From 1089ea001ee1b90ea7b219757f56d9631a99755e Mon Sep 17 00:00:00 2001
From: zhangchenghong <1598349064@qq.com>
Date: Tue, 15 Oct 2024 11:08:02 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 39 +++------------------------------------
 1 file changed, 3 insertions(+), 36 deletions(-)

diff --git a/function.py b/function.py
index b1b6d46..d2ed52e 100644
--- a/function.py
+++ b/function.py
@@ -47,52 +47,19 @@ from collections import Counter
 
   
 # 这里写自己的代码实现  
-def classify(input_vector, dataSet, labels, k=3):  
+def classify(input_vector, dataSet, labels, k=2):  
     """  
     使用 KNN 算法对输入样本进行分类。  
   
     参数:  
-    - input_vector (numpy array): 输入待分类的样本（1xN）。  
-    - dataSet (numpy array): 训练数据集（MxN）。  
+	    - input_vector (numpy array): 输入待分类的样本（1xN）。  
+	    - dataSet (numpy array): 训练数据集（MxN）。  
     - labels (list or numpy array): 训练集对应的标签。  
     - k (int): kNN 算法参数，默认值为 3。  
   
     返回:  
     - int: 预测的分类标签。  
     """  
-   # 在 classify 函数中实现权重投票
-    distances = np.sum((input_vector - dataSet) ** 2, axis=1)
-    sorted_indices = distances.argsort()
-    k_nearest_distances = distances[sorted_indices[:k]]
-    k_nearest_labels = labels[sorted_indices[:k]]
-    
-    # 计算权重（距离的倒数）
-    weights = 1 / k_nearest_distances
-    # 归一化权重
-    weights /= weights.sum()
-    
-    # 计算加权投票
-    weighted_votes = np.zeros((10,))
-    for label, weight in zip(k_nearest_labels, weights):
-        weighted_votes[label] += weight
-    
-    return np.argmax(weighted_votes)
-
-# 在 load_dataset 函数中添加特征缩放
-def load_dataset(directory):
-    feature_list = []
-    label_list = []
-    for file in os.listdir(directory):
-        file_path = os.path.join(directory, file)
-        feature_vector = img_to_vector(file_path)
-        feature_list.append(feature_vector)
-        label_list.append(int(file.split('_')[0]))
-    feature_matrix = np.array(feature_list)
-    label_array = np.array(label_list)
-    
-    # 特征缩放
-    feature_matrix = feature_matrix / 255.0
-    return feature_matrix, label_array
     # 计算输入向量与训练集之间的欧氏距离
     distances = np.sum((input_vector - dataSet) ** 2, axis=1) ** 0.5
     # 根据距离排序并获取索引