From 483f2bdb945bf212dc62b51143698ba907851511 Mon Sep 17 00:00:00 2001
From: 43606070 <43606070@qq.com>
Date: Sun, 20 Oct 2024 11:37:00 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 56 ++++++++++++++---------------------------------------
 1 file changed, 15 insertions(+), 41 deletions(-)

diff --git a/function.py b/function.py
index dbf400c..dacd14c 100644
--- a/function.py
+++ b/function.py
@@ -77,45 +77,19 @@ if __name__ == "__main__":
     # 加载训练数据集
     train_data, train_labels = load_data("trainingDigits")  # 使用正确的函数名 load_data
     
-    # 加载测试样本 (此处为简单测试，实际使用时应替换为完整的测试集)
-    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"
-    )
-    
-    # 将测试样本转换为向量，并进行分类
-    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}")
+    # 加载测试数据集
+    test_data, test_labels = load_data("testDigits")  # 加载测试集文件夹中的所有数据
+
+    # 测试集预测结果
+    correct_count = 0
+    total_count = len(test_labels)
+
+    for i in range(total_count):
+        predicted_label = classify(test_data[i], train_data, train_labels)
+        print(f"测试样本 {i+1} - 真实标签: {test_labels[i]}, 预测标签: {predicted_label}")
+        if predicted_label == test_labels[i]:
+            correct_count += 1
+
+    accuracy = correct_count / total_count
+    print(f"分类精度: {accuracy * 100:.2f}%")