils/helper_functions.py

utils/helper_functions.py

@@ -277,11 +277,12 @@ def quantized_influence(arr1: np.ndarray, arr2: np.ndarray, k: int = 16, use_dag
     unique_values = np.unique(arr1_quantized)
     
     # Compute the global average of quantized arr2
+    total_samples = len(arr2_quantized)
     y_bar_global = np.mean(arr2_quantized)  
     
     # Compute weighted local averages and normalize
     weighted_local_averages = [(np.mean(arr2_quantized[arr1_quantized == val]) - y_bar_global)**2 * len(arr2_quantized[arr1_quantized == val])**2 for val in unique_values]
-    qim = np.mean(weighted_local_averages) / np.std(arr2_quantized)  # Calculate the quantized influence measure
+    qim = np.sum(weighted_local_averages) / (total_samples * np.std(arr2_quantized))  # Calculate the quantized influence measure
 
     if use_dagger:
         # If use_dagger is True, compute local estimates and map them to unique quantized values