更新 function.py

function.py

@@ -98,12 +98,13 @@ def load_dataset(directory):
                 data_set.append(img_to_vector(os.path.join(root, file)))
     data_set = np.array(data_set).reshape(len(data_set), - 1)
 
-    # 找到数据集中的最大值和最小值
-    data_min = np.min(data_set)
-    data_max = np.max(data_set)
+    # # 找到数据集中的最大值和最小值
+    # data_min = np.min(data_set)
+    # data_max = np.max(data_set)
 
     # 归一化数据
-    data_set = (data_set - data_min) / (data_max - data_min)
+    # data_set = (data_set - data_min) / (data_max - data_min)
+    data_set = data_set / 255.0
 
     return data_set, np.array(labels)
 
@@ -146,14 +147,9 @@ if __name__ == "__main__":
         "00000000000001111111110000000000"
         "00000000000000000111100000000000"
     )
-    # 对测试数据进行归一化
-    test_data_vector = np.array([int(char) for char in test_data]).reshape(1, 1024)
-    test_data_min = np.min(test_data_vector)
-    test_data_max = np.max(test_data_vector)
-    test_data_vector = (test_data_vector - test_data_min) / (test_data_max - test_data_min)
     
     predicted_label = classify(
-        np.array([int(char) for char in test_data]).reshape(1, 1024),
+        np.array([int(char) for char in test_data]).reshape(1, 1024) / 255.0,
         train_data,
         train_labels,
     )