From d3bce57a27c44561c1f82db8627b2ec2c38396fc Mon Sep 17 00:00:00 2001
From: cy <939601402@qq.com>
Date: Tue, 15 Oct 2024 20:46:54 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 function.py | 84 ++++++++++++++++++++++++++---------------------------
 1 file changed, 42 insertions(+), 42 deletions(-)

diff --git a/function.py b/function.py
index c2f8278..e64cd0e 100644
--- a/function.py
+++ b/function.py
@@ -100,47 +100,47 @@ def load_dataset(directory):
 
 
 if __name__ == "__main__":
-    # # 以下代码可用于测试，可自行随意修改
-    # train_data, train_labels = load_dataset("trainingDigits")
+    # 以下代码可用于测试，可自行随意修改
+    train_data, train_labels = load_dataset("trainingDigits")
 
-    # test_data = (
-    #     "00000000000000110000000000000000"
-    #     "00000000000011111000000000000000"
-    #     "00000000000011111100000000000000"
-    #     "00000000001111111110000000000000"
-    #     "00000000001111111110000000000000"
-    #     "00000000001111111100000000000000"
-    #     "00000000011111111000000000000000"
-    #     "00000000111111111000000000000000"
-    #     "00000000111111110000000000000000"
-    #     "00000000111111110000000000000000"
-    #     "00000000111111110000000000000000"
-    #     "00000000111111110000000000000000"
-    #     "00000000111111100000000000000000"
-    #     "00000001111111100000000000000000"
-    #     "00000001111111100000000000000000"
-    #     "00000001111111100000000000000000"
-    #     "00000001111111111111000000000000"
-    #     "00000001111111111111111000000000"
-    #     "00000001111111111111111100000000"
-    #     "00000011111111111111111110000000"
-    #     "00000011111111111111111110000000"
-    #     "00000001111111111111111110000000"
-    #     "00000001111111111111111110000000"
-    #     "00000000111111111100111111100000"
-    #     "00000000111111111000111111100000"
-    #     "00000000011111111001111111100000"
-    #     "00000000011111111111111111000000"
-    #     "00000000001111111111111111000000"
-    #     "00000000000111111111111111000000"
-    #     "00000000000011111111111100000000"
-    #     "00000000000001111111110000000000"
-    #     "00000000000000000111100000000000"
-    # )
-    # predicted_label = classify(
-    #     np.array([int(char) for char in test_data]).reshape(1, 1024),
-    #     train_data,
-    #     train_labels,
-    # )
+    test_data = (
+        "00000000000000110000000000000000"
+        "00000000000011111000000000000000"
+        "00000000000011111100000000000000"
+        "00000000001111111110000000000000"
+        "00000000001111111110000000000000"
+        "00000000001111111100000000000000"
+        "00000000011111111000000000000000"
+        "00000000111111111000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111111111000000000000"
+        "00000001111111111111111000000000"
+        "00000001111111111111111100000000"
+        "00000011111111111111111110000000"
+        "00000011111111111111111110000000"
+        "00000001111111111111111110000000"
+        "00000001111111111111111110000000"
+        "00000000111111111100111111100000"
+        "00000000111111111000111111100000"
+        "00000000011111111001111111100000"
+        "00000000011111111111111111000000"
+        "00000000001111111111111111000000"
+        "00000000000111111111111111000000"
+        "00000000000011111111111100000000"
+        "00000000000001111111110000000000"
+        "00000000000000000111100000000000"
+    )
+    predicted_label = classify(
+        np.array([int(char) for char in test_data]).reshape(1, 1024),
+        train_data,
+        train_labels,
+    )
 
-    # print(f"预测的标签: {predicted_label}")
\ No newline at end of file
+    print(f"预测的标签: {predicted_label}")
\ No newline at end of file