From dddf61b7a35cee55add3fe8a52ddfcc52c4ccf81 Mon Sep 17 00:00:00 2001
From: dong <2938690875@qq.com>
Date: Wed, 16 Oct 2024 18:37:05 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 29 +++--------------------------
 1 file changed, 3 insertions(+), 26 deletions(-)

diff --git a/function.py b/function.py
index 8de62e1..012044f 100644
--- a/function.py
+++ b/function.py
@@ -58,11 +58,7 @@ def classify(input_vector, dataSet, labels, k=3):
     """
     使用欧式距离的 kNN 分类算法。
     """
-    distances = [np.linalg.norm(input_vector - x) for x in dataSet]
-    k_indices = np.argsort(distances)[:k]
-    k_nearest_labels = [labels[i] for i in k_indices]
-    most_common = Counter(k_nearest_labels).most_common(1)
-    return most_common[0][0]
+    
 
 
 # 这里也写自己的代码实现
@@ -78,25 +74,7 @@ def load_dataset(directory):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-    features = []
-    labels = []
-    for filename in os.listdir(directory):
-        filepath = os.path.join(directory, filename)
-
-        # 检查文件是否为图片类型（如 png 或 jpg）
-        if filename.endswith(".png") or filename.endswith(".jpg"):
-            # 从文件名提取标签
-            label = int(filename.split('_')[0])
-
-            # 加载图片并转换为灰度图
-            image = Image.open(filepath).convert('L')
-
-            # 展平为一维向量并添加到特征列表中
-            feature = np.array(image).flatten()
-            features.append(feature)
-            labels.append(label)
-
-    return np.array(features), np.array(labels)
+    
 
 
 # 这里还写自己的代码实现
@@ -111,8 +89,7 @@ def img_to_vector(filename):
     Returns:
     - np.array: 二值图像向量。
     """
-    image = Image.open(filename).convert('L')
-    return np.array(image).flatten()
+    
 
 
 if __name__ == "__main__":