From d18773adc3724b091179b1f7bbf8ee40f10a2149 Mon Sep 17 00:00:00 2001
From: zhangchenghong <1598349064@qq.com>
Date: Tue, 22 Oct 2024 20:51:05 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 255 +++++++++++++++++++++++++---------------------------
 1 file changed, 123 insertions(+), 132 deletions(-)

diff --git a/function.py b/function.py
index c72d396..161cc84 100644
--- a/function.py
+++ b/function.py
@@ -28,136 +28,127 @@ import os
 id = "202210423215"
 name = "章程鸿·"
 
-
-import numpy as np
-import os
-from collections import Counter
-
-
-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）。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数，默认值为 2。
-
-    返回:
-    - int: 预测的分类标签。
-    """
-    # 计算马氏距离
-    centered_dataSet = dataSet - np.mean(dataSet, axis=0)
-    centered_input_vector = input_vector - np.mean(dataSet, axis=0)
-    cov_matrix = np.cov(centered_dataSet.T)
-    if np.linalg.det(cov_matrix) == 0:
-        raise ValueError("协方差矩阵不可逆")
-    inv_cov_matrix = np.linalg.inv(cov_matrix)
-    mahalanobis_distances = np.sqrt(np.sum(centered_input_vector.dot(inv_cov_matrix) * centered_input_vector, axis=1))
-
-    sorted_indices = mahalanobis_distances.argsort()
-    k_nearest_distances = mahalanobis_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)
-
-
-def load_dataset(directory):
-    """
-    加载数据集中的图像，并将其转换为特征向量和标签。
-
-    参数:
-    - directory (str): 数据文件夹路径。
-
-    返回:
-            - np.array: 特征数据（Mx1024）。
-            - np.array: 标签数据（Mx1）。
-    """
-    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
-
-
-def img_to_vector(filename):
-    """
-    将二值图像转换为向量。
-
-    Args:
-    - filename (str): 图像文件路径。
-
-    Returns:
-    - np.array: 二值图像向量。
-    """
-    vector = np.zeros((1024,))
-    with open(filename) as file:
-        for i, line in enumerate(file):
-            vector[i * 32:i * 32 + 32] = np.array([int(char) for char in line.strip()])
-    return vector
-
-
-if __name__ == "__main__":
-    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,
-    )
-
+import numpy as np  
+import os  
+from collections import Counter  
+  
+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）。  
+        - labels (list or numpy array): 训练集对应的标签。  
+        - k (int): kNN 算法参数，默认值为 2。  
+  
+    返回:  
+        - int: 预测的分类标签。  
+    """  
+    # 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)  
+    # 根据距离排序并获取索引  
+    sorted_indices = distances.argsort()  
+    # 选择距离最近的k个点  
+    k_nearest_labels = labels[sorted_indices[:k]]  
+      
+    # 统计k个点中各类别的数量  
+    count = Counter(k_nearest_labels)  
+    # 返回数量最多的类别标签  
+    return count.most_common(1)[0][0]  
+  
+def load_dataset(directory):  
+    """  
+    加载数据集中的图像，并将其转换为特征向量和标签。  
+  
+    参数:  
+    - directory (str): 数据文件夹路径。  
+  
+    返回:  
+        - np.array: 特征数据（Mx1024）。  
+        - np.array: 标签数据（Mx1）。  
+    """  
+    # 初始化特征数据列表和标签列表  
+    feature_list = []  
+    label_list = []  
+    # 遍历目录中的所有文件  
+    for file in os.listdir(directory):  
+        # 获取文件的完整路径  
+        file_path = os.path.join(directory, file)  
+        # 将图像文件转换为向量  
+        feature_vector = img_to_vector(file_path)  
+        # 将特征向量添加到列表中  
+        feature_list.append(feature_vector)  
+        # 获取文件名中的标签并添加到标签列表中  
+        label_list.append(int(file.split('_')[0]))  
+    # 将特征列表和标签列表转换为numpy数组  
+    feature_matrix = np.array(feature_list)  
+    label_array = np.array(label_list)  
+    # 特征缩放（如果需要）  
+    feature_matrix = feature_matrix / 255.0  
+    return feature_matrix, label_array  
+  
+def img_to_vector(filename):  
+    """  
+    将二值图像转换为向量。  
+  
+    Args:  
+    - filename (str): 图像文件路径。  
+  
+    Returns:  
+    - np.array: 二值图像向量。  
+    """  
+    # 初始化向量  
+    vector = np.zeros((1024,))  # 创建一个一维数组  
+    # 打开文件并逐行读取  
+    with open(filename) as file:  
+        for i, line in enumerate(file):  
+            # 将每行的数字字符转换为整数并存储到向量中  
+            vector[i*32:i*32+32] = np.array([int(char) for char in line.strip()])  
+    return vector  
+  
+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")  
+  
+    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}")
-