From cc43766dcfb5947dae965fba98633a15021e316b Mon Sep 17 00:00:00 2001
From: heart20003 <1789588961@qq.com>
Date: Thu, 17 Oct 2024 00:21:59 +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 | 49 +++++++++++++++++--------------------------------
 1 file changed, 17 insertions(+), 32 deletions(-)

diff --git a/function.py b/function.py
index 207a95d..3627e82 100644
--- a/function.py
+++ b/function.py
@@ -43,38 +43,23 @@ name = "陈垣豪"
 # 这里写自己的代码实现
 def classify0(inX, dataSet, labels, k=0):
     def classify0(inX, dataSet, labels, k=3):
-    """
-    KNN 分类算法。
-
-    Args:
-    - inX (numpy array): 输入待分类的样本。
-    - dataSet (numpy array): 训练数据集。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数，请**自行设置默认值**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
+        distances = np.sqrt(np.sum((dataSet - inX) ** 2, axis=1))
+        # 获取距离最小的 k 个样本的索引
+        sorted_indices = np.argsort(distances)[:k]
     
-    # 计算输入样本与训练集中每个样本的欧氏距离
-    distances = np.sqrt(np.sum((dataSet - inX) ** 2, axis=1))
+        # 获取 k 个最近邻的标签
+        nearest_labels = [labels[i] for i in sorted_indices]
     
-    # 获取距离最小的 k 个样本的索引
-    sorted_indices = np.argsort(distances)[:k]
-    
-    # 获取 k 个最近邻的标签
-    nearest_labels = [labels[i] for i in sorted_indices]
-    
-    # 统计 k 个最近邻中每个类别的出现次数
-    label_count = {}
-    for label in nearest_labels:
-        if label in label_count:
-            label_count[label] += 1
-        else:
-            label_count[label] = 1
-    
-    # 找到出现次数最多的标签
-    predicted_label = max(label_count, key=label_count.get)
-    
-    return predicted_label
+        # 统计 k 个最近邻中每个类别的出现次数
+        label_count = {}
+        for label in nearest_labels:
+            if label in label_count:
+                label_count[label] += 1
+            else:
+                label_count[label] = 1
+        
+        # 找到出现次数最多的标签
+        predicted_label = max(label_count, key=label_count.get)
+        
+        return predicted_label